From Wages to Compute
The Shifting Landscape of Work, Wealth (and Public Finances) in the Age of Artificial Intelligence
Imagine waking up in a world where, overnight, millions of the world's best workers have been duplicated—infinitely knowledgeable, tireless, and perfectly coordinated. This isn’t a sci-fi scenario; it’s the rapidly approaching reality of Artificial General Intelligence (AGI) and its subsequent evolution into Artificial Super Intelligence (ASI).
Just as the steam engine sparked the Industrial Revolution, albeit far more potently and rapidly, AGI threatens to profoundly reshape our economic and social landscapes.
Recent breakthroughs in AI have transformed AGI from theory to imminent reality. Sam Altman recently remarked, "We are now confident we know how to build AGI as traditionally understood. By 2025, we may witness the first AI agents joining the workforce and materially transforming company outputs."
Companies will spend less on labor. Some of that money will go to compute (data centers, model providers, software applications) and some of that money will go to bottom-lines.
This assertion demands immediate attention. AGI—and eventually ASI—will fundamentally alter our economies, social structures, and the nature of work itself.
I. Understanding AGI and ASI: Defining Our Future
AGI refers to autonomous systems capable of outperforming humans across most tasks. Yet its true potency lies in its digital nature: AGI systems can be instantly replicated, merged, evolved, and scaled in ways humans simply cannot. Imagine companies duplicating their most capable employees millions of times, flawlessly transferring knowledge, and deploying specialist teams across countless projects simultaneously.
ASI, on the other hand, represents intelligence exponentially superior to even humanity's greatest minds. Dario Amodei describes ASI as initially comparable to a Nobel laureate but quickly becoming infinitely smarter. Though the exact timeline is uncertain, experts believe progression from AGI to ASI could occur rapidly, a scenario dubbed the "fast takeoff", based on the fact that models will get so good they can speed up the process of developing models in the first place, eventually self-improving autonomously—a goal actively pursued by leading AI labs.
II. The Rapid Path to AGI: Progress and Indicators
The emergence of AGI won’t be marked by singular discrete events but rather by gradual and continuous progress. Systems close to AGI, such as OpenAI's o3 reasoning model—a follow-up to the initial o1 model leveraging test-time compute—are here, and the pace of model releases has accelerated. OpenAI safety researcher Stephen McAleer underscores this urgency, stating recently, "Many researchers at frontier labs seriously consider short timelines to AGI, yet few outside these labs discuss it sufficiently."
These advancements aren’t merely technical feats; they signal a fundamental transformation in organizational productivity. Companies hesitant about adopting AI will struggle to compete against rivals benefiting from AI-driven efficiencies.
III. Work Automation: A New Economic Paradigm
Reasoning models will power agentic software, capable of independent planning, decision-making, and adaptive action. These technologies will radically reshape both white-collar (agentic software) and blue-collar (robotics) work. Tim Urban of Wait But Why anticipates that within two decades, humanoid robots and autonomous drones performing everyday tasks will seem as ordinary as smartphones today.
The first wave of work automation is already happening in customer support. Klarna, a European fintech company, claims that AI manages two-thirds of its support tickets, generating a $40 million profit boost.
But this trend extends to other types of work, such as software development (Devin, Replit Agent, Cursor Agent), pre-sales automation (Apollo, Clay), legal services (Harvey, Igual, Lexter.ai), and many other sectors.
IV. Dramatic Health Improvements and Extended Lifespans
As AGI systems become increasingly intelligent and capable of independent scientific inquiry, their impact on healthcare and longevity research will be transformative. Imagine replicating entire research teams with a few commands, with thousands of the world’s best scientists simultaneously tackling diverse medical challenges. Dario Amodei envisions AI functioning as virtual biologists, revolutionizing biological research by accelerating experimentation and innovation exponentially.
As AGI systems become increasingly intelligent and capable of independent scientific inquiry, their impact on healthcare and longevity research will be transformative. We will devise cures to most diseases, dramatically reducing early deaths, and maybe even revolutionizing longevity. We will all live to be 120.
V. Economic Implication #1: From Wages to Compute
Automation will drastically reduce company spending on human labor, redirecting some of the saved resources towards technology providers (cloud computing, specialized AI chips, software agents, which I'm bundling into the term “compute”). Not all of it will be redirected, tho. The difference will improve bottom-lines.
For example, in the past, a company had to hire a designer (freelance or full time) or a firm that itself hired a designer (freelance or full time) to create an ad. But in its latest release, OpenAI has a model that does pretty darn good ads (and mind you, this is the worst these ads will ever be, because the models are getting better at a super fast pace):
Will all designers be replaced for now? Of course not. But you can clearly see that there will be dislocation in money from wages to compute. And the same is happening in customer support, coding, driving, and increasingly to many other areas of the economy.
Labor dislocations due to AGI (and much longer lifespans) may lead to double-whammy spending pressure for all governments, and thus a constant struggle against ever-increasing deficits from ever increasing welfare payments.
If we extrapolate this migration to physical robots, corporate financial statements will change structurally: opex from human wages will decline and some of these savings will migrate towards capital expenditures for purchasing robots, temporarily boosting profitability (until we get RaaS: robot-as-a-service).
This productivity revolution poses a paradox: reducing human wages simultaneously shrinks consumer purchasing power, potentially threatening economic stability unless new economic frameworks emerge. That leads us to economic implication #2.
VI. Economic Implication #2: Less Disposable Income, More Leisure (and Expanded Welfare)
Mass automation will initially decrease aggregate wages, and thus wage incomes, reducing aggregate disposable income, yet increasing leisure time. This shift might temporarily boost sectors like entertainment, education, and travel. However, without income redistribution, lowered consumer demand could dampen economic growth, with “no one left to buy stuff”.
Governments will need to proactively mitigate unemployment through mechanisms like Universal Basic Income (UBI), as traditional unemployment schemes may not cope with widespread structural displacement. And given that we'll probably live much longer (see IV. Dramatic Health Improvements and Extended Lifespans), these welfare payments will have to go on for much longer.
This may lead to spending pressure for all governments, and thus a constant struggle against ever-increasing deficits from ever increasing welfare payments.
VII. Aside - Economics of Abundance: Rethinking Monetary Policy?
AI-driven abundance could fundamentally alter traditional economic constraints, particularly inflation driven by government spending. Historically, increased spending risks inflation when production capacity is limited and marginal costs are significant. However, AI technologies drastically reduce marginal costs, approaching near-zero for digital and automated products. In such a scenario, traditional monetary policy designed to manage scarcity may become obsolete.
Efficiency from automation is going to be highly deflationary. Would that make room to accommodate printing of money without moderation?
Instead, governments could shift towards managing abundance. Economic growth might become predominantly demand-driven, with AI systems dynamically responding to increased consumer purchasing power. Rising government expenditures, including UBI, would act less as inflationary threats and more as signals instructing AI-driven economies to ramp up production immediately. Thus, monetary policy might evolve from managing inflation and scarcity toward managing equitable distribution and incentivizing sustainable, abundant production.
VIII. The Challenge(s) Ahead
People, companies, and governments need to actively consider what's coming, and prepare for it. What happens when everybody lives to 120 and beyond? When most diseases are cured? When most jobs are automated? These are very hard questions that deal with a scenario that may seem far away but IS NOT.