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Expertise gets more valuable as AI improves, but only under conditions. The AI has to take over work that eats your time without replacing your judgment. You also need enough knowledge to direct and check what it produces. And the workflow has to be built so your judgment reaches the final decision.
The evidence is mixed. In some studies AI sped people up and helped less-experienced workers catch up. In a radiology experiment, AI help didn’t improve expert diagnoses on average. The 2025 National Academies report Artificial Intelligence and the Future of Work puts it this way: AI is “a tool with the potential to enhance human labor and create new forms of valuable work – but this is not an inevitable outcome.”
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Why the claim is plausible
AI makes some things cheap: first drafts, searches, summaries, and side-by-side comparisons of options. When those get cheap, the scarce part of the work moves elsewhere. It moves to deciding what problem is worth solving, noticing what context is missing, spotting an output that is wrong, choosing between trade-offs, dealing with other people, and being accountable for the result.
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Automation versus augmentation
The distinction matters for your own work. Task automation hands a subtask to a machine. Output goes up, but your judgment may not. Expertise augmentation happens when you use the system to extend what you can do while keeping enough knowledge to direct and evaluate it. The same tool can do either, depending on how it is deployed and how you use it.
What the evidence shows
All figures below are specific to the study, task and population described. They are not general productivity estimates.
Professional writing: faster, with a narrower skill gap
The National Academies summarizes a 2023 experiment by Noy and Zhang in which college graduates did professional-writing tasks with ChatGPT (version 3.5). Work time fell by roughly 40%, and average quality rose slightly. Less-skilled writers improved more. This does not show that someone with no relevant expertise would do well. The participants were educated people doing tasks they could judge.
Customer support: gains that favored newer workers
The same report summarizes a study by Brynjolfsson, Li and Raymond (2023). A chatbot suggested responses to human support agents, and chat resolutions per hour rose 14% on average. Less-experienced workers using the tool approached the productivity of experts. The humans stayed in the loop. The result doesn’t automatically carry over to autonomous customer-facing systems or to other jobs.
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There is a counterpoint for experts here: when AI lifts novices toward expert output, the edge that comes from routine know-how shrinks. What holds its value is the judgment the tool doesn’t supply, such as handling unusual cases and knowing when a suggestion is wrong.
Radiology: expertise did not guarantee a good partnership
In a radiology experiment summarized by the National Academies, the AI’s predictions were more accurate than almost two-thirds of the participants’ assessments. Yet AI assistance did not improve radiologists’ diagnostic quality on average. Contextual information available to the radiologists did improve quality. The lesson is about design: a skilled person plus a strong tool can still underperform if the tool is integrated badly or if people over-rely on it or discount it.
Evidence review: a case study, not a benchmark
A UK Department for Science, Innovation and Technology summary (published 23 April 2025) described a comparative exercise, commissioned by DSIT and DCMS and run by the Behavioural Insights Team, on AI-assisted rapid evidence review. The AI-assisted approach took 23% less time overall and 56% less time on analysis and synthesis. The page itself says: “This study is effectively a case-study, and therefore the results are not generalisable.” Treat it as an illustration of where time can be saved, not as a forecast.
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The National Academies committee says rigorous, representative workplace evidence on complementarity and substitution is still limited, and that the available studies neither confirm nor reject its framework conclusively. Its report is a 200-page consensus study with a US focus. It describes the future of work as dependent on how AI is developed and adopted, plus training and institutional choices. So whether your expertise gains value depends partly on decisions made by employers and policymakers, not only on your skills.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to tell whether AI will raise or lower the value of your expertise
| Question | Favors expertise | Favors substitution |
|---|---|---|
| Task structure | Novel, ambiguous or high-stakes | Routine and well-defined |
| Your baseline skill | Competent or frontier practitioner who can judge output | Work where novices can reach expert level with suggestions |
| AI’s role | Drafting, retrieval, suggestion that you review | Autonomous action with little review |
| Context | Local, historical, interpersonal or situational knowledge only you hold | All relevant information is already in the system |
| Accountability | A qualified person can catch errors and answers for them | Nobody is positioned to check, or no one is responsible |
| Outcome measured | Quality, error rate, customer experience, learning | Speed and volume alone |
A gain on one measure does not imply gains on the others. Faster output with more errors is not a win for anyone.
Which skills to build
An OECD report on AI and skills (2026) says fewer than 1% of workers will need advanced AI-specific skills such as programming or model development. It points instead to digital skills, using and interpreting data, managerial skills, problem-solving, creativity and innovation. For most people, the practical list is:
- Deep knowledge of your own field. It is what lets you recognize a bad output and ask a better question.
- Digital fluency and data interpretation. Know what a tool can and can’t do, and read numbers critically.
- Problem framing. Defining the goal well often determines whether the output is useful.
- Deliberate practice at checking. Compare AI answers against sources you trust, and note where they fail in your domain.
- Judgment about trust. Decide in advance which tasks you will let AI draft, which you will verify line by line, and which you will not delegate.
Don’t assume interpersonal skills automatically rise in value. OECD says empathy, communication and teamwork remain essential in many jobs. It also reports early signals that demand for some social skills may fall in parts of Europe under algorithmic management, and says it is too early for firm conclusions.
When to trust an AI answer
- Check the stakes. The higher the cost of an error, the more of the answer you should verify independently.
- Test it against what you know. If you can’t judge any part of the answer, you are not in a position to rely on it.
- Add the context the tool lacks. The radiology result suggests context can matter more than a prediction on its own.
- Form your view before you look. Anchoring on a suggestion is one way assistance goes wrong.
- Know who is accountable. If it’s you, review accordingly.
Verdict
The headline holds in a narrower form: as AI gets better, expertise matters more where people use it to extend their reach while keeping the knowledge to steer and check it. It matters less where the tool lets novices match experts on routine tasks, and it does little where the workflow sidelines human judgment. The evidence is a set of task-specific studies, not a labor-market forecast, so the safest move is to build knowledge you can use to evaluate machine output.
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