“There tends to be a debate between being an expert or generalist in the era of AI. So far, the experts appear to have the upper hand, and that’s not slowing down.” – Aaron Levie – Box CEO
The central tension is not whether AI can generate more output, but who can most effectively direct that output towards something valuable. As the cost of first drafts, search, synthesis, and routine analysis falls, the scarce resource shifts towards judgement: deciding what problem to solve, knowing when the system has gone off course, and recognising whether the result is actually good enough to use. That is why expertise has not been diluted by AI as much as some expected; it has become more visible, more leveraged, and in many settings more valuable 1,4,13.
That pattern fits the evidence emerging across productivity research. The OECD has argued that AI can raise output and labour productivity, but its estimates are measured rather than euphoric, with annual total-factor productivity gains in the range of 0,25 to 0,6 percentage points over a decade in the most AI-ready economies 1. At the same time, OECD work on experimental studies finds that generative AI often improves efficiency in writing, summarising, editing, translation, and coding, while the size of the gains depends on task fit, user skill, and the ability to evaluate outputs 13. The implication is straightforward: AI is not a universal equaliser. It is a force multiplier that rewards people who already know how to ask sharper questions and judge the answers more rigorously 1,13.
That is also why the debate between expert and generalist is so persistent. Generalists can now get started faster across a wider range of tasks, and that creates the impression that breadth is winning. Yet the harder part of the work has not gone away. The more an agent can do, the more valuable it becomes to understand the boundaries of what it should do, where it is likely to fail, and how to verify the result. MIT Sloan’s discussion of a study on highly skilled workers found that AI improved performance when used within its capability boundary, but performance dropped when users pushed it beyond that boundary, reinforcing the importance of expert review and cognitive effort 11. In other words, AI reduces the barrier to entry, but it does not remove the barrier to reliable judgement 11.
Expertise as a control system
The most useful way to understand this shift is to think of expertise as a control system rather than a repository of static knowledge. In that model, AI produces candidate outputs at speed, but the human expert sets the objective, monitors for drift, and corrects the path when the system starts optimising the wrong thing. This is particularly obvious in fields where the right answer is not merely a technically plausible one, but one that is contextually defensible, legally sound, commercially coherent, or strategically wise. Research on human-AI collaboration has increasingly described the value of hybrid decision-making, where machine speed and human judgement are combined rather than treated as substitutes 2,5.
The review literature also suggests why the expert advantage persists. AI is especially powerful where a task is structured, repeatable, and easy to verify, but it is far less reliable where the task depends on tacit knowledge, domain-specific standards, or incomplete information. A legal brief, a finance model, or a product strategy memo may all look polished in draft form, yet each requires a different kind of expert filter to determine whether the assumptions are sound and the reasoning holds together. OECD analysis has noted that better decision-making, sense-making, and forecasting are among the most important benefits of AI, while also warning against over-reliance because errors can propagate quickly when human judgement is deferred to machines 5.
The same pattern is visible in empirical productivity studies. Generative AI often helps less experienced workers the most because it compensates for gaps in fluency, structure, and speed of execution 4,13. That does not contradict the claim that experts gain more leverage. It means the gains are different in kind. Novices may get a larger lift in basic throughput, but experts can use the same tools to expand scope, test more alternatives, and compress hours of routine labour into a shorter decision cycle. The result is not simple replacement, but a widening of the gap between people who can merely produce text and people who can identify what the text should achieve 4,11.
Why judgement is becoming scarcer
There is a deeper reason why expertise is becoming more valuable: AI makes mediocre output cheaper, but it does not make good judgement cheaper. The tools can draft, summarise, classify, and propose, but they cannot reliably supply institutional memory, ethical responsibility, or the tacit sense of what will work in a specific environment. That is why the burden on experts often increases rather than falls. A senior professional using AI may spend less time on first-pass production, but more time reviewing, redrafting, and stress-testing the agent’s recommendations. This is not a sign that the technology has failed. It is a sign that the high-value portion of the job has moved upstream into framing and downstream into verification 5,11.
That shift also explains why expertise development itself matters more, not less. There is a risk that people mistake tool fluency for domain competence, especially when AI can make a novice look more polished than would once have been possible. But polish is not the same as depth. Studies and policy work alike warn that automated workflows can erode opportunities for deliberate practice if organisations allow the machine to take over the work that builds skill 14. If the next generation of professionals learns only how to prompt, rather than how to evaluate, they may gain short-term speed while losing the slower capabilities that actually make AI useful in serious work 14.
That is where the strategic implication becomes clearest. AI does not eliminate the need for experts; it changes the economics of expertise. Someone who knows the field can now work across a wider surface area, examine more possibilities, and delegate more routine preparation to the machine. This produces real leverage in consulting, law, software, finance, research, and operations, where the value of a senior person’s hour lies less in typing and more in synthesis, prioritisation, and risk management 1,11,13. The market consequence is a premium on people who can combine technical literacy with deep domain knowledge, and on organisations that know how to design workflows around human oversight rather than around blind automation 5,12.
The objection from the generalist camp
The strongest objection is that AI will flatten the value of specialism because broad competence becomes cheap. There is truth in that, but only up to a point. AI does let more people participate in more kinds of work, and that broadens opportunity. It also means that many routine tasks once protected by gatekeeping are now easier to attempt. Yet the very accessibility of these tools makes evaluation harder, not easier, because plausible output arrives faster than human review capacity. The more generalist the user, the more likely they are to accept fluent but weak answers. The more expert the user, the more likely they are to exploit the model’s strengths while detecting its blind spots 2,11,13.
This is why the old distinction between doing and knowing becomes less useful than the distinction between generating and governing. AI is very good at generation. Experts remain essential for governance. They decide what counts as evidence, which trade-offs matter, and whether a recommendation aligns with the underlying objective. In practical terms, that means the future belongs less to the person who can ask any question and more to the person who can ask the right one, interpret the answer, and revise the problem definition when needed. That is the part of work that still resists automation, and it is precisely why the upper hand has, for now, stayed with expertise 1,5,11.
The longer-term question is not whether AI will create generalists, but what kind of generalists it will create. The most durable generalist may be someone with enough depth in one area to judge quality, plus enough breadth to transfer that judgement across adjacent tasks. That is a very different profile from a shallow all-rounder. As AI spreads, the winners are likely to be those who use breadth as an interface and depth as an anchor, because the machine can help with expansion but cannot supply the standards by which expansion is worth anything at all 2,12,14.
References
1. Miracle or Myth? Assessing the macroeconomic productivity gains … – 2024-11-22 – https://www.oecd.org/en/publications/miracle-or-myth-assessing-the-macroeconomic-productivity-gains-from-artificial-intelligence_b524a072-en.html
2. THE SYNERGY BETWEEN EXPERT JUDGMENT AND AI SYSTEM … – 2025-12-22 – https://jomsa.science/index.php/jomsa/article/view/145
3. The impact of Artificial Intelligence on productivity, distribution and … – 2024-04-16 – https://www.oecd.org/en/publications/the-impact-of-artificial-intelligence-on-productivity-distribution-and-growth_8d900037-en.html
4. The impact of Artificial Intelligence on productivity, distribution and growth: Key mechanisms, initial evidence and policy challenges – https://www.oecd.org/content/dam/oecd/en/publications/reports/2024/04/the-impact-of-artificial-intelligence-on-productivity-distribution-and-growth_d54e2842/8d900037-en.pdf
5. How artificial intelligence is accelerating the digital government … – 2025-09-18 – https://www.oecd.org/en/publications/2025/06/governing-with-artificial-intelligence_398fa287/full-report/how-artificial-intelligence-is-accelerating-the-digital-government-journey_d9552dc7.html
6. Incorporating experts’ judgment into machine learning models – https://www.sciencedirect.com/science/article/abs/pii/S0957417423006206
7. Rethinking Human Judgment in the Age of Generative AI – https://arxiv.org/html/2512.10961v1?ref=grigio.org
8. AI, productivity – 2025-04-02 – https://www.oecd.org/en/events/2025/05/will-ai-make-us-more-productive–oecd-experiences-and-policies.html
9. AI and work – OECD – 2025-09-22 – https://www.oecd.org/en/topics/sub-issues/ai-and-work.html
10. AI, Productivity, and Labor Markets: A Review of the Empirical … – 2026-02-05 – https://laweconcenter.org/resources/ai-productivity-and-labor-markets-a-review-of-the-empirical-evidence/
11. How generative AI can boost highly skilled workers’ productivity – 2023-10-19 – https://mitsloan.mit.edu/ideas-made-to-matter/how-generative-ai-can-boost-highly-skilled-workers-productivity
12. How Expert Collaboration with AI Systems Shapes Their … – https://arxiv.org/pdf/2504.12654
13. Unlocking productivity with generative AI: Evidence from … – 2025-07-08 – https://www.oecd.org/en/blogs/2025/07/unlocking-productivity-with-generative-ai-evidence-from-experimental-studies.html
14. The Impact of Artificial Intelligence on Expertise Development – https://experts.umn.edu/en/publications/the-impact-of-artificial-intelligence-on-expertise-development-im
15. 5x Output Per Senior Hour: AI Amplifies Domain Expertise | Pertama … – 2026-02-26 – https://www.pertamapartners.com/insights/ai-leverage-5x-output-per-senior-hour
