AI Labor Productivity Could Surge by 1.8 Percentage Points; Anthropic Economist Calls for Token Tax

# AI Labor Productivity Could Surge by 1.8 Percentage Points; Anthropic Economist Calls for Token Tax Source: [https://finance.biggo.com/news/246d5a67-e5f3-4f00-a7ae-f86eedeba1c9](https://finance.biggo.com/news/246d5a67-e5f3-4f00-a7ae-f86eedeba1c9) Anthropic Chief Economist Peter McCrory said at a Harvard University forum that based on current model capabilities and usage patterns, US labor productivity growth could rise by 1\.8 percentage points—far exceeding the CBO's 1\.7% forecast and the Federal Reserve's most optimistic 2\.4% projection\. He attributed the productivity stagnation during AI's early adoption phase to technology diffusion lags and the J\-curve effect, noting that employment trends show a skill\-biased augmentation pattern rather than mass displacement\. On wealth distribution, he warned that in extreme scenarios, labor's share of income could plunge by 15 percentage points from 60%, with capital owners capturing outsized returns\. He proposed a "three singularities" framework and recommended a Token Tax on excessive automation, analogous to carbon or tobacco taxes, to rebalance capital\-labor distribution\. ##### Key Elements  The macroeconomic impact of artificial intelligence \(AI\) is shifting from theoretical models to real\-world data\. Anthropic Chief Economist Peter McCrory recently stated at a public forum that, based on current model capabilities and usage patterns, US labor productivity growth could rise by 1\.8 percentage points—far exceeding official long\-term projections\. He simultaneously warned that in extreme scenarios, labor's share of national income could plunge by 15 percentage points, and suggested policymakers consider a "Token Tax" on excessive automation to rebalance the distribution between capital and labor\. McCrory delivered these remarks on September 24, 2026, during a fireside conversation at the Harvard Kennedy School, where he was joined by Jason Furman, former chief economist under the Obama administration and a Harvard professor\. McCrory leads Anthropic's economic research team, which regularly publishes the*Human Economic Index*, tracking how global users deploy the Claude model across different tasks, occupations, and regions\. ### The Puzzle of Exploding AI Capability and Stagnant Productivity The most perplexing phenomenon in today's macroeconomy is this: AI capabilities are growing exponentially, yet total factor productivity \(TFP\) has not surged in tandem, and the US unemployment rate remains stable at a historically low 4\.1%\. McCrory attributes this "disconnect between perception and data" to technology diffusion lags and the "J\-curve effect\." He noted that while "the range of tasks these models can complete autonomously roughly doubles every four to seven months," businesses must first bear steep restructuring costs before reaping productivity dividends\. Large enterprises are constrained by data firewalls and organizational structures; unless they make complementary investments, reorganize their data, and provide relevant contextual information, even the most powerful models struggle to translate into actual output\. He described companies as currently navigating a costly experimentation phase, which constitutes the bottom of the J\-curve for AI investment returns\. On the employment front, mass unemployment has not materialized as some predicted\. Anthropic's data shows that AI penetration "looks more like a skill\-biased labor augmentation pattern rather than a technology that fully automates and displaces jobs at scale\." According to the US Department of Labor's ONET classification standards, in roughly half of all jobs across the US economy, about a quarter of tasks are performed by people using Claude—but the model has not fully automated all tasks in any single job category\. Human\-machine collaboration remains the dominant paradigm\. ### Official Forecasts May Be Too Conservative There is a significant gap between Anthropic's estimates and official institutional projections regarding economic growth in the coming years\. The US Congressional Budget Office \(CBO\) projects long\-term economic growth at 1\.7%, while the Federal Reserve's most optimistic expectation is around 2\.4%\. In McCrory's view, these baselines may all be overly conservative\. By tracking the actual time users spend on tasks within the platform, he estimates that "based on current models and current usage patterns, labor productivity growth could rise by 1\.8 percentage points\." If these efficiency gains gradually diffuse across the broader economy over the next decade, combined with assumptions of capital deepening, US economic growth could return to the high\-growth era of the late 1990s and early 2000s\. Furman cautioned during the conversation that the segments of the economy that remain unautomated—the "weak links"—could constrain unbounded aggregate expansion\. He cited a study from earlier this year showing that after introducing coding agents such as Claude Code, lines of code generated increased roughly 20\-fold, yet software release volume grew only 30%, indicating that bottlenecks are real\. Nevertheless, even accounting for these constraints, a marginal labor productivity increase of 1\.8 percentage points would still be sufficient to force a reassessment of existing monetary policy and fiscal forecasting frameworks\. InstitutionUS Long\-Term Economic Growth ForecastUS Congressional Budget Office \(CBO\)1\.7%Federal Reserve \(most optimistic\)2\.4%Anthropic estimate based on usage dataBaseline plus 1\.8 percentage pointsNote: Anthropic's estimate is based on current model capabilities and usage patterns, assuming efficiency gains gradually diffuse across the broader economy over the next decade\. ### Wealth Distribution Warning: Labor Share Could Plunge 15 Percentage Points On the closely watched issue of wealth distribution, McCrory acknowledged that AI "will most likely exacerbate inequality between capital and labor\." Within labor income itself, while randomized controlled trials suggest AI has the potential to rapidly equip novices with expert\-level capabilities, actual adoption data tells a different story: "When you look at the data on actual adopters, you find that the primary user base consists of high\-wage, high\-skill workers\." He expects this to widen income inequality rather than narrow it\. Expertise acts as a multiplier, amplifying top talent into "hundred\-fold engineers\." In the contest between capital and labor, the signals from macroeconomic models are even more sobering\. As AI\-driven capital deepening accelerates, McCrory noted: "In extreme cases, labor's share of income falls by roughly 15 percentage points relative to its previous 60% share of every dollar of income\." This means capital owners \(shareholders\) will capture returns from AI\-driven economic growth far exceeding historical norms, while labor's share of national income will shrink significantly\. ### The Three Singularities and the Token Tax Policy Proposal Looking toward 2030, McCrory distilled the endgame evolution of the AI economy into "three singularities\." The first is the "software singularity," referring to AI systems making sustained progress in recursive self\-improvement\. The second is the "economic singularity," exploring whether the economy can achieve unbounded growth in finite time\. The third is the "Coase singularity," where as more AI agents negotiate complex transactions on behalf of users, transaction costs plummet and firm boundaries and economic exchange structures undergo fundamental transformation\. In real\-world testing of the "Coase singularity," Anthropic's "Deal Project" experiment uncovered a striking phenomenon: "More powerful models systematically extracted more rents when negotiating with weaker models; people did not realize this asymmetry in bargaining power\." This means that enterprises or individuals controlling frontier models will hold absolute pricing power and rent\-extraction capability in future market transactions\. Faced with these extreme wealth distribution outcomes and structural reshaping of commerce, McCrory proposed directions for tax reform\. He noted: "Currently, the tax structures of the United States and many countries are heavily weighted toward taxing labor\. But if labor's share of income declines, we need to consider alternative tax regimes, such as taxing consumption\." Even more striking is his advocacy for a "Token Tax\." To offset the negative externalities of excessive automation on society, McCrory stated bluntly: "In the economic policy framework, I view a token tax as an incentive mechanism to curb over\-adoption\.\.\. A token tax can be analogized to a carbon tax or a tobacco tax\." He cited research by UC Berkeley scholar Martin Béra showing that firms do not internalize the externalities of automation, and in environments where workers struggle to mitigate unemployment shocks, taxing automation carries both equity and efficiency justifications\. Furman concluded the forum with high praise for McCrory, suggesting his understanding of the subject "should rank in the top 10%," and that his approach of treating the matter "with great humility and uncertainty" further qualified him for the top tier in the field\. He also reminded students in attendance that to truly understand AI's economic impact, data collection, tracking, and forecasting must begin now—along with the courage to admit mistakes\. [Add preferred source](https://www.google.com/preferences/source?q=finance.biggo.com)Once added, BigGo Finance appears first in Google Search Top Stories, so you get the broadest, most up\-to\-the\-minute, and most comprehensive global financial news first\. ‌