The Anatomy of Work in the Age of AI
Not whether AI will change work, but what kind of work — and which workers — will endure
Former President Obama recently shared an interview with Dario Amodei, CEO of Anthropic, who warned in Axios that AI could eliminate up to 50% of entry-level white-collar jobs within five years, potentially raising unemployment to 10–20%. Economists and institutions like the IMF and the World Economic Forum are sounding similar alarms.
This kind of forecast is no longer hypothetical. It reframes the urgency of our conversation: not whether AI will change work, but what kind of work — and which workers — will endure.
In this post, I want to offer a clearer way to think about that question. Drawing on recent research with colleagues, I propose a framework that breaks down jobs into their smallest units — skills — and then divides each skill into two distinct components: decision-making and execution. This distinction, I argue, is essential to understanding where humans still matter, how AI is reshaping labor, and what we should be measuring, teaching, and building toward.
If we can’t say what a job is, we can’t reason about what AI is replacing.
The Wrong Debate
Much of the public debate still hinges on a narrow question: Will AI replace us, or will it help us? But this framing misses the deeper structural shift underway. It treats jobs as flat, undifferentiated collections of tasks, as if replacing a few steps is equivalent to automating the whole. In reality, most jobs are complex systems of judgment, adaptation, and execution. And not all parts of that system are equally exposed to AI. If we want to understand how work is changing — and what skills will remain valuable — we need a more granular lens.
Ada’s Shift
Consider Ada, a mid-level software engineer. Just a few years ago, her day revolved around writing code, debugging features, documenting functions, and reviewing pull requests. Today, GitHub Copilot writes much of the scaffolding. GPT drafts her documentation. Internal models flag bugs before she sees them. Her technical execution has been accelerated — even outsourced — by AI. But her job hasn’t disappeared. It has shifted.
What Ada does has changed. What Ada is responsible for has not.
What matters now is whether Ada can decide what to build, why it matters, how to navigate tradeoffs that aren’t in the training data — and crucially, whether the result is even correct. Her value no longer lies in execution — it lies in judgment and verification. This isn’t just a shift in what she does — it’s a shift in what makes her valuable. Execution can be delegated. Judgment cannot.
The Real Divide
Ada’s story isn’t just a sign of change—it’s a window into the deeper structure of modern work. In our research, we argue that to understand how AI is reshaping labor, we need to go beyond jobs and tasks, and examine the composition of skills themselves
Every skill, we propose, has two layers:
Decision-level skills: choosing goals, framing problems, evaluating tradeoffs
Action-level skills: executing plans through tools, language, or procedures
This distinction is foundational to our framework. It allows us to model worker capabilities and job demands more precisely. And once you see the split, it’s everywhere.
A doctor uses AI to flag anomalies in a scan — but must decide when to intervene, and why.
An analyst uses GPT to draft a report — but must decide which story the data tells.
A teacher uses AI to generate exercises — but must decide how to adapt them for a struggling student.
A programmer uses Copilot to write code — but must decide what to build, how to build it, and in what language, architecture, or paradigm.
AI acts. Humans decide. That’s the frontier.
This isn’t a cosmetic difference. It’s a deep structural boundary — one that shapes who adds value, who adapts, and who gets displaced.
The distinction between decision and action is becoming the new fault line in labor.
A New Lens on Success
This structure isn’t just descriptive—it makes success measurable. We model each worker using an ability profile that captures their expected strength and variability at each subskill. Given a job’s subskill demands, we can compute the probability of success—a quantitative match between a worker’s capabilities and a job’s needs. This also lets us explore how training, support, or AI tools can shift that success rate—and identify where small changes make a big difference.
One of our key findings is that decision-level skills often act as a bottleneck: small improvements can trigger phase transitions, where success probability jumps sharply rather than smoothly. A little more judgment can be worth far more than a lot more action.
We also show how merging two workers—or pairing a worker with an AI system—can significantly boost job success by combining complementary skills. This framework yields four canonical pairings—each capturing a mode of human or hybrid productivity:
Strong decision + strong action: the ideal worker, high success probability.
Strong decision + weak action: can be aided by AI to improve execution.
Weak decision + strong action: needs human or institutional support to frame and evaluate problems.
Weak decision + weak action: unlikely to succeed without extensive support or retraining.
This pairing framework also models human-AI collaboration. AI tools excel in action-level tasks, reducing execution noise. But they cannot compensate for poor decisions. Conversely, humans with decision strength but noisy action skills can leverage AI to execute more reliably.
AI can make your actions sharper. But it takes another human—or a better-trained self—to make your decisions wiser.
Rethinking Upskilling
Most upskilling initiatives focus on teaching people how to use tools — how to code in Python, use Excel, write better prompts. But these are action-level skills. They train people to do — not to decide. And as AI becomes more fluent in these domains, such training risks becoming obsolete even faster.
What our model shows is that the most durable leverage comes from strengthening decision-level abilities: learning how to formulate the right problem, evaluate competing goals, and adapt strategy under uncertainty. These skills are harder to teach, harder to measure — but they’re also harder to replace.
Reskilling should not mean trading one automatable task for another. It should mean elevating workers into roles where human judgment is indispensable — and building the scaffolding to support that transition.
The goal isn’t to out-code AI. It’s to out-decide it.
But understanding skill composition isn’t just useful for support or upskilling—it changes how we identify and select talent in the first place.
Beyond the Average: Hiring for Complements, Not Proxies
Traditional hiring often relies on blunt proxies—credentials, test scores, résumé keywords—that collapse a person’s skill into a single metric. But real talent is rarely uniform. Someone may struggle with polish but excel at solving hard problems. Another may be smooth in execution but shallow in judgment. Conventional systems force institutions to average across these traits—hiring for perceived overall competence rather than true fit.
Our framework breaks that bind. By distinguishing decision-level and action-level abilities, and modeling how they combine, we can identify complementary strengths—either across people or in partnership with AI. This makes it possible to spot a high-judgment candidate with messy execution and pair them with tools that stabilize output. Or to recognize an efficient executor who thrives when decisions are pre-structured by a manager or teammate.
You no longer have to bet on the average. You can hire for the right complement.
This shift doesn’t just improve hiring. It makes evaluation more precise, support more targeted, and teams more balanced. It also disrupts the performative incentives of current systems, where polish often trumps insight, and fluency overshadows depth. If we can build systems that recognize decision strength—even in the absence of perfect execution—we can unlock talent that today gets overlooked.
Designing for the Future of Work
Ada’s story is only the beginning. Her challenge—learning how to decide, not just how to do—is quickly becoming the central challenge for institutions.
The AI wave isn’t just increasing efficiency. It’s reshaping the anatomy of work, separating action from decision, and forcing us to ask: what remains uniquely human?
Economist Daron Acemoglu argues that the most dangerous trend in recent technological history isn’t automation itself—it’s the way we’ve used automation to displace workers rather than augment them. Over the past few decades, many technologies have replaced mid-skill jobs without meaningfully improving productivity. The result: wage stagnation, rising inequality, and a polarized labor market. Acemoglu’s call is clear—we need to redirect innovation toward task-augmenting technologies that enhance human capability rather than erode it.
Our framework offers a concrete way to act on that vision. By distinguishing between decision- and action-level skills, and modeling how they interact with AI, we can design technologies—and institutions—that truly support human strengths.
But the real challenge is not theoretical. It’s institutional.
If we continue to train, hire, and evaluate people based on action-level outputs, we will misread talent, mistrain workers, and misdesign systems. Worse, we will cede the future of work to automation by default—not because AI is more capable, but because we forgot how to measure what humans uniquely contribute.
Our model not only reframes how we think about work, but also offers a practical tool for institutions. By enabling fine-grained evaluations along decision- and action-level dimensions, it allows for more accurate assessments—not just in hiring, but also in upskilling, promotions, task allocation, and even layoff decisions. Instead of collapsing a worker’s abilities into a single metric, we can now ask: where does their judgment shine? Where is support or augmentation most effective?
We need tools, metrics, and institutions that can recognize decision-level excellence—not just output volume. Otherwise, we’ll keep mistaking speed for insight, and productivity for progress. That means rethinking how we evaluate students, support workers, and guide innovation. It means building systems that value judgment, verification, ethical reasoning, and strategic adaptation—the things that don’t show up in a prompt but define good decisions.
The question is no longer whether humans have a role. The question is whether we’re designing for it.
This is the work ahead. And we’ll need to be as thoughtful about decisions as we’ve been clever with tools.
This essay draws on a forthcoming paper, A Mathematical Framework for AI-Human Integration in Work, by L. Elisa Celis, Lingxiao Huang, and Nisheeth K. Vishnoi, to appear at ICML 2025. You can read the full paper here: https://arxiv.org/abs/2505.23432
In most industries, every level jobs are about grunt work, plain execution.
Go fix these bugs. In a software company.
Or go make this PowerPoint deck. In management consulting.
Or go build this model in Excel. In investment banking.
The expectation is that in a few years, the junior employee will become capable of exercising judgement and then they will become more senior. Well, those skills are qualitatively different, and the current AI boom is exposing that difference!
Being a really good human calculator doesn't train one to become an algebraist. That distinction is going to be driven home in practically every industry in the next decade or so.
This is also why I think the LLM hype cycle is going to disappoint a lot of people who are expecting miracles.
It is a good breakdown, but I feel it is missing an unknown unknown. AI may enable new kinds of work that we aren’t seeing at all right now. This is focus on incremental changes to the current works stack. If you were to talk about ‘level designers’ for FPS games to someone even from 1970s it would make no sense to them, and we may similarly have future jobs that make no sense now.