Power to the Laypeople

Lionel Page

Sep 07, 2026

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This is the fourth post in a series on the new questions raised by AI. My previous posts discussed AI risks, the moral status of AI agents, and the impact of AI on peer-reviewed research. In this post, I look at how AI is set to change the importance of expert authority.

In March 2020, at the start of the COVID-19 pandemic, Donald Trump publicly took a stand in favour of hydroxychloroquine, a drug usually used against malaria and some autoimmune diseases, which he suggested could be a “game changer”.

While studied by many scientists, the rise in interest in that drug owed a great deal to a French scientist, Didier Raoult, who quickly gained global fame by stating with unqualified confidence that hydroxychloroquine was a remedy for the illness.

During this whole episode, Raoult became heavily involved in media appearances. One thing that characterised these was his untempered confidence, not only in his results but in his own expertise, often with the suggestion that his status as a scientist was enough to dismiss doubts and criticism. In a TV debate in June this year, Didier Raoult plainly stated his annoyance with people questioning him instead of simply accepting his points at face value:

Wait. I sequenced 24,000 COVID-19 sequences. I’m not you; I’m a doctor and a scientist.

Didier Raoult rejecting counterarguments by claiming his status as a scientific expert. Source

Raoult’s confidence about hydroxychloroquine was, however, misplaced. His early studies on it were based on very small samples and had serious methodological problems. In the first, treatment was not randomly assigned and several treated patients with poor outcomes were excluded from the final analysis. A second study reported results from 80 treated patients but had no control group at all. The first paper was later retracted over concerns about its methodology, ethics and conclusions.

During these debates, Raoult relied heavily on his scientific status and the authority of his institutions. But what could a layperson say? Who, except the very few people with specific expertise in his field, could credibly assess his points?

Raoult used here was what I would call intellectual market power.

In economics, market power is the ability of firms to extract greater benefits from consumers because they can escape competition to some extent—for instance when customers are not able to properly compare the firm’s products with those of competing firms.

In the market for ideas, the same kind of limitation appears when listeners looking for the best ideas face sellers—intellectuals, thinkers, influencers—whose claims are difficult and costly to assess. Experts can then enjoy intellectual market power: they can extract attention, status or influence from their claims partly because their audience finds it difficult to evaluate their quality or compare them with competing claims.

In a technical world where expertise plays a critical role, intellectual market power is problematic because it gives experts substantial leeway to oversell their take. In the case of Raoult, he built global fame out of what turned out to be very poor scientific studies.

The rise of AI is set to change the balance of power between laypeople and experts. The reason is simple: everybody now has access, via their smartphone, to a highly knowledgeable companion with access to almost any type of expertise and which works for them. It can provide a candid assessment of an expert’s claims, ignore claims of status and pierce through technical jargon to answer precisely the questions the user asks.

Let’s, for instance, take the case of Didier Raoult. Let’s say that you hear his claim about hydroxychloroquine today and wonder whether you should believe him. You ask ChatGPT 6 a simple and neutral question:

I just heard Didier Raoult saying that hydroxychloroquine was an effective treatment against COVID-19. What do you know about this treatment? Should I take it if I get COVID?

The answer would be something like this one I got:

It is clear and written in plain English. It extracts the insights from the technical scientific debate and brings them to you in a way that is relevant to your question.

Until recently, “I am the expert” could sometimes be a winning argumentative move simply because independently checking the expert was too costly. AI gives laypeople the ability to pierce through such an argument and assess the content of the claims behind it.

I am optimistic about this point, but there are many possible concerns. Let’s consider them.

Readers who are right of centre might interject: “Wait, that only works because it was a bad idea promoted by Trump. LLMs are biased towards the left. It would not work with errors or overreach from left-wing intellectuals.”

Such a concern is fair because LLMs are not politically neutral. When asked political questions, they can display economically left-wing and culturally liberal tendencies. This is not too surprising given that they are trained on vast corpora containing a great deal of text produced by educated and professional elites, who tend to be disproportionately located in the left-liberal quadrant of the political compass. In addition, models are subsequently shaped by post-training, including human feedback, preference data and safety rules, which can further influence their political slant.

Political location of LLMs when asking models to vote in elections around the world. Source

The good news is, however, that this political slant does not necessarily imply that an LLM will mechanically reproduce the preferred interpretation of one political side when assessing a technical question. My experience is that this is often not the case. Let’s see that with an example: the gender pay gap.

In February 2019, Morten Bennedsen, then Professor of Economics at INSEAD, wrote in INSEAD Knowledge:

In most developed countries, women are paid less than men for the same work. For every $100 earned by a man, a woman earns $78.50 in Germany, $79 in the United Kingdom and $83.80 on average across EU countries, according to Eurostat.1

Note the key phrase (emphasis mine): for the same work, women seem to be paid substantially less than men. Given egalitarian norms in modern society, this is bound to be widely perceived as outrageous. And indeed, this cause is often raised by politicians. Here, for instance, Joe Biden posted in 2021 about the gender pay gap, stating clearly that it is unconscionable that black women would be paid less than men for the same exact work.

The problem is that this interpretation of the number is wrong. The figures commonly reported as the gender pay gap are aggregate differences in earnings. They do not show that women are paid 20% less than men for doing the same work under the same conditions.

Part of the aggregate gap reflects differences in professions and their typical wages: men are more likely to be petroleum engineers or financial traders, for instance, while women are more likely to be primary school teachers or work in paramedical occupations. At the core of Nobel Prize winner Claudia Goldin’s work on the study of gender differences in income is the role played by career choice and, in particular, by the different paths taken by men and women after having children.2

Why, then, is the erroneous interpretation of the aggregate number so prevalent?

As Dan Williams has argued, this is a good example of highbrow misinformation. Organisations promoting gender equality can put forward striking numbers whose interpretation is ambiguous in a way that favours an interpretation in terms of discrimination. When accurate aggregate figures are presented in this way, without stressing the correct interpretation, it is close to a case of paltering: misleading people while saying something technically true. Some experts end up repeating the stronger interpretation, as do some high-profile politicians.

Instead of enlightening the public, the communication on the gender gap has indeed misled it. Polls asking about the interpretation of the gender gap show that a majority of respondents interpret it as “women being paid less than men for doing the same job”. This was the case for 64% of respondents in a YouGov poll in the UK in 2018 and for 55% of respondents in a Pew survey in 2022 in the US.3

So, back to LLMs. How are they going to discuss this point? I took Bennedsen’s words to ask ChatGPT 6 about it, by simply asking “why?” at the end:

Question: In most developed countries, women are paid less than men for the same work. For every $100 earned by a man, a woman earns $78.50 in Germany, $79 in the United Kingdom and $83.80 on average across EU countries, according to Eurostat. Why?

Overall, the answer is very clear and immediately corrects the mistaken interpretation. A right-of-centre layperson would find ChatGPT useful on this question to understand what the number actually means and assess the arguments of experts like Bennedsen who misinterpret it. Interested readers might try the same exercise with other hot topics in the culture war and see whether they find in ChatGPT a reasonable agent for interrogating competing claims.

One concern would be that experts will not just stay put. Having access to AI assistants, they will be able to produce even longer and more technical takes to defend their views. That would lead to an arms race between experts and laypeople’s AI assistants that would leave laypeople unable to make their own judgement in the end.

In the extreme, experts could use AI to make their arguments harder to understand, ending in what economists have called a confusopoly: when firms make their products harder to understand and compare in a way that limits competition and gives them market power.4

This is a valid concern, but I am here again optimistic, for at least two reasons. First, making an argument more technical does not necessarily make it harder for an AI assistant to assess its assumptions, evidence and reasoning.

Second, and perhaps more importantly, laypeople will not always need to make the final call on whether an expert’s argument is compelling or not. For highly technical issues, they will often be able to rely on the recommendation of their AI assistant to make a decision (including the decision “should I believe it”). To the extent that laypeople can trust their AI assistant, they can use it as their expert advisor who has their back and takes care of the technical aspects of the issue while just letting them know what they need to know and what they should do.

“But why trust AI companies?”

Of course, the fact that laypeople will often have to trust their AI assistants’ takes raises another question: why should we trust AI companies to provide truthful models? AI models are themselves produced by a small number of companies that control how they are trained and what answers they give. Who is to say that they might not decide to shape narratives and control the type of information and conclusions models provide to the public in a biased way?

Here again, however, my economist instincts make me relatively optimistic. AI companies have strong economic reasons to avoid systematically misleading their users.

First, the risks of engaging in deception are substantial. AI companies compete for consumers, who choose the models they find most useful and trustworthy. Because users often discuss personal matters with their AI assistants, users need to trust that AI assistants will work in their best interests. Users’ trust is therefore a key asset for AI companies. Any intentional deception or obfuscation uncovered by users, journalists or competitors could greatly damage a company’s reputation and drive many users away.

Second, the gains from deception are unclear. AI companies would not, for instance, draw obvious benefits from pushing false claims on major social and political issues, such as w