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Regulatory Comments Resumen Ejecutivo Agradecemos la oportunidad de presentar nuestros comentarios al Informe Preliminar (en adelante, el Informe[1]) publicado por la Autoridad Investigadora (AI) de la Comisión . . .
Agradecemos la oportunidad de presentar nuestros comentarios al Informe Preliminar (en adelante, el Informe[1]) publicado por la Autoridad Investigadora (AI) de la Comisión Federal de Competencia Económica (COFECE), luego de culminada su investigación sobre la competencia en el mercado de comercio electrónico. El International Center for Law and Economics (“ICLE”) es un think-tank global de políticas públicas e investigación, no partidista y sin fines de lucro, fundado con el objetivo de construir las bases intelectuales para políticas sensatas y económicamente fundamentadas. ICLE promueve el uso de las metodologías del Análisis Económico del Derecho para informar los debates de política pública, y tiene una larga experiencia en la evaluación de leyes y políticas de competencia. El interés de ICLE es garantizar que la aplicaciones de las leyes de competencia y el impacto de la regulación sobre la competencia se base en reglas claras, precedentes establecidos, evidencia y un análisis económico sólido.
El Informe ha sido emitido en el marco de un procedimiento contemplado en la Ley de Competencia de México, conocido como “Investigaciones para Determinar Facilidades Esenciales o Barreras a la Competencia”, en virtud del cual COFECE iniciará una investigación “cuando existan elementos que sugieran que no existen condiciones efectivas de competencia en un mercado”. La AI es responsable de emitir un informe de investigación preliminar y proponer medidas correctivas. El Pleno de la COFECE podrá posteriormente adoptar o rechazar la propuesta.
Nuestros comentarios sugieren respetuosamente a los Comisionados de la COFECE no seguir las recomendaciones de la AI en lo que se refiere a la competencia en el mercado de comercio electrónico. Si bien el Informe es un esfuerzo loable por comprender el mercado de marketplaces y proteger la competencia en él —competencia que ha sido beneficiosa para los consumidores mexicanos—sus conclusiones y recomendaciones no siguen la evidencia ni los métodos y principios generalmente aceptados del Derecho de la Libre Competencia.
En primer lugar, de acuerdo con la Ley de Competencia mexicana, cualquier investigación debe apuntar a eliminar “restricciones al funcionamiento eficiente de los mercados”. Sin embargo, según información disponible públicamente, Amazon y Mercado Libre (MeLi), las dos empresas identificadas como “dominantes” en el informe, debe su éxito al hecho de que gozan de la preferencia de los consumidores, y cuentan la confianza de éstos, antes que a la existencia de “barreras a la competencia”. El informe también parece ignorar los beneficios para el consumidor que ofrecen los modelos de negocio de Amazon y MeLi (es decir, productos y servicios más baratos, entrega rápida, acceso más fácil a la información para comparar productos, etc.).
En segundo lugar, el Informe define un mercado relevante irrazonablemente “estrecho”, que incluye sólo “mercados en línea en múltiples categorías de productos y que operan a nivel nacional”. Esta definición de mercado ignora a otros minoristas en línea (como Shein o Temu) porque venden una selección menos amplia de productos, agregadores de comercio electrónico (como Google Shopping) porque son “meros intermediarios” que conectan compradores y vendedores, sitios propios de vendedores (como Apple o Adidas) porque no operan en diversas categorías, así como tiendas físicas. Esta definición, artificialmente estrecha, distorsiona drásticamente la participación de mercado de Amazon y MeLi, haciéndola parecer mucho mayor de lo que realmente es.
En tercer lugar, esta distorsionada definición del mercado relevante conduce hacia la errada conclusión de que Amazon y MeLi ostentan una posición dominante, un requisito previo para la adopción de medidas aplicables a dichas empresas. Esta conclusión es errada porque el Informe utiliza un concepto de “barreras a la entrada” que parece considerar cualquier costo que enfrenten los nuevos participantes como una barrera a la entrada que protege a Amazon y MeLi de la competencia. Como explicamos más adelante, estos costos son costos comerciales regulares, no barreras específicas del mercado que impiden la entrada de nuevos actores. En efecto, la evidencia muestra que, efectivamente, han estado entrando regularmente nuevas empresas en el mercado.
Finalmente, el informe sugiere remedios que perjudicarían a los consumidores en lugar de beneficiarlos. El Informe sugiere obligar a Amazon y MeLi a separar sus servicios de streaming (como Amazon Prime) de sus programas de fidelización. Esto perjudicaría a los consumidores que actualmente disfrutan de beneficios combinados a un precio más bajo. Además, exigir que las plataformas sean interoperables conlas otros proveedores de logística sofocaría la innovación y la inversión, ya que estas plataformas no aprovecharían los beneficios de su infraestructura digital. Esta interoperabilidad obligatoria también podría perjudicar a los consumidores, quienes pueden atribuir fallas relacionadas con la entrega a los marketplaces, en lugar de a los proveedores de logística responsables de ellas, creando así un típico problema de “free-riding”.
El Informe ha sido emitido en el marco de un procedimiento contemplado en el artículo 94 de la Ley de Competencia de México, conocido como “Investigaciones para Determinar Facilidades Esenciales o Barreras a la Competencia”. Según esta disposición, la COFECE iniciará una investigación “cuando existan elementos que sugieran que no existen condiciones de competencia efectiva en un mercado”. La investigación debería apuntar a determinar la existencia de “barreras a la competencia y al libre acceso a los mercados” o de “facilidades esenciales”.
La AI es responsable de emitir un informe de investigación preliminar y proponer medidas correctivas. El Informe deberá identificar el mercado objeto de la investigación con el fin de que cualquier persona interesada aporte elementos durante la investigación. Una vez finalizada la investigación, la AI emitirá un Informe, incluyendo las medidas correctivas que se consideren necesarias para eliminar las restricciones al funcionamiento eficiente del mercado. Los agentes económicos potencialmente afectados por las medidas correctivas propuestas tienen la oportunidad de comentar y aportar evidencia. El Pleno de la COFECE puede posteriormente adoptar o rechazar las propuestas.
Entendemos y elogiamos las preocupaciones de la COFECE sobre la competencia en los mercados, pero cualquier investigación debe apuntar a eliminar “las restricciones al funcionamiento eficiente de los mercados”, el propósito de la Ley de Competencia de México, según su Artículo 2. Las conclusiones y recomendaciones del Informe no parecen considerar las eficiencias generadas por los marketplaces líderes, lo que puede explicar por qué gozan de la preferencia de los consumidores.
De hecho, según información públicamente disponible, Amazon y MeLi, las dos empresas identificadas como “dominantes” en el informe, son debe su éxito a la preferencia y confianza de los consumidores. Según una fuente[2], por ejemplo:
La popularidad del marketplace de Amazon en México se basa en gran medida en la satisfacción del cliente. Amazon es la segunda plataforma de comercio electrónico más apreciada en México, según una encuesta de Kantar, con un índice de satisfacción de 8.5 sobre 10. El feedback de los consumidores también es esencial para el éxito del mercado de Amazon, ya que permite a los compradores realizar compras exitosas. . Las reseñas de los consumidores también son esenciales para el éxito del marketplace de Amazon, ya que permiten a los compradores realizar compras informadas. Las buenas críticas destacan la velocidad y confiabilidad de Amazon (el énfasis es nuestro).
Según un estudio publicado por el Instituto Federal de Telecomunicaciones (IFT) sobre el uso de plataformas digitales durante la pandemia de Covid-19, el 75.8% de los usuarios afirma estar satisfecho o muy satisfecho con las aplicaciones y páginas web que utiliza para comprar en línea. Precisamente MeLi y Amazon fueron las plataformas más mencionadas con un 67,3% y un 30,3% de menciones, respectivamente.[3]
El informe también parece ignorar los beneficios para el consumidor que ofrecen los modelos de negocio de Amazon MeLi (es decir, productos y servicios más baratos, entrega rápida, acceso más fácil a la información para comparar productos, etc.).
El Informe encuentra evidencia preliminar de que “no existen condiciones de competencia efectiva en el Mercado Relevante de Vendedores y en el Mercado Relevante de Compradores”, así como la existencia de tres “Barreras a la Competencia” que generan restricciones al funcionamiento eficiente de dichos mercados.
Las supuestas barreras consisten en:
Para eliminar estas supuestas barreras, el Informe propone tres remedios que se aplicarían a Amazon y MeLi:
No estamos de acuerdo con las conclusiones y recomendaciones del Informe por las razones que se exponen a continuación:
Antes que un procedimiento de “abuso de posición dominante”, la investigación de mercado que condujo a la emisión del Informe fue el “procedimiento cuasi-regulatorio” descrito líneas arriba. Pero la redacción del artículo 94 de la Ley Federal de Competencia Económica de México (bajo la cual se autorizó la investigación) sugiere contundentemente que la COFECE tiene que establecer (no simplemente afirmar que existe) una “ausencia de competencia efectiva”. Esto implicaría que existe una “falla del mercado” que impide la competencia, o que existe un agente económico con una posición dominante. El informe intenta mostrar esto último, pero lo hace de manera poco convincente.
Para determinar si una determinada empresa tiene una “posición dominante” (poder monopólico), las agencias de competencia deben primero definir un “mercado relevante” en el que la conducta o modelo de negocio cuestionado tenga un efecto. Aunque es común que las autoridades antimonopolio definan de manera restrictiva los mercados relevantes (a menudo, cuanto más pequeño es el mercado, más fácil es descubrir que el hipotético monopolista es, de hecho, un monopolista), creemos que el Informe va demasiado lejos en el caso que nos ocupa.
El Informe parece seguir el (mal) ejemplo de su homólogo estadounidense, la Comisión Federal de Comercio (FTC). Como explica Geoffrey Manne en un informe sobre la reciente denuncia[5] por monopolización de la FTC contra Amazon:
La denuncia de la FTC contra Amazon describe dos mercados relevantes en los que supuestamente se han producido daños anticompetitivos: (1) el “mercado de los grandes supermercados en línea” y (2) el “mercado de servicios de marketplaces en línea”. … la demanda de la FTC limita el mercado de los supermercados en línea únicamente a las tiendas en línea, y lo limita aún más a las tiendas que tienen una “gran amplitud y profundidad” de productos. Esto último significa tiendas en línea que venden prácticamente todas las categorías de productos (“como artículos deportivos, artículos de cocina, indumentaria y electrónica de consumo”) y que también tienen una amplia variedad de marcas dentro de cada categoría (como Nike, Under Armour, Adidas , etc.). En la práctica, esta definición excluye los canales privados de marcas líderes (como la tienda en línea de Nike), así como las tiendas en línea que se centran en una categoría particular de productos (como el enfoque de Wayfair en muebles). También excluye las tiendas físicas que todavía representan la gran mayoría de las transacciones minoristas. Las empresas con importantes ventas en línea y físicas podrían contar, pero sólo sus ventas en línea se considerarían parte del mercado.[6]
La denuncia de la FTC contra Amazon describe dos mercados relevantes en los que supuestamente se han producido daños anticompetitivos: (1) el “mercado de los grandes supermercados en línea” y (2) el “mercado de servicios de marketplaces en línea”.
… la demanda de la FTC limita el mercado de los supermercados en línea únicamente a las tiendas en línea, y lo limita aún más a las tiendas que tienen una “gran amplitud y profundidad” de productos. Esto último significa tiendas en línea que venden prácticamente todas las categorías de productos (“como artículos deportivos, artículos de cocina, indumentaria y electrónica de consumo”) y que también tienen una amplia variedad de marcas dentro de cada categoría (como Nike, Under Armour, Adidas , etc.). En la práctica, esta definición excluye los canales privados de marcas líderes (como la tienda en línea de Nike), así como las tiendas en línea que se centran en una categoría particular de productos (como el enfoque de Wayfair en muebles). También excluye las tiendas físicas que todavía representan la gran mayoría de las transacciones minoristas. Las empresas con importantes ventas en línea y físicas podrían contar, pero sólo sus ventas en línea se considerarían parte del mercado.[6]
El Informe hace algo similar. Define dos mercados relevantes;
Ambos mercados, sin embargo, están definidos de forma irrazonablemente restrictiva. Al alegar que los grandes mercados en línea “se han posicionado como una importante opción”, la agencia ignora la competencia de otros minoristas, tanto on-line como off-line. El Informe ignora otras plataformas de comercio electrónico, como Shein[7] y Temu[8] de China, que han ganado tanto popularidad como participación en el mercado publicitario. El Informe tampoco menciona los agregadores de comercio electrónico como Google Shopping, que permiten a los consumidores buscar casi cualquier producto, compararlos y encontrar ofertas competitivas; así como la competencia de sitios web de comercio electrónico propiedad de los propios vendedores, como Apple o Adidas.
Esta exclusión es, por decir lo menos, discutible. Para competir con una “super-tienda online”, las tiendas online no tienen que contar necesariamente con la misma gama de productos que tienen Amazon o MeLi, porque “los consumidores compran productos, no tipos de tiendas”[9]:
De hecho, parte de la supuesta ventaja de las compras en línea (cuando es una ventaja) es que los consumidores no tienen que agrupar las compras para minimizar los costos de transacción de visitar físicamente a un minorista tradicional. Mientras tanto, otra parte de la ventaja de las compras en línea es la facilidad de comparar precios: los consumidores ni siquiera tienen que cerrar una ventana de Amazon en sus computadoras para verificar alternativas, precios y disponibilidad en otros lugares. Todo esto socava la afirmación de que el “one-stop shopping” es una característica definitoria del supuesto mercado relevante.[10]
El Informe también parece ignorar la competencia que representan por los minoristas tradicionales, que disciplinarían cualquier intento de Amazon o Meli de explotar su poder de mercado. Por supuesto, cuántos consumidores podrían cambiar de proveedor y en qué medida eso afectaría a los marketplaces en cuestión son cuestiones empíricas. Pero no hay duda de que al menos algunos consumidores podrían cambiarse. Sobre el particular, es importante recordar que la competencia se produce en los márgenes. En consecuencia, no es necesario que todos los consumidores cambien para afectar las ventas y las ganancias de una empresa.
El informe hace mención a las ventas a través de las redes sociales, pero no las incluye en el mercado relevante. Desde nuestro punto de vista las redes sociales como canal de ventas deben considerarse como un sustituto razonable de Amazon y Meli, considerando que el 85% de las pequeñas y medianas empresas recurrieron a Facebook, Instagram y WhatsApp durante la pandemia de Covid-19 para publicitar y vender sus productos.[11] La Guía Comercial publicada por la Administración de Comercio Internacional del Departamento de Comercio de Estados Unidos para México informa que “los compradores mexicanos están muy influenciados por las redes sociales a la hora de realizar compras. El cuarenta y tres por ciento de los compradores de comercio electrónico han comprado a través de comercio conversacional o comercio electrónico (ventas a través de Facebook o WhatsApp) y el 29 por ciento a través de “lives” o transmisiones en vivo”.[12]
También hay evidencia empírica de que Amazon no sólo compite, sino que compite intensamente con otros canales de distribución, y tiene un efecto neto positivo en el bienestar de los consumidores mexicanos. Un artículo[13] de 2022 encontró que:
El mismo documento concluye que la entrada al mercado de productos vendidos y entregados por Amazon dio lugar a reducciones de precios de hasta un 28%.[14] A la luz de esta evidencia, creemos que es un error suponer que mercados como Amazon y MeLi no compiten con otros minoristas. Por tanto, estos últimos deberían incluirse en el mercado relevante.
Por si esta estrecha definición del mercado relevante no fuera suficiente, el informe combina las cuotas de mercado de Amazon y MeLi, para concluir que, ambas empresas ostentan más del 85% de las ventas y transacciones en el Mercado Relevante de Vendedores durante el periodo analizado, y el Índice Herfindahl-Hirschman (HHI) supera los dos mil puntos (por tanto, el mercado sería “altamente concentrado”). Asimismo, en el “Mercado Relevante de Compradores” el HHI se estimó, para 2022, en 1614 unidades, y los tres principales participantes concentran el 61% (sesenta y uno por ciento) del mercado. En ambos mercados, los demás participantes tienen una participación significativamente menor.
Pero ¿por qué combinar la cuota de mercado de Amazon y MeLi, como si actuaran como una sola empresa? Dada la definición de mercado de la AI, Amazon y MeLi (por lo menos) estarían compitiendo entre sí. El continuo crecimiento del mercado y la evolución de las respectivas cuotas de mercado de las empresas indican que así es. Un artículo de 2020, por ejemplo, informa que:
Cadenas de autoservicios, departamentales y nativas digitales tienen un objetivo en común: ser quien acapare más mercado en el comercio electrónico en México. En esta batalla, Amazon y Mercado Libre se ponen a la cabeza, pues son las dos firmas que concentran casi un cuarto del total de mercado de este rubro. Al cierre de 2019, Amazon contaba con un cuota de mercado del 13.4%, que lo colocaba al frente de los demás competidores. Ese mismo año, con 11.4% se encontraba Mercado Libre”.[15]
Cadenas de autoservicios, departamentales y nativas digitales tienen un objetivo en común: ser quien acapare más mercado en el comercio electrónico en México. En esta batalla, Amazon y Mercado Libre se ponen a la cabeza, pues son las dos firmas que concentran casi un cuarto del total de mercado de este rubro.
Al cierre de 2019, Amazon contaba con un cuota de mercado del 13.4%, que lo colocaba al frente de los demás competidores. Ese mismo año, con 11.4% se encontraba Mercado Libre”.[15]
También es inconsistente con la hipótesis de un mercado con “barreras a la competencia” el hecho de que el mercado de comercio electrónico está creciendo continuamente en México, que ahora es el segundo mercado de comercio electrónico más grande de América Latina.[16]
Es sólo sobre la base de una descripción distorsionada del mercado relevante que puede arribarse a la conclusión de que Amazon y MeLi tienen “el poder de fijar precios” (otra forma de decir “poder de monopolio”). Teniendo en cuenta lo explicado líneas arriba, esa conclusión debe rechazarse.
Incluso si se acepta la definición de mercado del Informe y, por lo tanto, se considera que Amazon y MeLi tienen una participación de mercado significativa, ambas empresas aún podrían enfrentar la competencia de nuevos participantes, atraídos al mercado por los precios más altos (u otras condiciones “explotativas”) que cobrarían a los consumidores. Según el Informe, sin embargo, existen varias barreras que obstaculizan “la entrada y la expansión” en ambos mercados relevantes. Entre ellos, el Informe menciona, por ejemplo:
Sin embargo, y contrariamente a lo que afirma el Informe, estos son costos de hacer negocios, no “barreras de entrada”. Como explicó convincentemente Richard Posner, el término “barrera de entrada” se utiliza comúnmente para describir cualquier obstáculo o costo que enfrentan los entrantes al mercado[17]. Pero según esta definición (aparentemente adoptada por el Informe), cualquier costo es una barrera de entrada. Basándose en la definición más precisa de George Stigler, Posner sugirió definir una barrera de entrada como “una condición que impone a un nuevo entrante costos de producción a largo plazo más altos que los que soportan las empresas que ya están en el mercado”.[18] En otras palabras, bien entendida, una barrera a la entrada es un costo asumido por los nuevos participantes, que no fue asumido por los ya actores establecidos.
La definición de “barreras de entrada” de la AI también contradice la definición dada por la sección IV del artículo 3 de la Ley de Competencia de México, según la cual una barrera a la competencia es:
Cualquier característica estructural del mercado, acto o hecho realizado por Agentes Económicos con el propósito o efecto de impedir el acceso a competidores o limitar su capacidad para competir en los mercados; que impida o distorsione el proceso de competencia y libre acceso a los mercados, así como cualquier disposición legal emitida por cualquier nivel de gobierno que impida o distorsione indebidamente el proceso de competencia y libre acceso a los mercados.
Por supuesto, Amazon y MeLi tienen algunas ventajas sobre otras empresas en términos de infraestructura, conocimientos, escala y goodwill. Pero esas ventajas no cayeron del cielo. Amazon y MeLi los construyeron con el tiempo, invirtiendo (y continuando invirtiendo) a menudo enormes cantidades para lograrlo. Incluso los “efectos de red”, a menudo considerados como una fuente inevitable de monopolio, no son un obstáculo definitivo para la competencia. Como han señalado Evans y Schmalensee:
la investigación sistemática sobre plataformas en línea realizada por varios autores, incluido uno de nosotros, muestra una considerable rotación en el liderazgo de las plataformas en línea en períodos inferiores a una década. luego está la colección de plataformas muertas o marchitas que salpican este sector, incluidas blackberry y windows en los sistemas operativos de teléfonos inteligentes, aol en mensajería, orkut en redes sociales y yahoo en medios masivos en línea.[19]
La idea de que Amazon y MeLi están protegidas por barreras de entrada también se contradice con la entrada de nuevos rivales, como Shein y Temu.
Como se explicó anteriormente, el Informe también combina erróneamente las participaciones de mercado de Mercado Libre y Amazon, para alcanzar una participación de mercado combinada del 85% (ochenta y cinco por ciento) de las ventas y transacciones en el Mercado Relevante de Vendedores; y luego combina la participación de mercado de los tres principales participantes del mercado en el Mercado Relevante para Compradores para alcanzar una participación de mercado del 61% (sesenta y uno por ciento) del mercado. Esto es muy problemático, ya que esas empresas no son una sola entidad económica y, por lo tanto, presumiblemente (a falta de evidencia de colusión) debe asumirse que compiten entre sí.
En todo caso, las cuotas de mercado producidas por el Informe sólo conducen a un IHH alto, lo que a su vez muestra que el mercado está “altamente concentrado” (si se acepta la estrecha definición de mercado del Informe). Pero la concentración es un pobre indicador del poder de mercado. Los economistas han estudiado la relación entre la concentración y diversos indicios potenciales de efectos anticompetitivos (precio, margen, ganancias, tasa de rendimiento, etc.) durante décadas, y la evidencia empírica es más que suficiente para decir que la concentración podría conducir a problemas de competencia.[20] No es per se una prueba de falta de competencia, y mucho menos de una posición dominante.
Como resumió recientemente Chad Syverson:
Quizás el problema conceptual más profundo de la concentración como medida del poder de mercado es que es un resultado, no un determinante central inmutable de cuán competitivo es una industria o un mercado… Como resultado, la concentración es peor que un simple barómetro poco preciso del poder de mercado. En realidad, ni siquiera podemos saber en general en qué dirección está orientado el barómetro.[21]
Incluso si aceptáramos la definición de mercado relevante sugerida por el Informe y su determinación de la existencia de una posición dominante, los remedios propuestos —que podrían resumirse en la separación obligatoria de los servicios de streaming de Amazon y MeLi de sus programas de fidelización (como Prime de Amazon) y hacer que (al menos parte de) sus plataformas sean “interoperables” con otros servicios logísticos—perjudicaría a los consumidores, en lugar de beneficiarlos.
Amazon Prime, por ejemplo, ofrece a los consumidores muchos beneficios atractivos: acceso a streaming de vídeo y música; ofertas y descuentos especiales; y, por último, pero no menos importante, envío gratuito en dos días. Según el Informe, estos “son una estrategia artificial que atrae y retiene a los compradores, a la vez que reduce que los compradores y vendedores usen marketplaces alternativos.”
No está del todo claro qué significa el término “artificial” en este contexto, pero parece implicar algo fuera de los límites de la competencia “natural”. Sin embargo, la estrategia de negocio que describe el Informe es la definición misma de competencia. Las empresas que compiten en un mercado siempre eligen un “paquete” de atributos que combinan en un solo producto. En cierta medida “apuestan” por un conjunto de características (funcionalidad, materiales, términos y condiciones) que implican asumir determinado costos, que luego ofrecen a un precio determinado, que puede ser asumido por clientes dispuestos (o no). Incluso con información imperfecta, los mercados (es decir, los vendedores y los consumidores) son los agentes mejor calificados para “decidir” el nivel apropiado de “agrupación” de un producto, no las agencias de competencia o los tribunales.
Un mandato para desagregar los servicios de streaming en realidad degradaría la experiencia de los consumidores online, quienes tendrían que contratar y pagar esos servicios por separado[22]. La prestación independiente de dichos servicios no se beneficiaría de las economías de escala y alcance de Amazon o MeLi y, por tanto, sería más cara. Ofrecer más beneficios a los consumidores a un precio determinado es lo precisamente lo que queremos que hagan los competidores. Tratar el beneficio para el consumidor como un daño es un contrasentido para el Derecho y las políticas de competencia (y, de hecho, para la noción misma de competencia).
Por otro lado, el informe también propone ordenar la apertura del Buy Box y modificar sus reglas, a fin de que sea neutral para todos los proveedores de logística. Exigir que se permita a dichos proveedores ofrecer sus servicios en Amazon o Mercado Libre equivale a considerar estas plataformas como “operadores comunes”, tal como los legisladores y reguladores hicieron con las antiguas redes de telefonía del siglo XX. Sin embargo, esta clasificación y las reglas que de ella se derivan (neutralidad y regulación de precios, entre otras) fueron diseñadas para mercados con monopolios naturales, donde la competencia no es posible, o incluso indeseable.[23] Pero no hay evidencia de que este sea el caso de los marketplaces de comercio electrónico. Por el contrario, las plataformas digitales son mucho más competitivas. En este contexto, el aplicara éstas las normas del tipo “common carrier” sólo crearía “free-riding” e incentivos negativos para la inversión y la innovación (tanto por parte de los actuales participantes del mercado como de los nuevos entrantes). Los vendedores y proveedores de logística tienen muchas otras opciones para acceder a los consumidores. No existe ninguna justificación económica o legal para ordenar su acceso mandatorio a las plataformas de Amazon o MeLi.
En resumen, las conclusiones erróneas del Informe conducen a soluciones aún peores. Tales soluciones no promoverían la competencia en México ni beneficiarían a los consumidores.
[1] El texto completo de el Informe (en su versión pública) está disponible en el siguiente enlace: https://www.cofece.mx/wp-content/uploads/2024/02/Dictamen_Preliminar_Version_Publica.pdf.
[2] La Patria, ¿Qué tan popular es el marketplace de Amazon en México? (23 Apr. 2023), https://www.lapatria.com/publirreportaje/que-tan-popular-es-el-marketplace-de-amazon-en-mexico.
[3] Instituto Federal de Telecomunicaciones, Adopción, Uso y satisfacción de las aplicaciones y herramientas digitales para compras y banca en línea, videollamadas, redes sociales, salud y trámites gubernamentales en tiempos de Covid-19 (Jan 19, 2022), https://www.ift.org.mx/sites/default/files/contenidogeneral/usuarios-y-audiencias/aplicacionesyherramientasdigitalesentiemposdecovid19.pdf.
[4] El “Buy Box” o, traduciendo literalmente el “Recuadro de compra” es un cuadro que normalmente se encuentra en el lado derecho de la página web del marketplace cuando los clientes buscan un producto. Estar en esta casilla es una ventaja para el vendedor porque no solo resalta su producto, sino que también facilita el proceso de pago. Por supuesto, esto también es una ventaja para los consumidores, que pueden encontrar y comprar productos más rápido.
[5] Ver: https://www.ftc.gov/legal-library/browse/cases-proceedings/1910129-1910130-amazoncom-inc-amazon-ecommerce.
[6] Geoffrey A. Manne, Gerrymandered Market Definitions in FTC v. Amazon (Jan. 26, 2024), https://laweconcenter.org/resources/gerrymandered-market-definitions-in-ftc-v-amazon.
[7] Ver, por ejemplo: Krystal Hu y Arriana McLymore, Exclusive: Fast-fashion giant Shein plans Mexico factory, Reuters (Mayo 24, 2023), https://www.reuters.com/business/retail-consumer/fast-fashion-giant-shein-plans-mexico-factory-sources-2023-05-24.
[8] Ver, por ejemplo: Rising E-commerce Star: The Emergence of Temu in Mexico, BNN (Sep. 25, 2023), https://bnnbreaking.com/finance-nav/rising-e-commerce-star-the-emergence-of-temu-in-mexico.
[9] Geoffrey A. Manne, Ibid.
[10] Ibid.
[11] Expansión, El 85% de las Pymes usa redes sociales para vender en línea (28 Jul. 2021), https://expansion.mx/tecnologia/2021/07/28/el-85-de-las-pymes-usa-redes-sociales-para-vender-en-linea.
[12] International Trade Organization, Mexico – Country Commercial Guide, (Nov. 5, 2023), https://www.trade.gov/country-commercial-guides/mexico-ecommerce.
[13] Raymundo Campos Vázquez et al., Amazon’s Effect on Prices: The Case of Mexico, Centro de Estudios Económicos, Documentos de Trabajo, Nro. II (2022), https://cee.colmex.mx/dts/2022/DT-2022-2.pdf.
[14] Ibid, p. 23.
[15] El CEO, Amazon y Mercado Libre se disputan la corona del comercio electrónico en México (Mar 17, 2020), https://elceo.com/negocios/amazon-y-mercado-libre-se-discuten-la-corona-del-comercio-electronico-en-mexico.
[16] “Over the last few years, online buying and selling have gained considerable ground in Mexico, so much so that the country has positioned itself as the second largest e-commerce market in Latin America. With a rapidly increasing online buying population, it was forecast that nearly 70 million Mexicans would be shopping on the internet in 2023, a figure that would grow by over 26 percent by 2027.”). Stephanie Chevalier, E-commerce market share in Latin American and the Caribbean 2023, by country, Statista, March 25, 2024, https://www.statista.com/statistics/434042/mexico-most-visited-retail-websites.
[17] Richard Posner, Antitrust Law (2nd. Ed. 2001), pp.73-74.
[18] Ibid., p. 74.
[19] David S.Evans and Richard Schmalensee, Debunking the “network effects” bogeyman, Regulation (Winter 2017-2018), at 39, https://www.cato.org/sites/cato.org/files/serials/files/regulation/2017/12/regulation-v40n4-1.pdf.
[20] Sólo para citar alguos de los ejemplos más relevante de una amplia literatura, ver: Steven Berry, Martin Gaynor, & Fiona Scott Morton, Do Increasing Markups Matter? Lessons from Empirical Industrial Organization, 33J. Econ. Perspectives 44 (2019); Richard Schmalensee, Inter-Industry Studies of Structure and Performance, in 2 Handbook of Industrial Organization 951-1009 (Richard Schmalensee & Robert Willig, eds., 1989); William N. Evans, Luke M. Froeb, & Gregory J. Werden, Endogeneity in the Concentration-Price Relationship: Causes, Consequences, and Cures, 41 J. Indus. Econ. 431 (1993); Steven Berry, Market Structure and Competition, Redux, FTC Micro Conference (Nov. 2017), available at https://www.ftc.gov/system/files/documents/public_events/1208143/22_-_steven_berry_keynote.pdf; Nathan Miller, et al., On the Misuse of Regressions of Price on the HHI in Merger Review, 10 J. Antitrust Enforcement 248 (2022).
[21] Chad Syverson, Macroeconomics and Market Power: Context, Implications, and Open Questions 33 J. Econ. Persp. 23, (2019) at 26.
[22] Ver, sobre el particular: Alden Abbott, FTC’s Amazon Complaint: Perhaps the Greatest Affront to Consumer and Producer Welfare in Antitrust History, Truth on the Market (September 27, 2023), https://truthonthemarket.com/2023/09/27/ftcs-amazon-complaint-perhaps-the-greatest-affront-to-consumer-and-producer-welfare-in-antitrust-history.
[23] Ver, por ejemplo: Giuseppe Colangelo y Oscar Borgogno, App Stores as Public Utilities?, Truth on the Market (January 19, 2022), https://truthonthemarket.com/2022/01/19/app-stores-as-public-utilities.
Popular Media Cuando los procesos internos de una plataforma de comercio electrónico fallan, o cuando simplemente sus directivos o empleados toman decisiones equivocadas —digamos, enviando un pedido . . .
Cuando los procesos internos de una plataforma de comercio electrónico fallan, o cuando simplemente sus directivos o empleados toman decisiones equivocadas —digamos, enviando un pedido a una dirección incorrecta— un consumidor o un grupo de consumidores se ven perjudicados. Estos errores son fácilmente subsanables si la plataforma en cuestión tiene un buen proceso de atención al cliente.
Read the full piece here.
Scholarship Executive Summary A prominent theme in debates about US national privacy legislation is whether federal law should preempt state law. A federal statute could create . . .
A prominent theme in debates about US national privacy legislation is whether federal law should preempt state law. A federal statute could create one standard for markets that are obviously national in scope. Another approach is to allow states to be “laboratories of democracy” that adopt different laws so they can discover the best ones.
We propose a federal statute requiring states to recognize contractual choice-of-law provisions, so companies and consumers can choose what state privacy law to adopt. Privacy would continue to be regulated at the state level. However, the federal government would provide for jurisdictional competition among states, such that companies operating nationally could comply with the privacy laws of any one state.
Our proposed approach would foster a double competition aimed at discerning and delivering on consumers’ true privacy interests: market competition to deliver privacy policies that consumers prefer and competition among states to develop the best privacy laws.
Unlike a single federal privacy law, this approach would provide 50 competing privacy regimes for national firms. The choice-of-law approach can trigger competition and innovation in privacy practices while preserving a role for meaningful state privacy regulation.
The question of preemption of state law by the federal government has bedeviled debates about privacy regulation in the United States. A prominent theme is to propose a national privacy policy that largely preempts state policies to create one standard for markets that are obviously national. Another approach is to allow states to be “laboratories of democracy” that adopt different laws, with the hope that they will adopt the best rules over time. Both approaches have substantial costs and weaknesses.
The alternative approach we propose would foster a double competition aimed at discerning and delivering on consumers’ true privacy interests: market competition to deliver privacy policies that consumers prefer and competition among states to develop the best privacy laws. Indeed, our proposal aims to obtain the best features—and avoid the worst features—of both a federal regime and a multistate privacy law regime by allowing firms and consumers to agree on compliance with the single regime of their choosing.
Thus, we propose a federal statute requiring states to recognize contractual choice-of-law provisions, so companies and consumers can choose what state privacy law to adopt. Privacy would continue to be regulated at the state level. However, the federal government would provide for jurisdictional competition among states, and companies operating nationally could comply with the privacy laws of any one state.
Unlike a single federal privacy law, this approach would provide 50 competing privacy regimes for national firms. Protecting choice of law can trigger competition and innovation in privacy practices while preserving a role for meaningful state privacy regulation.
A strong impetus for federal privacy legislation is the opportunity national and multinational businesses see to alleviate the expense and liability of having a patchwork of privacy statutes with which they must comply in the United States. Absent preemptive legislation, they could conceivably operate under 50 different state regimes, which would increase costs and balkanize their services and policies without coordinate gains for consumers. Along with whether a federal statute should have a private cause of action, preempting state law is a top issue when policymakers roll up their sleeves and discuss federal privacy legislation.
But while the patchwork argument is real, it may be overstated. There are unlikely ever to be 50 distinct state regimes; rather, a small number of state legislation types is likely, as jurisdictions follow each other’s leads and group together, including by promulgating model state statutes.[1] States don’t follow the worst examples from their brethren, as the lack of biometric statutes modeled on Illinois’s legislation illustrates.[2]
Along with fewer “patches,” the patchwork’s costs will tend to diminish over time as states land on relatively stable policies, allowing compliance to be somewhat routinized.
Nonetheless, the patchwork is far from ideal. It is costly to firms doing business nationally. It costs small firms more per unit of revenue, raising the bar to new entry and competition. And it may confuse consumers about what their protections are (though consumers don’t generally assess privacy policies carefully anyway).
Federal preemption has many weaknesses and costs as well. Foremost, it may not deliver meaningful privacy to consumers. This is partially because “privacy” is a congeries of interests and values that defy capture.[3] Different people prioritize different privacy issues differently. In particular, the elites driving and influencing legislation may prioritize certain privacy values differently from consumers, so legislation may not serve most consumers’ actual interests.[4]
Those in the privacy-regulation community sometimes assume that passing privacy legislation ipso facto protects privacy, but that is not a foregone conclusion. The privacy regulations issued under the Gramm-Leach-Bliley Act (concerning financial services)[5] and the Health Insurance Portability and Accountability Act (concerning health care)[6] did not usher in eras of consumer confidence about privacy in their respective fields.
The short-term benefits of preempting state law may come with greater long-term costs. One cost is the likely drop in competition among firms around privacy. Today, as some have noted, “Privacy is actually a commercial advantage. . . . It can be a competitive advantage for you and build trust for your users.”[7] But federal privacy regulation seems almost certain to induce firms to treat compliance as the full measure of privacy to offer consumers. Efforts to outperform or ace out one another will likely diminish.[8]
Another long-term cost of preempting state law is the drop in competition among states to provide well-tuned privacy and consumer-protection legislation. Our federal system’s practical genius, which Justice Louis Brandeis articulated 90 years ago in New State Ice v. Liebmann, is that state variation allows natural experiments in what best serves society—business and consumer interests alike.[9] Because variations are allowed, states can amend their laws individually, learn from one another, adapt, and converge on good policy.
The economic theory of federalism draws heavily from the Tiebout model.[10] Charles Tiebout argued that competing local governments could, under certain conditions, produce public goods more efficiently than the national government could. Local governments act as firms in a marketplace for taxes and public goods, and consumer-citizens match their preferences to the providers. Efficient allocation requires mobile people and resources, enough jurisdictions with the freedom to set their own laws, and limited spillovers among jurisdictions (effects of one jurisdiction’s policies on others).
A related body of literature on “market-preserving federalism” argues that strong and self-reinforcing limits on national and local power can preserve markets and incentivize economic growth and development.[11] The upshot of this literature is that when local jurisdictions can compete on law, not only do they better match citizens’ policy preferences, but the rules tend toward greater economic efficiency.
In contrast to the economic gains from decentralization, moving authority over privacy from states to the federal government may have large political costs. It may deepen Americans’ growing dissatisfaction with their democracy. Experience belies the ideal of responsive national government when consumers, acting as citizens, want to learn about or influence the legislation and regulation that governs more and more areas of their lives. The “rejectionist” strain in American politics that Donald Trump’s insurgency and presidency epitomized may illustrate deep dissatisfaction with American democracy that has been growing for decades. Managing a highly personal and cultural
issue like privacy through negotiation between large businesses and anonymous federal regulators would deepen trends that probably undermine the government’s legitimacy.
To put a constitutional point on it, preempting states on privacy contradicts the original design of our system, which assigned limited powers to the federal government.[12] The federal government’s enumerated powers generally consist of national public goods—particularly defense. The interstate commerce clause, inspired by state parochialism under the Articles of Confederation, exists to make commerce among states (and with tribes) regular; it is not rightly a font of power to regulate the terms and conditions of commerce generally.[13]
Preempting state law does not necessarily lead to regulatory certainty, as is often imagined. Section 230 of the Communications Decency Act may defeat once and for all the idea that federal legislation creates certainty.[14] More than a quarter century after its passage, it is hotly debated in Congress and threatened in the courts.[15]
The Fair Credit Reporting Act (FCRA) provides a similar example.[16] Passed in 1970, it comprehensively regulated credit reporting. Since then, Congress has amended it dozens of times, and regulators have made countless alterations through interpretation and enforcement.[17] The Consumer Financial Protection Bureau recently announced a new inquiry into data brokering under the FCRA.[18] That is fine, but it illustrates that the FCRA did not solve problems and stabilize the law. It just moved the jurisdiction to Washington, DC.
Meanwhile, as regulatory theory predicts, credit reporting has become a three-horse race.[19] A few slow-to-innovate firms have captured and maintained dominance thanks partially to the costs and barriers to entry that uniform regulation creates.
Legal certainty may be a chimera while business practices and social values are in flux. Certainty develops over time as industries settle into familiar behaviors and roles.
One way to deal with this highly complex issue is to promote competition for laws. The late, great Larry Ribstein, with several coauthors over the years, proposed one such legal mechanism: a law market empowered by choice-of-law statutes.[20] Drawing on the notion of market competition as a discovery process,[21] Ribstein and Henry Butler explained:
In order to solve the knowledge problem and to create efficient legal technologies, the legal system can use the same competitive process that encourages innovation in the private sector—that is, competition among suppliers of law. As we will see, this entails enforcing contracts among the parties regarding the applicable law. The greater the knowledge problem the more necessary it is to unleash markets for law to solve the problem.[22]
The proposal set forth below promotes just such competition and solves the privacy-law patchwork problem without the costs of federal preemption. It does this through a simple procedural regulation requiring states to enforce choice-of-law terms in privacy contracts, rather than through a heavy-handed, substantive federal law. Inspired by Butler and Ribstein’s proposal for pluralist insurance regulation,[23] the idea is to make the choice of legal regime a locus of privacy competition.
Modeled on the US system of state incorporation law, our proposed legislation would leave firms generally free to select the state privacy law under which they do business nationally. Firms would inform consumers, as they must to form a contract, that a given state’s laws govern their policies. Federal law would ensure that states respect those choice-of-law provisions, which would be enforced like any other contract term.
This would strengthen and deepen competition around privacy. If firms believed privacy was a consumer interest, they could select highly protective state laws and advertise that choice, currying consumer favor. If their competitors chose relatively lax state law, they could advertise to the public the privacy threats behind that choice. The process would help hunt out consumers’ true interests through an ongoing argument before consumers. Businesses’ and consumers’ ongoing choices— rather than a single choice by Congress followed by blunt, episodic amendments—would shape the privacy landscape.
The way consumers choose in the modern marketplace is a broad and important topic that deserves further study and elucidation. It nevertheless seems clear—and it is rather pat to observe—that consumers do not carefully read privacy policies and balance their implications. Rather, a hive mind of actors including competitors, advocates, journalists, regulators, and politicians pore over company policies and practices. Consumers take in branding and advertising, reputation, news, personal recommendations, rumors, and trends to decide on the services they use and how they use them.
That detail should not be overlooked: Consumers may use services differently based on the trust they place in them to protect privacy and related values. Using an information-intensive service is not a proposition to share everything or nothing. Consumers can and do shade their use and withhold information from platforms and services depending on their perceptions of whether the privacy protections offered meet their needs.
There is reason to be dissatisfied with the modern marketplace, in which terms of service and privacy policies are offered to the individual consumer on a “take it or leave it” basis. There is a different kind of negotiation, described above, between the hive mind and large businesses. But when the hive mind and business have settled on terms, individuals cannot negotiate bespoke policies reflecting their particular wants and needs. This collective decision-making may be why some advocates regard market processes as coercive. They do not offer custom choices to all but force individual consumers into channels cut by all.
The solution that orthodox privacy advocates offer does not respond well to this problem, because they would replace “take it or leave it” policies crafted in the crucible of the marketplace with “take it or leave it” policies crafted in a political and regulatory crucible. Their prescriptions are sometimes to require artificial notice and “choice,” such as whether to accept cookies when one visits websites. This, as experience shows, does not reach consumers when they are interested in choosing.
Choice of law in privacy competition is meant to preserve manifold choices when and where consumers make their choices, such as at the decision to transact, and then let consumers choose how they use the services they have decided to adopt. Let new entrants choose variegated privacy-law regimes, and consumers will choose among them. That does not fix the whole problem, but at least it doesn’t replace consumer choice with an “expert” one-size-fits-all choice.
In parallel to business competition around privacy choice of law, states would compete with one another to provide the most felicitous environment for consumers and businesses. Some states would choose more protection, seeking the rules businesses would choose to please privacy-conscious consumers. Others might choose less protection, betting that consumers prefer goods other than information control, such as free, convenient, highly interactive, and custom services.
Importantly, this mechanism would allow companies to opt in to various privacy regimes based on the type of service they offer, enabling a degree of fine-tuning appropriate for different industries and different activities that no alternative would likely offer. This would not only result in the experimentation and competition of federalism but also enable multiple overlapping privacy-regulation regimes, avoiding the “one-size-doesn’t-fit-all” problem.
While experimentation continued, state policies would probably rationalize and converge over time. There are institutions dedicated to this, such as the Uniform Law Commission, which is at its best when it harmonizes existing laws based on states’ experience.[24]
It is well within the federal commerce power to regulate state enforcement of choice-of-law provisions, because states may use them to limit interjurisdictional competition. Controlling that is precisely what the commerce power is for. Utah’s recent Social Media Regulation Act[25] barred enforcement of choice-of-law provisions, an effort to regulate nationally from a state capital. Federally backing contractual choice-of-law selections would curtail this growing problem.
At the same time, what our proposed protections for choice-of-law rules do is not much different from what contracts already routinely do and courts enforce in many industries. Contracting parties often specify the governing state’s law and negotiate for the law that best suits their collective needs.
Indeed, sophisticated business contracts increasingly include choice-of-law clauses that state the law that the parties wish to govern their relationship. In addition to settling uncertainty, these clauses might enable the contracting parties to circumvent those states’ laws they deem to be undesirable.[26]
This practice is not only business-to-business. Consumers regularly enter into contracts that include choice-of-law clauses—including regarding privacy law. Credit card agreements, stock and mutual fund investment terms, consumer-product warranties, and insurance contracts, among many other legal agreements, routinely specify the relevant state law that will govern.
In these situations, the insurance company, manufacturer, or mutual fund has effectively chosen the law. The consumer participates in this choice only to the same extent that she participates in any choices related to mass-produced products and services, that is, by deciding whether to buy the product or service.[27]
Allowing contracting parties to create their own legal certainty by contract would likely rankle states. Indeed, “we might expect governments to respond with hostility to the enforcement of choice-of-law clauses. In fact, however, the courts usually do enforce choice-of-law clauses.”[28] With some states trying to regulate nationally and some effectively doing so, the choice the states collectively face is having a role in privacy regulation or no role at all. Competition is better for them than exclusion from the field or minimization of their role through federal preemption of state privacy law. This proposal thus advocates simple federal legislation that preserves firms’ ability to make binding choice-of-law decisions and states’ ability to retain a say in the country’s privacy-governance regime.
Some privacy advocates may object that state laws will not sufficiently protect consumers.[29] Indeed, there is literature arguing that federalism will produce a race to the bottom (i.e., competition leading every state to effectively adopt the weakest law possible), for example, when states offer incorporation laws that are the least burdensome to business interests in a way that arguably diverges from public or consumer interests.[30]
The race-to-the-bottom framing slants the issues and obscures ever-present trade-offs, however. Rules that give consumers high levels of privacy come at a cost in social interaction, price, and the quality of the goods they buy and services they receive. It is not inherently “down” or bad to prefer cheap or free goods and plentiful, social, commercial interaction. It is not inherently “up” or good to opt for greater privacy.
The question is what consumers want. The answers to that question—yes, plural—are the subject of constant research through market mechanisms when markets are free to experiment and are functioning well. Consumers’ demands can change over time through various mechanisms, including experience with new technologies and business models. We argue for privacy on the terms consumers want. The goal is maximizing consumer welfare, which sometimes means privacy and sometimes means sharing personal information in the interest of other goods. There is no race to the bottom in trading one good for another.
Yet the notion of a race to the bottom persists—although not without controversy. In the case of Delaware’s incorporation statutes, the issue is highly contested. Many scholars argue that the state’s rules are the most efficient—that “far from exploiting shareholders, . . . these rules actually benefit shareholders by increasing the wealth of corporations chartered in states with these rules.”[31]
As always, there are trade-offs, and the race-to-the-bottom hypothesis requires some unlikely assumptions. Principally, as Jonathan Macey and Geoffrey Miller discuss, the assumption that state legislators are beholden to the interests of corporations over other constituencies vying for influence. As Macey and Miller explain, the presence of a powerful lobby of specialized and well-positioned corporate lawyers (whose interests are not the same as those of corporate managers) transforms the analysis and explains the persistence and quality of Delaware corporate law.[32]
In much the same vein, there are several reasons to think competition for privacy rules would not succumb to a race to the bottom.
First, if privacy advocates are correct, consumers put substantial pressure on companies to adopt stricter privacy policies. Simply opting in to the weakest state regime would not, as with corporate law, be a matter of substantial indifference to consumers but would (according to advocates) run contrary to their interests. If advocates are correct, firms avoiding stronger privacy laws would pay substantial costs. As a result, the impetus for states to offer weaker laws would be diminished. And, consistent with Macey and Miller’s “interest-group theory” of corporate law,[33] advocates themselves would be important constituencies vying to influence state privacy laws. Satisfying these advocates may benefit state legislators more than satisfying corporate constituencies does.
Second, “weaker” and “stronger” would not be the only dimensions on which states would compete for firms to adopt their privacy regimes. Rather, as mentioned above, privacy law is not one-size-fits-all. Different industries and services entail different implications for consumer interests. States could compete to specialize in offering privacy regimes attractive to distinct industries based on interest groups with particular importance to their economies. Minnesota (home of the Mayo Clinic) and Ohio (home of the Cleveland Clinic), for example, may specialize in health care and medical privacy, while California specializes in social media privacy.
Third, insurance companies are unlikely to be indifferent to the law that the companies they cover choose. Indeed, to the extent that insurers require covered firms to adopt specific privacy practices to control risk, those insurers would likely relish the prospect of outsourcing the oversight of these activities to state law enforcers. States could thus compete to mimic large insurers’ privacy preferences—which would by no means map onto “weaker” policies—to induce insurers to require covered firms to adopt their laws.
If a race to the bottom is truly a concern, the federal government could offer a 51st privacy alternative (that is, an optional federal regime as an alternative to the states’ various privacy laws). Assuming federal privacy regulation would be stricter (an assumption inherent in the race-to-the-bottom objection to state competition), such an approach would ensure that at least one sufficiently strong opt-in privacy regime would always be available. Among other things, this would preclude firms from claiming that no option offers a privacy regime stronger than those of the states trapped in the (alleged) race to the bottom.
Choice of law exists to a degree in the European Union, a trading bloc commonly regarded as uniformly regulated (and commonly regarded as superior on privacy because of a bias toward privacy over other goods). The General Data Protection Regulation (GDPR) gives EU member states broad authority to derogate from its provisions and create state-level exemptions. Article 23 of the GDPR allows states to exempt themselves from EU-wide law to safeguard nine listed broad governmental and public interests.[34] And Articles 85 through 91 provide for derogations, exemptions, and powers to impose additional requirements relative to the GDPR for a number of “specific data processing situations.”[35]
Finally, Article 56 establishes a “lead supervisory authority” for each business.[36] In the political, negotiated processes under the GDPR, this effectively allows companies to shade their regulatory obligations and enforcement outlook through their choices of location. For the United States’ sharper rule-of-law environment, we argue that the choice of law should be articulate and clear.
The precise contours of a federal statute protecting choice-of-law terms in contracts will determine whether it successfully promotes interfirm and interstate competition. Language will also determine its political salability.
Questions include: What kind of notice, if any, should be required to make consumers aware that they are dealing with a firm under a law regime not their own? Consumers are notoriously unwilling to investigate privacy terms—or any other contract terms—in advance, and when considering the choice of law, they would probably not articulate it to themselves. But the competitive dynamics described earlier would probably communicate relevant information to consumers even without any required notice. As always, competitors will have an incentive to ensure consumers are appropriately well-informed when they can diminish their rivals or elevate themselves in comparison by doing so.[37]
Would there be limits on which state’s laws a firm could choose? For example, could a company choose the law of a state where neither the company nor the consumer is domiciled? States would certainly argue that a company should not be able to opt out of the law of the state where it is domiciled. The federal legislation we propose would allow unlimited choice. Such a choice is important if the true benefits of jurisdictional competition are to be realized.
A federal statute requiring states to enforce choice-of-law terms should not override state law denying enforcement of choice-of-law terms that are oppressive, unfair, or improperly bargained for. In cases such as Carnival Cruise Lines v. Shute[38] and The Bremen v. Zapata Off-Shore Co.,[39] the Supreme Court has considered whether forum-selection clauses in contracts might be invalid. The Court has generally upheld such clauses, but they can be oppressive if they require plaintiffs in Maine to litigate in Hawaii, for example, without a substantial reason why Hawaii courts are the appropriate forum. Choice-of-law terms do not impose the cost of travel to remote locations, but they could be used not to establish the law governing the parties but rather to create a strategic advantage unrelated to the law in litigation. Deception built into a contract’s choice-of-law terms should remain grounds for invalidating the contract under state law, even if the state is precluded from barring choice-of-law terms by statute.
The race-to-the-bottom argument raises the question of whether impeding states from overriding contractual choice-of-law provisions would be harmful to state interests, especially since privacy law concerns consumer rights. However, there are reasons to believe race-to-the-bottom incentives would be tempered by greater legal specialization and certainty and by state courts’ ability to refuse to enforce choice-of-law clauses in certain limited circumstances. As Erin O’Hara and Ribstein put it:
Choice-of law clauses reduce uncertainty about the parties’ legal rights and obligations and enable firms to operate in many places without being subject to multiple states’ laws. These reduced costs may increase the number of profitable transactions and thereby increase social wealth. Also, the clauses may not change the results of many cases because courts in states that prohibit a contract term might apply the more lenient law of a state that has close connections with the parties even without a choice-of-law clause.[40]
Determining when, exactly, a state court can refuse to enforce a firm’s choice of privacy law because of excessive leniency is tricky, but the federal statute could set out a framework for when a court could apply its own state’s law. Much like the independent federal alternative discussed above, specific minimum requirements in the federal law could ensure that any race to the bottom that does occur can go only so far. Of course, it would be essential that any such substantive federal requirements be strictly limited, or else the benefits of jurisdictional competition would be lost.
The converse to the problem of a race to the bottom resulting from state competition is the “California effect”—the prospect of states adopting onerous laws from which no company (or consumer) can opt out. States can regulate nationally through one small tendril of authority: the power to prevent businesses and consumers from agreeing on the law that governs their relationships. If a state regulates in a way that it thinks will be disfavored, it will bar choice-of-law provisions in contracts so consumers and businesses cannot exercise their preference.
Utah’s Social Media Regulation Act, for example, includes mandatory age verification for all social media users,[41] because companies must collect proof that consumers are either of age or not in Utah. To prevent consumers and businesses from avoiding this onerous requirement, Utah bars waivers of the law’s requirements “notwithstanding any contract or choice-of-law provision in a contract.”[42] If parties could choose their law, that would render Utah’s law irrelevant, so Utah cuts off that avenue. This demonstrates the value of a proposal like the one contemplated here.
Creating a federal policy to stop national regulation coming from state capitols, while still preserving competition among states and firms, is unique. Congress usually creates its own policy and preempts states in that area to varying degrees. There is a well-developed law around this type of preemption, which is sometimes implied and sometimes expressed in statute.[43] Our proposal does not operate that way. It merely withdraws state authority to prevent parties from freely contracting about the law that applies to them.
A second minor challenge exists regarding the subject matter about which states may not regulate choice of law. Barring states from regulating choice of law entirely is an option, but if the focus is on privacy only, the preemption must be couched to allow regulation of choice of law in other areas. Thus, the scope of “privacy” must be in the language.
Finally, the withdrawal of state authority should probably be limited to positive enactments, such as statutes and regulations, leaving intact common-law practice related to choice-of-law provisions.[44] “Statute,” “enactment,” and “provision” are preferable in preemptive language to “law,” which is ambiguous.
These challenges, and possibly more, are tentatively addressed in the following first crack at statutory language, inspired by several preemptive federal statutes, including the Employee Retirement Income Security Act of 1974,[45] the Airline Deregulation Act,[46] the Federal Aviation Administration Authorization Act of 1994,[47] and the Federal Railroad Safety Act.[48]
A state, political subdivision of a state, or political authority of at least two states may not enact or enforce any statute, regulation, or other provision barring the adoption or application of any contractual choice-of-law provision to the extent it affects contract terms governing commercial collection, processing, security, or use of personal information.
This report introduces a statutory privacy framework centered on individual states and consistent with the United States’ constitutional design. But it safeguards companies from the challenge created by the intersection of that design and the development of modern commerce and communication, which may require them to navigate the complexities and inefficiencies of serving multiple regulators. It fosters an environment conducive to jurisdictional competition and experimentation.
We believe giving states the chance to compete under this approach should be explored in lieu of consolidating privacy law in the hands of one central federal regulator. Competition among states to provide optimal legislation and among businesses to provide optimal privacy policies will help discover and deliver on consumers’ interests, including privacy, of course, but also interactivity, convenience, low costs, and more.
Consumers’ diverse interests are not known now, and they cannot be predicted reliably for the undoubtedly interesting technological future. Thus, it is important to have a system for discovering consumers’ interests in privacy and the regulatory environments that best help businesses serve consumers. It is unlikely that a federal regulatory regime can do these things. The federal government could offer a 51st option in such a system, of course, so advocates for federal involvement could see their approach tested alongside the states’ approaches.
[1] See Uniform Law Commission, “What Is a Model Act?,” https://www.uniformlaws.org/acts/overview/modelacts.
[2] 740 Ill. Comp. Stat. 14/15 (2008).
[3] See Jim Harper, Privacy and the Four Categories of Information Technology, American Enterprise Institute, May 26, 2020, https://www.aei.org/research-products/report/privacy-and-the-four-categories-of-information-technology.
[4] See Jim Harper, “What Do People Mean by ‘Privacy,’ and How Do They Prioritize Among Privacy Values? Preliminary Results,” American Enterprise Institute, March 18, 2022, https://www.aei.org/research-products/report/what-do-people-mean-by-privacy-and-how-do-they-prioritize-among-privacy-values-preliminary-results.
[5] Gramm-Leach-Bliley Act, 15 U.S.C. 6801, § 501 et seq.
[6] Health Insurance Portability and Accountability Act of 1996, Pub. L. No. 104-191, § 264.
[7] Estelle Masse, quoted in Ashleigh Hollowell, “Is Privacy Only for the Elite? Why Apple’s Approach Is a Marketing Advantage,” VentureBeat, October 18, 2022, https://venturebeat.com/security/is-privacy-only-for-the-elite-why-apples-approach-is-a-marketing-advantage.
[8] Competition among firms regarding privacy is common, particularly in digital markets. Notably, Apple has implemented stronger privacy protections than most of its competitors have, particularly with its App Tracking Transparency framework in 2021. See, for example, Brain X. Chen, “To Be Tracked or Not? Apple Is Now Giving Us the Choice,” New York Times, April 26, 2021, https://www.nytimes.com/2021/04/26/technology/personaltech/apple-app-tracking-transparency.html. For Apple, this approach is built into the design of its products and offers what it considers a competitive advantage: “Because Apple designs both the iPhone and processors that offer heavy-duty processing power at low energy usage, it’s best poised to offer an alternative vision to Android developer Google which has essentially built its business around internet services.” Kif Leswing, “Apple Is Turning Privacy into a Business Advantage, Not Just a Marketing Slogan,” CNBC, June 8, 2021, https://www.cnbc.com/2021/06/07/apple-is-turning-privacy-into-a-business-advantage.html. Apple has built a substantial marketing campaign around these privacy differentiators, including its ubiquitous “Privacy. That’s Apple.” slogan. See Apple, “Privacy,” https://www.apple.com/privacy. Similarly, “Some of the world’s biggest brands (including Unilever, AB InBev, Diageo, Ferrero, Ikea, L’Oréal, Mars, Mastercard, P&G, Shell, Unilever and Visa) are focusing on taking an ethical and privacy-centered approach to data, particularly in the digital marketing and advertising context.” Rachel Dulberg, “Why the World’s Biggest Brands Care About Privacy,” Medium, September 14, 2021, https://uxdesign.cc/who-cares-about-privacy-ed6d832156dd.
[9] New State Ice Co. v. Liebmann, 285 US 262, 311 (1932) (Brandeis, J., dissenting) (“To stay experimentation in things social and economic is a grave responsibility. Denial of the right to experiment may be fraught with serious consequences to the Nation. It is one of the happy incidents of the federal system that a single courageous State may, if its citizens choose, serve as a laboratory; and try novel social and economic experiments without risk to the rest of the country.”).
[10] See Charles M. Tiebout, “A Pure Theory of Local Expenditures,” Journal of Political Economy 64, no. 5 (1956): 416–24, https://www.jstor.org/stable/1826343.
[11] See, for example, Barry R. Weingast, “The Economic Role of Political Institutions: Market-Preserving Federalism and Economic Development,” Journal of Law, Economics, & Organization 11, no. 1 (April 1995): 1 31, https://www.jstor.org/stable/765068; Yingyi Qian and Barry R. Weingast, “Federalism as a Commitment to Preserving Market Incentives,” Journal of Economic Perspectives 11, no. 4 (Fall 1997): 83–92, https://www.jstor.org/stable/2138464; and Rui J. P. de Figueiredo Jr. and Barry R. Weingast, “Self-Enforcing Federalism,” Journal of Law, Economics, & Organization 21, no. 1 (April 2005): 103–35, https://www.jstor.org/stable/3554986.
[12] See US Const. art. I, § 8 (enumerating the powers of the federal Congress).
[13] See generally Randy E. Barnett, Restoring the Lost Constitution: The Presumption of Liberty (Princeton, NJ: Princeton University Press, 2014), 274–318.
[14] Protection for Private Blocking and Screening of Offensive Material, 47 U.S.C. 230.
[15] See Geoffrey A. Manne, Ben Sperry, and Kristian Stout, “Who Moderates the Moderators? A Law & Economics Approach to Holding Online Platforms Accountable Without Destroying the Internet,” Rutgers Computer & Technology Law Journal 49, no. 1 (2022): 39–53, https://laweconcenter.org/wp-content/uploads/2021/11/Stout-Article-Final.pdf (detailing some of the history of how Section 230 immunity expanded and differs from First Amendment protections); Meghan Anand et al., “All the Ways Congress Wants to Change Section 230,” Slate, August 30, 2023, https://slate.com/technology/2021/03/section-230 reform-legislative-tracker.html (tracking every proposal to amend or repeal Section 230); and Technology & Marketing Law Blog, website, https://blog.ericgoldman.org (tracking all Section 230 cases with commentary).
[16] Fair Credit Reporting Act, 15 U.S.C. § 1681 et seq.
[17] See US Federal Trade Commission, Fair Credit Reporting Act: 15 U.S.C. § 1681, May 2023, https://www.ftc.gov/system/files/ftc_gov/pdf/fcra-may2023-508.pdf (detailing changes to the Fair Credit Reporting Act and its regulations over time).
[18] US Federal Reserve System, Consumer Financial Protection Bureau, “CFPB Launches Inquiry into the Business Practices of Data Brokers,” press release, May 15, 2023, https://www.consumerfinance.gov/about-us/newsroom/cfpb-launches-inquiry-into-the-business-practices-of-data-brokers.
[19] US Federal Reserve System, Consumer Financial Protection Bureau, List of Consumer Reporting Companies, 2021, 8, https://files.consumerfinance.gov/f/documents/cfpb_consumer-reporting-companies-list_03-2021.pdf (noting there are “three big nationwide providers of consumer reports”).
[20] See, for example, Erin A. O’Hara and Larry E. Ribstein, The Law Market (Oxford, UK: Oxford University Press, 2009); Erin A. O’Hara O’Connor and Larry E. Ribstein, “Conflict of Laws and Choice of Law,” in Procedural Law and Economics, ed. Chris William Sanchirico (Northampton, MA: Edward Elgar Publishing, 2012), in Encyclopedia of Law and Economics, 2nd ed., ed. Gerrit De Geest (Northampton, MA: Edward Elgar Publishing, 2009); and Bruce H. Kobayashi and Larry E. Ribstein, eds., Economics of Federalism (Northampton, MA: Edward Elgar Publishing, 2007).
[21] See F. A. Hayek, “The Use of Knowledge in Society,” American Economic Review 35, no. 4 (September 1945): 519–30, https://www.jstor.org/stable/1809376?seq=12.
[22] Henry N. Butler and Larry E. Ribstein, “Legal Process for Fostering Innovation” (working paper, George Mason University, Antonin Scalia Law School, Fairfax, VA), 2, https://masonlec.org/site/rte_uploads/files/Butler-Ribstein-Entrepreneurship-LER.pdf.
[23] See Henry N. Butler and Larry E. Ribstein, “The Single-License Solution,” Regulation 31, no. 4 (Winter 2008–09): 36–42, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1345900.
[24] See Uniform Law Commission, “Acts Overview,” https://www.uniformlaws.org/acts/overview.
[25] Utah Code Ann. § 13-63-101 et seq. (2023).
[26] O’Hara and Ribstein, The Law Market, 5.
[27] O’Hara and Ribstein, The Law Market, 5.
[28] O’Hara and Ribstein, The Law Market, 5.
[29] See Christiano Lima-Strong, “The U.S.’s Sixth State Privacy Law Is Too ‘Weak,’ Advocates Say,” Washington Post, March 30, 2023, https://www.washingtonpost.com/politics/2023/03/30/uss-sixth-state-privacy-law-is-too-weak-advocates-say.
[30] See, for example, William L. Cary, “Federalism and Corporate Law: Reflections upon Delaware,” Yale Law Journal 83, no. 4 (March 1974): 663–705, https://openyls.law.yale.edu/bitstream/handle/20.500.13051/15589/33_83YaleLJ663_1973_1974_.pdf (arguing Delaware could export the costs of inefficiently lax regulation through the dominance of its incorporation statute).
[31] Jonathan R. Macey and Geoffrey P. Miller, “Toward an Interest-Group Theory of Delaware Corporate Law,” Texas Law Review 65, no. 3 (February 1987): 470, https://openyls.law.yale.edu/bitstream/handle/20.500.13051/1029/Toward_An_Interest_Group_Theory_of_Delaware_Corporate_Law.pdf. See also Daniel R. Fischel, “The ‘Race to the Bottom’ Revisited: Reflections on Recent Developments in Delaware’s Corporation Law,” Northwestern University Law Review 76, no. 6 (1982): 913–45, https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?referer=&httpsredir=1&article=2409&context=journal_articles.
[32] Macey and Miller, “Toward an Interest-Group Theory of Delaware Corporate Law.”
[33] Macey and Miller, “Toward an Interest-Group Theory of Delaware Corporate Law.”
[34] Commission Regulation 2016/679, General Data Protection Regulation art. 23.
[35] Commission Regulation 2016/679, General Data Protection Regulation art. 85–91.
[36] Commission Regulation 2016/679, General Data Protection Regulation art. 56.
[37] See the discussion in endnote 8.
[38] Carnival Cruise Lines v. Shute, 499 US 585 (1991).
[39] The Bremen v. Zapata, 407 US 1 (1972).
[40] O’Hara and Ribstein, The Law Market, 8.
[41] See Jim Harper, “Perspective: Utah’s Social Media Legislation May Fail, but It’s Still Good for America,” Deseret News, April 6, 2023, https://www.aei.org/op-eds/utahs-social-media-legislation-may-fail-but-its-still-good-for-america.
[42] Utah Code Ann. § 13-63-401 (2023).
[43] See Bryan L. Adkins, Alexander H. Pepper, and Jay B. Sykes, Federal Preemption: A Legal Primer, Congressional Research Service, May 18, 2023, https://sgp.fas.org/crs/misc/R45825.pdf.
[44] Congress should not interfere with interpretation of choice-of-law provisions. These issues are discussed in Tanya J. Monestier, “The Scope of Generic Choice of Law Clauses,” UC Davis Law Review 56, no. 3 (February 2023): 959–1018, https://digitalcommons.law.buffalo.edu/cgi/viewcontent.cgi?article=2148&context=journal_articles.
[45] Employee Retirement Income Security Act of 1974, 29 U.S.C. § 1144(a).
[46] Airline Deregulation Act, 49 U.S.C. § 41713(b).
[47] Federal Aviation Administration Authorization Act of 1994, 49 U.S.C. § 14501.
[48] Federal Railroad Safety Act, 49 U.S.C. § 20106.
Regulatory Comments Executive Summary We welcome the opportunity to comment on the European Commission’s call for contributions on competition in “Virtual Worlds”.[1] The International Center for Law . . .
We welcome the opportunity to comment on the European Commission’s call for contributions on competition in “Virtual Worlds”.[1] The International Center for Law & Economics (“ICLE”) is a nonprofit, nonpartisan global research and policy center founded with the goal of building the intellectual foundations for sensible, economically grounded policy. ICLE promotes the use of law & economics methodologies to inform public-policy debates and has longstanding expertise in the evaluation of competition law and policy. ICLE’s interest is to ensure that competition law remains grounded in clear rules, established precedent, a record of evidence, and sound economic analysis.
The metaverse is an exciting and rapidly evolving set of virtual worlds. As with any new technology, concerns about the potential risks and negative consequences that the metaverse may bring have moved policymakers to explore how best to regulate this new space.
From the outset, it is important to recognize that simply because the metaverse is new does not mean that competition in this space is unregulated or somehow ineffective. Existing regulations may not explicitly or exclusively target metaverse ecosystems, but a vast regulatory apparatus already covers most aspects of business in virtual worlds. This includes European competition law, the Digital Markets Act (“DMA”), the General Data Protection Act (“GDPR), the Digital Services Act (“DSA”), and many more. Before it intervenes in this space, the commission should carefully consider whether there are any metaverse-specific problems not already addressed by these legal provisions.
This sense that competition intervention would be premature is reinforced by three important factors.
The first is that competition appears particularly intense in this space (Section I). There are currently multiple firms vying to offer compelling virtual worlds. At the time of writing, however, none appears close to dominating the market. In turn, this intense competition will encourage platforms to design services that meet consumers’ demands, notably in terms of safety and privacy. Nor does the market appear likely to fall into the hands of one of the big tech firms that command a sizeable share of more traditional internet services. Meta notoriously has poured more than $3.99 billion into its metaverse offerings during the first quarter of 2023, in addition to $13.72 billion the previous calendar year.[2] Despite these vast investments and a strategic focus on metaverse services, the company has, thus far, struggled to achieve meaningful traction in the space.[3]
Second, the commission’s primary concern appears to be that metaverses will become insufficiently “open and interoperable”.[4] But to the extent that these ecosystems do, indeed, become closed and proprietary, there is no reason to believe this to be a problem. Closed and proprietary ecosystems have several features that may be attractive to consumers and developers (Section II). These include improved product safety, performance, and ease of development. This is certainly not to say that closed ecosystems are always better than more open ones, but rather that it would be wrong to assume that one model or the other is optimal. Instead, the proper balance depends on tradeoffs that markets are better placed to decide.
Finally, timing is of the essence (Section III). Intervening so early in a fledgling industry’s life cycle is like shooting a moving target from a mile away. New rules or competition interventions might end up being irrelevant. Worse, by signaling that metaverses will be subject to heightened regulatory scrutiny for the foreseeable future, the commission may chill investment from the very firms is purports to support. In short, the commission should resist the urge to intervene so long as the industry is not fully mature.
The Commission is right to assume, in its call for contributions, that the extent to which metaverse services compete with each other (and continue to do so in the future) will largely determine whether they fulfil consumers’ expectations and meet the safety and trustworthiness requirements to which the commission aspires. As even the left-leaning Lessig put it:
Markets regulate behavior in cyberspace too. Prices structures often constrain access, and if they do not, then busy signals do. (America Online (AOL) learned this lesson when it shifted from an hourly to a flat-rate pricing plan.) Some sites on the web charge for access, as on-line services like AOL have for some time. Advertisers reward popular sites; online services drop unpopular forums. These behaviors are all a function of market constraints and market opportunity, and they all reflect the regulatory role of the market.[5]
Indeed, in a previous call for contributions, the Commission implicitly recognized the important role that competition plays, although it frames the subject primarily in terms of the problems that would arise if competition ceased to operate:
There is a risk of having a small number of big players becoming future gatekeepers of virtual worlds, creating market entry barriers and shutting out EU start-ups and SMEs from this emerging market. Such a closed ecosystem with the prevalence of proprietary systems can negatively affect the protection of personal information and data, the cybersecurity and the freedom and openness of virtual worlds at the same time.[6]
It is thus necessary to ask whether there is robust competition in the market for metaverse services. The short answer is a resounding yes.
While there is no precise definition of what constitutes a metaverse—much less a precise definition of the relevant market—available data suggests the space is highly competitive. This is evident in the fact that even a major global firm like Meta—having invested billions of dollars in its metaverse branch (and having rebranded the company accordingly)—has struggled to gain traction.[7]
Other major players in the space include the likes of Roblox, Fortnite, and Minecraft, which all have somewhere between 70 and 200 million active users.[8] This likely explains why Meta’s much-anticipated virtual world struggled to gain meaningful traction with consumers, stalling at around 300,000 active users.[9] Alongside these traditional players, there are also several decentralized platforms that are underpinned by blockchain technology. While these platforms have attracted massive investments, they are largely peripheral in terms of active users, with numbers often only in the low thousands.[10]
There are several inferences that can be drawn from these limited datasets. For one, it is clear that the metaverse industry is not yet fully mature. There are still multiple paradigms competing for consumer attention: game-based platforms versus social-network platforms; traditional platforms versus blockchain platforms, etc. In the terminology developed by David Teece, the metaverse industry has not yet reached a “paradigmatic” stage. It is fair to assume there is still significant scope for the entry of differentiated firms.[11]
It is also worth noting that metaverse competition does not appear to exhibit the same sort of network effects and tipping that is sometimes associated with more traditional social networks.[12] Despite competing for nearly a decade, no single metaverse project appears to be running away with the market.[13] This lack of tipping might be because these projects are highly differentiated.[14] It may also be due to the ease of multi-homing among them.[15]
More broadly, it is far from clear that competition will lead to a single metaverse for all uses. Different types of metaverse services may benefit from different user interfaces, graphics, and physics engines. This cuts in favor of multiple metaverses coexisting, rather than all services coordinating within a single ecosystem. Competition therefore appears likely lead to the emergence of multiple differentiated metaverses, rather than a single winner.
Ultimately, competition in the metaverse industry is strong and there is little sense these markets are about to tip towards a single firm in the year future.
As alluded to in the previous subsection, the world’s largest and most successful metaverse entrants to date are traditional videogaming platforms that have various marketplaces and currencies attached.[16] In other words, decentralized virtual worlds built upon blockchain technology remain marginal.
This has important policy implications. The primary legal issues raised by metaverses are the same as those encountered on other digital marketplaces. This includes issues like minor fraud, scams, and children buying content without their parents’ authorization.[17] To the extent these harms are not adequately deterred by existing laws, metaverse platforms themselves have important incentives to police them. In turn, these incentives may be compounded by strong competition among platforms.
Metaverses are generally multi-sided platforms that bring together distinct groups of users, including consumers and content creators. In order to maximize the value of their ecosystems, platforms have an incentive to balance the interests of these distinct groups.[18] In practice, this will often mean offering consumers various forms of protection against fraud and scams and actively policing platforms’ marketplaces. As David Evans puts it:
But as with any community, there are numerous opportunities for people and businesses to create negative externalities, or engage in other bad behavior, that can reduce economic efficiency and, in the extreme, lead to the tragedy of the commons. Multi-sided platforms, acting selfishly to maximize their own profits, often develop governance mechanisms to reduce harmful behavior. They also develop rules to manage many of the same kinds of problems that beset communities subject to public laws and regulations. They enforce these rules through the exercise of property rights and, most importantly, through the “Bouncer’s Right” to exclude agents from some quantum of the platform, including prohibiting some agents from the platform entirely…[19]
While there is little economic research to suggest that competition directly increases hosts’ incentive to policy their platforms, it stands to reason that doing so effectively can help platforms to expand the appeal of their ecosystems. This is particularly important for metaverse services whose userbases remain just a fraction of the size they could ultimately reach. While 100 or 200 million users already comprises a vast ecosystem, it pales in comparison to the sometimes billions of users that “traditional” online platforms attract.
The bottom line is that the market for metaverses is growing. This likely compounds platforms’ incentives to weed out undesirable behavior, thereby complementing government efforts to achieve the same goal.
In its call for contributions, the commission seems concerned that the metaverse competition may lead to closed ecosystems that may be less beneficial to consumers than more open ones. But if this is indeed the commission’s fear, it is largely unfounded.
There are many benefits to closed ecosystems. Choosing the optimal degree of openness entails tradeoffs. At the very least, this suggests that policymakers should be careful not to assume that opening platforms up will systematically provide net benefits to consumers.
To understand why open (and weakly propertized) platforms are not always better for consumers, it is worth looking at past competition enforcement in the online space. Recent interventions by competition authorities have generally attempted (or are attempting) to move platforms toward more openness and less propertization. For their part, these platforms are already tremendously open (as the “platform” terminology implies) and attempt to achieve a delicate balance between centralization and decentralization.
The Microsoft cases and the Apple investigation both sought or seek to bring more openness and less propertization to those respective platforms. Microsoft was made to share proprietary data with third parties (less propertization) and to open its platform to rival media players and web browsers (more openness).[20] The same applies to Apple. Plaintiffs in private antitrust litigation brought in the United States[21] and government enforcement actions in Europe[22] are seeking to limit the fees that Apple can extract from downstream rivals (less propertization), as well as to ensure that it cannot exclude rival mobile-payments solutions from its platform (more openness).
The various cases that were brought by EU and U.S. authorities against Qualcomm broadly sought to limit the extent to which it was monetizing its intellectual property.[23] The European Union’s Amazon investigation centers on the ways in which the company uses data from third-party sellers (and, ultimately, the distribution of revenue between those sellers and Amazon).[24] In both cases, authorities are ultimately trying to limit the extent to which firms can propertize their assets.
Finally, both of the EU’s Google cases sought to bring more openness to the company’s main platform. The Google Shopping decision sanctioned Google for purportedly placing its services more favorably than those of its rivals.[25] The separate Android decision sought to facilitate rival search engines’ and browsers’ access to the Android ecosystem. The same appears to be true of ongoing litigation brought by state attorneys general in the United States.[26]
Much of the same can be said of the numerous regulatory initiatives pertaining to digital markets. Indeed, draft regulations being contemplated around the globe mimic the features of the antitrust/competition interventions discussed above. For instance, it is widely accepted that Europe’s DMA effectively transposes and streamlines the enforcement of the theories harm described above.[27] Similarly, several scholars have argued that the proposed American Innovation and Choice Online Act (“AICOA”) in the United States largely mimics European competition policy.[28] The legislation would ultimately require firms to open up their platforms, most notably by forcing them to treat rival services as they would their own and to make their services more interoperable with those rivals.[29]
What is striking about these decisions and investigations is the extent to which authorities are pushing back against the very features that distinguish the platforms they are investigating. Closed (or relatively closed) platforms are forced to open up, and firms with highly propertized assets are made to share them (or, at the very least, monetize them less aggressively).
All of this would not be very interesting if it weren’t for a final piece of the puzzle: the model of open and shared platforms that authorities apparently favor has traditionally struggled to gain traction with consumers. Indeed, there seem to be vanishingly few successful consumer-oriented products and services in this space.
There have been numerous attempts to introduce truly open consumer-oriented operating systems in both the mobile and desktop segments. Most have ended in failure. Ubuntu and other flavors of the Linux operating system remain fringe products. There have been attempts to create open-source search engines, but they have not met with success.[30] The picture is similar in the online retail space. Amazon appears to have beaten eBay, despite the latter being more open and less propertized. Indeed, Amazon has historically charged higher fees than eBay and offers sellers much less freedom in the ways in which they may sell their goods.[31]
This theme is repeated in the standardization space. There have been innumerable attempts to impose open, royalty-free standards. At least in the mobile-internet industry, few (if any) of these have taken off. Instead, proprietary standards such as 5G and WiFi have been far more successful. That pattern is repeated in other highly standardized industries, like digital-video formats. Most recently, the proprietary Dolby Vision format seems to be winning the war against the open HDR10+ format.[32]
This is not to say that there haven’t been any successful examples of open, royalty-free standards. Internet protocols, blockchain, and Wikipedia all come to mind. Nor does it mean that we will not see more decentralized goods in the future. But by and large, firms and consumers have not yet taken to the idea of fully open and shared platforms. Or, at least, those platforms have not yet achieved widespread success in the marketplace (potentially due to supply-side considerations, such as the difficulty of managing open platforms or the potentially lower returns to innovation in weakly propertized ones).[33] And while some “open” projects have achieved tremendous scale, the consumer-facing side of these platforms is often dominated by intermediaries that opt for much more traditional business models (think of Coinbase in the blockchain space, or Android’s use of Linux).
The preceding section posited a recurring reality: the digital platforms that competition authorities wish to bring into existence are fundamentally different from those that emerge organically. But why have authorities’ ideal platforms, so far, failed to achieve truly meaningful success?
Three potential explanations come to mind. First, “closed” and “propertized” platforms might systematically—and perhaps anticompetitively—thwart their “open” and “shared” rivals. Second, shared platforms might fail to persist (or grow pervasive) because they are much harder to monetize, and there is thus less incentive to invest in them. This is essentially a supply-side explanation. Finally, consumers might opt for relatively closed systems precisely because they prefer these platforms to marginally more open ones—i.e., a demand-side explanation.
In evaluating the first conjecture, the key question is whether successful “closed” and “propertized” platforms overcame their rivals before or after they achieved some measure of market dominance. If success preceded dominance, then anticompetitive foreclosure alone cannot explain the proliferation of the “closed” and “propertized” model.[34]
Many of today’s dominant platforms, however, often overcame open/shared rivals, well before they achieved their current size. It is thus difficult to make the case that the early success of their business models was due to anticompetitive behavior. This is not to say these business models cannot raise antitrust issues, but rather that anticompetitive behavior is not a good explanation for their emergence.
Both the second and the third conjectures essentially ask whether “closed” and “propertized” might be better adapted to their environment than “open” and “shared” rivals.
In that respect, it is not unreasonable to surmise that highly propertized platforms would generally be easier to monetize than shared ones. For example, to monetize open-source platforms often requires relying on complementarities, which tend to be vulnerable to outside competition and free-riding.[35] There is thus a natural incentive for firms to invest and innovate in more propertized environments. In turn, competition enforcement that limits a platform’s ability to propertize their assets may harm innovation.
Similarly, authorities should reflect on whether consumers really want the more “competitive” ecosystems that they are trying to design. The European Commission, for example, has a long track record of seeking to open digital platforms, notably by requiring that platform owners do not preinstall their own web browsers (the Microsoft decisions are perhaps the most salient example). And yet, even after these interventions, new firms have kept using the very business model that the commission reprimanded, rather than the “pro-consumer” model it sought to impose on the industry. For example, Apple tied the Safari browser to its iPhones; Google went to some length to ensure that Chrome was preloaded on devices; and Samsung phones come with Samsung Internet as default.[36] Yet this has not ostensibly steered consumers away from those platforms.
Along similar lines, a sizable share of consumers opt for Apple’s iPhone, which is even more centrally curated than Microsoft Windows ever was (and the same is true of Apple’s MacOS). In other words, it is hard to claim that opening platforms is inherently good for consumers when those same consumers routinely opt for platforms with the very features that policymakers are trying to eliminate.
Finally, it is worth noting that the remedies imposed by competition authorities have been anything but successes. Windows XP N (the version of Windows that came without Windows Media Player) was an unmitigated flop, selling a paltry 1,787 copies.[37] Likewise, the internet-browser “ballot box” imposed by the commission was so irrelevant to consumers that it took months for authorities to notice that Microsoft had removed it, in violation of the commission’s decision.[38]
One potential inference is that consumers do not value competition interventions that make dominant ecosystems marginally more open and less propertized. There are also many reasons why consumers might prefer “closed” systems (at least, relative to the model favored by many policymakers), even when they must pay a premium for them.
Take the example of app stores. Maintaining some control over the apps that can access the store enables platforms to easily weed out bad actors. Similarly, controlling the hardware resources that each app can use may greatly improve device performance. Indeed, it may be that a measure of control facilitates the very innovations that consumers demand. Therefore, “authorities and courts should not underestimate the indispensable role control plays in achieving coordination and coherence in the context of systemic ef?ciencies. Without it, the attempted novelties and strategies might collapse under their own complexity.”[39]
Relatively centralized platforms can eliminate negative externalities that “bad” apps impose on rival apps and consumers.[40] This is especially true when consumers will tend to attribute dips in performance to the overall platform, rather than to a particular app.[41] At the same time, they can take advantage of positive externalities to improve the quality of the overall platform.
And it is surely the case that consumers prefer to make many of their decisions at the inter-platform level, rather than within each platform. In simple terms, users arguably make their most important decision when they choose between an Apple or Android smartphone (or a Mac and a PC, etc.). In doing so, they can select their preferred app suite with one simple decision. They might thus purchase an iPhone because they like the secure App Store, or an Android smartphone because they like the Chrome Browser and Google Search. Absent false information at the time of the initial platform decision, this decision will effectively incorporate expectations about subsequent constraints.[42]
Furthermore, forcing users to make too many “within-platform” choices may undermine a product’s attractiveness. Indeed, it is difficult to create a high-quality reputation if each user’s experience is fundamentally different.[43] In short, contrary to what antitrust authorities appear to believe, closed platforms might give most users exactly what they desire.
All of this suggests that consumers and firms often gravitate spontaneously toward both closed and highly propertized platforms, the opposite of what the commission and other competition authorities tend to favor. The reasons for this trend are still misunderstood, and mostly ignored. Too often it is simply assumed that consumers benefit from more openness, and that shared/open platforms are the natural order of things. Instead, what some regard as “market failures” may in fact be features that explain the rapid emergence of the digital economy.
When considering potential policy reforms targeting the metaverse, policymakers would be wrong to assume openness (notably, in the form of interoperability) and weak propertization are always objectively superior. Instead, these platform designs entail important tradeoffs. Closed metaverse ecosystems may lead to higher consumer safety and better performance, while interoperable systems may reduce the frictions consumers face when moving from one service to another. There is little reason to believe policymakers are in a better position to weigh these tradeoffs than consumers, who vote with their virtual feet.
A final important argument against intervening today is that the metaverse industry is nowhere near mature. Tomorrow’s competition-related challenges and market failures might not be the same as today’s. This makes it exceedingly difficult for policymakers to design appropriate remedies and increases the risk that intervention might harm innovation.
As of 2023, the entire metaverse industry (both hardware and software) is estimated to be worth somewhere in the vicinity of $80 billion, and projections suggest this could grow by a factor of 10 by 2030.[44] Growth projections of this sort are notoriously unreliable. But in this case, they do suggest there is some consensus that the industry is not fully fledged.
Along similar lines, it remains unclear what types of metaverse services will gain the most traction with consumers, what sorts of hardware consumers will use to access these services, and what technologies will underpin the most successful metaverse platforms. In fact, it is still an open question whether the metaverse industry will foster any services that achieve widespread consumer adoption in the foreseeable future.[45] In other words, it is not exactly clear what metaverse products and services the Commission should focus on in the first place.
Given these uncertainties, competition intervention in the metaverse appears premature. Intervening so early in the industry’s life cycle is like aiming at a moving target. Ensuing remedies might end up being irrelevant before they have any influence on the products that firms develop. More worryingly, acting now signals that the metaverse industry will be subject to heightened regulatory scrutiny for the foreseeable future. In turn, this may deter large platforms from investing in the European market. It also may funnel venture-capital investments away from the European continent.
Competition intervention in burgeoning industries is no free lunch. The best evidence concerning these potential costs comes from the GDPR. While privacy regulation is obviously not the same as competition law, the evidence concerning the GDPR suggests that heavy-handed intervention may, at least in some instances, slow down innovation and reduce competition.
The most-cited empirical evidence concerning the effects of the GDPR comes from a paper by Garrett Johnson and co-authors, who link the GDPR to widespread increases to market concentration, particularly in the short-term:
We show that websites’ vendor use falls after the European Union’s (EU’s) General Data Protection Regulation (GDPR), but that market concentration also increases among technology vendors that provide support services to websites…. The week after the GDPR’s enforcement, website use of web technology vendors falls by 15% for EU residents. Websites are relatively more likely to retain top vendors, which increases the concentration of the vendor market by 17%. Increased concentration predominantly arises among vendors that use personal data, such as cookies, and from the increased relative shares of Facebook and Google-owned vendors, but not from website consent requests. Although the aggregate changes in vendor use and vendor concentration dissipate by the end of 2018, we find that the GDPR impact persists in the advertising vendor category most scrutinized by regulators.[46]
Along similar lines, an NBER working paper by Jian Jia and co-authors finds that enactment of the GDPR markedly reduced venture-capital investments in Europe:
Our findings indicate a negative differential effect on EU ventures after the rollout of GDPR relative to their US counterparts. These negative effects manifest in the overall number of financing rounds, the overall dollar amount raised across rounds, and in the dollar amount raised per individual round. Specifically, our findings suggest a $3.38 million decrease in the aggregate dollars raised by EU ventures per state per crude industry category per week, a 17.6% reduction in the number of weekly venture deals, and a 39.6% decrease in the amount raised in an average deal following the rollout of GDPR.[47]
In another paper, Samuel Goldberg and co-authors find that the GDPR led to a roughly 12% reduction in website pageviews and e-commerce revenue in Europe.[48] Finally, Rebecca Janssen and her co-authors show that the GDPR decreased the number of apps offered on Google’s Play Store between 2016 and 2019:
Using data on 4.1 million apps at the Google Play Store from 2016 to 2019, we document that GDPR induced the exit of about a third of available apps; and in the quarters following implementation, entry of new apps fell by half.[49]
Of course, the body of evidence concerning the GDPR’s effects is not entirely unambiguous. For example, Rajkumar Vekatesean and co-authors find that the GDPR had mixed effects on the returns of different types of firms.[50] Other papers also show similarly mixed effects.[51]
Ultimately, the empirical literature concerning the effects of the GDPR shows that regulation—in this case, privacy protection—is no free lunch. Of course, this does not mean that competition intervention targeting the metaverse would necessarily have these same effects. But in the absence of a clear market failure to solve, it is unclear why policymakers should run such a risk in the first place.
In the end, competition intervention in the metaverse is unlikely to be costless. The metaverse is still in its infancy, regulation could deter essential innovation, and the commission has thus far failed to identify any serious market failures that warrant public intervention. The result is that the commission’s call for contributions appears premature or, in other words, that the commission is putting the meta-cart before the meta-horse.
[1] Competition in Virtual Worlds and Generative AI – Calls for contributions, European Commission (Jan. 9, 2024) https://competition-policy.ec.europa.eu/document/download/e727c66a-af77-4014-962a-7c9a36800e2f_en?filename=20240109_call-for-contributions_virtual-worlds_and_generative-AI.pdf (hereafter, “Call for Contributions”).
[2] Jonathan Vaian, Meta’s Reality Labs Records $3.99 Billion Quarterly Loss as Zuckerberg Pumps More Cash into Metaverse, CNBC (Apr. 26, 2023), https://www.cnbc.com/2023/04/26/metas-reality-labs-unit-records-3point99-billion-first-quarter-loss-.html.
[3] Alan Truly, Horizon Worlds Leak: Only 1 in 10 Users Return & Web Launch Is Coming, Mixed News (Mar. 3, 2023), https://mixed-news.com/en/horizon-worlds-leak-only-1-in-10-users-return-web-launch-coming; Kevin Hurler, Hey Fellow Kids: Meta Is Revamping Horizon Worlds to Attract More Teen Users, Gizmodo (Feb. 7, 2023), https://gizmodo.com/meta-metaverse-facebook-horizon-worlds-vr-1850082068; Emma Roth, Meta’s Horizon Worlds VR Platform Is Reportedly Struggling to Keep Users, The Verge (Oct. 15, 2022), https://www.theverge.com/2022/10/15/23405811/meta-horizon-worlds-losing-users-report; Paul Tassi, Meta’s ‘Horizon Worlds’ Has Somehow Lost 100,000 Players in Eight Months, Forbes, (Oct. 17, 2022), https://www.forbes.com/sites/paultassi/2022/10/17/metas-horizon-worlds-has-somehow-lost-100000-players-in-eight-months/?sh=57242b862a1b.
[4] Call for Contributions, supra note 1. (“6) Do you expect the technology incorporated into Virtual World platforms, enabling technologies of Virtual Worlds and services based on Virtual Worlds to be based mostly on open standards and/or protocols agreed through standard-setting organisations, industry associations or groups of companies, or rather the use of proprietary technology?”).
[5] Less Lawrence Lessig, The Law of the Horse: What Cyberlaw Might Teach, 113 Harv. L. Rev. 508 (1999).
[6] Virtual Worlds (Metaverses) – A Vision for Openness, Safety and Respect, European Commission, https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/13757-Virtual-worlds-metaverses-a-vision-for-openness-safety-and-respect/feedback_en?p_id=31962299H.
[7] Catherine Thorbecke, What Metaverse? Meta Says Its Single Largest Investment Is Now in ‘Advancing AI’, CNN Business (Mar. 15, 2023), https://www.cnn.com/2023/03/15/tech/meta-ai-investment-priority/index.html; Ben Marlow, Mark Zuckerberg’s Metaverse Is Shattering into a Million Pieces, The Telegraph (Apr. 23, 2023), https://www.telegraph.co.uk/business/2023/04/21/mark-zuckerbergs-metaverse-shattering-million-pieces; Will Gendron, Meta Has Reportedly Stopped Pitching Advertisers on the Metaverse, BusinessInsider (Apr. 18, 2023), https://www.businessinsider.com/meta-zuckerberg-stopped-pitching-advertisers-metaverse-focus-reels-ai-report-2023-4.
[8] Mansoor Iqbal, Fortnite Usage and Revenue Statistics, Business of Apps (Jan. 9, 2023), https://www.businessofapps.com/data/fortnite-statistics; Matija Ferjan, 76 Little-Known Metaverse Statistics & Facts (2023 Data), Headphones Addict (Feb. 13, 2023), https://headphonesaddict.com/metaverse-statistics.
[9] James Batchelor, Meta’s Flagship Metaverse Horizon Worlds Struggling to Attract and Retain Users, Games Industry (Oct. 17, 2022), https://www.gamesindustry.biz/metas-flagship-metaverse-horizon-worlds-struggling-to-attract-and-retain-users; Ferjan, id.
[10] Richard Lawler, Decentraland’s Billion-Dollar ‘Metaverse’ Reportedly Had 38 Active Users in One Day, The Verge (Oct. 13, 2022), https://www.theverge.com/2022/10/13/23402418/decentraland-metaverse-empty-38-users-dappradar-wallet-data; The Sandbox, DappRadar, https://dappradar.com/multichain/games/the-sandbox (last visited May 3, 2023); Decentraland, DappRadar, https://dappradar.com/multichain/social/decentraland (last visited May 3, 2023).
[11] David J. Teece, Profiting from Technological Innovation: Implications for Integration, Collaboration, Licensing and Public Policy, 15 Research Policy 285-305 (1986), https://www.sciencedirect.com/science/article/abs/pii/0048733386900272.
[12] Geoffrey Manne & Dirk Auer, Antitrust Dystopia and Antitrust Nostalgia: Alarmist Theories of Harm in Digital Markets and Their Origins, 28 Geo. Mason L. Rev. 1279 (2021).
[13] Roblox, Wikipedia, https://en.wikipedia.org/wiki/Roblox (last visited May 3, 2023); Minecraft, Wikipedia, https://en.wikipedia.org/wiki/Minecraft (last visited May 3, 2023); Fortnite, Wikipedia, https://en.wikipedia.org/wiki/Fortnite (last visited May 3, 2023); see Fiza Chowdhury, Minecraft vs Roblox vs Fortnite: Which Is Better?, Metagreats (Feb. 20, 2023), https://www.metagreats.com/minecraft-vs-roblox-vs-fortnite.
[14] Marc Rysman, The Economics of Two-Sided Markets, 13 J. Econ. Perspectives 134 (2009) (“First, if standards can differentiate from each other, they may be able to successfully coexist (Chou and Shy, 1990; Church and Gandal, 1992). Arguably, Apple and Microsoft operating systems have both survived by specializing in different markets: Microsoft in business and Apple in graphics and education. Magazines are an obvious example of platforms that differentiate in many dimensions and hence coexist.”).
[15] Id. at 134 (“Second, tipping is less likely if agents can easily use multiple standards. Corts and Lederman (forthcoming) show that the fixed cost of producing a video game for one more standard have reduced over time relative to the overall fixed costs of producing a game, which has led to increased distribution of games across multiple game systems (for example, PlayStation, Nintendo, and Xbox) and a less-concentrated game system market.”).
[16] What Are Fortnite, Roblox, Minecraft and Among Us? A Parent’s Guide to the Most Popular Online Games Kids Are Playing, FTC Business (Oct. 5, 2021), https://www.ftc.net/blog/what-are-fortnite-roblox-minecraft-and-among-us-a-parents-guide-to-the-most-popular-online-games-kids-are-playing; Jay Peters, Epic Is Merging Its Digital Asset Stores into One Huge Marketplace, The Verge (Mar. 22, 2023), https://www.theverge.com/2023/3/22/23645601/epic-games-fab-asset-marketplace-state-of-unreal-2023-gdc.
[17] Luke Winkie, Inside Roblox’s Criminal Underworld, Where Kids Are Scamming Kids, IGN (Jan. 2, 2023), https://www.ign.com/articles/inside-robloxs-criminal-underworld-where-kids-are-scamming-kids; Fake Minecraft Updates Pose Threat to Users, Tribune (Sept. 11, 2022), https://tribune.com.pk/story/2376087/fake-minecraft-updates-pose-threat-to-users; Ana Diaz, Roblox and the Wild West of Teenage Scammers, Polygon (Aug. 24, 2019) https://www.polygon.com/2019/8/24/20812218/roblox-teenage-developers-controversy-scammers-prison-roleplay; Rebecca Alter, Fortnite Tries Not to Scam Children and Face $520 Million in FTC Fines Challenge, Vulture (Dec. 19, 2022), https://www.vulture.com/2022/12/fortnite-epic-games-ftc-fines-privacy.html; Leonid Grustniy, Swindle Royale: Fortnite Scammers Get Busy, Kaspersky Daily (Dec. 3, 2020), https://www.kaspersky.com/blog/top-four-fortnite-scams/37896.
[18] See, generally, David Evans & Richard Schmalensee, Matchmakers: The New Economics of Multisided Platforms (Harvard Business Review Press, 2016).
[19] David S. Evans, Governing Bad Behaviour By Users of Multi-Sided Platforms, Berkley Technology Law Journal 27:2 (2012), 1201.
[20] See Case COMP/C-3/37.792, Microsoft, OJ L 32 (May 24, 2004). See also, Case COMP/39.530, Microsoft (Tying), OJ C 120 (Apr. 26, 2013).
[21] See Complaint, Epic Games, Inc. v. Apple Inc., 493 F. Supp. 3d 817 (N.D. Cal. 2020) (4:20-cv-05640-YGR).
[22] See European Commission Press Release IP/20/1073, Antitrust: Commission Opens Investigations into Apple’s App Store Rules (Jun. 16, 2020); European Commission Press Release IP/20/1075, Antitrust: Commission Opens Investigation into Apple Practices Regarding Apple Pay (Jun. 16, 2020).
[23] See European Commission Press Release IP/18/421, Antitrust: Commission Fines Qualcomm €997 Million for Abuse of Dominant Market Position (Jan. 24, 2018); Federal Trade Commission v. Qualcomm Inc., 969 F.3d 974 (9th Cir. 2020).
[24] See European Commission Press Release IP/19/4291, Antitrust: Commission Opens Investigation into Possible Anti-Competitive Conduct of Amazon (Jul. 17, 2019).
[25] See Case AT.39740, Google Search (Shopping), 2017 E.R.C. I-379. See also, Case AT.40099 (Google Android), 2018 E.R.C.
[26] See Complaint, United States v. Google, LLC, (2020), https://www.justice.gov/opa/pr/justice-department-sues-monopolist-google-violating-antitrust-laws; see also, Complaint, Colorado et al. v. Google, LLC, (2020), available at https://coag.gov/app/uploads/2020/12/Colorado-et-al.-v.-Google-PUBLIC-REDACTED-Complaint.pdf.
[27] See, e.g., Giorgio Monti, The Digital Markets Act: Institutional Design and Suggestions for Improvement, Tillburg L. & Econ. Ctr., Discussion Paper No. 2021-04 (2021), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3797730 (“In sum, the DMA is more than an enhanced and simplified application of Article 102 TFEU: while the obligations may be criticised as being based on existing competition concerns, they are forward-looking in trying to create a regulatory environment where gatekeeper power is contained and perhaps even reduced.”) (Emphasis added).
[28] See, e.g., Aurelien Portuese, “Please, Help Yourself”: Toward a Taxonomy of Self-Preferencing, Information Technology & Innovation Foundation (Oct. 25, 2021), available at https://itif.org/sites/default/files/2021-self-preferencing-taxonomy.pdf. (“The latest example of such weaponization of self-preferencing by antitrust populists is provided by Sens. Amy Klobuchar (D-MN) and Chuck Grassley (R-IA). They introduced legislation in October 2021 aimed at prohibiting the practice.2 However, the legislation would ban self-preferencing only for a handful of designated companies—the so-called “covered platforms,” not the thousands of brick-and-mortar sellers that daily self-preference for the benefit of consumers. Mimicking the European Commission’s Digital Markets Act prohibiting self-preferencing, Senate and the House bills would degrade consumers’ experience and undermine competition, since self-preferencing often benefits consumers and constitutes an integral part, rather than an abnormality, of the process of competition.”).
[29] Efforts to saddle platforms with “non-discrimination” constraints are tantamount to mandating openness. See Geoffrey A. Manne, Against the Vertical Discrimination Presumption, Foreword, Concurrences No. 2-2020 (2020) at 2 (“The notion that platforms should be forced to allow complementors to compete on their own terms, free of constraints or competition from platforms is a species of the idea that platforms are most socially valuable when they are most ‘open.’ But mandating openness is not without costs, most importantly in terms of the effective operation of the platform and its own incentives for innovation.”).
[30] See, e.g., Klint Finley, Your Own Private Google: The Quest for an Open Source Search Engine, Wired (Jul. 12, 2021), https://www.wired.com/2012/12/solar-elasticsearch-google.
[31] See Brian Connolly, Selling on Amazon vs. eBay in 2021: Which Is Better?, JungleScout (Jan. 12, 2021), https://www.junglescout.com/blog/amazon-vs-ebay; Crucial Differences Between Amazon and eBay, SaleHOO, https://www.salehoo.com/educate/selling-on-amazon/crucial-differences-between-amazon-and-ebay (last visited Feb. 8, 2021).
[32] See, e.g., Dolby Vision Is Winning the War Against HDR10 +, It Requires a Single Standard, Tech Smart, https://voonze.com/dolby-vision-is-winning-the-war-against-hdr10-it-requires-a-single-standard (last visited June 6, 2022).
[33] On the importance of managers, see, e.g., Nicolai J Foss & Peter G Klein, Why Managers Still Matter, 56 MIT Sloan Mgmt. Rev., 73 (2014) (“In today’s knowledge-based economy, managerial authority is supposedly in decline. But there is still a strong need for someone to define and implement the organizational rules of the game.”).
[34] It is generally agreed upon that anticompetitive foreclosure is possible only when a firm enjoys some degree of market power. Frank H. Easterbrook, Limits of Antitrust, 63 Tex. L. Rev. 1, 20 (1984) (“Firms that lack power cannot injure competition no matter how hard they try. They may injure a few consumers, or a few rivals, or themselves (see (2) below) by selecting ‘anticompetitive’ tactics. When the firms lack market power, though, they cannot persist in deleterious practices. Rival firms will offer the consumers better deals. Rivals’ better offers will stamp out bad practices faster than the judicial process can. For these and other reasons many lower courts have held that proof of market power is an indispensable first step in any case under the Rule of Reason. The Supreme Court has established a market power hurdle in tying cases, despite the nominally per se character of the tying offense, on the same ground offered here: if the defendant lacks market power, other firms can offer the customer a better deal, and there is no need for judicial intervention.”).
[35] See, e.g., Josh Lerner & Jean Tirole, Some Simple Economics of Open Source, 50 J. Indus. Econ. 197 (2002).
[36] See Matthew Miller, Thanks, Samsung: Android’s Best Mobile Browser Now Available to All, ZDNet (Aug. 11, 2017), https://www.zdnet.com/article/thanks-samsung-androids-best-mobile-browser-now-available-to-all.
[37] FACT SHEET: Windows XP N Sales, RegMedia (Jun. 12, 2009), available at https://regmedia.co.uk/2009/06/12/microsoft_windows_xp_n_fact_sheet.pdf.
[38] See Case COMP/39.530, Microsoft (Tying), OJ C 120 (Apr. 26, 2013).
[39] Konstantinos Stylianou, Systemic Efficiencies in Competition Law: Evidence from the ICT Industry, 12 J. Competition L. & Econ. 557 (2016).
[40] See, e.g., Steven Sinofsky, The App Store Debate: A Story of Ecosystems, Medium (Jun. 21, 2020), https://medium.learningbyshipping.com/the-app-store-debate-a-story-of-ecosystems-938424eeef74.
[41] Id.
[42] See, e.g., Benjamin Klein, Market Power in Aftermarkets, 17 Managerial & Decision Econ. 143 (1996).
[43] See, e.g., Simon Hill, What Is Android Fragmentation, and Can Google Ever Fix It?, DigitalTrends (Oct. 31, 2018), https://www.digitaltrends.com/mobile/what-is-android-fragmentation-and-can-google-ever-fix-it.
[44] Metaverse Market Revenue Worldwide from 2022 to 2030, Statista, https://www.statista.com/statistics/1295784/metaverse-market-size (last visited May 3, 2023); Metaverse Market by Component (Hardware, Software (Extended Reality Software, Gaming Engine, 3D Mapping, Modeling & Reconstruction, Metaverse Platform, Financial Platform), and Professional Services), Vertical and Region – Global Forecast to 2027, Markets and Markets (Apr. 27, 2023), https://www.marketsandmarkets.com/Market-Reports/metaverse-market-166893905.html; see also, Press Release, Metaverse Market Size Worth $ 824.53 Billion, Globally, by 2030 at 39.1% CAGR, Verified Market Research (Jul. 13, 2022), https://www.prnewswire.com/news-releases/metaverse-market-size-worth–824-53-billion-globally-by-2030-at-39-1-cagr-verified-market-research-301585725.html.
[45] See, e.g., Megan Farokhmanesh, Will the Metaverse Live Up to the Hype? Game Developers Aren’t Impressed, Wired (Jan. 19, 2023), https://www.wired.com/story/metaverse-video-games-fortnite-zuckerberg; see also Mitch Wagner, The Metaverse Hype Bubble Has Popped. What Now?, Fierce Electronics (Feb. 24, 2023), https://www.fierceelectronics.com/embedded/metaverse-hype-bubble-has-popped-what-now.
[46] Garret A. Johnson, et al., Privacy and Market Concentration: Intended and Unintended Consequences of the GDPR, Forthcoming Management Science 1 (2023).
[47] Jian Jia, et al., The Short-Run Effects of GDPR on Technology Venture Investment, NBER Working Paper 25248, 4 (2018), available at https://www.nber.org/system/files/working_papers/w25248/w25248.pdf.
[48] Samuel G. Goldberg, Garrett A. Johnson, & Scott K. Shriver, Regulating Privacy Online: An Economic Evaluation of GDPR (2021), available at https://www.ftc.gov/system/files/documents/public_events/1588356/johnsongoldbergshriver.pdf.
[49] Rebecca Janßen, Reinhold Kesler, Michael Kummer, & Joel Waldfogel, GDPR and the Lost Generation of Innovative Apps, Nber Working Paper 30028, 2 (2022), available at https://www.nber.org/system/files/working_papers/w30028/w30028.pdf.
[50] Rajkumar Venkatesan, S. Arunachalam & Kiran Pedada, Short Run Effects of Generalized Data Protection Act on Returns from AI Acquisitions, University of Virginia Working Paper 6 (2022), available at: https://conference.nber.org/conf_papers/f161612.pdf. (“On average, GDPR exposure reduces the ROA of firms. We also find that GDPR exposure increases the ROA of firms that make AI acquisitions for improving customer experience, and cybersecurity. Returns on AI investments in innovation and operational efficiencies are unaffected by GDPR.”)
[51] For a detailed discussion of the empirical literature concerning the GDPR, see Garrett Johnson, Economic Research on Privacy Regulation: Lessons From the GDPR And Beyond, NBER Working Paper 30705 (2022), available at https://www.nber.org/system/files/working_papers/w30705/w30705.pdf.
Regulatory Comments Executive Summary We thank the European Commission for launching this consultation on competition in generative AI. The International Center for Law & Economics (“ICLE”) is . . .
We thank the European Commission for launching this consultation on competition in generative AI. The International Center for Law & Economics (“ICLE”) is a nonprofit, nonpartisan global research and policy center founded with the goal of building the intellectual foundations for sensible, economically grounded policy. ICLE promotes the use of law & economics methodologies to inform public-policy debates and has longstanding expertise in the evaluation of competition law and policy. ICLE’s interest is to ensure that competition law remains grounded in clear rules, established precedent, a record of evidence, and sound economic analysis.
In our comments, we express concern that policymakers may equate the rapid rise of generative AI services with a need to intervene in these markets when, in fact, the opposite is true. As we explain, the rapid growth of AI markets, as well as the fact that new market players are thriving, suggests competition is intense. If incumbent firms could easily leverage their dominance into burgeoning generative AI markets, we would not have seen the growth of generative AI unicorns such as OpenAI, Midjourney, and Anthropic, to name but a few.
Of course, this is not to say that generative AI markets are not important—quite the opposite. Generative AI is already changing the ways that many firms do business and improving employee productivity in many industries.[1] The technology is also increasingly useful in the field of scientific research, where it has enabled creation of complex models that expand scientists’ reach.[2] Against this backdrop, Commissioner Margrethe Vestager was right to point out that it “is fundamental that these new markets stay competitive, and that nothing stands in the way of businesses growing and providing the best and most innovative products to consumers.”[3]
But while sensible enforcement is of vital importance to maintain competition and consumer welfare, knee-jerk reactions may yield the opposite outcomes. As our comments explain, overenforcement in the field of generative AI could cause the very harms that policymakers seek to avert. For instance, preventing so-called “big tech” firms from competing in these markets (for example, by threatening competition intervention as soon as they embed generative AI services in their ecosystems or seek to build strategic relationships with AI startups) may thwart an important source of competition needed to keep today’s leading generative-AI firms in check. In short, competition in AI markets is important, but trying naïvely to hold incumbent tech firms back out of misguided fears they will come to dominate this space is likely to do more harm than good.
Our comment proceeds as follows. Section I summarizes recent calls for competition intervention in generative AI markets. Section II argues that many of these calls are underpinned by fears of data-related incumbency advantages (often referred to as “data-network effects”). Section III explains why these effects are unlikely to play a meaningful role in generative-AI markets. Section IV concludes by offering five key takeaways to help policymakers (including the Commission) better weigh the tradeoffs inherent to competition intervention in generative-AI markets.
It was once (and frequently) said that Google’s “data monopoly” was unassailable: “If ‘big data’ is the oil of the information economy, Google has Standard Oil-like monopoly dominance—and uses that control to maintain its dominant position.”[4] Similar claims of data dominance have been attached to nearly all large online platforms, including Facebook (Meta), Amazon, and Uber.[5]
While some of these claims continue even today (for example, “big data” is a key component of the U.S. Justice Department’s (DOJ) Google Search and adtech antitrust suits),[6] a shiny new data target has emerged in the form of generative artificial intelligence (AI). The launch of ChatGPT in November 2022, as well as the advent of AI image-generation services like Midjourney and Dall-E, have dramatically expanded the public’s conception of what is—and what might be—possible to achieve with generative-AI technologies built on massive datasets.
While these services remain in the early stages of mainstream adoption and remain in the throes of rapid, unpredictable technological evolution, they nevertheless already appear to be on the radar of competition policymakers around the world. Several antitrust enforcers appear to believe that, by acting now, they can avoid the “mistakes” that were purportedly made during the formative years of Web 2.0.[7] These mistakes, critics assert, include failing to appreciate the centrality of data in online markets, as well as letting mergers go unchecked and allowing early movers to entrench their market positions.[8] As Lina Khan, chair of the U.S. Federal Trade Commission (FTC), put it: “we are still reeling from the concentration that resulted from Web 2.0, and we don’t want to repeat the mis-steps of the past with AI”.[9]
This response from the competition-policy world is deeply troubling. Rather than engage in critical self-assessment and adopt an appropriately restrained stance, the enforcement community appears to be champing at the bit. Rather than assessing their prior assumptions based on the current technological moment, enforcers’ top priority appears to be figuring out how to rapidly and almost reflexively deploy existing competition tools to address the presumed competitive failures presented by generative AI.[10]
It is increasingly common for competition enforcers to argue that so-called “data-network effects” serve not only to entrench incumbents in those markets where the data is collected, but also confer similar, self-reinforcing benefits in adjacent markets. Several enforcers have, for example, prevented large online platforms from acquiring smaller firms in adjacent markets, citing the risk that they could use their vast access to data to extend their dominance into these new markets.[11]
They have also launched consultations to ascertain the role that data plays in AI competition. For instance, in an ongoing consultation, the European Commission asks: “What is the role of data and what are its relevant characteristics for the provision of generative AI systems and/or components, including AI models?”[12] Unsurprisingly, the FTC has likewise been bullish about the risks posed by incumbents’ access to data. In comments submitted to the U.S. Copyright Office, for example, the FTC argued that:
The rapid development and deployment of AI also poses potential risks to competition. The rising importance of AI to the economy may further lock in the market dominance of large incumbent technology firms. These powerful, vertically integrated incumbents control many of the inputs necessary for the effective development and deployment of AI tools, including cloud-based or local computing power and access to large stores of training data. These dominant technology companies may have the incentive to use their control over these inputs to unlawfully entrench their market positions in AI and related markets, including digital content markets.[13]
Certainly, it stands to reason that the largest online platforms—including Alphabet, Meta, Apple, and Amazon—should have a meaningful advantage in the burgeoning markets for generative-AI services. After all, it is widely recognized that data is an essential input for generative AI.[14] This competitive advantage should be all the more significant, given that these firms have been at the forefront of AI technology for more than a decade. Over this period, Google’s DeepMind and AlphaGo and Meta’s have routinely made headlines.[15] Apple and Amazon also have vast experience with AI assistants, and all of these firms use AI technology throughout their platforms.[16]
Contrary to what one might expect, however, the tech giants have, to date, been largely unable to leverage their vast data troves to outcompete startups like OpenAI and Midjourney. At the time of writing, OpenAI’s ChatGPT appears to be, by far, the most successful chatbot,[17] despite the large tech platforms’ apparent access to far more (and more up-to-date) data.
In these comments, we suggest that there are important lessons to glean from these developments, if only enforcers would stop to reflect. The meteoric rise of consumer-facing AI services should offer competition enforcers and policymakers an opportunity for introspection. As we explain, the rapid emergence of generative-AI technology may undercut many core assumptions of today’s competition-policy debates, which have largely focused on the rueful after-effects of the purported failure of 20th-century antitrust to address the allegedly manifest harms of 21st-century technology. These include the notions that data advantages constitute barriers to entry and can be leveraged to project dominance into adjacent markets; that scale itself is a market failure to be addressed by enforcers; and that the use of consumer data is inherently harmful to those consumers.
Proponents of tougher interventions by competition enforcers into digital markets often cite data-network effects as a source of competitive advantage and barrier to entry (though terms like “economies of scale and scope” may offer more precision).[18] The crux of the argument is that “the collection and use of data creates a feedback loop of more data, which ultimately insulates incumbent platforms from entrants who, but for their data disadvantage, might offer a better product.”[19] This self-reinforcing cycle purportedly leads to market domination by a single firm. Thus, it is argued, for example, that Google’s “ever-expanding control of user personal data, and that data’s critical value to online advertisers, creates an insurmountable barrier to entry for new competition.”[20]
Right off the bat, it is important to note the conceptual problem these claims face. Because data can be used to improve the quality of products and/or to subsidize their use, the idea of data as an entry barrier suggests that any product improvement or price reduction made by an incumbent could be a problematic entry barrier to any new entrant. This is tantamount to an argument that competition itself is a cognizable barrier to entry. Of course, it would be a curious approach to antitrust if competition were treated as a problem, as it would imply that firms should under-compete—i.e., should forego consumer-welfare enhancements—in order to inculcate a greater number of firms in a given market simply for its own sake.[21]
Meanwhile, actual economic studies of data-network effects have been few and far between, with scant empirical evidence to support the theory.[22] Andrei Hagiu and Julian Wright’s theoretical paper offers perhaps the most comprehensive treatment of the topic to date.[23] The authors ultimately conclude that data-network effects can be of different magnitudes and have varying effects on firms’ incumbency advantage.[24] They cite Grammarly (an AI writing-assistance tool) as a potential example: “As users make corrections to the suggestions offered by Grammarly, its language experts and artificial intelligence can use this feedback to continue to improve its future recommendations for all users.”[25]
This is echoed by other economists who contend that “[t]he algorithmic analysis of user data and information might increase incumbency advantages, creating lock-in effects among users and making them more reluctant to join an entrant platform.”[26] Crucially, some scholars take this logic a step further, arguing that platforms may use data from their “origin markets” in order to enter and dominate adjacent ones:
First, as we already mentioned, data collected in the origin market can be used, once the enveloper has entered the target market, to provide products more efficiently in the target market. Second, data collected in the origin market can be used to reduce the asymmetric information to which an entrant is typically subject when deciding to invest (for example, in R&D) to enter a new market. For instance, a search engine could be able to predict new trends from consumer searches and therefore face less uncertainty in product design.[27]
This possibility is also implicit in Hagiu and Wright’s paper.[28] Indeed, the authors’ theoretical model rests on an important distinction between within-user data advantages (that is, having access to more data about a given user) and across-user data advantages (information gleaned from having access to a wider user base). In both cases, there is an implicit assumption that platforms may use data from one service to gain an advantage in another market (because what matters is information about aggregate or individual user preferences, regardless of its origin).
Our review of the economic evidence suggests that several scholars have, with varying degrees of certainty, raised the possibility that incumbents may leverage data advantages to stifle competitors in their primary market or in adjacent ones (be it via merger or organic growth). As we explain below, however, there is ultimately little evidence to support such claims. Policymakers have, however, been keenly receptive to these limited theoretical findings, basing multiple decisions on these theories, often with little consideration given to the caveats that accompany them.[29]
Indeed, it is remarkable that, in its section on “[t]he data advantage for incumbents,” the “Furman Report” created for the UK government cited only two empirical economic studies, and they offer directly contradictory conclusions with respect to the question of the strength of data advantages.[30] Nevertheless, the Furman Report concludes that data “may confer a form of unmatchable advantage on the incumbent business, making successful rivalry less likely,”[31] and adopts without reservation “convincing” evidence from non-economists that have no apparent empirical basis.[32]
In the Google/Fitbit merger proceedings, the European Commission found that the combination of data from Google services with that of Fitbit devices would reduce competition in advertising markets:
Giving [sic] the large amount of data already used for advertising purposes that Google holds, the increase in Google’s data collection capabilities, which goes beyond the mere number of active users for which Fitbit has been collecting data so far, the Transaction is likely to have a negative impact on the development of an unfettered competition in the markets for online advertising.[33]
As a result, the Commission cleared the merger on the condition that Google refrain from using data from Fitbit devices for its advertising platform.[34] The Commission will likely focus on similar issues during its ongoing investigation of Microsoft’s investment into OpenAI.[35]
Along similar lines, the FTC’s complaint to enjoin Meta’s purchase of a virtual-reality (VR) fitness app called “Within” relied, among other things, on the fact that Meta could leverage its data about VR-user behavior to inform its decisions and potentially outcompete rival VR-fitness apps: “Meta’s control over the Quest platform also gives it unique access to VR user data, which it uses to inform strategic decisions.”[36]
The DOJ’s twin cases against Google also implicate data leveraging and data barriers to entry. The agency’s adtech complaint charges that “Google intentionally exploited its massive trove of user data to further entrench its monopoly across the digital advertising industry.”[37] Similarly, in its search complaint, the agency argues that:
Google’s anticompetitive practices are especially pernicious because they deny rivals scale to compete effectively. General search services, search advertising, and general search text advertising require complex algorithms that are constantly learning which organic results and ads best respond to user queries; the volume, variety, and velocity of data accelerates the automated learning of search and search advertising algorithms.[38]
Finally, updated merger guidelines published in recent years by several competition enforcers cite the acquisition of data as a potential source of competition concerns. For instance, the FTC and DOJ’s newly published guidelines state that “acquiring data that helps facilitate matching, sorting, or prediction services may enable the platform to weaken rival platforms by denying them that data.”[39] Likewise, the UK Competition and Markets Authority (CMA) warns against incumbents acquiring firms in order to obtain their data and foreclose other rivals:
Incentive to foreclose rivals… 7.19(e) Particularly in complex and dynamic markets, firms may not focus on short term margins but may pursue other objectives to maximise their long-run profitability, which the CMA may consider. This may include… obtaining access to customer data….[40]
Incentive to foreclose rivals…
7.19(e) Particularly in complex and dynamic markets, firms may not focus on short term margins but may pursue other objectives to maximise their long-run profitability, which the CMA may consider. This may include… obtaining access to customer data….[40]
In short, competition authorities around the globe have been taking an increasingly aggressive stance on data-network effects. Among the ways this has manifested is in basing enforcement decisions on fears that data collected by one platform might confer a decisive competitive advantage in adjacent markets. Unfortunately, these concerns rest on little to no empirical evidence, either in the economic literature or the underlying case records.
Given the assertions canvassed in the previous section, it would be reasonable to assume that firms such as Google, Meta, and Amazon should be in pole position to dominate the burgeoning market for generative AI. After all, these firms have not only been at the forefront of the field for the better part of a decade, but they also have access to vast troves of data, the likes of which their rivals could only dream when they launched their own services. Thus, the authors of the Furman Report caution that “to the degree that the next technological revolution centres around artificial intelligence and machine learning, then the companies most able to take advantage of it may well be the existing large companies because of the importance of data for the successful use of these tools.”[41]
To date, however, this is not how things have unfolded—although it bears noting these markets remain in flux and the competitive landscape is susceptible to change. The first significantly successful generative-AI service was arguably not from either Meta—which had been working on chatbots for years and had access to, arguably, the world’s largest database of actual chats—or Google. Instead, the breakthrough came from a previously unknown firm called OpenAI.
OpenAI’s ChatGPT service currently holds an estimated 60% of the market (though reliable numbers are somewhat elusive).[42] It broke the record for the fastest online service to reach 100 million users (in only a couple of months), more than four times faster than the previous record holder, TikTok.[43] Based on Google Trends data, ChatGPT is nine times more popular worldwide than Google’s own Bard service, and 14 times more popular in the United States.[44] In April 2023, ChatGPT reportedly registered 206.7 million unique visitors, compared to 19.5 million for Google’s Bard.[45] In short, at the time we are writing, ChatGPT appears to be the most popular chatbot. The entry of large players such as Google Bard or Meta AI appear to have had little effect thus far on its market position.[46]
The picture is similar in the field of AI-image generation. As of August 2023, Midjourney, Dall-E, and Stable Diffusion appear to be the three market leaders in terms of user visits.[47] This is despite competition from the likes of Google and Meta, who arguably have access to unparalleled image and video databases by virtue of their primary platform activities.[48]
This raises several crucial questions: how have these AI upstarts managed to be so successful, and is their success just a flash in the pan before Web 2.0 giants catch up and overthrow them? While we cannot answer either of these questions dispositively, we offer what we believe to be some relevant observations concerning the role and value of data in digital markets.
A first important observation is that empirical studies suggest that data exhibits diminishing marginal returns. In other words, past a certain point, acquiring more data does not confer a meaningful edge to the acquiring firm. As Catherine Tucker put it following a review of the literature: “Empirically there is little evidence of economies of scale and scope in digital data in the instances where one would expect to find them.”[49]
Likewise, following a survey of the empirical literature on this topic, Geoffrey Manne and Dirk Auer conclude that:
Available evidence suggests that claims of “extreme” returns to scale in the tech sector are greatly overblown. Not only are the largest expenditures of digital platforms unlikely to become proportionally less important as output increases, but empirical research strongly suggests that even data does not give rise to increasing returns to scale, despite routinely being cited as the source of this effect.[50]
In other words, being the firm with the most data appears to be far less important than having enough data. This lower bar may be accessible to far more firms than one might initially think possible. And obtaining enough data could become even easier—that is, the volume of required data could become even smaller—with technological progress. For instance, synthetic data may provide an adequate substitute to real-world data,[51] or may even outperform real-world data.[52] As Thibault Schrepel and Alex Pentland surmise:
[A]dvances in computer science and analytics are making the amount of data less relevant every day. In recent months, important technological advances have allowed companies with small data sets to compete with larger ones.[53]
Indeed, past a certain threshold, acquiring more data might not meaningfully improve a service, where other improvements (such as better training methods or data curation) could have a large impact. In fact, there is some evidence that excessive data impedes a service’s ability to generate results appropriate for a given query: “[S]uperior model performance can often be achieved with smaller, high-quality datasets than massive, uncurated ones. Data curation ensures that training datasets are devoid of noise, irrelevant instances, and duplications, thus maximizing the efficiency of every training iteration.”[54]
Consider, for instance, a user who wants to generate an image of a basketball. Using a model trained on an indiscriminate range and number of public photos in which a basketball appears surrounded by copious other image data, the user may end up with an inordinately noisy result. By contrast, a model trained with a better method on fewer, more carefully selected images, could readily yield far superior results.[55] In one important example:
[t]he model’s performance is particularly remarkable, given its small size. “This is not a large language model trained on the whole Internet; this is a relatively small transformer trained for these tasks,” says Armando Solar-Lezama, a computer scientist at the Massachusetts Institute of Technology, who was not involved in the new study…. The finding implies that instead of just shoving ever more training data into machine-learning models, a complementary strategy might be to offer AI algorithms the equivalent of a focused linguistics or algebra class.[56]
Platforms’ current efforts are thus focused on improving the mathematical and logical reasoning of large language models (LLMs), rather than maximizing training datasets.[57] Two points stand out. The first is that firms like OpenAI rely largely on publicly available datasets—such as GSM8K—to train their LLMs.[58] Second, the real challenge to create cutting-edge AI is not so much in collecting data, but rather in creating innovative AI-training processes and architectures:
[B]uilding a truly general reasoning engine will require a more fundamental architectural innovation. What’s needed is a way for language models to learn new abstractions that go beyond their training data and have these evolving abstractions influence the model’s choices as it explores the space of possible solutions. We know this is possible because the human brain does it. But it might be a while before OpenAI, DeepMind, or anyone else figures out how to do it in silicon.[59]
[B]uilding a truly general reasoning engine will require a more fundamental architectural innovation. What’s needed is a way for language models to learn new abstractions that go beyond their training data and have these evolving abstractions influence the model’s choices as it explores the space of possible solutions.
We know this is possible because the human brain does it. But it might be a while before OpenAI, DeepMind, or anyone else figures out how to do it in silicon.[59]
Furthermore, it is worth noting that the data most relevant to startups in a given market may not be those data held by large incumbent platforms in other markets, but rather data specific to the market in which the startup is active or, even better, to the given problem it is attempting to solve:
As Andres Lerner has argued, if you wanted to start a travel business, the data from Kayak or Priceline would be far more relevant. Or if you wanted to start a ride-sharing business, data from cab companies would be more useful than the broad, market-cross-cutting profiles Google and Facebook have. Consider companies like Uber, Lyft and Sidecar that had no customer data when they began to challenge established cab companies that did possess such data. If data were really so significant, they could never have competed successfully. But Uber, Lyft and Sidecar have been able to effectively compete because they built products that users wanted to use—they came up with an idea for a better mousetrap. The data they have accrued came after they innovated, entered the market and mounted their successful challenges—not before.[60]
The bottom line is that data is not the be-all and end-all that many in competition circles make it out to be. While data may often confer marginal benefits, there is little sense these are ultimately decisive.[61] As a result, incumbent platforms’ access to vast numbers of users and data in their primary markets might only marginally affect their AI competitiveness.
A related observation is that firms’ capabilities and other features of their products arguably play a more important role than the data they own.[62] Examples of this abound in digital markets. Google overthrew Yahoo, despite initially having access to far fewer users and far less data; Google and Apple overcame Microsoft in the smartphone OS market despite having comparatively tiny ecosystems (at the time) to leverage; and TikTok rose to prominence despite intense competition from incumbents like Instagram, which had much larger user bases. In each of these cases, important product-design decisions (such as the PageRank algorithm, recognizing the specific needs of mobile users,[63] and TikTok’s clever algorithm) appear to have played a far more significant role than initial user and data endowments (or lack thereof).
All of this suggests that the early success of OpenAI likely has more to do with its engineering decisions than what data it did (or did not) own. Going forward, OpenAI and its rivals’ ability to offer and monetize compelling stores offering custom versions of their generative-AI technology will arguably play a much larger role than (and contribute to) their ownership of data.[64] In other words, the ultimate challenge is arguably to create a valuable platform, of which data ownership is a consequence, but not a cause.
It is also important to note that, in those instances where it is valuable, data does not just fall from the sky. Instead, it is through smart business and engineering decisions that firms can generate valuable information (which does not necessarily correlate with owning more data).
For instance, OpenAI’s success with ChatGPT is often attributed to its more efficient algorithms and training models, which arguably have enabled the service to improve more rapidly than its rivals.[65] Likewise, the ability of firms like Meta and Google to generate valuable data for advertising arguably depends more on design decisions that elicit the right data from users, rather than the raw number of users in their networks.
Put differently, setting up a business so as to extract and organize the right information is more important than simply owning vast troves of data.[66] Even in those instances where high-quality data is an essential parameter of competition, it does not follow that having vaster databases or more users on a platform necessarily leads to better information for the platform.
Indeed, if data ownership consistently conferred a significant competitive advantage, these new firms would not be where they are today. This does not mean that data is worthless, of course. Rather, it means that competition authorities should not assume that the mere possession of data is a dispositive competitive advantage, absent compelling empirical evidence to support such a finding. In this light, the current wave of decisions and competition-policy pronouncements that rely on data-related theories of harm are premature.
As we explain above, data (network effects) are not the source of barriers to entry that they are sometimes made out to be. The picture is far more nuanced. Indeed, as economist Andres Lerner demonstrated almost a decade ago (and the assessment is only truer today):
Although the collection of user data is generally valuable for online providers, the conclusion that such benefits of user data lead to significant returns to scale and to the entrenchment of dominant online platforms is based on unsupported assumptions. Although, in theory, control of an “essential” input can lead to the exclusion of rivals, a careful analysis of real-world evidence indicates that such concerns are unwarranted for many online businesses that have been the focus of the “big data” debate.[67]
While data can be an important part of the competitive landscape, incumbents’ data advantages are far less pronounced than today’s policymakers commonly assume. In that respect, five main lessons emerge:
These lessons have important ramifications for competition-policy debates over the competitive implications of data in technologically evolving areas.
First, it is not surprising that startups, rather than incumbents, have taken an early lead in generative AI (and in Web 2.0 before it). After all, if data-incumbency advantages are small or even nonexistent, then smaller and more nimble players may have an edge over established tech platforms. This is all the more likely given that, despite significant efforts, the biggest tech platforms were unable to offer compelling generative-AI chatbots and image-generation services before the emergence of ChatGPT, Dall-E, Midjourney, etc.
This failure suggests that, in a process akin to Clayton Christensen’s “innovator’s dilemma,”[68] something about the incumbent platforms’ existing services and capabilities was holding them back in those markets. Of course, this does not necessarily mean that those same services or capabilities could not become an advantage when the generative-AI market starts addressing issues of monetization and scale.[69] But it does mean that assumptions about a firm’s market power based on its possession of data are off the mark.
Another important implication is that, paradoxically, policymakers’ efforts to prevent Web 2.0 platforms from competing freely in generative AI markets may ultimately backfire and lead to less, not more, competition. Indeed, OpenAI is currently acquiring a sizeable lead in generative AI. While competition authorities might like to think that other startups will emerge and thrive in this space, it is important not to confuse desires with reality. While there currently exists a vibrant AI-startup ecosystem, there is at least a case to be made that the most significant competition for today’s AI leaders will come from incumbent Web 2.0 platforms—although nothing is certain at this stage. Policymakers should beware not to stifle that competition on the misguided assumption that competitive pressure from large incumbents is somehow less valuable to consumers than that which originates from smaller firms.
Finally, even if there were a competition-related market failure to be addressed in the field of generative AI (which is anything but clear), it is unclear that the remedies being contemplated would do more good than harm. Some of the solutions that have been put forward have highly ambiguous effects on consumer welfare. Scholars have shown that, e.g., mandated data sharing—a solution championed by EU policymakers, among others—may sometimes dampen competition in generative-AI markets.[70] This is also true of legislation like the General Data Protection Regulation (GDPR), which makes it harder for firms to acquire more data about consumers—assuming such data is, indeed, useful to generative-AI services.[71]
In sum, it is a flawed understanding of the economics and practical consequences of large agglomerations of data that lead competition authorities to believe that data-incumbency advantages are likely to harm competition in generative AI markets—or even in the data-intensive Web 2.0 markets that preceded them. Indeed, competition or regulatory intervention to “correct” data barriers and data network and scale effects is liable to do more harm than good.
[1] See, e.g., Michael Chui, et al., The Economic Potential of Generative AI: The Next Productivity Frontier, McKinsey (Jun. 14, 2023), https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-AI-the-next-productivity-frontier.
[2] See, e. g., Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F. Miller III, & Animashree Anandkumar, State-Specific Protein–Ligand Complex Structure Prediction with a Multiscale Deep Generative Model, 6 Nature Machine Intelligence, 195-208 (2024); see also, Jaemin Seo, Sang Kyeun Kim, Azarakhsh Jalalvand, Rory Conlin, Andrew Rothstein, Joseph Abbate, Keith Erickson, Josiah Wai, Ricardo Shousha, & Egemen Kolemen, Avoiding Fusion Plasma Tearing Instability with Deep Reinforcement Learning, 626 Nature, 746-751 (2024).
[3] See, e.g., Press Release, Commission Launches Calls for Contributions on Competition in Virtual Worlds and Generative AI, European Commission (Jan. 9, 2024), https://ec.europa.eu/commission/presscorner/detail/en/IP_24_85.
[4] Nathan Newman, Taking on Google’s Monopoly Means Regulating Its Control of User Data, Huffington Post (Sep. 24, 2013), http://www.huffingtonpost.com/nathan-newman/taking-on-googlesmonopol_b_3980799.html.
[5] See, e.g., Lina Khan & K. Sabeel Rahman, Restoring Competition in the U.S. Economy, in Untamed: How to Check Corporate, Financial, and Monopoly Power (Nell Abernathy, Mike Konczal, & Kathryn Milani, eds., 2016), at 23 (“From Amazon to Google to Uber, there is a new form of economic power on display, distinct from conventional monopolies and oligopolies…, leverag[ing] data, algorithms, and internet-based technologies… in ways that could operate invisibly and anticompetitively.”); Mark Weinstein, I Changed My Mind—Facebook Is a Monopoly, Wall St. J. (Oct. 1, 2021), https://www.wsj.com/articles/facebook-is-monopoly-metaverse-users-advertising-platforms-competition-mewe-big-tech-11633104247 (“[T]he glue that holds it all together is Facebook’s monopoly over data…. Facebook’s data troves give it unrivaled knowledge about people, governments—and its competitors.”).
[6] See, generally, Abigail Slater, Why “Big Data” Is a Big Deal, The Reg. Rev. (Nov. 6, 2023), https://www.theregreview.org/2023/11/06/slater-why-big-data-is-a-big-deal; Amended Complaint at ¶36, United States v. Google, 1:20-cv-03010- (D.D.C. 2020); Complaint at ¶37, United States V. Google, 1:23-cv-00108 (E.D. Va. 2023), https://www.justice.gov/opa/pr/justice-department-sues-google-monopolizing-digital-advertising-technologies (“Google intentionally exploited its massive trove of user data to further entrench its monopoly across the digital advertising industry.”).
[7] See, e.g., Press Release, European Commission, supra note 3; Krysten Crawford, FTC’s Lina Khan Warns Big Tech over AI, SIEPR (Nov. 3, 2020), https://siepr.stanford.edu/news/ftcs-lina-khan-warns-big-tech-over-ai (“Federal Trade Commission Chair Lina Khan delivered a sharp warning to the technology industry in a speech at Stanford on Thursday: Antitrust enforcers are watching what you do in the race to profit from artificial intelligence.”) (emphasis added).
[8] See, e.g., John M. Newman, Antitrust in Digital Markets, 72 Vand. L. Rev. 1497, 1501 (2019) (“[T]he status quo has frequently failed in this vital area, and it continues to do so with alarming regularity. The laissez-faire approach advocated for by scholars and adopted by courts and enforcers has allowed potentially massive harms to go unchecked.”); Bertin Martins, Are New EU Data Market Regulations Coherent and Efficient?, Bruegel Working Paper 21/23 (2023), https://www.bruegel.org/working-paper/are-new-eu-data-market-regulations-coherent-and-efficient (“Technical restrictions on access to and re-use of data may result in failures in data markets and data-driven services markets.”); Valéria Faure-Muntian, Competitive Dysfunction: Why Competition Law Is Failing in a Digital World, The Forum Network (Feb. 24, 2021), https://www.oecd-forum.org/posts/competitive-dysfunction-why-competition-law-is-failing-in-a-digital-world.
[9] See Rana Foroohar, The Great US-Europe Antitrust Divide, FT (Feb. 5, 2024), https://www.ft.com/content/065a2f93-dc1e-410c-ba9d-73c930cedc14.
[10] See, e.g., Press Release, European Commission, supra note 3.
[11] See infra, Section II. Commentators have also made similar claims; see, e.g., Ganesh Sitaram & Tejas N. Narechania, It’s Time for the Government to Regulate AI. Here’s How, Politico (Jan. 15, 2024) (“All that cloud computing power is used to train foundation models by having them “learn” from incomprehensibly huge quantities of data. Unsurprisingly, the entities that own these massive computing resources are also the companies that dominate model development. Google has Bard, Meta has LLaMa. Amazon recently invested $4 billion into one of OpenAI’s leading competitors, Anthropic. And Microsoft has a 49 percent ownership stake in OpenAI — giving it extraordinary influence, as the recent board struggles over Sam Altman’s role as CEO showed.”).
[12] Press Release, European Commission, supra note 3.
[13] Comment of U.S. Federal Trade Commission to the U.S. Copyright Office, Artificial Intelligence and Copyright, Docket No. 2023-6 (Oct. 30, 2023), at 4, https://www.ftc.gov/legal-library/browse/advocacy-filings/comment-federal-trade-commission-artificial-intelligence-copyright (emphasis added).
[14] See, e.g. Joe Caserta, Holger Harreis, Kayvaun Rowshankish, Nikhil Srinidhi, & Asin Tavakoli, The Data Dividend: Fueling Generative AI, McKinsey Digital (Sep. 15, 2023), https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-data-dividend-fueling-generative-ai (“Your data and its underlying foundations are the determining factors to what’s possible with generative AI.”).
[15] See, e.g., Tim Keary, Google DeepMind’s Achievements and Breakthroughs in AI Research, Techopedia (Aug. 11, 2023), https://www.techopedia.com/google-deepminds-achievements-and-breakthroughs-in-ai-research; See, e.g., Will Douglas Heaven, Google DeepMind Used a Large Language Model to Solve an Unsolved Math Problem, MIT Technology Review (Dec. 14, 2023), https://www.technologyreview.com/2023/12/14/1085318/google-deepmind-large-language-model-solve-unsolvable-math-problem-cap-set; see also, A Decade of Advancing the State-of-the-Art in AI Through Open Research, Meta (Nov. 30, 2023), https://about.fb.com/news/2023/11/decade-of-advancing-ai-through-open-research; see also, 200 Languages Within a Single AI Model: A Breakthrough in High-Quality Machine Translation, Meta, https://ai.meta.com/blog/nllb-200-high-quality-machine-translation (last visited Jan. 18, 2023).
[16] See, e.g., Jennifer Allen, 10 Years of Siri: The History of Apple’s Voice Assistant, Tech Radar (Oct. 4, 2021), https://www.techradar.com/news/siri-10-year-anniversary; see also Evan Selleck, How Apple Is Already Using Machine Learning and AI in iOS, Apple Insider (Nov. 20, 2023), https://appleinsider.com/articles/23/09/02/how-apple-is-already-using-machine-learning-and-ai-in-ios; see also, Kathleen Walch, The Twenty Year History Of AI At Amazon, Forbes (July 19, 2019), https://www.forbes.com/sites/cognitiveworld/2019/07/19/the-twenty-year-history-of-ai-at-amazon.
[17] See infra Section III.
[18] See, e.g., Cédric Argenton & Jens Prüfer, Search Engine Competition with Network Externalities, 8 J. Comp. L. & Econ. 73, 74 (2012).
[19] John M. Yun, The Role of Big Data in Antitrust, in The Global Antitrust Institute Report on the Digital Economy (Joshua D. Wright & Douglas H. Ginsburg, eds., Nov. 11, 2020) at 233, https://gaidigitalreport.com/2020/08/25/big-data-and-barriers-to-entry/#_ftnref50; see also, e.g., Robert Wayne Gregory, Ola Henfridsson, Evgeny Kaganer, & Harris Kyriakou, The Role of Artificial Intelligence and Data Network Effects for Creating User Value, 46 Acad. of Mgmt. Rev. 534 (2020), final pre-print version at 4, http://wrap.warwick.ac.uk/134220) (“A platform exhibits data network effects if, the more that the platform learns from the data it collects on users, the more valuable the platform becomes to each user.”); see also, Karl Schmedders, José Parra-Moyano, & Michael Wade, Why Data Aggregation Laws Could be the Answer to Big Tech Dominance, Silicon Republic (Feb. 6, 2024), https://www.siliconrepublic.com/enterprise/data-ai-aggregation-laws-regulation-big-tech-dominance-competition-antitrust-imd.
[20] Nathan Newman, Search, Antitrust, and the Economics of the Control of User Data, 31 Yale J. Reg. 401, 409 (2014) (emphasis added); see also id. at 420 & 423 (“While there are a number of network effects that come into play with Google, [“its intimate knowledge of its users contained in its vast databases of user personal data”] is likely the most important one in terms of entrenching the company’s monopoly in search advertising…. Google’s overwhelming control of user data… might make its dominance nearly unchallengeable.”).
[21] See also Yun, supra note 19 at 229 (“[I]nvestments in big data can create competitive distance between a firm and its rivals, including potential entrants, but this distance is the result of a competitive desire to improve one’s product.”).
[22] For a review of the literature on increasing returns to scale in data (this topic is broader than data-network effects) see Geoffrey Manne & Dirk Auer, Antitrust Dystopia and Antitrust Nostalgia: Alarmist Theories of Harm in Digital Markets and Their Origins, 28 Geo Mason L. Rev. 1281, 1344 (2021).
[23] Andrei Hagiu & Julian Wright, Data-Enabled Learning, Network Effects, and Competitive Advantage, 54 RAND J. Econ. 638 (2023).
[24] Id. at 639. The authors conclude that “Data-enabled learning would seem to give incumbent firms a competitive advantage. But how strong is this advantage and how does it differ from that obtained from more traditional mechanisms…”.
[25] Id.
[26] Bruno Jullien & Wilfried Sand-Zantman, The Economics of Platforms: A Theory Guide for Competition Policy, 54 Info. Econ. & Pol’y 10080, 101031 (2021).
[27] Daniele Condorelli & Jorge Padilla, Harnessing Platform Envelopment in the Digital World, 16 J. Comp. L. & Pol’y 143, 167 (2020).
[28] See Hagiu & Wright, supra note 23.
[29] For a summary of these limitations, see generally Catherine Tucker, Network Effects and Market Power: What Have We Learned in the Last Decade?, Antitrust (2018) at 72, available at https://sites.bu.edu/tpri/files/2018/07/tucker-network-effects-antitrust2018.pdf; see also Manne & Auer, supra note 22, at 1330.
[30] See Jason Furman, Diane Coyle, Amelia Fletcher, Derek McAuley, & Philip Marsden (Dig. Competition Expert Panel), Unlocking Digital Competition (2019) at 32-35 (“Furman Report”), available at https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/785547/unlocking_digital_competition_furman_review_web.pdf.
[31] Id. at 34.
[32] Id. at 35. To its credit, it should be noted, the Furman Report does counsel caution before mandating access to data as a remedy to promote competition. See id. at 75. That said, the Furman Report does maintain that such a remedy should certainly be on the table because “the evidence suggests that large data holdings are at the heart of the potential for some platform markets to be dominated by single players and for that dominance to be entrenched in a way that lessens the potential for competition for the market.” Id. In fact, the evidence does not show this.
[33] Case COMP/M.9660 — Google/Fitbit, Commission Decision (Dec. 17, 2020) (Summary at O.J. (C 194) 7), available at https://ec.europa.eu/competition/mergers/cases1/202120/m9660_3314_3.pdf at 455.
[34] Id. at 896.
[35] See Natasha Lomas, EU Checking if Microsoft’s OpenAI Investment Falls Under Merger Rules, TechCrunch (Jan. 9, 2024), https://techcrunch.com/2024/01/09/openai-microsoft-eu-merger-rules.
[36] Amended Complaint at 11, Meta/Zuckerberg/Within, Fed. Trade Comm’n. (2022) (No. 605837), available at https://www.ftc.gov/system/files/ftc_gov/pdf/D09411%20-%20AMENDED%20COMPLAINT%20FILED%20BY%20COUNSEL%20SUPPORTING%20THE%20COMPLAINT%20-%20PUBLIC%20%281%29_0.pdf.
[37] Amended Complaint (D.D.C), supra note 6 at ¶37.
[38] Amended Complaint (E.D. Va), supra note 6 at ¶8.
[39] Merger Guidelines, US Dep’t of Justice & Fed. Trade Comm’n (2023) at 25, available at https://www.ftc.gov/system/files/ftc_gov/pdf/2023_merger_guidelines_final_12.18.2023.pdf.
[40] Merger Assessment Guidelines, Competition and Mkts. Auth (2021) at ¶7.19(e), available at https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1051823/MAGs_for_publication_2021_–_.pdf.
[41] Furman Report, supra note 30, at ¶4.
[42] See, e.g., Chris Westfall, New Research Shows ChatGPT Reigns Supreme in AI Tool Sector, Forbes (Nov. 16, 2023), https://www.forbes.com/sites/chriswestfall/2023/11/16/new-research-shows-chatgpt-reigns-supreme-in-ai-tool-sector/?sh=7de5de250e9c.
[43] See Krystal Hu, ChatGPT Sets Record for Fastest-Growing User Base, Reuters (Feb. 2, 2023), https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01; Google: The AI Race Is On, App Economy Insights (Feb. 7, 2023), https://www.appeconomyinsights.com/p/google-the-ai-race-is-on.
[44] See Google Trends, https://trends.google.com/trends/explore?date=today%205-y&q=%2Fg%2F11khcfz0y2,%2Fg%2F11ts49p01g&hl=en (last visited, Jan. 12, 2024) and https://trends.google.com/trends/explore?date=today%205-y&geo=US&q=%2Fg%2F11khcfz0y2,%2Fg%2F11ts49p01g&hl=en (last visited Jan. 12, 2024).
[45] See David F. Carr, As ChatGPT Growth Flattened in May, Google Bard Rose 187%, Similarweb Blog (Jun. 5, 2023), https://www.similarweb.com/blog/insights/ai-news/chatgpt-bard.
[46] See Press Release, Introducing New AI Experiences Across Our Family of Apps and Devices, Meta (Sep. 27, 2023), https://about.fb.com/news/2023/09/introducing-ai-powered-assistants-characters-and-creative-tools; Sundar Pichai, An Important Next Step on Our AI Journey, Google Keyword Blog (Feb. 6, 2023), https://blog.google/technology/ai/bard-google-ai-search-updates.
[47] See Ion Prodan, 14 Million Users: Midjourney’s Statistical Success, Yon (Aug. 19, 2023), https://yon.fun/midjourney-statistics; see also Andrew Wilson, Midjourney Statistics: Users, Polls, & Growth [Oct 2023], ApproachableAI (Oct. 13, 2023), https://approachableai.com/midjourney-statistics.
[48] See Hema Budaraju, New Ways to Get Inspired with Generative AI in Search, Google Keyword Blog (Oct. 12, 2023), https://blog.google/products/search/google-search-generative-ai-october-update; Imagine with Meta AI, Meta (last visited Jan. 12, 2024), https://imagine.meta.com.
[49] Catherine Tucker, Digital Data, Platforms and the Usual [Antitrust] Suspects: Network Effects, Switching Costs, Essential Facility, 54 Rev. Indus. Org. 683, 686 (2019).
[50] Manne & Auer, supra note 22, at 1345.
[51] See, e.g., Stefanie Koperniak, Artificial Data Give the Same Results as Real Data—Without Compromising Privacy, MIT News (Mar. 3, 2017), https://news.mit.edu/2017/artificial-data-give-same-results-as-real-data-0303 (“[Authors] describe a machine learning system that automatically creates synthetic data—with the goal of enabling data science efforts that, due to a lack of access to real data, may have otherwise not left the ground. While the use of authentic data can cause significant privacy concerns, this synthetic data is completely different from that produced by real users—but can still be used to develop and test data science algorithms and models.”).
[52] See, e.g., Rachel Gordon, Synthetic Imagery Sets New Bar in AI Training Efficiency, MIT News (Nov. 20, 2023), https://news.mit.edu/2023/synthetic-imagery-sets-new-bar-ai-training-efficiency-1120 (“By using synthetic images to train machine learning models, a team of scientists recently surpassed results obtained from traditional ‘real-image’ training methods.).
[53] Thibault Schrepel & Alex ‘Sandy’ Pentland, Competition Between AI Foundation Models: Dynamics and Policy Recommendations, MIT Connection Science Working Paper (Jun. 2023), at 8.
[54] Igor Susmelj, Optimizing Generative AI: The Role of Data Curation, Lightly (last visited Jan. 15, 2024), https://www.lightly.ai/post/optimizing-generative-ai-the-role-of-data-curation.
[55] See, e.g., Xiaoliang Dai, et al., Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack, ArXiv (Sep. 27, 2023) at 1, https://ar5iv.labs.arxiv.org/html/2309.15807 (“[S]upervised fine-tuning with a set of surprisingly small but extremely visually appealing images can significantly improve the generation quality.”); see also, Hu Xu, et al., Demystifying CLIP Data, ArXiv (Sep. 28, 2023), https://arxiv.org/abs/2309.16671.
[56] Lauren Leffer, New Training Method Helps AI Generalize like People Do, Sci. Am. (Oct. 26, 2023), https://www.scientificamerican.com/article/new-training-method-helps-ai-generalize-like-people-do (discussing Brendan M. Lake & Marco Baroni, Human-Like Systematic Generalization Through a Meta-Learning Neural Network, 623 Nature 115 (2023)).
[57] Timothy B. Lee, The Real Research Behind the Wild Rumors about OpenAI’s Q* Project, Ars Technica (Dec. 8, 2023), https://arstechnica.com/ai/2023/12/the-real-research-behind-the-wild-rumors-about-openais-q-project.
[58] Id.; see also GSM8K, Papers with Code (last visited Jan. 18, 2023), available at https://paperswithcode.com/dataset/gsm8k; MATH Dataset, GitHub (last visited Jan. 18, 2024), available at https://github.com/hendrycks/math.
[59] Lee, supra note 57.
[60] Geoffrey Manne & Ben Sperry, Debunking the Myth of a Data Barrier to Entry for Online Services, Truth on the Market (Mar. 26, 2015), https://truthonthemarket.com/2015/03/26/debunking-the-myth-of-a-data-barrier-to-entry-for-online-services (citing Andres V. Lerner, The Role of ‘Big Data’ in Online Platform Competition (Aug. 26, 2014), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2482780.).
[61] See Catherine Tucker, Digital Data as an Essential Facility: Control, CPI Antitrust Chron. (Feb. 2020), at 11 (“[U]ltimately the value of data is not the raw manifestation of the data itself, but the ability of a firm to use this data as an input to insight.”).
[62] Or, as John Yun puts it, data is only a small component of digital firms’ production function. See Yun, supra note 19, at 235 (“Second, while no one would seriously dispute that having more data is better than having less, the idea of a data-driven network effect is focused too narrowly on a single factor improving quality. As mentioned in supra Section I.A, there are a variety of factors that enter a firm’s production function to improve quality.”).
[63] Luxia Le, The Real Reason Windows Phone Failed Spectacularly, History–Computer (Aug. 8, 2023), https://history-computer.com/the-real-reason-windows-phone-failed-spectacularly.
[64] Introducing the GPT Store, Open AI (Jan. 10, 2024), https://openai.com/blog/introducing-the-gpt-store.
[65] See Michael Schade, How ChatGPT and Our Language Models are Developed, OpenAI, https://help.openai.com/en/articles/7842364-how-chatgpt-and-our-language-models-are-developed; Sreejani Bhattacharyya, Interesting Innovations from OpenAI in 2021, AIM (Jan. 1, 2022), https://analyticsindiamag.com/interesting-innovations-from-openai-in-2021; Danny Hernadez & Tom B. Brown, Measuring the Algorithmic Efficiency of Neural Networks, ArXiv (May 8, 2020), https://arxiv.org/abs/2005.04305.
[66] See Yun, supra note 19 at 235 (“Even if data is primarily responsible for a platform’s quality improvements, these improvements do not simply materialize with the presence of more data—which differentiates the idea of data-driven network effects from direct network effects. A firm needs to intentionally transform raw, collected data into something that provides analytical insights. This transformation involves costs including those associated with data storage, organization, and analytics, which moves the idea of collecting more data away from a strict network effect to more of a ‘data opportunity.’”).
[67] Lerner, supra note 60, at 4-5 (emphasis added).
[68] See Clayton M. Christensen, The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail (2013).
[69] See David J. Teece, Dynamic Capabilities and Strategic Management: Organizing for Innovation and Growth (2009).
[70] See Hagiu & Wright, supra note 23, at 23 (“We use our dynamic framework to explore how data sharing works: we find that it in-creases consumer surplus when one firm is sufficiently far ahead of the other by making the laggard more competitive, but it decreases consumer surplus when the firms are sufficiently evenly matched by making firms compete less aggressively, which in our model means subsidizing consumers less.”); see also Lerner, supra note 60.
[71] See, e.g., Hagiu & Wright, id. (“We also use our model to highlight an unintended consequence of privacy policies. If such policies reduce the rate at which firms can extract useful data from consumers, they will tend to increase the incumbent’s competitive advantage, reflecting that the entrant has more scope for new learning and so is affected more by such a policy.”); Jian Jia, Ginger Zhe Jin, & Liad Wagman, The Short-Run Effects of the General Data Protection Regulation on Technology Venture Investment, 40 Marketing Sci. 593 (2021) (finding GDPR reduced investment in new and emerging technology firms, particularly in data-related ventures); James Campbell, Avi Goldfarb, & Catherine Tucker, Privacy Regulation and Market Structure, 24 J. Econ. & Mgmt. Strat. 47 (2015) (“Consequently, rather than increasing competition, the nature of transaction costs implied by privacy regulation suggests that privacy regulation may be anti-competitive.”).
Amicus Brief Dear Justice Guerrero and Associate Justices, In accordance with California Rule of Court 8.500(g), we are writing to urge the Court to grant the Petition . . .
Dear Justice Guerrero and Associate Justices,
In accordance with California Rule of Court 8.500(g), we are writing to urge the Court to grant the Petition for Review filed by Petitioner Gilead Sciences, Inc. (“Petitioner” or “Gilead”) on February 21, 2024, in the above-captioned matter.
We agree with Petitioner that the Court of Appeal’s finding of a duty of reasonable care in this case “is such a seismic change in the law and so fundamentally wrong, with such grave consequences, that this Court’s review is imperative.” (Pet. 6.) The unprecedented duty of care put forward by the Court of Appeal—requiring prescription drug manufacturers to exercise reasonable care toward users of a current drug when deciding when to bring a new drug to market (Op. 11)—would have far-reaching, harmful implications for innovation that the Court of Appeal failed properly to weigh.
If upheld, this new duty of care would significantly disincentivize pharmaceutical innovation by allowing juries to second-guess complex scientific and business decisions about which potential drugs to prioritize and when to bring them to market. The threat of massive liability simply for not developing a drug sooner would make companies reluctant to invest the immense resources needed to bring new treatments to patients. Perversely, this would deprive the public of lifesaving and less costly new medicines. And the prospective harm from the Court of Appeal’s decision is not limited only to the pharmaceutical industry.
We urge the Court to grant the Petition for Review and to hold that innovative firms do not owe the users of current products a “duty to innovate” or a “duty to market”—that is, that firms cannot be held liable to users of a current product for development or commercialization decisions on the basis that those decisions could have facilitated the introduction of a less harmful, alternative product.
The International Center for Law & Economics (“ICLE”) is a nonprofit, non-partisan global research and policy center aimed at building the intellectual foundations for sensible, economically grounded policy. ICLE promotes the use of law and economics methodologies and economic learning to inform policy debates. It also has longstanding expertise in evaluating law and policy relating to innovation and the legal environment facing commercial activity. In this letter, we wish to briefly highlight some of the crucial considerations concerning the effect on innovation incentives that we believe would arise from the Court of Appeal’s ruling in this case.[1]
The Court of Appeal’s ruling marks an unwarranted departure from decades of products-liability law requiring plaintiffs to prove that the product that injured them was defective. Expanding liability to products never even sold is an unprecedented, unprincipled, and dangerous approach to product liability. Plaintiffs’ lawyers may seek to apply this new theory to many other beneficial products, arguing manufacturers should have sold a superior alternative sooner. This would wreak havoc on innovation across industries.
California Civil Code § 1714 does not impose liability for “fail[ing] to take positive steps to benefit others,” (Brown v. USA Taekwondo (2021) 11 Cal.5th 204, 215), and Plaintiffs did not press a theory that the medicine they received was defective. Moreover, the product included all the warnings required by federal and state law. Thus, Plaintiffs’ case—as accepted by the Court of Appeal—is that they consumed a product authorized by the FDA, that they were fully aware of its potential side effects, but maybe they would have had fewer side effects had Gilead made the decision to accelerate (against some indefinite baseline) the development of an alternative medicine. To call this a speculative harm is an understatement, and to dismiss Gilead’s conduct as unreasonable because motivated by a crass profit motive, (Op. at 32), elides many complicated facts that belie such a facile assertion.
A focus on the narrow question of profits for a particular drug misunderstands the inordinate complexity of pharmaceutical development and risks seriously impeding the rate of drug development overall. Doing so
[over-emphasizes] the recapture of “excess” profits on the relatively few highly profitable products without taking into account failures or limping successes experienced on the much larger number of other entries. If profits were held to “reasonable” levels on blockbuster drugs, aggregate profits would almost surely be insufficient to sustain a high rate of technological progress. . . . If in addition developing a blockbuster is riskier than augmenting the assortment of already known molecules, the rate at which important new drugs appear could be retarded significantly. Assuming that important new drugs yield substantial consumers’ surplus untapped by their developers, consumers would lose along with the drug companies. Should a tradeoff be required between modestly excessive prices and profits versus retarded technical progress, it would be better to err on the side of excessive profits. (F. M. Scherer, Pricing, Profits, and Technological Progress in the Pharmaceutical Industry, 7 J. Econ. Persp. 97, 113 (1993)).
Indeed, Plaintiffs’ claim on this ground is essentially self-refuting. If the “superior” product they claim was withheld for “profit” reasons was indeed superior, then Plaintiffs could have expected to make a superior return on that product. Thus, Plaintiffs claim they were allegedly “harmed” by not having access to a product that Petitioners were not yet ready to market, even though Petitioners had every incentive to release a potentially successful alternative as soon as possible, subject to a complex host of scientific and business considerations affecting the timing of that decision.
Related, the Court of Appeal’s decision rests on the unfounded assumption that Petitioner “knew” TAF was safer than TDF after completing Phase I trials. This ignores the realities of the drug development process and the inherent uncertainty of obtaining FDA approval, even after promising early results. Passing Phase I trials, which typically involve a small number of healthy volunteers, is a far cry from having a marketable drug. According to the Biotechnology Innovation Organization, only 7.9% of drugs that enter Phase I trials ultimately obtain FDA approval.[2] (Biotechnology Innovation Organization, Clinical Development Success Rates and Contributing Factors 2011-2020, Fig. 8b (2021), available at https://perma.cc/D7EY-P22Q.) Even after Phase II trials, which assess efficacy and side effects in a larger patient population, the success rate is only about 15.1%. (Id.) Thus, at the time Gilead decided to pause TAF development, it faced significant uncertainty about whether TAF would ever reach the market, let alone ultimately prove safer than TDF.
Moreover, the clock on Petitioner’s patent exclusivity for TAF was ticking throughout the development process. Had Petitioner “known” that TAF was a safer and more effective drug, it would have had every incentive to bring it to market as soon as possible to maximize the period of patent protection and the potential to recoup its investment. The fact that Petitioner instead chose to focus on TDF strongly suggests that it did not have the level of certainty the Court of Appeal attributed to it.
Although conventional wisdom has often held otherwise, economists generally dispute the notion that companies have an incentive to unilaterally suppress innovation for economic gain.
While rumors long have circulated about the suppression of a new technology capable of enabling automobiles to average 100 miles per gallon or some new device capable of generating electric power at a fraction of its current cost, it is rare to uncover cases where a worthwhile technology has been suppressed altogether. (John J. Flynn, Antitrust Policy, Innovation Efficiencies, and the Suppression of Technology, 66 Antitrust L.J. 487, 490 (1998)).
Calling such claims “folklore,” the economists Armen Alchian and William Allen note that, “if such a [technology] did exist, it could be made and sold at a price reflecting the value of [the new technology], a net profit to the owner.” (Armen A. Alchian & William R. Allen, Exchange & Production: Competition, Coordination, & Control (1983), at 292). Indeed, “even a monopolist typically will have an incentive to adopt an unambiguously superior technology.” (Joel M. Cohen and Arthur J. Burke, An Overview of the Antitrust Analysis of Suppression of Technology, 66 Antitrust L.J. 421, 429 n. 28 (1998)). While nominal suppression of technology can occur for a multitude of commercial and technological reasons, there is scant evidence that doing so coincides with harm to consumers, except where doing so affirmatively interferes with market competition under the antitrust laws—a claim not advanced here.
One reason the tort system is inapt for second-guessing commercial development and marketing decisions is that those decisions may be made for myriad reasons that do not map onto the specific safety concern of a products-liability action. For example, in the 1930s, AT&T abandoned the commercial development of magnetic recording “for ideological reasons. . . . Management feared that availability of recording devices would make customers less willing to use the telephone system and so undermine the concept of universal service.” (Mark Clark, Suppressing Innovation: Bell Laboratories and Magnetic Recording, 34 Tech. & Culture 516, 520-24 (1993)). One could easily imagine arguments that coupling telephones and recording devices would promote safety. But the determination of whether safety or universal service (and the avoidance of privacy invasion) was a “better” basis for deciding whether to pursue the innovation is not within the ambit of tort law (nor the capability of a products-liability jury). And yet, it would necessarily become so if the Court of Appeal’s decision were to stand.
The Court of Appeal notes that “a duty that placed manufacturers ‘under an endless obligation to pursue ever-better new products or improvements to existing products’ would be unworkable and unwarranted,” (Op. 10), yet avers that “plaintiffs are not asking us to recognize such a duty” because “their negligence claim is premised on Gilead’s possession of such an alternative in TAF; they complain of Gilead’s knowing and intentionally withholding such a treatment….” (Id).
From an economic standpoint, this is a distinction without a difference.
Both a “duty to invent” and a “duty to market” what is already invented would increase the cost of bringing any innovative product to market by saddling the developer with an expected additional (and unavoidable) obligation as a function of introducing the initial product, differing only perhaps by degree. Indeed, a “duty to invent” could conceivably be more socially desirable because in that case a firm could at least avoid liability by undertaking the process of discovering new products (a socially beneficial activity), whereas the “duty to market” espoused by the Court of Appeal would create only the opposite incentive—the incentive never to gain knowledge of a superior product on the basis of which liability might attach.[3]
And public policy is relevant. This Court in Brown v. Superior Court, (44 Cal. 3d 1049 (1988)), worried explicitly about the “[p]ublic policy” implications of excessive liability rules for the provision of lifesaving drugs. (Id. at 1063-65). As the Court in Brown explained, drug manufacturers “might be reluctant to undertake research programs to develop some pharmaceuticals that would prove beneficial or to distribute others that are available to be marketed, because of the fear of large adverse monetary judgments.” (Id. at 1063). The Court of Appeal agreed, noting that “the court’s decision [in Brown] was grounded in public policy concerns. Subjecting prescription drug manufacturers to strict liability for design defects, the court worried, might discourage drug development or inflate the cost of otherwise affordable drugs.” (Op. 29).
In rejecting the relevance of the argument here, however, the Court of Appeal (very briefly) argued a) that Brown espoused only a policy against burdening pharmaceutical companies with a duty stemming from unforeseeable harms, (Op. 49-50), and b) that the relevant cost here might be “some failed or wasted efforts,” but not a reduction in safety. (Op. 51).[4] Both of these claims are erroneous.
On the first, the legalistic distinction between foreseeable and unforeseeable harm was not, in fact, the determinative distinction in Brown. Rather, that distinction was relevant only because it maps onto the issue of incentives. In the face of unforeseeable, and thus unavoidable, harm, pharmaceutical companies would have severely diminished incentives to innovate. While foreseeable harms might also deter innovation by imposing some additional cost, these costs would be smaller, and avoidable or insurable, so that innovation could continue. To be sure, the Court wanted to ensure that the beneficial, risk-reduction effects of the tort system were not entirely removed from pharmaceutical companies. But that meant a policy decision that necessarily reduced the extent of tort-based risk optimization in favor of the manifest, countervailing benefit of relatively higher innovation incentives. That same calculus applies here, and it is this consideration, not the superficial question of foreseeability, that animated this Court in Brown.
On the second, the Court of Appeal inexplicably fails to acknowledge that the true cost of the imposition of excessive liability risk from a “duty to market” (or “duty to innovate”) is not limited to the expenditure of wasted resources, but the non-expenditure of any resources. The court’s contention appears to contemplate that such a duty would not remove a firm’s incentive to innovate entirely, although it might deter it slightly by increasing its expected cost. But economic incentives operate at the margin. Even if there remains some profit incentive to continue to innovate, the imposition of liability risk simply for the act of doing so would necessarily reduce the amount of innovation (in some cases, and especially for some smaller companies less able to bear the additional cost, to the point of deterring innovation entirely). But even this reduction in incentive is a harm. The fact that some innovation may still occur despite the imposition of considerable liability risk is not a defense of the imposition of that risk; rather, it is a reason to question its desirability, exactly as this Court did in Brown.
Innovation is a long-term, iterative process fraught with uncertainty. At the outset of research and development, it is impossible to know whether a potential new drug will ultimately prove superior to existing drugs. Most attempts at innovation fail to yield a marketable product, let alone one that is significantly safer or more effective than its predecessors. Deciding whether to pursue a particular line of research depends on weighing myriad factors, including the anticipated benefits of the new drug, the time and expense required to develop it, and its financial viability relative to existing products. Sometimes, potentially promising drug candidates are not pursued fully, even if theoretically “better” than existing drugs to some degree, because the expected benefits are not sufficient to justify the substantial costs and risks of development and commercialization.
If left to stand, the Court of Appeal’s decision would mean that whenever this stage of development is reached for a drug that may offer any safety improvement, the manufacturer will face potential liability for failing to bring that drug to market, regardless of the costs and risks involved in its development or the extent of the potential benefit. Such a rule would have severe unintended consequences that would stifle innovation.
First, by exposing manufacturers to liability on the basis of early-stage research that has not yet established a drug candidate’s safety and efficacy, the Court of Appeal’s rule would deter manufacturers from pursuing innovations in the first place. Drug research involves constant iteration, with most efforts failing and the potential benefits of success highly uncertain until late in the process. If any improvement, no matter how small or tentative, could trigger liability for failing to develop the new drug, manufacturers will be deterred from trying to innovate at all.
Second, such a rule would force manufacturers to direct scarce resources to developing and commercializing drugs that offer only small or incremental benefits because failing to do so would invite litigation. This would necessarily divert funds away from research into other potential drugs that could yield greater advancements. Further, as each small improvement is made, it reduces the relative potential benefit from, and therefore the incentive to undertake, further improvements. Rather than promoting innovation, the Court of Appeal’s decision would create incentives that favor small, incremental changes over larger, riskier leaps with the greatest potential to significantly advance patient welfare.
Third, and conversely, the Court of Appeal’s decision would set an unrealistic and dangerous standard of perfection for drug development. Pharmaceutical companies should not be expected to bring only the “safest” version of a drug to market, as this would drastically increase the time and cost of drug development and deprive patients of access to beneficial treatments in the meantime.
Fourth, the threat of liability would lead to inefficient and costly distortions in how businesses organize their research and development efforts. To minimize the risk of liability, manufacturers may avoid integrating ongoing research into existing product lines, instead keeping the processes separate unless and until a potential new technology is developed that offers benefits so substantial as to clearly warrant the costs and liability exposure of its development in the context of an existing drug line. Such an incentive would prevent potentially beneficial innovations from being pursued and would increase the costs of drug development.
Finally, the ruling would create perverse incentives that could actually discourage drug companies from developing and introducing safer alternative drugs. If bringing a safer drug to market later could be used as evidence that the first-generation drug was not safe enough, companies may choose not to invest in developing improved versions at all in order to avoid exposing themselves to liability. This would, of course, directly undermine the goal of increasing drug safety overall.
The Court of Appeal gave insufficient consideration to these severe policy consequences of the duty it recognized. A manufacturer’s decision when to bring a potentially safer drug to market involves complex trade-offs that courts are ill-equipped to second-guess—particularly in the limited context of a products-liability determination.
The Court of Appeal’s novel “duty to market” any known, less-harmful alternative to an existing product would deter innovation to the detriment of consumers. The Court of Appeal failed to consider how its decision would distort incentives in a way that harms the very patients the tort system is meant to protect. This Court should grant review to address these important legal and policy issues and to prevent this unprecedented expansion of tort liability from distorting manufacturers’ incentives to develop new and better products.
[1] No party or counsel for a party authored or paid for this amicus letter in whole or in part.
[2] It is important to note that this number varies with the kind of medicine involved, but across all categories of medicines there is a high likelihood of failure subsequent to Phase I trials.
[3] To the extent the concern is with disclosure of information regarding a potentially better product, that is properly a function of the patent system, which requires public disclosure of new ideas in exchange for the receipt of a patent. (See Brenner v. Manson, 383 U.S. 519, 533 (1966) (“one of the purposes of the patent system is to encourage dissemination of information concerning discoveries and inventions.”)). Of course, the patent system preserves innovation incentives despite the mandatory disclosure of information by conferring an exclusive right to the inventor to use the new knowledge. By contrast, using the tort system as an information-forcing device in this context would impose risks and costs on innovation without commensurate benefit, ensuring less, rather than more, innovation.
[4] The Court of Appeal makes a related argument when it claims that “the duty does not require manufacturers to perfect their drugs, but simply to act with reasonable care for the users of the existing drug when the manufacturer has developed an alternative that it knows is safer and at least equally efficacious. Manufacturers already engage in this type of innovation in the ordinary course of their business, and most plaintiffs would likely face a difficult road in establishing a breach of the duty of reasonable care.” (Op. at 52-3).
Regulatory Comments Dear Chair Khan, Commissioners Slaughter and Bedoya, and General Counsel Dasgupta, The International Center for Law & Economics (ICLE) respectfully submits this letter in response . . .
Dear Chair Khan, Commissioners Slaughter and Bedoya, and General Counsel Dasgupta,
The International Center for Law & Economics (ICLE) respectfully submits this letter in response to your June 29, 2023, NPRM regarding amendments to the premerger notification rules that implement the Hart-Scott-Rodino Antitrust Improvements Act (HSR Act) and to the Premerger Notification and Report Form and Instructions.
ICLE is a nonprofit, nonpartisan research center working to promote the use of law & economics methodologies to inform public policy debate. We have a long history of participation in regulatory proceedings relating to competition and antitrust law, including recent revisions to the merger guidelines[1] and the proposed revisions to the HSR premerger notification process.[2] We are consistently grateful for the opportunity to participate in proceedings such as these.
We write to express our concern about an important omission in the FTC’s proposed changes to the premerger notification form: its failure to address the requirements of the Regulatory Flexibility Act (RFA).[3] We appreciate your interest in this matter and the opportunity to share our concern with your offices.
This concern involves two legislative frameworks: the HSR premerger notification process and the requirements of the RFA. Under the HSR Act’s amendments to the Clayton Act, firms engaging in mergers above a statutorily defined minimum value[4]—including many that would involve smaller businesses to which the RFA applies—are required to provide information about a proposed merger to the FTC and the Department of Justice (DOJ) before the transaction can close. To bolster information gathering in merger enforcement, the FTC (with concurrence from the DOJ) proposed an extensive set of amendments to the filing process outlined by the HSR Act.[5] The proposed changes to the HSR process would dramatically expand the disclosure obligations for merging companies that meet the minimum valuation threshold.[6]
Under the RFA, federal agencies “shall prepare and make available for public comment an initial regulatory flexibility analysis. . . describ[ing] the impact of the proposed rule on small entities,”[7] except where “the head of the agency certifies that the rule will not, if promulgated, have a significant economic impact on a substantial number of small entities.”[8] If the agency believes the amendment will not have a substantial impact on small businesses, then it must provide “the factual basis” for its conclusion.[9]
The statement certifying that the proposed HSR changes in the NPRM won’t affect small businesses reads, in full:
Because of the size of the transactions necessary to invoke an HSR Filing, the premerger notification rules rarely, if ever, affect small entities. The 2000 amendments to the Act exempted all transactions valued at $50 million or less, with subsequent automatic adjustments to take account of changes in Gross National Product resulting in a current threshold of $111 million. Further, none of the proposed amendments expands the coverage of the premerger notification rules in a way that would affect small entities. Accordingly, the Commission certifies that these proposed amendments will not have a significant economic impact on a substantial number of small entities.[10]
Unfortunately, this is insufficient to satisfy the requirements of the RFA. Although the FTC stresses the $111 million HSR threshold to assert that small entities will not be affected, the Small Business Administration (SBA) “generally defines a small business as an independent business having fewer than 500 employees.”[11] The SBA also offers more detailed, industry-specific identification of small businesses.[12] Indeed, the NPRM cites to the SBA’s own standards, but these, too, do not align with the FTC’s “factual statement,” and it is not evident that the Commission sufficiently delved into those standards to understand their relevance for the size thresholds under the HSR Act.
Even a quick review of the SBA’s “Small Business Size Standards by NAICS Industry” table reveals that the SBA classifies the size of a firm based on either annual receipts or number of employees,[13] depending upon the characteristics of their industry.[14] Neither of these, it should go without saying, is the same as a “size-of-transaction” threshold under the HSR Act. Nor does the NPRM’s “factual basis” statement contain information sufficient to determine that there is any correlation between the SBA’s size thresholds and the size of a transaction (which typically represents something between the discounted present value of a firm’s expected returns under new ownership and current ownership over an indefinite time period).
Despite the FTC’s claim that the $111 million deal threshold will ensure that small businesses are not substantially affected, the agency’s own data from 2022 shows that nearly a quarter of all HSR filings covered transactions involving firms with sales of $50 million or less.[15] The same data shows that, out of the 3029 reported transactions in 2022, 513 involved firms with between $50 and $100 million in sales and 305 with between $100 and $150 million in sales. Here, again, the SBA’s metrics for identifying small businesses bear emphasis: where the SBA relies on dollar values instead of employee headcounts to define small businesses at all, it does so based on annual average receipts, not on the overall value of the firm.
This distinction underscores a point made in a letter filed by the App Association, a trade group representing small technology firms, that it is important not to conflate valuation with size.[16] A company, such as an innovative tech startup, can have a small number of employees but a high value based on projected sales, intellectual property, and forthcoming products. Indeed, the App Association notes that a number of its members are already subject to HSR disclosures and that that number can only increase under the proposed amendments.[17]
To provide further context regarding whether many of these deals involve small businesses, a 2013 CrunchBase dataset showed that the average successful American startup sold for $242.9 million.[18] Furthermore, the FTC’s 2022 HSR report highlights at least one challenged transaction involving a small business: Meta/Within.[19] Within, a virtual-reality startup with 58 employees, was acquired by Meta for $400 million.[20] Not only was there an HSR filing, but the FTC attempted to challenge the transaction—and lost in district court.[21]
Small businesses are clearly burdened by the HSR premerger notification requirements—and this burden would only increase under the proposed changes. By the FTC’s own estimate the new requirements would quadruple the hours required to prepare an HSR filing and raise costs by $350 million. By other, more realistic estimates, that increase in work hours would entail a cost of more than $1.6 billion[22]—or, indeed, considerably more.[23] There is no question that drastically increasing the cost of merger filings will make it much harder for small businesses to merge or be acquired, which is a primary form of success for small businesses.
Indeed, the NPRM’s proposed changes are, in part, specifically designed to affect small businesses. “Acquisitions of small companies can cause harm, including in sectors where competition occurs on a local level. . . . Thus, the Commission proposes several changes to expand the requirements for information related to prior acquisitions beyond what is currently required by Item 8.”[24] Furthermore, “given the difficulties in determining the value of small or nascent companies, the Commission believes it would be less burdensome for filers to report all acquisitions. . . .”[25] Indeed, the FTC is aware of the potential burden on small businesses that such an approach would entail, but nevertheless aims to ensure that its proposed rules “still captur[e] acquisitions of entities worth less than $10 million.”[26]
And there is yet a further problem: These concerns take into account only the direct costs the NPRM would impose on small businesses. But, as the National Federation of Independent Business highlighted in 2023, several agencies have arguably failed to comply with the RFA by failing to consider indirect effects on small businesses.[27] Obviously, there is no such analysis provided here—and, indeed, as noted above, a clear intent of the NPRM is to affect the likelihood of small-business acquisition by reducing the incentive for firms to serially acquire small businesses. Doing so, of course, reduces funders’ incentives to invest in startups and small businesses and raises these companies’ cost of capital. Arguably that increase is itself a direct cost, but certainly its indirect effect is incredibly significant to the health of small businesses in the U.S.
The dramatic changes to the HSR premerger notification requirements proposed by the FTC have already created substantial uncertainty within the antitrust bar. Procedural defects such as failing to comply with the requirements of the RFA increase the likelihood that any rules adopted by the FTC will be challenged in court. This would increase the uncertainty (and thus the cost) surrounding the HSR process. This would be an unfortunate outcome. Fortunately, it is one that can be avoided if the FTC addresses these issues prior to finalizing its proposed rules.
[1] Geoffrey A. Manne, Dirk Auer, Brian Albrecht, Eric Fruits, Daniel J. Gilman, & Lazar Radic, Comments of the International Center for Law and Economics on the FTC & DOJ Draft Merger Guidelines, International Center for Law and Economics (Sept 18, 2023), https://laweconcenter.org/resources/comments-of-the-international-center-for-law-and-economics-on-the-ftc-doj-draft-merger-guidelines/.
[2] Brian Albrecht, Dirk Auer, Daniel J. Gilman, Gus Hurwitz, & Geoffrey A. Manne, Comments of the International Center for Law & Economics on Proposed Changes to the Premerger Notification Rules, International Center for Law and Economics (Sept 27,2023), https://laweconcenter.org/resources/comments-of-the-international-center-for-law-economics-on-proposed-changes-to-the-premerger-notification-rules/.
[3] 5 U.S.C. §§ 601-612 (2018).
[4] 15 U.S.C. § 18a(a)(2) (2018).
[5] NPRM, 88 FR 42178 (Jun. 29, 2023).
[6] See id. at 42208 (estimating the hours and expenses required to comply with the new rules). According to antitrust practitioners, however, the NPRM’s estimate likely substantially underestimates the true burden and cost of the proposed rules. See, e.g., Sean Heather, Antitrust Experts Reject FTC/DOJ Changes to Merger Process, Chamber of Commerce (Sept 19, 2023), https://www.uschamber.com/finance/antitrust/antitrust-experts-reject-ftc-doj-changes-to-merger-process.
[7] 5 U.S.C. § 603(a) (2018).
[8] 5 U.S.C. § 605(b) (2018).
[9] Id.
[10] NPRM, 88 FR 42178, 42208 (Jun. 29, 2023).
[11] Frequently Asked Questions, U.S. Small Bus. Admin. Off. of Advoc. (2023), https://advocacy.sba.gov/wp-content/uploads/2023/03/Frequently-Asked-Questions-About-Small-Business-March-2023-508c.pdf.
[12] See 13 CFR § 121.101, et seq. (1996)
[13] 13 CFR § 121.201 (2024).
[14] Indeed, the SBA’s standards entail a review of a wide range of such characteristics. See 13 CFR § 121.102 (1996) (“SBA considers economic characteristics comprising the structure of an industry, including degree of competition, average firm size, start-up costs and entry barriers, and distribution of firms by size. It also considers technological changes, competition from other industries, growth trends, historical activity within an industry, unique factors occurring in the industry which may distinguish small firms from other firms, and the objectives of its programs and the impact on those programs of different size standard levels.”).
[15] See Fed. Trade Comm’n and Dept of Just., Hart-Scott-Rodino Annual Report (2022), at Table IX, available at https://www.ftc.gov/system/files/ftc_gov/pdf/FY2022HSRReport.pdf.
[16] See Letter from Morgan Reed, President of App Association, to Lina Khan, Chair of Fed. Trade. Comm’n and Members of Congress (Feb 1, 2024), available at https://actonline.org/wp-content/uploads/App-Association-HSR-RFA-Ltr-1-Feb-2024-1.pdf.
[17] See id.
[18] See Mark Lennon, CrunchBase Reveals: The Average Successful Startup Raises $41M, Exits at $242.9M, TechCrunch (Dec 14, 2013), https://techcrunch.com/2013/12/14/crunchbase-reveals-the-average-successful-startup-raises-41m-exits-at-242-9m.
[19] See Fed. Trade Comm’n and Dept of Just., Hart-Scott-Rodino Annual Report (2022), available at https://www.ftc.gov/system/files/ftc_gov/pdf/FY2022HSRReport.pdf.
[20] See, e.g., Within (Virtual Reality) Overview, Pitchbook (last visited Feb. 29, 2024), https://pitchbook.com/profiles/company/117068-59#overview.
[21] In the Matter of Meta/Zuckerberg/Within, Fed. Trade Comm’n Docket No. 9411 (Aug. 11, 2022), https://www.ftc.gov/legal-library/browse/cases-proceedings/221-0040-metazuckerbergwithin-matter.
[22] See Albrecht, et al., supra note 2, at 7 (“The U.S. Chamber of Commerce conducted ‘a survey of 70 antitrust practitioners asking them questions about the proposed revisions to the HSR merger form and the new draft merger guides.’ Based on average answers from the survey respondents, the new rules would increase compliance costs by $1.66 billion, almost five times the FTC’s $350 million estimate.”).
[23] See id. (“For the current rules, the average survey response puts the cost of compliance at $79,569. Assuming there are 7,096 filings (as the FTC projects for FY 23), the total cost under the current rules would be $565 million. Under the new rules, the average survey response estimates the expected cost of compliance to be $313,828 per transaction, for a total cost of $2.23 billion.”) (emphasis added).
[24] NPRM, 88 FR 42178, 42203 (Jun. 29, 2023).
[25] Id. at 42204 (emphasis added).
[26] Id. (emphasis added).
[27] See Rob Smith, The Regulatory Flexibility Act: Turning a Paper Tiger Into a Legitimate Constraint on One-Size-Fits-All Agency Rulemaking, NFIB Small Business Legal Center (May 2, 2023), https://strgnfibcom.blob.core.windows.net/nfibcom/NFIB-RFA-White-paper.pdf (collecting examples).
Scholarship I. Introduction It was once (and frequently) said that Google’s “data monopoly” was unassailable: “If ‘big data’ is the oil of the information economy, Google . . .
It was once (and frequently) said that Google’s “data monopoly” was unassailable: “If ‘big data’ is the oil of the information economy, Google has Standard Oil-like monopoly dominance — and uses that control to maintain its dominant position.”[1] Similar epithets have been hurled at virtually all large online platforms, including Facebook (Meta), Amazon, and Uber.[2]
While some of these claims continue even today (for example, “big data” is a key component of the U.S. Justice Department’s (“DOJ”) Google Search and AdTech antitrust suits),[3] a shiny new data target has emerged in the form of generative artificial intelligence. The launch of ChatGPT in November 2022, as well as the advent of AI image-generation services like Midjourney and Dall-E, have dramatically expanded people’s conception of what is, and what might be, possible to achieve with generative AI technologies built on massive data sets.
While these services remain in the early stages of mainstream adoption and are in the throes of rapid, unpredictable technological evolution, they nevertheless already appear on the radar of competition policymakers around the world. Several antitrust enforcers appear to believe that, by acting now, they can avoid the “mistakes” that were purportedly made during the formative years of Web 2.0.[4] These mistakes, critics assert, include failing to appreciate the centrality of data in online markets, as well as letting mergers go unchecked and allowing early movers to entrench their market positions.[5] As Lina Khan, Chair of the FTC, put it: “we are still reeling from the concentration that resulted from Web 2.0, and we don’t want to repeat the mis-steps of the past with AI”.[6]
In that sense, the response from the competition-policy world is deeply troubling. Instead of engaging in critical self-assessment and adopting an appropriately restrained stance, the enforcement community appears to be chomping at the bit. Rather than assessing their prior assumptions based on the current technological moment, enforcers’ top priority appears to be figuring out how to deploy existing competition tools rapidly and almost reflexively to address the presumed competitive failures presented by generative AI.[7]
It is increasingly common for competition enforcers to argue that so-called “data network effects” serve not only to entrench incumbents in the markets where that data is collected, but also confer similar, self-reinforcing benefits in adjacent markets. Several enforcers have, for example, prevented large online platforms from acquiring smaller firms in adjacent markets, citing the risk that they could use their vast access to data to extend their dominance into these new markets.[8] They have also launched consultations to ascertain the role that data plays in AI competition. For instance, in an ongoing consultation, the European Commission asks: “What is the role of data and what are its relevant characteristics for the provision of generative AI systems and/or components, including AI models?”[9] Unsurprisingly, the U.S. Federal Trade Commission (“FTC”) has been bullish about the risks posed by incumbents’ access to data. In comments submitted to the U.S. Copyright Office, for example, the FTC argued that:
The rapid development and deployment of AI also poses potential risks to competition. The rising importance of AI to the economy may further lock in the market dominance of large incumbent technology firms. These powerful, vertically integrated incumbents control many of the inputs necessary for the effective development and deployment of AI tools, including cloud-based or local computing power and access to large stores of training data. These dominant technology companies may have the incentive to use their control over these inputs to unlawfully entrench their market positions in AI and related markets, including digital content markets.[10]
Against this backdrop, it stands to reason that the largest online platforms—including Alphabet, Meta, Apple, and Amazon — should have a meaningful advantage in the burgeoning markets for generative AI services. After all, it is widely recognized that data is an essential input for generative AI.[11] This competitive advantage should be all the more significant given that these firms have been at the forefront of AI technology for more than a decade. Over this period, Google’s DeepMind and AlphaGo and Meta’s have routinely made headlines.[12] Apple and Amazon also have vast experience with AI assistants, and all of these firms use AI technology throughout their platforms.[13]
Contrary to what one might expect, however, the tech giants have, to date, been unable to leverage their vast data troves to outcompete startups like OpenAI and Midjourney. At the time of writing, OpenAI’s ChatGPT appears to be, by far, the most successful chatbot[14], despite the fact that large tech platforms arguably have access to far more (and more up-to-date) data.
This article suggests there are important lessons to be learned from the current technological moment, if only enforcers would stop to reflect. The meteoric rise of consumer-facing AI services should offer competition enforcers and policymakers an opportunity for introspection. As we explain, the rapid emergence of generative AI technology may undercut many core assumptions of today’s competition-policy debates — the rueful after-effects of the purported failure of 20th-century antitrust to address the allegedly manifest harms of 21st-century technology. These include the notions that data advantages constitute barriers to entry and can be leveraged to project dominance into adjacent markets; that scale itself is a market failure to be addressed by enforcers; and that the use of consumer data is inherently harmful to those consumers.
Proponents of tougher interventions by competition enforcers into digital markets often cite data network effects as a source of competitive advantage and barrier to entry (though terms like “economies of scale and scope” may offer more precision).[15] The crux of the argument is that “the collection and use of data creates a feedback loop of more data, which ultimately insulates incumbent platforms from entrants who, but for their data disadvantage, might offer a better product.”[16] This self-reinforcing cycle purportedly leads to market domination by a single firm. Thus, for Google, for example, it is argued that its “ever-expanding control of user personal data, and that data’s critical value to online advertisers, creates an insurmountable barrier to entry for new competition.”[17]
Right off the bat, it is important to note the conceptual problem of these claims. Because data is used to improve the quality of products and/or to subsidize their use, the idea of data as an entry barrier suggests that any product improvement or price reduction made by an incumbent could be a problematic entry barrier to any new entrant. This is tantamount to an argument that competition itself is a cognizable barrier to entry. Of course, it would be a curious approach to antitrust if this were treated as a problem, as it would imply that firms should under-compete — should forego consumer-welfare enhancements—in order to bring about a greater number of firms in a given market simply for its own sake.[18]
Meanwhile, actual economic studies of data network effects are few and far between, with scant empirical evidence to support the theory.[19] Andrei Hagiu and Julian Wright’s theoretical paper offers perhaps the most comprehensive treatment of the topic.[20] The authors ultimately conclude that data network effects can be of different magnitudes and have varying effects on firms’ incumbency advantage.[21] They cite Grammarly (an AI writing-assistance tool) as a potential example: “As users make corrections to the suggestions offered by Grammarly, its language experts and artificial intelligence can use this feedback to continue to improve its future recommendations for all users.”[22]
This is echoed by other economists who contend that “[t]he algorithmic analysis of user data and information might increase incumbency advantages, creating lock-in effects among users and making them more reluctant to join an entrant platform.”[23]
Crucially, some scholars take this logic a step further, arguing that platforms may use data from their “origin markets” in order to enter and dominate adjacent ones:
First, as we already mentioned, data collected in the origin market can be used, once the enveloper has entered the target market, to provide products more efficiently in the target market. Second, data collected in the origin market can be used to reduce the asymmetric information to which an entrant is typically subject when deciding to invest (for example, in R&D) to enter a new market. For instance, a search engine could be able to predict new trends from consumer searches and therefore face less uncertainty in product design.[24]
This possibility is also implicit in the paper by Hagiu and Wright.[25] Indeed, the authors’ theoretical model rests on an important distinction between within-user data advantages (that is, having access to more data about a given user) and across-user data advantages (information gleaned from having access to a wider user base). In both cases, there is an implicit assumption that platforms may use data from one service to gain an advantage in another market (because what matters is information about aggregate or individual user preferences, regardless of its origin).
Our review of the economic evidence suggests that several scholars have, with varying degrees of certainty, raised the possibility that incumbents may leverage data advantages to stifle competitors in their primary market or adjacent ones (be it via merger or organic growth). As we explain below, however, there is ultimately little evidence to support such claims.
Policymakers, however, have largely been receptive to these limited theoretical findings, basing multiple decisions on these theories, often with little consideration of the caveats that accompany them.[26] Indeed, it is remarkable that, in the Furman Report’s section on “[t]he data advantage for incumbents,” only two empirical economic studies are cited, and they offer directly contradictory conclusions with respect to the question of the strength of data advantages.[27] Nevertheless, the Furman Report concludes that data “may confer a form of unmatchable advantage on the incumbent business, making successful rivalry less likely,”[28] and adopts without reservation “convincing” evidence from non-economists with apparently no empirical basis.[29]
Giving [sic] the large amount of data already used for advertising purposes that Google holds, the increase in Google’s data collection capabilities, which goes beyond the mere number of active users for which Fitbit has been collecting data so far, the Transaction is likely to have a negative impact on the development of an unfettered competition in the markets for online advertising.[30]
As a result, the Commission cleared the merger on the condition that Google refrain from using data from Fitbit devices for its advertising platform.[31] The Commission will likely focus on similar issues during its ongoing investigation into Microsoft’s investment into OpenAI.[32]
Along similar lines, the FTC’s complaint to enjoin Meta’s purchase of a virtual-reality (VR) fitness app called “Within” relied, among other things, on the fact that Meta could leverage its data about VR-user behavior to inform its decisions and potentially outcompete rival VR-fitness apps: “Meta’s control over the Quest platform also gives it unique access to VR user data, which it uses to inform strategic decisions.”[33]
The U.S. Department of Justice’s twin cases against Google also raise data leveraging and data barriers to entry. The agency’s AdTech complaint that “Google intentionally exploited its massive trove of user data to further entrench its monopoly across the digital advertising industry.”[34] Similarly, in its Search complaint, the agency argues that:
Google’s anticompetitive practices are especially pernicious because they deny rivals scale to compete effectively. General search services, search advertising, and general search text advertising require complex algorithms that are constantly learning which organic results and ads best respond to user queries; the volume, variety, and velocity of data accelerates the automated learning of search and search advertising algorithms.[35]
Finally, the merger guidelines published by several competition enforcers cite the acquisition of data as a potential source of competitive concerns. For instance, the FTC and DOJ’s newly published guidelines state that “acquiring data that helps facilitate matching, sorting, or prediction services may enable the platform to weaken rival platforms by denying them that data.”[36] Likewise, the UK Competition and Markets Authority (“CMA”) warns against incumbents acquiring firms in order to obtain their data and foreclose other rivals:
Incentive to foreclose rivals… 7.19(e) Particularly in complex and dynamic markets, firms may not focus on short term margins but may pursue other objectives to maximise their long-run profitability, which the CMA may consider. This may include… obtaining access to customer data….[37]
7.19(e) Particularly in complex and dynamic markets, firms may not focus on short term margins but may pursue other objectives to maximise their long-run profitability, which the CMA may consider. This may include… obtaining access to customer data….[37]
In short, competition authorities around the globe are taking an aggressive stance on data network effects. Among the ways this has manifested is in basing enforcement decisions on fears that data collected by one platform might confer a decisive competitive advantage in adjacent markets. Unfortunately, these concerns rest on little to no empirical evidence, either in the economic literature or the underlying case records.
Given the assertions canvassed in the previous section, it seems reasonable to assume that firms such as Google, Meta, and Amazon would be in pole position to dominate the burgeoning market for generative AI. After all, these firms have not only been at the forefront of the field for the better part of a decade, but they also have access to vast troves of data, the likes of which their rivals could only dream when they launched their own services. Thus the authors of the Furman Report caution that “to the degree that the next technological revolution centres around artificial intelligence and machine learning, then the companies most able to take advantage of it may well be the existing large companies because of the importance of data for the successful use of these tools.[38]
At the time of writing, however, this is not how things have unfolded — although it bears noting these markets remain in flux and the competitive landscape is susceptible to change. The first significantly successful generative AI service was arguably not from either Meta—which had been working on chatbots for years and had access to, arguably, the world’s largest database of actual chats—or Google. Instead, the breakthrough came from a previously unknown firm called OpenAI.
OpenAI’s ChatGPT service currently holds an estimated 60% of the market (though reliable numbers are somewhat elusive).[39] It broke the record for the fastest online service to reach 100 million users (in only a couple of months), more than four times faster than the previous record holder, TikTok.[40] Based on Google Trends data, ChatGPT is nine times more popular than Google’s own Bard service worldwide, and 14 times more popular in the U.S.[41] In April 2023, ChatGPT reportedly registered 206.7 million unique visitors, compared to 19.5 million for Google’s Bard.[42] In short, at the time of writing, ChatGPT appears to be the most popular chatbot. And, so far, the entry of large players such as Google Bard or Meta AI appear to have had little effect on its market position.[43]
The picture is similar in the field of AI image generation. As of August 2023, Midjourney, Dall-E, and Stable Diffusion appear to be the three market leaders in terms of user visits.[44] This is despite competition from the likes of Google and Meta, who arguably have access to unparalleled image and video databases by virtue of their primary platform activities.[45]
This raises several crucial questions: how have these AI upstarts managed to be so successful, and is their success just a flash in the pan before Web 2.0 giants catch up and overthrow them? While we cannot answer either of these questions dispositively, some observations concerning the role and value of data in digital markets would appear to be relevant.
A first important observation is that empirical studies suggest data exhibits diminishing marginal returns. In other words, past a certain point, acquiring more data does not confer a meaningful edge to the acquiring firm. As Catherine Tucker puts it, following a review of the literature: “Empirically there is little evidence of economies of scale and scope in digital data in the instances where one would expect to find them.”[46]
Likewise, following a survey of the empirical literature on this topic, Geoffrey Manne & Dirk Auer conclude that:
Available evidence suggests that claims of “extreme” returns to scale in the tech sector are greatly overblown. Not only are the largest expenditures of digital platforms unlikely to become proportionally less important as output increases, but empirical research strongly suggests that even data does not give rise to increasing returns to scale, despite routinely being cited as the source of this effect.[47]
In other words, being the firm with the most data appears to be far less important than having enough data, and this lower bar may be accessible to far more firms than one might initially think possible.
And obtaining enough data could become even easier — that is, the volume of required data could become even smaller — with technological progress. For instance, synthetic data may provide an adequate substitute to real-world data[48] — or may even outperform real-world data.[49] As Thibault Schrepel and Alex Pentland point out, “advances in computer science and analytics are making the amount of data less relevant every day. In recent months, important technological advances have allowed companies with small data sets to compete with larger ones.”[50]
Indeed, past a certain threshold, acquiring more data might not meaningfully improve a service, where other improvements (such as better training methods or data curation) could have a large effect. In fact, there is some evidence that excessive data impedes a service’s ability to generate results appropriate for a given query: “[S]uperior model performance can often be achieved with smaller, high-quality datasets than massive, uncurated ones. Data curation ensures that training datasets are devoid of noise, irrelevant instances, and duplications, thus maximizing the efficiency of every training iteration.”[51]
Consider, for instance, a user who wants to generate an image of a basketball. Using a model trained on an indiscriminate range and number of public photos in which a basketball appears, but is surrounded by copious other image data, the user may end up with an inordinately noisy result. By contrast, a model trained with a better method on fewer, more-carefully selected images, could readily yield far superior results.[52] In one important example,
[t]he model’s performance is particularly remarkable, given its small size. “This is not a large language model trained on the whole Internet; this is a relatively small transformer trained for these tasks,” says Armando Solar-Lezama, a computer scientist at the Massachusetts Institute of Technology, who was not involved in the new study…. The finding implies that instead of just shoving ever more training data into machine-learning models, a complementary strategy might be to offer AI algorithms the equivalent of a focused linguistics or algebra class.[53]
Current efforts are thus focused on improving the mathematical and logical reasoning of large language models (“LLMs”), rather than maximizing training datasets.[54] Two points stand out. The first is that firms like OpenAI rely largely on publicly available datasets — such as GSM8K — to train their LLMs.[55] Second, the real challenge to create cutting-edge AI is not so much in collecting data, but rather in creating innovative AI training processes and architectures:
[B]uilding a truly general reasoning engine will require a more fundamental architectural innovation. What’s needed is a way for language models to learn new abstractions that go beyond their training data and have these evolving abstractions influence the model’s choices as it explores the space of possible solutions. We know this is possible because the human brain does it. But it might be a while before OpenAI, DeepMind, or anyone else figures out how to do it in silicon.[56]
We know this is possible because the human brain does it. But it might be a while before OpenAI, DeepMind, or anyone else figures out how to do it in silicon.[56]
Furthermore, it is worth noting that the data most relevant to startups operating in a given market may not be those data held by large incumbent platforms in other markets, but rather data specific to the market in which the startup is active or, even better, to the given problem it is attempting to solve:
As Andres Lerner has argued, if you wanted to start a travel business, the data from Kayak or Priceline would be far more relevant. Or if you wanted to start a ride-sharing business, data from cab companies would be more useful than the broad, market-cross-cutting profiles Google and Facebook have. Consider companies like Uber, Lyft and Sidecar that had no customer data when they began to challenge established cab companies that did possess such data. If data were really so significant, they could never have competed successfully. But Uber, Lyft and Sidecar have been able to effectively compete because they built products that users wanted to use — they came up with an idea for a better mousetrap. The data they have accrued came after they innovated, entered the market and mounted their successful challenges — not before.[57]
The bottom line is that data is not the be-all and end-all that many in competition circles rather casually make it out to be.[58] While data may often confer marginal benefits, there is little sense these are ultimately decisive.[59] As a result, incumbent platforms’ access to vast numbers of users and data in their primary markets might only marginally affect their AI competitiveness.
A related observation is that firms’ capabilities and other features of their products arguably play a more important role than the data they own.[60] Examples of this abound in digital markets. Google overthrew Yahoo, despite initially having access to far fewer users and far less data; Google and Apple overcame Microsoft in the smartphone OS market despite having comparatively tiny ecosystems (at the time) to leverage; and TikTok rose to prominence despite intense competition from incumbents like Instagram, which had much larger user bases. In each of these cases, important product-design decisions (such as the PageRank algorithm, recognizing the specific needs of mobile users,[61] and TikTok’s clever algorithm) appear to have played a far greater role than initial user and data endowments (or lack thereof).
All of this suggests that the early success of OpenAI likely has more to do with its engineering decisions than the data it did (or did not) own. And going forward, OpenAI and its rivals’ ability to offer and monetize compelling stores offering custom versions of their generative AI technology will arguably play a much larger role than (and contribute to) their ownership of data.[62] In other words, the ultimate challenge is arguably to create a valuable platform, of which data ownership is a consequence, but not a cause.
It is also important to note that, in those instances where it is valuable, data does not just fall from the sky. Instead, it is through smart business and engineering decisions that firms can generate valuable information (which does not necessarily correlate with owing more data).
For instance, OpenAI’s success with ChatGPT is often attributed to its more efficient algorithms and training models, which arguably have enabled the service to improve more rapidly than its rivals.[63] Likewise, the ability of firms like Meta and Google to generate valuable data for advertising arguably depends more on design decisions that elicit the right data from users, rather than the raw number of users in their networks.
Put differently, setting up a business so as to generate the right information is more important than simply owning vast troves of data.[64] Even in those instances where high-quality data is an essential parameter of competition, it does not follow that having vaster databases or more users on a platform necessarily leads to better information for the platform.
Given what precedes, it seems clear that OpenAI and other generative AI startups’ early success, as well as their chances of prevailing in the future, hinge on a far broader range of factors than the mere ownership of data. Indeed, if data ownership consistently conferred a significant competitive advantage, these new firms would not be where they are today. This does not mean that data is worthless, of course. Rather, it means that competition authorities should not assume that merely possessing data is a dispositive competitive advantage, absent compelling empirical evidence to support such a finding. In this light, the current wave of decisions and competition-policy pronouncements that rely on data-related theories of harm are premature.
As we explain above, data (network effects) are not the source of barriers to entry that they are sometimes made out to be; rather, the picture is far more nuanced. Indeed, as economist Andres Lerner demonstrated almost a decade ago (and the assessment is only truer today):
Although the collection of user data is generally valuable for online providers, the conclusion that such benefits of user data lead to significant returns to scale and to the entrenchment of dominant online platforms is based on unsupported assumptions. Although, in theory, control of an “essential” input can lead to the exclusion of rivals, a careful analysis of real-world evidence indicates that such concerns are unwarranted for many online businesses that have been the focus of the “big data” debate.[65]
While data can be an important part of the competitive landscape, incumbent data advantages are far less pronounced than today’s policymakers commonly assume. In that respect, five main lessons emerge:
First, it is not surprising that startups, rather than incumbents, have taken an early lead in generative AI (and in Web 2.0 before it). After all, if data-incumbency advantages are small or even nonexistent, then smaller and more nimble players may have an edge over established tech platforms. This is all the more likely given that, despite significant efforts, the biggest tech platforms were unable to offer compelling generative AI chatbots and image-generation services before the emergence of ChatGPT, Dall-E, Midjourney, etc. This failure suggests that, in a process akin to Christensen’s Innovator’s Dilemma,[66] something about their existing services and capabilities was holding them back in those markets. Of course, this does not necessarily mean that those same services/capabilities could not become an advantage when the generative AI market starts addressing issues of monetization and scale.[67] But it does mean that assumptions of a firm’s market power based on its possession of data are off the mark.
Another important implication is that, paradoxically, policymakers’ efforts to prevent Web 2.0 platforms from competing freely in generative AI markets may ultimately backfire and lead to less, not more, competition. Indeed, OpenAI is currently acquiring a sizeable lead in generative AI. While competition authorities might like to think that other startups will emerge and thrive in this space, it is important not to confuse desires with reality. For, while there is a vibrant AI-startup ecosystem, there is at least a case to be made that the most significant competition for today’s AI leaders will come from incumbent Web 2.0 platforms — although nothing is certain at this stage. Policymakers should beware not to stifle that competition on the misguided assumption that competitive pressure from large incumbents is somehow less valuable to consumers than that which originates from smaller firms.
Finally, even if there were a competition-related market failure to be addressed (which is anything but clear) in the field of generative AI, it is unclear that contemplated remedies would do more good than harm. Some of the solutions that have been put forward have highly ambiguous effects on consumer welfare. Scholars have shown that mandated data sharing — a solution championed by EU policymakers, among others — may sometimes dampen competition in generative AI markets.[68] This is also true of legislation like the GDPR that make it harder for firms to acquire more data about consumers — assuming such data is, indeed, useful to generative AI services.[69]
In sum, it is a flawed understanding of the economics and practical consequences of large agglomerations of data that lead competition authorities to believe that data-incumbency advantages are likely to harm competition in generative AI markets — or even in the data-intensive Web 2.0 markets that preceded them. Indeed, competition or regulatory intervention to “correct” data barriers and data network and scale effects is liable to do more harm than good.
[1] Nathan Newman, Taking on Google’s Monopoly Means Regulating Its Control of User Data, Huffington Post (Sep. 24, 2013), http://www.huffingtonpost.com/nathan-newman/taking-on-googlesmonopol_b_3980799.html.
[2] See e.g. Lina Khan & K. Sabeel Rahman, Restoring Competition in the U.S. Economy, in Untamed: How to Check Corporate, Financial, and Monopoly Power (Nell Abernathy, Mike Konczal, & Kathryn Milani, eds., 2016), at 23 (“From Amazon to Google to Uber, there is a new form of economic power on display, distinct from conventional monopolies and oligopolies…, leverag[ing] data, algorithms, and internet-based technologies… in ways that could operate invisibly and anticompetitively.”); Mark Weinstein, I Changed My Mind — Facebook Is a Monopoly, Wall St. J. (Oct. 1, 2021), https://www.wsj.com/articles/facebook-is-monopoly-metaverse-users-advertising-platforms-competition-mewe-big-tech-11633104247 (“[T]he glue that holds it all together is Facebook’s monopoly over data…. Facebook’s data troves give it unrivaled knowledge about people, governments — and its competitors.”).
[3] See generally Abigail Slater, Why “Big Data” Is a Big Deal, The Reg. Rev. (Nov. 6, 2023), https://www.theregreview.org/2023/11/06/slater-why-big-data-is-a-big-deal/; Amended Complaint at ¶36, United States v. Google, 1:20-cv-03010- (D.D.C. 2020); Complaint at ¶37, United States v. Google, 1:23-cv-00108 (E.D. Va. 2023), https://www.justice.gov/opa/pr/justice-department-sues-google-monopolizing-digital-advertising-technologies (“Google intentionally exploited its massive trove of user data to further entrench its monopoly across the digital advertising industry.”).
[4] See e.g. Press Release, European Commission, Commission Launches Calls for Contributions on Competition in Virtual Worlds and Generative AI (Jan. 9, 2024), https://ec.europa.eu/commission/presscorner/detail/en/IP_24_85; Krysten Crawford, FTC’s Lina Khan warns Big Tech over AI, SIEPR (Nov. 3, 2020), https://siepr.stanford.edu/news/ftcs-lina-khan-warns-big-tech-over-ai (“Federal Trade Commission Chair Lina Khan delivered a sharp warning to the technology industry in a speech at Stanford on Thursday: Antitrust enforcers are watching what you do in the race to profit from artificial intelligence.”) (emphasis added).
[5] See e.g. John M. Newman, Antitrust in Digital Markets, 72 Vand. L. Rev. 1497, 1501 (2019) (“[T]he status quo has frequently failed in this vital area, and it continues to do so with alarming regularity. The laissez-faire approach advocated for by scholars and adopted by courts and enforcers has allowed potentially massive harms to go unchecked.”); Bertin Martins, Are New EU Data Market Regulations Coherent and Efficient?, Bruegel Working Paper 21/23 (2023), available at https://www.bruegel.org/working-paper/are-new-eu-data-market-regulations-coherent-and-efficient (“Technical restrictions on access to and re-use of data may result in failures in data markets and data-driven services markets.”); Valéria Faure-Muntian, Competitive Dysfunction: Why Competition Law Is Failing in a Digital World, The Forum Network (Feb. 24, 2021), https://www.oecd-forum.org/posts/competitive-dysfunction-why-competition-law-is-failing-in-a-digital-world.
[6] Rana Foroohar, The Great US-Europe Antitrust Divide, FT (Feb. 5, 2024), https://www.ft.com/content/065a2f93-dc1e-410c-ba9d-73c930cedc14.
[7] See e.g. Press Release, European Commission, supra note 5.
[8] See infra, Section II. Commentators have also made similar claims. See, e.g., Ganesh Sitaram & Tejas N. Narechania, It’s Time for the Government to Regulate AI. Here’s How, Politico (Jan. 15, 2024) (“All that cloud computing power is used to train foundation models by having them “learn” from incomprehensibly huge quantities of data. Unsurprisingly, the entities that own these massive computing resources are also the companies that dominate model development. Google has Bard, Meta has LLaMa. Amazon recently invested $4 billion into one of OpenAI’s leading competitors, Anthropic. And Microsoft has a 49 percent ownership stake in OpenAI — giving it extraordinary influence, as the recent board struggles over Sam Altman’s role as CEO showed.”).
[9] Press Release, European Commission, supra note 5.
[10] Comment of U.S. Federal Trade Commission to the U.S. Copyright Office, Artificial Intelligence and Copyright, Docket No. 2023-6 (Oct. 30, 2023) at 4, available at https://www.ftc.gov/legal-library/browse/advocacy-filings/comment-federal-trade-commission-artificial-intelligence-copyright (emphasis added).
[11] See, e.g. Joe Caserta, Holger Harreis, Kayvaun Rowshankish, Nikhil Srinidhi, and Asin Tavakoli, The data dividend: Fueling generative AI, McKinsey Digital (Sept. 15, 2023), https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-data-dividend-fueling-generative-ai (“Your data and its underlying foundations are the determining factors to what’s possible with generative AI.”).
[12] See e.g. Tim Keary, Google DeepMind’s Achievements and Breakthroughs in AI Research, Techopedia (Aug. 11, 2023), https://www.techopedia.com/google-deepminds-achievements-and-breakthroughs-in-ai-research; See e.g. Will Douglas Heaven, Google DeepMind used a large language model to solve an unsolved math problem, MIT Technology Review (Dec. 14, 2023), https://www.technologyreview.com/2023/12/14/1085318/google-deepmind-large-language-model-solve-unsolvable-math-problem-cap-set/; See also A Decade of Advancing the State-of-the-Art in AI Through Open Research, Meta (Nov. 30, 2023), https://about.fb.com/news/2023/11/decade-of-advancing-ai-through-open-research/; See also 200 languages within a single AI model: A breakthrough in high-quality machine translation, Meta, https://ai.meta.com/blog/nllb-200-high-quality-machine-translation/ (last visited Jan. 18, 2023).
[13] See e.g. Jennifer Allen, 10 years of Siri: the history of Apple’s voice assistant, Tech Radar (Oct. 4, 2021), https://www.techradar.com/news/siri-10-year-anniversary; see also Evan Selleck, How Apple is already using machine learning and AI in iOS, Apple Insider (Nov. 20, 2023), https://appleinsider.com/articles/23/09/02/how-apple-is-already-using-machine-learning-and-ai-in-ios; see also Kathleen Walch, The Twenty Year History Of AI At Amazon, Forbes (July 19, 2019), https://www.forbes.com/sites/cognitiveworld/2019/07/19/the-twenty-year-history-of-ai-at-amazon/?sh=1734bcb268d0.
[14] See infra Section III.
[15] See e.g. Cédric Argenton & Jens Prüfer, Search Engine Competition with Network Externalities, 8 J. Comp. L. & Econ. 73, 74 (2012); Mark A. Lemley & Matthew Wansley, Coopting Disruption (February 1, 2024), https://ssrn.com/abstract=4713845.
[16] John M. Yun, The Role of Big Data in Antitrust, in The Global Antitrust Institute Report on the Digital Economy (Joshua D. Wright & Douglas H. Ginsburg, eds., Nov. 11, 2020) at 233, available at https://gaidigitalreport.com/2020/08/25/big-data-and-barriers-to-entry/#_ftnref50. See also e.g. Robert Wayne Gregory, Ola Henfridsson, Evgeny Kaganer, & Harris Kyriakou, The Role of Artificial Intelligence and Data Network Effects for Creating User Value, 46 Acad. of Mgmt. Rev. 534 (2020), final pre-print version at 4, available at http://wrap.warwick.ac.uk/134220) (“A platform exhibits data network effects if, the more that the platform learns from the data it collects on users, the more valuable the platform becomes to each user.”). See also Karl Schmedders, José Parra-Moyano & Michael Wade, Why Data Aggregation Laws Could be the Answer to Big Tech Dominance, Silicon Republic (Feb. 6, 2024), https://www.siliconrepublic.com/enterprise/data-ai-aggregation-laws-regulation-big-tech-dominance-competition-antitrust-imd.
[17] Nathan Newman, Search, Antitrust, and the Economics of the Control of User Data, 31 Yale J. Reg. 401, 409 (2014) (emphasis added). See also id. at 420 & 423 (“While there are a number of network effects that come into play with Google, [“its intimate knowledge of its users contained in its vast databases of user personal data”] is likely the most important one in terms of entrenching the company’s monopoly in search advertising…. Google’s overwhelming control of user data… might make its dominance nearly unchallengeable.”).
[18] See also Yun, supra note 17 at 229 (“[I]nvestments in big data can create competitive distance between a firm and its rivals, including potential entrants, but this distance is the result of a competitive desire to improve one’s product.”).
[19] For a review of the literature on increasing returns to scale in data (this topic is broader than data network effects) see Geoffrey Manne & Dirk Auer, Antitrust Dystopia and Antitrust Nostalgia: Alarmist Theories of Harm in Digital Markets and Their Origins, 28 Geo Mason L. Rev. 1281, 1344 (2021).
[20] Andrei Hagiu & Julian Wright, Data-Enabled Learning, Network Effects, and Competitive Advantage, 54 RAND J. Econ. 638 (2023) (final preprint available at https://andreihagiu.com/wp-content/uploads/2022/08/Data-enabled-learning-Final-RAND-Article.pdf).
[21] Id. at 2. The authors conclude that “Data-enabled learning would seem to give incumbent firms a competitive advantage. But how strong is this advantage and how does it differ from that obtained from more traditional mechanisms….”
[22] Id.
[23] Bruno Jullien & Wilfried Sand-Zantman, The Economics of Platforms: A Theory Guide for Competition Policy, 54 Info. Econ. & Pol’y 10080, 101031 (2021).
[24] Daniele Condorelli & Jorge Padilla, Harnessing Platform Envelopment in the Digital World, 16 J. Comp. L. & Pol’y 143, 167 (2020).
[25] See Hagiu & Wright, supra note 21.
[26] For a summary of these limitations, see generally Catherine Tucker, Network Effects and Market Power: What Have We Learned in the Last Decade?, Antitrust (Spring 2018) at 72, available at https://sites.bu.edu/tpri/files/2018/07/tucker-network-effects-antitrust2018.pdf. See also Manne & Auer, supra note 20, at 1330.
[27] See Jason Furman, Diane Coyle, Amelia Fletcher, Derek McAuley & Philip Marsden (Dig. Competition Expert Panel), Unlocking Digital Competition (2019) at 32-35 (“Furman Report”), available at https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/785547/unlocking_digital_competition_furman_review_web.pdf.
[28] Id. at 34.
[29] Id. at 35. To its credit, it should be noted, the Furman Report does counsel caution before mandating access to data as a remedy to promote competition. See id. at 75. That said, the Furman Report does maintain that such a remedy should certainly be on the table because “the evidence suggests that large data holdings are at the heart of the potential for some platform markets to be dominated by single players and for that dominance to be entrenched in a way that lessens the potential for competition for the market.” Id. In fact, the evidence does not show this.
[30] Case COMP/M.9660 — Google/Fitbit, Commission Decision (Dec. 17, 2020) (Summary at O.J. (C 194) 7), available at https://ec.europa.eu/competition/mergers/cases1/202120/m9660_3314_3.pdf at 455.
[31] Id. at 896.
[32] See Natasha Lomas, EU Checking if Microsoft’s OpenAI Investment Falls Under Merger Rules, TechCrunch (Jan. 9, 2024), https://techcrunch.com/2024/01/09/openai-microsoft-eu-merger-rules/.
[33] Amended Complaint at 11, Meta/Zuckerberg/Within, Fed. Trade Comm’n. (2022) (No. 605837), available at https://www.ftc.gov/system/files/ftc_gov/pdf/D09411%20-%20AMENDED%20COMPLAINT%20FILED%20BY%20COUNSEL%20SUPPORTING%20THE%20COMPLAINT%20-%20PUBLIC%20%281%29_0.pdf.
[34] Amended Complaint (D.D.C), supra note 4, at ¶37.
[35] Amended Complaint (E.D. Va), supra note 4, at ¶8.
[36] US Dep’t of Justice & Fed. Trade Comm’n, Merger Guidelines (2023) at 25, https://www.ftc.gov/system/files/ftc_gov/pdf/2023_merger_guidelines_final_12.18.2023.pdf.
[37] Competition and Mkts. Auth., Merger Assessment Guidelines (2021) at ¶7.19(e), https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1051823/MAGs_for_publication_2021_–_.pdf.
[38] Furman Report, supra note 28, at ¶4.
[39] See e.g. Chris Westfall, New Research Shows ChatGPT Reigns Supreme in AI Tool Sector, Forbes (Nov. 16, 2023), https://www.forbes.com/sites/chriswestfall/2023/11/16/new-research-shows-chatgpt-reigns-supreme-in-ai-tool-sector/?sh=7de5de250e9c.
[40] See Krystal Hu, ChatGPT Sets Record for Fastest-Growing User Base, Reuters (Feb. 2, 2023), https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/; Google: The AI Race Is On, App Economy Insights (Feb. 7, 2023), https://www.appeconomyinsights.com/p/google-the-ai-race-is-on.
[41] See Google Trends, https://trends.google.com/trends/explore?date=today%205-y&q=%2Fg%2F11khcfz0y2,%2Fg%2F11ts49p01g&hl=en (last visited, Jan. 12, 2024) and https://trends.google.com/trends/explore?date=today%205-y&geo=US&q=%2Fg%2F11khcfz0y2,%2Fg%2F11ts49p01g&hl=en (last visited Jan. 12, 2024).
[42] See David F. Carr, As ChatGPT Growth Flattened in May, Google Bard Rose 187%, Similarweb Blog (June 5, 2023), https://www.similarweb.com/blog/insights/ai-news/chatgpt-bard/.
[43] See Press Release, Meta, Introducing New AI Experiences Across Our Family of Apps and Devices (Sept. 27, 2023), https://about.fb.com/news/2023/09/introducing-ai-powered-assistants-characters-and-creative-tools/; Sundar Pichai, An Important Next Step on Our AI Journey, Google Keyword Blog (Feb. 6, 2023), https://blog.google/technology/ai/bard-google-ai-search-updates/.
[44] See Ion Prodan, 14 Million Users: Midjourney’s Statistical Success, Yon (Aug. 19, 2023), https://yon.fun/midjourney-statistics/. See also Andrew Wilson, Midjourney Statistics: Users, Polls, & Growth [Oct 2023], ApproachableAI (Oct. 13, 2023), https://approachableai.com/midjourney-statistics/.
[45] See Hema Budaraju, New Ways to Get Inspired with Generative AI in Search, Google Keyword Blog (Oct. 12, 2023), https://blog.google/products/search/google-search-generative-ai-october-update/; Imagine with Meta AI, Meta (last visited Jan. 12, 2024), https://imagine.meta.com/.
[46] Catherine Tucker, Digital Data, Platforms and the Usual [Antitrust] Suspects: Network Effects, Switching Costs, Essential Facility, 54 Rev. Indus. Org. 683, 686 (2019).
[47] Manne & Auer, supra note 20, at 1345.
[48] See e.g. Stefanie Koperniak, Artificial Data Give the Same Results as Real Data—Without Compromising Privacy, MIT News (Mar. 3, 2017), https://news.mit.edu/2017/artificial-data-give-same-results-as-real-data-0303 (“[Authors] describe a machine learning system that automatically creates synthetic data—with the goal of enabling data science efforts that, due to a lack of access to real data, may have otherwise not left the ground. While the use of authentic data can cause significant privacy concerns, this synthetic data is completely different from that produced by real users—but can still be used to develop and test data science algorithms and models.”).
[49] See e.g. Rachel Gordon, Synthetic Imagery Sets New Bar in AI Training Efficiency, MIT News (Nov. 20, 2023), https://news.mit.edu/2023/synthetic-imagery-sets-new-bar-ai-training-efficiency-1120 (“By using synthetic images to train machine learning models, a team of scientists recently surpassed results obtained from traditional ‘real-image’ training methods.).
[50] Thibault Schrepel & Alex ‘Sandy’ Pentland, Competition Between AI Foundation Models: Dynamics and Policy Recommendations, MIT Connection Science Working Paper (Jun. 2023), at 8.
[51] Igor Susmelj, Optimizing Generative AI: The Role of Data Curation, Lightly (last visited Jan 15, 2024), https://www.lightly.ai/post/optimizing-generative-ai-the-role-of-data-curation.
[52] See e.g. Xiaoliang Dai, et al., Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack , ArXiv (Sep. 27, 2023) at 1, https://ar5iv.labs.arxiv.org/html/2309.15807 (“[S]upervised fine-tuning with a set of surprisingly small but extremely visually appealing images can significantly improve the generation quality.”). See also Hu Xu, et al., Demystifying CLIP Data, ArXiv (Sep. 28, 2023), https://arxiv.org/abs/2309.16671.
[53] Lauren Leffer, New Training Method Helps AI Generalize like People Do, Sci. Am. (Oct. 26, 2023), https://www.scientificamerican.com/article/new-training-method-helps-ai-generalize-like-people-do/ (discussing Brendan M. Lake & Marco Baroni, Human-Like Systematic Generalization Through a Meta-Learning Neural Network, 623 Nature 115 (2023)).
[54] Timothy B. Lee, The Real Research Behind the Wild Rumors about OpenAI’s Q* Project, Ars Technica (Dec. 8, 2023), https://arstechnica.com/ai/2023/12/the-real-research-behind-the-wild-rumors-about-openais-q-project/.
[55] Id. See also GSM8K, Papers with Code (last visited Jan. 18, 2023), available at https://paperswithcode.com/dataset/gsm8k; MATH Dataset, GitHub (last visited Jan. 18, 2024), available at https://github.com/hendrycks/math.
[56] Lee, supra note 55.
[57] Geoffrey Manne & Ben Sperry, Debunking the Myth of a Data Barrier to Entry for Online Services, Truth on the Market (Mar. 26, 2015), https://truthonthemarket.com/2015/03/26/debunking-the-myth-of-a-data-barrier-to-entry-for-online-services/ (citing Andres V. Lerner, The Role of ‘Big Data’ in Online Platform Competition (Aug. 26, 2014), available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2482780.).
[58] See e.g., Lemley & Wansley, supra note 18, at 22 (“Incumbents have all that information. It would be difficult for a new entrant to acquire similar datasets independently….”).
[59] See Catherine Tucker, Digital Data as an Essential Facility: Control, CPI Antitrust Chron. (Feb. 2020) at 11 (“[U]ltimately the value of data is not the raw manifestation of the data itself, but the ability of a firm to use this data as an input to insight.”).
[60] Or, as John Yun puts it, data is only a small component of digital firms’ production function. See Yun, supra note 17, at 235 (“Second, while no one would seriously dispute that having more data is better than having less, the idea of a data-driven network effect is focused too narrowly on a single factor improving quality. As mentioned in supra Section I.A, there are a variety of factors that enter a firm’s production function to improve quality.”).
[61] Luxia Le, The Real Reason Windows Phone Failed Spectacularly, History–Computer (Aug. 8, 2023), https://history-computer.com/the-real-reason-windows-phone-failed-spectacularly/.
[62] Introducing the GPT Store, Open AI (Jan. 10, 2024), https://openai.com/blog/introducing-the-gpt-store.
[63] See Michael Schade, How ChatGPT and Our Language Models are Developed, OpenAI, https://help.openai.com/en/articles/7842364-how-chatgpt-and-our-language-models-are-developed; Sreejani Bhattacharyya, Interesting innovations from OpenAI in 2021, AIM (Jan. 1, 2022), https://analyticsindiamag.com/interesting-innovations-from-openai-in-2021/; Danny Hernadez & Tom B. Brown, Measuring the Algorithmic Efficiency of Neural Networks, ArXiv (May 8, 2020), available at https://arxiv.org/abs/2005.04305.
[64] See Yun, supra note 17 at 235 (“Even if data is primarily responsible for a platform’s quality improvements, these improvements do not simply materialize with the presence of more data—which differentiates the idea of data-driven network effects from direct network effects. A firm needs to intentionally transform raw, collected data into something that provides analytical insights. This transformation involves costs including those associated with data storage, organization, and analytics, which moves the idea of collecting more data away from a strict network effect to more of a ‘data opportunity.’”).
[65] Lerner, supra note 58, at 4-5 (emphasis added).
[66] See Clayton M. Christensen, The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail (2013).
[67] See David J. Teece, Dynamic Capabilities and Strategic Management: Organizing for Innovation and Growth (2009).
[68] See Hagiu and Wright, supra note 21, at 4 (“We use our dynamic framework to explore how data sharing works: we find that it in-creases consumer surplus when one firm is sufficiently far ahead of the other by making the laggard more competitive, but it decreases consumer surplus when the firms are sufficiently evenly matched by making firms compete less aggressively, which in our model means subsidizing consumers less.”). See also Lerner, supra note 58.
[69] See e.g. Hagiu & Wright, id. (“We also use our model to highlight an unintended consequence of privacy policies. If such policies reduce the rate at which firms can extract useful data from consumers, they will tend to increase the incumbent’s competitive advantage, reflecting that the entrant has more scope for new learning and so is affected more by such a policy.”); Jian Jia, Ginger Zhe Jin & Liad Wagman, The Short-Run Effects of the General Data Protection Regulation on Technology Venture Investment, 40 Marketing Sci. 593 (2021) (finding GDPR reduced investment in new and emerging technology firms, particularly in data-related ventures); James Campbell, Avi Goldfarb, & Catherine Tucker, Privacy Regulation and Market Structure, 24 J. Econ. & Mgmt. Strat. 47 (2015) (“Consequently, rather than increasing competition, the nature of transaction costs implied by privacy regulation suggests that privacy regulation may be anti-competitive.”).
TOTM As Greg Werden has noted, the process of defining the relevant market in an antitrust case doesn’t just finger which part of the economy is allegedly . . .
As Greg Werden has noted, the process of defining the relevant market in an antitrust case doesn’t just finger which part of the economy is allegedly affected by the challenged conduct, but it also “identifies the competitive process alleged to be harmed.” Unsurprisingly, plaintiffs in such proceedings (most commonly, antitrust enforcers) often seek to set exceedingly narrow parameters for relevant markets in order to bolster their case. In the extreme, these artificially constrained definitions sketch what can only be called “gerrymandered” markets—obscuring rather than illuminating the competitive processes at issue.
This unfortunate tendency is exemplified in the Federal Trade Commission’s (FTC) recent complaint against Amazon, which describes two relevant markets in which anticompetitive harm has allegedly occurred: (1) the “online superstore market” and (2) the “online marketplace services market.” Because both markets are exceedingly narrow, they grossly inflate Amazon’s apparent market share and minimize the true extent of competition. Moreover, by lumping together wildly different products and wildly different sellers into single “cluster markets,” the FTC misapprehends the nature of competition relating to the challenged conduct.
