The Efficiency Trap

Refreshed in August 2026. Reported figures are distinguished from model assumptions and author-defined scenarios; forecasts remain hypotheses, not observed outcomes.

On January 27, 2025, as markets panicked over DeepSeek's open-source model undercutting Western AI pricing by an order of magnitude, Satya Nadella posted four words on X that reframed the entire debate: "Jevons paradox strikes again!"

The reference was to William Stanley Jevons, a Victorian-era economist who noticed something counterintuitive about James Watt's steam engine. Watt's design was four to five times more fuel-efficient than the older Newcomen engine. The expectation: Britain would burn less coal. What actually happened: coal consumption soared. Cheap energy didn't conserve fuel. It made steam power viable for thousands of new applications. "It is wholly a confusion of ideas," Jevons wrote in The Coal Question (1865), "to suppose that the economical use of fuel is equivalent to a diminished consumption. The very contrary is the truth."

Nadella's argument, stripped to its core: as AI makes cognitive work cheaper, we will demand vastly more of it.

That was thirteen months ago. The evidence since then supports both sides, which is exactly the problem.

The headlines from January 2026: U.S. employers announced 108,435 job cuts, the worst January since 2009. But the category matters: employers explicitly attributed 7,624 of those cuts, or 7%, to AI. The Challenger report therefore supports a sharp layoff month and some AI-linked displacement, not the claim that AI caused most of the cuts. IMF Managing Director Kristalina Georgieva described AI as "hitting the labor market like a tsunami," while Sam Altman warned that some companies were also "AI washing" layoffs they would have made anyway.

The headlines from February 2026: preliminary estimates suggested a productivity acceleration, and major technology companies continued to raise infrastructure spending. Subsequent official releases put 2025 nonfarm-business labor-productivity growth at 2.1% and private-business total factor productivity at 0.8%. Those measures do not isolate AI's contribution. Rising investment alongside falling inference prices is consistent with a Jevons effect, but it does not establish one by itself.

Both sets of headlines are true. Displacement and demand explosion are not competing theories. They are different phases of the same process, and distinguishing them requires asking a question that neither headline addresses: in any given sector, where does cognitive labor sit in the cost structure? The answer determines whether cheaper AI creates a Jevons explosion or an agricultural collapse — and who captures the surplus from either outcome. Tracing that question across three decades of probable economic diffusion is the task of this essay.

I. The Augmentation Era (2025–2035): The J-Curve Decade

The most common error in forecasting AI's labor market impact is mistaking capability for diffusion. AI can already generate code, draft contracts, and perform competitively on selected medical-image and professional benchmarks. Benchmark performance is not clinical parity, professional judgment, or improved real-world outcomes. Adoption also varies widely by task, firm, and industry.

Brynjolfsson, Daniel Rock, and Chad Syverson documented this lag in their 2021 paper "The Productivity J-Curve" (American Economic Journal: Macroeconomics). General-purpose technologies show decreased measured productivity during initial adoption because firms must invest in intangible complements (training, process redesign, organizational restructuring) before benefits materialize.

The historical precedent is instructive. Paul David's 1990 study, "The Dynamo and the Computer" (American Economic Review), showed that electric power took approximately four decades to appear in productivity statistics after Edison's Pearl Street Station opened in 1882. Factories had to be completely redesigned, from multi-story steam-shaft-driven layouts to single-floor production lines with individual motors, before the gains arrived. Information technology followed a compressed but similar arc. Robert Solow observed in 1987 that you could "see the computer age everywhere but in the productivity statistics." The IT productivity boom didn't arrive until the mid-1990s, roughly two decades after widespread commercial adoption.

If adoption lags keep shortening, AI could be on a ten-to-fifteen-year timeline, placing the inflection point around 2035. The 2025 BLS estimates are compatible with an early bend in the curve, but they are also compatible with non-AI drivers and ordinary revision noise. Several more periods of sustained growth, plus better attribution, are needed before calling an inflection.

Not everyone accepts the historical analogy. Dario Amodei, CEO of Anthropic, argued in "Machines of Loving Grace" (2024) that AI could compress fifty to a hundred years of scientific progress into five to ten — "a country of geniuses in a datacenter." In a follow-up essay, "The Adolescence of Technology" (2025), he predicted that half of all entry-level white-collar jobs could be displaced within one to five years, a timeline that would collapse the J-Curve before institutions have time to adapt. If Amodei's capability estimates are correct, the augmentation era may be measured in years, not decades. The question is whether organizational inertia — the same force that kept factories running on steam-shaft layouts for decades after Edison — acts as a buffer or simply delays a harder landing.

The micro-evidence is accumulating faster than the macro statistics. Brynjolfsson, Li, and Raymond found that an AI assistant for customer service agents produced 14% average productivity gains, with 34% gains for less experienced workers. Noy and Zhang showed ChatGPT reduced time on writing tasks by 40% among college-educated professionals. Dell'Acqua and colleagues at Harvard found BCG consultants with AI access completed 12% more tasks, 25% faster, with 40% higher quality.

These are augmentation effects. The workers are still there. They are doing more.

A necessary caveat: all three studies measured narrowly defined tasks at individual organizations. The Brynjolfsson study covered customer service at a single company. Noy and Zhang used 453 professionals in a lab setting. Dell'Acqua's BCG experiment involved 758 consultants on tasks selected to be within AI's capability frontier — and notably, on tasks outside that frontier, AI users performed 19 percentage points worse than the control group. Whether these micro-level gains aggregate to economy-wide productivity growth is precisely the question that Acemoglu's macro framework (Section VI) challenges. The J-Curve theory provides a partial reconciliation: micro-gains are real but macro-effects can lag while implementation costs, organizational restructuring, and task heterogeneity absorb much of the surplus. The gap between micro-evidence and macro-data is consistent with the J-Curve, but it could also reflect limited diffusion, measurement error, or gains too narrow to move aggregate statistics.

The hypothesis for this era: By 2035, AI will drive productivity growth primarily through augmentation rather than replacement, with AI-complementary skills commanding wage premiums and new AI-native job categories absorbing some displaced workers. PwC's 2025 Global AI Jobs Barometer found a 56% advertised wage premium for jobs requiring AI skills. That is an observational comparison of job ads, not evidence that acquiring an AI skill causes a 56% raise. David Autor documented in his 2024 Quarterly Journal of Economics paper "New Frontiers" that 60% of employment in 2018 existed in job titles that did not exist in 1940.

Autor's finding reframes the question. The conversation assumes a binary: augment or automate. His historical evidence points to a third outcome that matters more: transformative technologies create entirely new categories of work that nobody anticipated. Nobody in 1990 predicted that "social media manager" or "data scientist" would be major job categories by 2020. The most significant employment effects of AI may be in roles that do not yet have names.

The biggest threat to this timeline is general-purpose robotics. If physical AI systems match the flexibility of large language models in manipulating the material world, full automation becomes cheaper than augmentation across most industries and the ten-year window collapses. Regulatory resistance in medicine and law could also steepen the J-Curve. And geographic concentration, with AI activity clustering in a handful of innovation hubs, could produce divergence worse than anything seen with previous general-purpose technologies. The January 2026 White House Council of Economic Advisers report, "Artificial Intelligence and the Great Divergence," draws explicit parallels to the Industrial Revolution's divide between industrializing and non-industrializing nations.

II. The Restructuring Era (2035–2045): The Jevons Explosion

Assume the augmentation story holds and organizations have spent a decade learning to use AI properly. What happens when cognitive work gets cheap? Not 20% cheaper. Ninety percent cheaper.

Jevons answered this in 1865. Cheap coal didn't mean less coal. It meant coal-powered everything. When a service becomes dramatically cheaper, existing users consume more and entirely new use cases become viable that were previously prohibitive. The combination can increase total spending despite radical price declines.

The proposed mechanism is visible in AI infrastructure: major providers increased capital spending while inference prices fell. That combination is consistent with expanding demand, but aggregate capital expenditure also includes data centers, networking, non-AI cloud workloads, and capacity built for expected rather than realized demand. It is a signal to track, not proof that efficiency caused consumption to rise.

But the original Jevons Paradox was about a depletable physical resource. Coal is rival and excludable: burning it in one engine means it cannot be burned in another. Cognitive services produced by AI are non-rival and have near-zero marginal cost of reproduction. An AI-generated legal brief can be copied infinitely at zero cost. That makes AI output more like software than like coal, and the economics of information goods (Shapiro and Varian, 1999) applies better than extractive-resource economics. The Jevons analogy requires a condition the essay must be honest about: sufficiently elastic demand.

Demand elasticity determines which cognitive sectors see Jevons explosions and which just get cheaper.

Consider the radiology example, which appears in nearly every optimistic AI forecast. If AI reduces interpretation costs by 90%, could MRI scans become standard for annual physicals and consumer wellness monitoring? The demand-expansion math is seductive: scans increase twentyfold, cost per scan drops tenfold, total spending doubles. But interpretation is only one component of scan cost. The MRI machine, technician time, and facility overhead (the dominant cost components) are unaffected by AI interpretation. A 90% reduction in interpretation cost does not produce a 90% reduction in total scan cost. Jevons effects require that the efficiency gains hit the binding cost constraint. In radiology, they may not.

Amodei's "marginal returns to intelligence" framework arrives at the same conclusion from the supply side: even unlimited AI intelligence hits diminishing returns wherever progress depends on physical experiments, regulatory cycles, or intrinsic complexity rather than cognitive effort. Radiology is constrained by machines and facilities. Software is constrained by thinking. The Jevons mechanism operates in the second category, not the first.

Contrast this with software development, where the binding constraint is cognitive labor: specification, coding, testing, debugging. Or content creation, where production cost is almost entirely human time. In these domains, demand elasticity is plausibly high enough for genuine Jevons effects. In domains where cognitive labor is a small share of total cost (manufacturing, logistics, construction), the effects will be muted. The question for each cognitive sector is whether it looks more like software (elastic, Jevons-compatible) or more like agriculture (inelastic, displacement-prone). When productivity in a sector with bounded demand improves dramatically, employment collapses rather than expands. Agriculture lost 95% of its workforce over a century despite enormous productivity gains. Treating "cognitive work" as a single category with uniform elasticity is the most common error in popular applications of the Jevons framework.

A second condition is sufficient competition to pass efficiency gains to consumers as price reductions. If providers capture the gains as oligopoly profit rather than passing them through, the demand-stimulating mechanism stalls. Foundation-model development and hyperscale infrastructure are concentrated among a small number of firms, making consumer pass-through an empirical question rather than an assumption.

Open-weight AI complicates the oligopoly story in ways that cut both for and against the Jevons thesis. DeepSeek's R1 release intensified price competition, while Meta's Llama series and Mistral's open-weight models let organizations avoid a per-token API license fee. Self-hosting is not free: compute, energy, engineering, security, and license conditions remain. Nor is a fixed six-to-twelve-month frontier lag established. If capable open-weight models continue to widen access and push down total costs, the competition condition for Jevons effects strengthens; the same change can also lower the barrier to automating tasks. Which effect dominates is an empirical question.

The hypothesis for this era: By 2045, global spending on high-elasticity cognitive services (software, content, analytics, design) will be 3–5x higher in real terms than 2035 levels, despite AI being orders of magnitude cheaper. New industries will emerge that are only viable with near-zero cognitive costs. But low-elasticity sectors will see efficiency captured as cost savings, not demand expansion.

III. The Energy Wall: Physical Limits on a Digital Paradox

The Jevons Paradox predicts demand expansion. But demand can only expand as fast as the physical infrastructure allows. And right now, the infrastructure is losing the race.

The International Energy Agency estimates global data center electricity consumption at 415 TWh in 2024, about 1.5% of global electricity. By 2030, the IEA projects this will more than double to 945 TWh, equivalent to Japan's entire current electricity demand. Goldman Sachs forecasts a 165% increase in data center power demand over the same period, with AI's share rising from roughly 14% of data center load to 35–50%.

These projections collide with a physical bottleneck: grid interconnection. At the end of 2024, roughly 1,400 GW of generation and 890 GW of storage were seeking U.S. grid connection—about 2,290 GW combined, according to Lawrence Berkeley National Laboratory's 2025 queue report. The median time from interconnection request to commercial operation has more than doubled since the early 2000s. Only 13% of capacity that submitted requests from 2000 to 2019 had reached commercial operation by the end of 2024; much of the rest was withdrawn or remained in process.

AI demand is scaling on a one-to-two-year cycle. Grid expansion takes five to ten. The timelines are fundamentally mismatched.

The consequences are already visible at the local level. Virginia, the world's largest data center market, devotes more than a quarter of Dominion Energy's electricity sales to data centers, according to the Virginia Joint Legislative Audit and Review Commission. In Ireland, EirGrid has imposed a de facto moratorium on new data center connections in the Dublin region through 2028 because of grid capacity concerns. These are not projections. They are current constraints on where new capacity can be sited.

This mismatch has triggered a nuclear renaissance. Microsoft signed a 20-year power purchase agreement to restart Three Mile Island Unit 1 (835 MW). Meta announced "Prometheus," a 6.6 GW nuclear procurement program spanning deals with Oklo, Vistra, and TerraPower. Google, Amazon, and OpenAI have all signed nuclear agreements of their own. OpenAI's flagship Stargate facility in Abilene, Texas includes an on-site natural gas plant because the grid cannot deliver enough power. The pattern is clear: AI companies are becoming energy companies out of necessity.

The irony is recursive. Nvidia reports large improvements in AI energy efficiency while the industry continues to deploy more accelerators and data-center capacity. That coexistence is compatible with a rebound effect, but vendor efficiency claims and shipment totals do not establish the counterfactual: energy use without those gains. Khowaja et al. develop the rebound hypothesis in "From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate" (ACM FAccT 2025). Measuring it requires workload-level energy, price, and demand data over time.

A double Jevons: cheaper AI drives more demand for AI services, and more efficient chips drive more demand for chips. Both are constrained by the same physical infrastructure: grid capacity, cooling water, and chip packaging.

Does the energy wall break the Jevons thesis? No, but it bends it. The correct analogy is the original coal story. Jevons was right that efficient steam engines increased coal demand. But coal supply constraints (mining capacity, transportation, labor) created price floors that moderated growth rates. The demand explosion was real but not infinite. AI will likely follow the same pattern: Jevons effects are real, but infrastructure constraints create a natural speed governor, keeping AI costs higher than pure algorithmic efficiency would allow and moderating the demand explosion to the pace at which physical supply can expand.

Epoch AI's comprehensive analysis concluded that electrical power is "the constraint likely to bind first" among all AI scaling bottlenecks. Jensen Huang called energy "the bottleneck" and placed it at the base of a "five-layer cake" for the AI industry. If the bottom layer constrains, everything above it constrains too.

IV. The Distribution Question: Who Captures the Surplus?

Even if the Jevons explosion materializes, it matters enormously who benefits. The efficiency surplus from AI has to go somewhere: to workers as higher wages, to consumers as lower prices, to capital owners as profits, or to specific geographies as concentrated growth. The early evidence suggests the flows are highly uneven.

The advertised wage premium is large but not causal. PwC's 2025 Global AI Jobs Barometer, which analyzed close to a billion job ads across six continents, found that jobs requiring AI skills advertised a 56% wage premium over comparable roles. The analysis is observational: it cannot establish how much of that difference comes from the skill itself, the occupations and employers demanding it, or worker selection. Other analyses suggest access and gains are uneven by income. The direction is concerning; the exact distribution mechanism still needs causal evidence.

The deeper shift could be from labor to capital. Authors of an IMF working paper modeled scenarios in which AI narrows wage inequality while widening wealth inequality as capital owners capture more of the surplus. That is a model result, not an observed distributional outcome. The Federal Reserve's Distributional Financial Accounts nevertheless show why the channel matters: corporate-equity ownership is highly concentrated, so profit-led gains would accrue unevenly.

The geographic concentration is stark. Brookings and other regional analyses find that AI jobs, investment, talent, and compute are concentrated in a relatively small set of U.S. metros. The exact shares vary with the dataset and definition of "AI," but the direction is consistent. Whether this concentration exceeds the IT era requires like-for-like historical measures.

The international distribution question is similarly unsettled. Foundation-model development, high-end compute, and venture investment are concentrated in a few countries, while outsourcing-heavy economies may face different exposure. Cross-country comparisons often combine incompatible measures—model counts, projected market size, jobs at risk, and announced infrastructure—so they are better treated as research questions than a single league table.

China offers a comparative hypothesis, not yet a natural experiment. Its AI ecosystem operates under different industrial policy, labor-market, and state–industry arrangements. Comparing worker displacement, retraining, and redeployment outcomes could test how much institutional design matters, but doing so would require comparable longitudinal measures and a credible counterfactual.

Institutions are a plausible moderator. An OECD study found that greater AI exposure was associated with lower wage inequality within occupations from 2014 to 2018. The authors explicitly caution that the evidence does not show AI caused broader changes in wage inequality; the period also predates generative AI's widespread adoption. Institutions may shape distribution, but this study does not identify the mechanism or establish cross-country causality.

The SAG-AFTRA and WGA agreements of 2023 established consent, compensation, and transparency protections for some uses of AI. They are a useful precedent, but bargaining power differs sharply across sectors. Cash-transfer research associated with Altman's pilot found a mix of effects, including more spending on basic needs and limited or short-lived changes on several labor and health measures; it does not support the blanket conclusion that cash "did not improve" health or work. The broader mechanisms for distributing any AI surplus remain contested.

Even the most optimistic projections acknowledge the contingency. Amodei's "Dream scenario" for developing economies — 20% annual GDP growth through AI-driven technology diffusion — carries an explicit caveat: it requires "strong efforts on our part." Benefits do not distribute themselves. He also identifies an "opt-out problem": populations that resist AI-enhanced services fall progressively further behind, creating feedback loops that compound existing inequality. The parallel to vaccine hesitancy is uncomfortable but instructive. When a technology's benefits are large and its adoption is uneven, the gap between adopters and holdouts widens faster than any redistribution mechanism can close.

V. The Institutional Era (2045–2055): The Perez Transition

Suppose the Jevons explosion happens in elastic sectors and the energy constraints bend without breaking. Productivity gains still need institutions to catch up, and that takes decades.

Carlota Perez's techno-economic paradigm theory (2002) documents this pattern: steam power required limited liability corporations and railway systems; electricity required scientific management and mass production; information technology required lean production and agile methodologies. Each general-purpose technology required new institutions, not just new tools. The technology always arrived decades before the institutions caught up, and the productivity gains came from the match between them.

The institutional adaptation is already underway, though in fragments. The EU AI Act, which entered force in stages beginning August 2024, created the first comprehensive regulatory framework for AI systems, classifying them by risk level and imposing transparency and audit requirements on high-risk applications. California's SB 1047, debated through 2024 and vetoed by Governor Newsom, would have required safety testing for frontier models above a compute threshold — the first attempt to regulate AI at the capability level rather than the application level. The SAG-AFTRA and WGA agreements of 2023 established consent requirements and compensation protections for AI use of human creative work, a template that other industries have yet to replicate. Singapore's Model AI Governance Framework takes a different approach entirely: voluntary, industry-led, emphasizing organizational accountability over prescriptive rules.

None of these frameworks resolves the deeper structural questions. Who owns the output of an AI system trained on copyrighted work? How should educational institutions prepare workers for roles that do not yet exist? What tax and transfer mechanisms can redistribute productivity gains without suppressing investment? The cases are also at different stages: Thomson Reuters v. Ross Intelligence produced a February 2025 summary-judgment ruling, while The New York Times v. OpenAI remained pending when this essay was refreshed. Neither single case will settle the full copyright landscape.

The hypothesis for this era: By 2055, the organizational structures that dominate the economy will bear little resemblance to 2025 corporations, just as a 2025 tech company bears little resemblance to a 1960s industrial conglomerate. The question is not whether institutions will adapt but how much damage accumulates during the lag between technological capability and institutional readiness.

Perez's historical analysis suggests these transitions are rarely smooth. They involve financial crises, political upheaval, and generational conflict before a new institutional framework stabilizes. The transition from the Victorian economy to the Progressive Era took decades of labor unrest, antitrust action, and regulatory invention. The AI transition will not be gentler.

VI. The Bear Case: What If the Pessimists Are Right?

The scenarios above assume AI will follow the historical pattern of general-purpose technologies. That assumption may be wrong. Three critiques are stronger than the optimistic case usually admits.

Robert Gordon's secular stagnation. Gordon's argument in The Rise and Fall of American Growth (2016) is not that innovation has slowed. It is that the "one-time-only" inventions of 1870–1970 (clean water, electricity, internal combustion, telecommunications) transformed material existence in ways that are, by definition, unrepeatable. Indoor plumbing can only be invented once. Gordon also identifies structural headwinds (rising inequality, educational stagnation, aging demographics, mounting government debt) that suppress growth regardless of technological progress. AI enthusiasts must explain not just why AI is transformative but why it overcomes headwinds that have been dragging on productivity for fifty years.

Daron Acemoglu's direction of innovation. Acemoglu's critique is more specific and more quantitatively damaging than most popular accounts suggest. In "The Simple Macroeconomics of AI" (NBER Working Paper 32487, 2024; published in Economic Policy, January 2025), he estimates that AI will increase total factor productivity by at most 0.53–0.66% over a decade, with cumulative GDP gains of only 1.1–1.6%. These numbers are an order of magnitude below the optimistic projections.

The mechanism: Acemoglu's task-based framework (developed with Pascual Restrepo) identifies displacement, productivity, and new task creation as the three forces shaping automation's labor market impact. The key insight is that automation can produce productivity gains and still harm workers if displacement exceeds new task creation. His concept of "so-so technologies"—automation that displaces workers without generating large productivity gains—applies uncomfortably well to some current AI deployments. Some firms appear to be cutting in anticipation of future capability, but the prevalence and causal contribution of that behavior are not yet well measured.

The measurement problem. GDP does not capture quality improvements from AI. Better recommendations, personalized content, faster service, free AI tools: none of this appears in productivity statistics. But the costs (displacement, retraining, energy consumption) do. William Nordhaus tested empirically whether economic data support accelerating growth consistent with transformative AI scenarios (American Economic Journal: Macroeconomics, 2021). His conclusion: they do not.

The three critiques point in different directions. Gordon says the big gains are behind us. Acemoglu says the gains are smaller than projected and may go to the wrong places. Nordhaus says the data do not show acceleration consistent with transformative scenarios. Meanwhile, 2025 BLS estimates show positive but not uniquely AI-attributable productivity growth, and providers continue to invest heavily in infrastructure. The debate is about magnitude, attribution, direction, and distribution.

VII. Tracking the Trajectory: How to Know Which Scenario Is Winning

These scenarios generate testable predictions.

By 2030, four indicators could distinguish the paths. The first is employment in software development and content creation, paired with output and price data that can separate demand growth from labor substitution. The second is the AI wage premium, measured with controls for occupation, employer, location, and worker selection. The third is whether labor statistics begin coding durable AI-native occupations. The fourth is data-center electricity demand alongside queue times, curtailment, and power prices. Any numeric thresholds used to classify these scenarios should be treated as author-chosen monitoring rules, not evidence-derived natural breakpoints.

By 2035, the macro picture should be clearer. Sustained total factor productivity growth, real spending and output in cognitive services, geographic concentration, and labor's share of income can be tracked together. No single threshold would validate the J-Curve: the claim needs a pattern connecting lower unit costs to higher total consumption while ruling out unrelated growth and measurement changes.

Conclusion: The Binding Constraint

Everything in this essay turns on where cognitive labor sits in the cost structure.

In software development, it is the cost structure. Specification, coding, testing, debugging — these are not secondary line items subordinate to machines and facilities. They are the product. Cut those costs in half and a company that could not justify a five-person engineering team ships with two. Cut them by ninety percent and businesses that never had software budgets start building custom tools. Content creation follows the same logic: writing, design, video production are pure cognitive labor. When production costs collapse in domains where labor is the binding constraint, cheaper does not mean less. It means more. That is the Jevons mechanism, and software and content are where it faces its cleanest test.

The numbers will be unambiguous. If the United States employs more software developers and content creators in 2030 than it does today, despite tools that already cut production time by a third, then demand is outrunning displacement. If both sectors have shrunk, the precedent is not coal. It is agriculture — a century of productivity gains, ninety-five percent of the workforce eliminated, because demand for food has a ceiling. What matters is whether cheaper cognitive labor opens new markets or merely lowers costs in existing ones.

Acemoglu's projection—0.53 to 0.66 percent TFP growth over a decade—is an estimate under a stated task-based model and its assumptions. It is not a direct measure of what AI can do, nor a forecast determined only by current incentives. The direction of deployment, market competition, consumer pass-through, and grid expansion can all change the outcome, but each channel needs evidence rather than inevitability.

Jevons was right about coal because the market worked: cheaper steam opened industries that burned more coal than the old ones ever had. Whether he is right about intelligence depends on whether the gains from cheaper cognition reach a broader economy or settle in the accounts of the companies that already dominate it.

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