Vasudev Singh

@__vasu_

Quant @ Lotus Technology Mgmt

New Delhi, India

Joined March 2017

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Alap Shah

@alapshah1

5 months ago

Every AI investment carries an implicit short on the consumer economy. Every dollar of margin expansion from replacing workers is dollar of household income lost. Part 3 of the Global Intelligence Crisis is out. Washington has no plan for the biggest labor disruption in history. So we’re starting a conversation around one- The American Prosperity Compact The Global Intelligence Crisis · Part Three The Path Forward This is the third part of a series about AI’s potential impact on the economy. Part One detailed my experience building with agentic AI and laid out the risk of what I'm calling the Intelligence Transition: the structural shift of cognitive labor from humans to machines and the economic reorganization it forces. Part Two, which I co-authored with Citrini, painted a hypothetical downside scenario for 2028 if no policy action is taken and displacement accelerates unchecked. Last month, the essays we authored clearly struck a nerve. The response exceeded anything we anticipated, and the drop in the markets was neither our expectation nor our intent. None of the ideas we presented were individually new, but connecting the dots into a coherent narrative clearly resonated with the world we are all living in. A world where we can all feel the ground shifting beneath us. A world in which for many of us our place and purpose is a bit less clear. A world in which the human intelligence we use to do our life’s work and earn a living looks increasingly competitive with AI. Something has gone wrong when the dominant emotional response to humanity's crowning technological achievement is dread. A common refrain on social media is that you have a few years to make enough money to avoid becoming part of the permanent underclass. I feel some of that dread myself, the fear of losing a sense of purpose, and I say that as someone without meaningful personal economic worries. This shouldn't be the prevailing mood at the dawn of a new golden age. With a properly designed policy framework, we can have both rapid AI progress and social stability. The second part of this essay proposes such a framework: the American Prosperity Compact, designed for today’s political climate. I’m an AI optimist and I’m not writing as a spectator. I’ve spent fifteen years building AI companies and twenty years investing in technology. I use frontier AI models every day to analyze companies, synthesize data and build products. In my experience, today's models approximate the cognitive abilities of a 125 IQ professional working at superhuman speed and endurance. They still miss things and need reminders for things humans don’t, but the trajectory of improvement is astonishing. I believe the ideal outcome from the culmination of humanity’s technological progress is genuinely within reach: broadly shared prosperity, an explosion of scientific discovery and democratized access to expertise. But we won’t get there without a plan to navigate the Intelligence Transition. Part A: The Reality on the Ground The Synthetic Short Every investment today in an AI company or beneficiary carries an implicit short on the consumer economy. Every dollar of margin expansion from replacing human workers is a dollar of household income lost from the demand side. The lost dollars are largely earned by middle-class families who spend almost all of it, while the margin expansion and potentially higher stock prices accrue to shareholder owners. Shareholders are composed primarily of wealthier households, foreign owners and pension funds, all of whom have high propensities to save rather than spend the incremental wealth. The mismatch is large. Of course, AI investment will lead to a productivity boom. But if the layoffs come fast enough and consumer spending drops in response, the handoff between higher productivity and faster GDP growth can easily break down until the Intelligence Transition has been navigated. This would ultimately be an aggregate demand problem, and the Fed would intervene aggressively, but it’s not clear that low or even negative rates alone would suffice to spur enough incremental demand to offset the largest ever shock to the cash flows and balance sheets of the American consumer, who collectively account for 68% of U.S. GDP and 100% of the electorate. The AI complex and the consumer economy are increasingly on opposite sides of the same trade. That is not sustainable and is not something that the AI complex, investors or society should want. Markets already sense this. Even before the latest geopolitical shock, US markets had stopped going up despite strong AI progress, a healthy economy and robust earnings growth. The software sector has been under heavy pressure for six months; since January, that pressure has spread to AI-exposed businesses in intermediation and financial services. The popular explanation cites concerns about AI capex sustainability. That’s part of it, but I think there is a deeper unease: the market is beginning to price a version of what we discussed in Parts One and Two. AI is clearly the largest technology shift of our lifetimes. What happens to the economy, existing market shares and employment on the other side? No one has a good answer, and that uncertainty alone is a drag on valuations. Addressing counterarguments to Part One and Two Many readers of my two previous essays argued that this moment will be similar to previous periods of profound economic shifts across history. As in the past, labor markets will adjust to automation and new job categories will arise to take the place of lost ones. The standard framework to think about this in labor economics is Autor, Levy and Murnane's landmark 2003 paper, which sorted all workplace tasks into four quadrants: routine cognitive (bookkeeping, clerical), routine manual (assembly, sorting), non-routine cognitive (analysis, management, persuasion) and non-routine manual (driving, food prep). Logically, routine jobs are easier to automate. Routine manual work had been declining since the dawn of the factory line, but since the 1970s, computers began replacing routine cognitive work. This was offset by the fact that non-routine cognitive work continued to grow, offering a path for displaced workers. AI threatens to break this framework, as it is the first technology that targets the non-routine cognitive work quadrant itself, the refuge that absorbed every prior wave of displaced workers. There is no higher tier to escape to. If we take seriously the idea that this time could be different, that AI is a different sort of technology, the risk may also be different and larger than anything the modern economy has experienced. Previous waves of automation displaced workers from specific tasks in specific industries over decades. The Intelligence Transition threatens to compress that disruption across the entire knowledge economy on a timeline measured in years. The second key counterargument from readers was that we won’t have sufficient compute for a mass jobs displacement scenario by 2028. While compute availability and cost is certainly tightening today, the AI industry’s track record on algorithmic improvement to reduce compute required for a unit of intelligence has been stellar. Inference costs have fallen 10x or more annually. This algorithmic progress will only accelerate if the labs focus on it in a compute constrained environment. Moreover, we are mobilizing global supply chains in previously unheard of ways to build more compute. We will spend nearly $1 trillion on AI capex globally this year, and at current growth rates we will be spending over 1% of global GDP on AI compute by 2028. We've Seen This Before The optimistic refrain is that technology always creates more jobs than it destroys. Over very long periods, that has been true. But the transition periods are brutal, and the people caught in them don't live on geological timescales. The Engels Pause is the canonical example. Using Robert Allen’s classic estimates, output per worker rose sharply between 1790 and 1840 while real wages rose much more slowly. For sixty years, the gains from the industrial revolution flowed almost entirely to capital while the people doing the work captured a small fraction of the wealth they were creating. The political debt came due in labor laws, a welfare state built under duress, and a political realignment that shaped British governance for a century. The mill owners of Manchester didn't price it. Neither did the investors. The China Shock, and more broadly the era of trade-driven manufacturing displacement is a more recent example. When China joined the World Trade Organization in 2001, the resulting import surge destroyed 2.0 to 2.4 million American jobs by 2011, according to Acemoglu et al. The losses were concentrated geographically, and the communities hit hardest are still depressed. Manufacturing employment dropped from 17 million to 11 million between 2000 and 2010. Mortality rose in trade-exposed areas. "Deaths of despair" became a research category. The political backlash from those job losses is the direct ancestor of today's tariff regime. We are still paying for the policy failure of the 2000s. A typical counterargument to automation-driven employment losses invokes the ATM. When automated teller machines rolled out across America, bank teller employment actually rose. ATMs made branches cheaper to operate, banks opened more branches, and tellers shifted from cash handling to relationship banking and selling financial products. Classic Jevons paradox: make an input cheaper, and demand for the output increases enough to preserve or increase demand for the input. But the ATM story has a second act that the optimists leave out. The technology that actually killed teller jobs was the iPhone. Mobile banking didn't automate some of the teller's tasks. It eliminated the reason to visit a branch at all. Once you could deposit checks, transfer funds and manage accounts from your phone, the demand for the entire branch model collapsed. Teller employment has fallen steadily since 2010. The distinction matters. When technology automates some tasks within a role, workers can redirect to higher-value tasks and the role survives or even expands. When technology automates the underlying need, the role disappears. AI is doing both simultaneously. The Layoffs Have Started Block's 40%+ layoffs announced on February 26th serve as a vivid case study. The company cut over 4,000 of its roughly 10,000 employees, with CEO Jack Dorsey citing AI explicitly as the driver. AI is playing double duty: a convenient catch-all for unwinding Covid-era hiring excess, and a genuine structural driver. As Dorsey stated: "Intelligence tools have changed what it means to build and run a company. A significantly smaller team, using the tools we're building, can do more and do it better. And intelligence tool capabilities are compounding faster every week. I don't think we're early to this realization. I think most companies are late." Since Block’s announcement, Meta is reportedly planning to cut ~16,000 workers (20% of its workforce) to help fund $135 billion in AI capex, Oracle is reportedly preparing to shed up to 30,000 roles (12-18% of staff), and Atlassian announced cuts of 1,500 (10%). The Skill Distribution Problem AI allows top performers to produce several times the output they could have a year ago. I can see this clearly across my teams. The trouble is in the distribution. If one person augmented by AI can do the work of three, you have less need for the other two. The top 10-15% of employees become much more valuable and more in demand than ever. The bottom 20-40% are in genuine danger. Through this process, employers get the double benefit of leaner teams with far lower coordination costs. This dynamic is not limited to engineering. It applies to legal research, financial analysis, marketing, design or any domain where AI can generate a first draft that a skilled practitioner can refine. The premium on judgment, taste and domain expertise goes up. The premium on simple execution goes way down. That's an inversion of how most knowledge-work careers are structured, where you start by executing and gradually learn your way into judgment. What happens when the first rung of the ladder disappears? The Entry-Level Collapse ServiceNow CEO Bill McDermott said it plainly on CNBC recently: college graduate unemployment “could easily go into the mid-30s in the next couple of years” as AI absorbs entry-level work. The data already shows early stress. The unemployment rate for recent college graduates has risen to 5.7%, up from 3.8% in 2023, the highest level in over a decade outside the pandemic. For the first time in modern data, recent graduate unemployment now exceeds the overall national rate. Recent graduates are the most exposed because they have the least accumulated judgment and domain expertise, the very things AI is sprinting to replicate today. Recent grads are merely the first group to face pressure from AI, the same logic will extend to more experienced professionals as models improve. Labor Market Supply-Demand Balance The zeitgeist is focused on which specific jobs disappear and whether new ones appear. This misses the more important dynamic: what replacement jobs pay. The labor market is one interconnected system. When a $100K accountant gets displaced and competes for jobs in the broader economy, she takes a job in retail, putting downward pressure on retail wages. When millions of displaced knowledge workers flood into the job market, the result is wage compression across the entire distribution. The people at the bottom get squeezed hardest. This is what happened with the China Shock. The cascade doesn't stop at white-collar displacement. The same AI acceleration that threatens knowledge work is accelerating robotics and physical-world automation. Autonomous driving is no longer hypothetical. Waymo’s autonomous rideshares operate in 10 cities and rival Lyft’s market share in SF, its most mature market. Tesla launched its robotaxi in Austin last June and plans seven more cities in 2026, with the explicit long-term goal of converting every Tesla on the road into an autonomous cab via software update. Collectively, truck driving, delivery trucks and rideshare provide over 4M jobs in the US. Warehouse automation, fast-food preparation, retail checkout and last-mile delivery by drone and robot: these are all in active and growing deployment throughout the U.S. today. This entire category of work is on a clear countdown. The blue-collar jobs that would have absorbed white-collar workers are themselves disappearing on a lag measured in years. The wave ultimately hits the entire consumer economy. Part B: The American Prosperity Compact I am not arguing for a punitive "AI tax" or for slowing frontier development. I’m a capitalist; I build companies and I invest in public and private markets. Free markets created the wealthiest society in human history, and it is vital we preserve them through the Intelligence Transition. But markets require functioning consumers. An economy where a generation of Americans cannot pay their mortgages is failing, not free. My solution is called the American Prosperity Compact. It is designed to close the synthetic short. It converts the unpriced tail risk of a disorderly transition into a defined, known cost structure that capital markets can underwrite. It keeps the consumer economy functioning while the productive economy reorganizes. The Compact is structured in four cascading, contingent parts. The Foundation lays out sensible labor market reforms for today. The Circuit Breaker triggers only if job displacement accelerates past certain thresholds. The Backstop triggers only if we see profound levels of job displacement. Finally, the Accelerator is where we play offense to make the economy work better in an AI world. If machines replace humans in the production function, the math eventually leads to taxing those machines. There are many new approaches such as a token tax or higher tax rates on agents. Some may be right for the 2030s, but none can pass in 2027. The Compact is designed for today’s political economy. Every mechanism builds on existing tax code, existing benefit structures and existing institutional capacity. The goal is a framework that can be enacted in the next year and operational soon thereafter. The tax mechanisms are deliberately concentrated at the corporate level, at the source of displacement itself- the companies experiencing the most margin expansion from replacing human labor with AI. This avoids the thornier issues of raising income tax brackets or blanket wealth taxes, especially on unrealized capital gains. Corporate rates are an ideal approach because in a world where AI drives large revenue growth and cost savings opportunities simultaneously, a moderately higher rate will not meaningfully deter corporate investment when the opportunity set is so large. The companies benefiting most from the Intelligence Transition will still be wildly profitable. The question is whether a portion of that windfall recirculates into the consumer economy or concentrates at the top while aggregate demand erodes beneath it. Building the tax system for a fully post-transition economy is a broader question for future work. This framework is the first step: what our politics can actually deliver in the next year. The Foundation The Foundation removes structural distortions that made sense in a 20th-century economy but are already liabilities today. It addresses pre-existing frictions and can be designed as revenue-neutral. Stop taxing labor more than AI. The employer side of Social Security and Medicare is funded by a 7.65% payroll tax on every dollar of wages. AI-generated output carries no equivalent cost. The employer side of the tax base should shift from wages to corporate value-added broadly: revenue minus purchased inputs, which equals wages plus profits. Because the base is broader, the rate can be lower while generating the same revenue. Companies that employ many people relative to output would actually pay less. Companies generating enormous output with minimal workforces would pay a bit more. Make benefits portable. The nature of work has already changed. Roughly 38% of the workforce does some form of freelance or independent work. AI will push more people toward portfolio careers, contracting and one-person firms. AI levels the playing field for entrepreneurship, the lifeblood of our economic dynamism and a core part of the American dream. But a would-be entrepreneur who can't leave her job because her kids' health insurance depends on it is a structural failure. Portable benefits that travel with the person, not the position, would unlock the risk-taking and business formation our economy needs. The goal is not to nationalize insurance but rather to decouple core benefits from any single employer so people can carry them across jobs, transitions and periods of self-employment. Pro-rata contribution accounts, where every employer or platform pays into a worker's portable fund based on hours worked, and expanded ACA marketplace subsidies that make individual coverage genuinely affordable, can make this real without nationalizing anything. The Circuit Breaker The Circuit Breaker triggers if the job displacement is rapid. I am not proposing we enact it tomorrow. I propose we design it now so it can trigger automatically if conditions warrant. Think of it as insurance. Designing this now, before it is needed, means economic and political tail risk converts into a defined cost structure. The trigger is labor's share of GDP. Today that number sits around 54%. If it stays there or increases, nothing happens. The Circuit Breaker only activates if labor share falls below a sustained threshold, likely in the low 50s. That would represent a structural break, not a cyclical dip. Until that threshold is crossed, the mechanism lies dormant. Two mechanisms once it triggers. First, a corporate displacement tax: a tax that scales automatically with the gap between labor's share of GDP and that threshold. The companies seeing the most margin expansion pay the highest rates. If margins are expanding while headcounts collapse, the tax goes up. If the labor market recovers and labor share climbs back above the threshold, rates step back down. The data decides. Second, targeted income support for displaced workers, funded by the corporate displacement tax. Built on Earned Income Tax Credit (EITC) principles: wage insurance where a worker whose new job pays substantially less than their old one receives partial income replacement for a transition period, phased so re-employment always beats waiting. Pair it with an expanded EITC with higher income ceilings, because as displaced white-collar professionals flood into the blue-collar labor market, wage compression pushes down incomes for everyone below them too. Crucially, both preserve the core EITC insight that earned it bipartisan support in the first place: every dollar of support is conditioned on work. To be clear: the EITC only works when jobs exist. If displacement outpaces job creation, we should broaden the definition of qualifying work to include schooling, retraining, caregiving and community service. Support remains contingent on contribution and commitment, not on employment specifically. There is a deeper reason fiscal stabilizers matter here. If AI simultaneously cuts costs and displaces workers, the Fed faces a contradiction it cannot resolve: deflation invites rate cuts, but rate cuts will not create jobs in categories that no longer exist. Central bankers have minimal experience implementing negative rates. Five central banks tried between 2012 and 2024 and none went below -0.75%. Japan ran the experiment for eight years without escaping deflation. Monetary policy has a structural floor. The Circuit Breaker adds the required fiscal policy boost to maintain household income through a secular restructuring rather than a temporary downturn. The Backstop I hope we never need the Backstop. If we do, it means household debt has become unserviceable and the financial system is under stress. Displacement and credit deterioration reinforce each other in a loop, and once that loop starts it is very difficult to stop. Mortgage delinquencies rise, housing prices soften and bank balance sheets take mark-to-market hits that tighten lending further. TARP is the apt analogue. The Backstop exists to break the loop before it starts. It includes two elements, funded by higher rates of the corporate displacement tax: Comprehensive income security. Extended income replacement, mortgage forbearance and healthcare continuity, designed to prevent a household liquidity crisis from becoming a banking solvency crisis. An American AI Dividend Fund modeled on the Alaska Permanent Fund. A portion of the increased corporate taxes would be invested on behalf of every American. Alaska does this through taxing resource extraction. The analogy extends as AI is built on the corpus of human knowledge and drives displacement through the existing economic system. Rather than a handout, this is a share of returns on a collectively created asset, making citizens shareholders in the transition The Accelerator The Foundation, the Circuit Breaker and the Backstop protect the downside. The Accelerator clears the institutional bottlenecks that would cap AI's GDP contribution even if the labor transition were managed perfectly. The defensive and offensive agendas are part of the same agenda. These are mostly sensible policies that economists have supported for years. They haven't been implemented because they involve clear constituencies that win and lose. The Intelligence Transition creates a unique opening to form the coalition needed to push them through. But that coalition only forms if the first three tiers of the Compact demonstrate a real commitment to protecting workers. Deregulation doesn’t work without a safety net. Energy and grid infrastructure. Energy, not compute, is now the binding constraint on AI growth. Permitting reform for datacenters, transmission, interconnection and nuclear power is a national security priority. China is building capacity aggressively to support its AI ambitions, and every year of U.S. permitting delay is a year of competitive ground lost. Moreover, the same AI driving energy demand is accelerating breakthroughs in fusion, next-gen solar, enhanced geothermal, battery chemistry and grid optimization, making the energy buildout and the clean energy transition the same project. (Continued in next Tweet)

Vasudev Singh

@__vasu_

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