In November 1962, Soviet Premier Nikita Khrushchev addressed the Central Committee plenum to vent his frustration over the glaring distortions created by the state’s central economic planning. His chief complaint concerned a metric known as val—gross industrial output measured by weight or volume. Lighting factories faced production quotas based on sheer tonnage, rather than quality or consumer demand. Their response was rational, if disastrous.

“The chandeliers grew heavier and heavier until they started pulling ceilings down,” Khrushchev noted. “The workers fulfilled the plan… but who needs this plan? To whom does it give light?”

As economists have since pointed out, this illustrates Goodhart’s law: When a measure becomes a target, it ceases to be a good measure. Central planning rewards producers for hitting numerical targets, even when doing so wastes resources and destroys economic value.

Decades later, the Federal Railroad Administration (FRA) appears to have adopted the Soviet lighting industry’s approach. Under pressure from executive mandates to deliver a high volume of deregulation, the FRA recently touted a record of 65 deregulatory actions finalized between January 2025 and late 2026.

On paper, that’s a staggering amount of regulatory relief. In practice, the FRA has simply manufactured heavy, low-value chandeliers.

Read the full piece here.

Your laptop’s memory has a job even when it isn’t working: being ready when you are. Sam Altman, CEO of OpenAI, the company behind ChatGPT, seems concerned about its work ethic.

Altman made the case during an Oct. 29 appearance on Cheeky Pint, the podcast hosted by Stripe co-founder John Collison. They were discussing why high prices hadn’t prompted memory manufacturers to expand supply faster. Altman challenged the idea that running artificial intelligence (AI) models on personal computers would ease the need for data centers. With memory scarce, he argued, centralized systems could put it to more consistent use.

Altman strikes me as earnest. I think he means it when he says that, from a “global allocation perspective,” it’s “quite terrible to have everybody have a bunch of the world’s limited supply of DRAM sitting in their own computer… being used 5% of the time.” DRAM—dynamic random-access memory—is the working memory computers use to run programs.

His premise is straightforward: Memory is scarce, so it should go wherever it gets the heaviest, most consistent use. By that logic, memory sitting idle in a laptop 95% of the time would serve us better in a data center, working almost continuously for thousands of users.

That view is understandable coming from someone whose firm needs vast data centers to run AI models. But it mistakes how often we use something for how much we value it. Assets we expect to use for years can justify their cost simply by being available when we need them. Privacy, control, and peace of mind also count, even if they don’t show up in a utilization rate.

More subtle versions of this mistake can be just as well-intentioned. In a recent interview with Ben Thompson, Palantir Chief Technology Officer Shyam Sankar aptly compared the current AI revolution to the spread of electricity in the late 19th and early 20th centuries. His central point is correct: Realizing the gains from this new technology requires redesigning how we work.

But the electricity analogy has limits. Altman’s complaint about idle memory shows where it begins to break down.

Read the full piece here.

Big Tech is spending a fortune to secure a future that competition regulators seem to think it already owns. Investors aren’t so sure. They’re losing patience with the industry’s artificial intelligence (AI) bill—and questioning what all that spending will buy.

Alphabet shares sank more than 12% last Thursday—the company’s worst trading day on record—after both revenue and profit missed expectations for the first time in nearly four years. Google’s core advertising business fell short of expectations, and free cash flow—the cash left after operating expenses and investments in assets such as data centers—turned negative by $9.1 billion. The company’s chief financial officer also warned that capital expenditures would increase significantly in 2027.

Amazon fared little better. Its shares closed down more than 4% ahead of its earnings release, then slid another 6% after hours when the company revealed that it had spent $75.9 billion on capital expenditures in a single quarter, while generating just $42.4 billion in cash from operations. Meta had already shed 6.98% the previous week over its own spending plans.

As The Economist, CNN, and CNBC all reported, investors increasingly question whether Big Tech’s massive AI spending will ever pay off. Alphabet’s results also stoked fears that consumers’ shift to AI chatbots threatens Google’s core search-advertising business.

It would be unwise to read too much into a few days of trading. Stock prices are noisy, and markets have swung between AI euphoria and anxiety more than once this year. But the sell-off sharpens a question for competition policymakers—one that sits uneasily with the regulatory frameworks they’ve spent the past five years building: What if the dominant platforms those frameworks label “gatekeepers” aren’t nearly as entrenched as policymakers assumed?

Read the full piece here.

Abstract

This paper explores how EU institutions look at the competition cybersecurity nexus. Through a content analysis of EU regulation and competition law cases, we uncover systematic patterns in the treatment of cybersecurity and reveal blind spots in bath legal reasoning and institutional design. Building on these findings, we propose a normative framework to guide policymakers and enforcement agencies looking to protect cybersecurity imperatives without distorting competition.

Read at SSRN.

Introduction: In this interview, Onyeka Aralu, a competition fellow at the International Centre for Law & Economics, discussed the dynamics behind Uber’s exit from Nigeria and what the country needs to attract long-term investment commitments. He spoke with Jide Akintunde, Managing Editor of Financial Nigeria publications.

Jide Akintunde (JA): Uber did not publicly provide a detailed explanation for its decision to exit Nigeria, although many observers have linked the move to the company’s broader shift towards autonomous mobility and robotaxi investments, amongst other reasons. What deductions can be made about Uber’s exit and Nigeria’s place in the company’s long-term strategy?

Onyeka Aralu (OA): Uber’s exit can be attributed to several factors: fierce local competition, an inability or unwillingness to fully adapt its business model to local conditions, and macroeconomic challenges such as currency volatility. Recent reporting also points to the fact that, due to the economic challenges, Uber was paying drivers more than it charged passengers on some short trips, in some cases by 23%. Its long-term strategy towards autonomous vehicles is a factor, although it is less important in the grand scheme of things, particularly in a market where autonomous deployment is not imminent.

Uber has said it remains committed to Africa. In fact, it still operates in Ghana, Kenya, South Africa and Egypt. While the company has exited Nigeria, it could re-enter the market once conditions are more suitable. The barriers to entry for ride-hailing are low; the key infrastructure is the platform itself. Moreover, Uber has acquired a level of brand recognition which is unlikely to diminish in the minds of Nigerians in the foreseeable future, which may facilitate entry at a future date if the Nigerian market becomes more attractive.

Read the full interview here.

Imagine you have a business that earns zero economic profit. It exactly covers its costs, including the return needed to attract investors, but nothing more. Is this the end state of competition? Is there anything left for competition to do?

Sure. If another company comes along with lower costs, it can drive prices down. That’s always a possibility, and an important one in real markets. But is there anything else competition can do in the more traditional sense, where new rivals take time to enter a market but eventually erode profits?

There’s one more way to compete. Suppose a company comes along that has no technological edge or lower costs. It can still compete on pricing—by changing the pricing structure, rather than simply setting a lower price.

The rival could charge an upfront fee, offer a bundled deal, or charge different customers different amounts. That last possibility takes us to price discrimination, which is usually described as simply a way for sellers to charge customers more.

The Federal Trade Commission (FTC) appears to share that view. It just ordered nine companies to report whether they charge different people different prices. The FTC worries that companies use data “to estimate the highest price they can charge each consumer.”

But price discrimination can also be how a competitor offers customers a better deal and wins their business. To see how, we need to start with the problem of paying for fixed costs—the expenses a business incurs regardless of how much it sells.

Read the full piece here.

The big news in the competition-plus space—where competition policy meets broader policy concerns—isn’t an antitrust matter at all. It’s New Mexico’s announcement of its “Historic $25 Billion Penalty Against Facebook” (Meta Platforms).

Here’s coverage from The New York Times, The Washington Post, The Wall Street Journal, and, for a bit of local flavor, the Albuquerque Journal.

That’s a big number for most human folks, if not for the large language models among us, but I have a few questions.

Read the full piece here.

Last year, Americans learned that parts of the nation’s air traffic control system still relied on floppy disks and paper strips. Washington had spent decades patching a system that needed replacing. The Universal Service Fund (USF) deserves the same overdue upgrade.