Pro-Worker AI

Adam highlighted: “By pro-worker AI, I mean AI that enables workers to do new or more sophisticated things by giving them information, by expanding their problem-solving and their expertise.”

The definition

Two categories, from the same underlying framework as Acemoglu and Restrepo - The Task Framework:

His own examples: “nurses now can do much more, journalists can do much more, electricians can do much more.” Note that all three are non-elite skilled occupations. That is not incidental — it’s the working-class-liberalism argument in technological form. See Working-Class Liberalism and Community Elbow Room.

And the exclusion is explicit: “Automation, again, narrowly construed, isn’t pro-worker AI.”

The part everyone gets wrong

Cowen sets a trap — a good one. If you want pro-worker AI, does a regulator inspect what Stripe and Mercatus are doing with their models and approve it? Acemoglu’s answer is unusually emphatic:

“I do not think that we know today or we desire today a regulator deciding what is pro-worker AI and what’s not, and then saying only pro-worker AI should be enabled. I’ve never argued that, and I do not believe that.”

And again: “No, I do not believe that we need to have a regulator that says, oh, only pro-worker AI is allowed. Absolutely not.”

What he wants instead is narrative and aspiration change among technologists, with public policy in a supporting role:

“Cultivating it has to start by changing the narrative, changing the aspirations of tech entrepreneurs and tech companies, and public policy has a role but has a supporting role in that.”

This is a much weaker lever than his critics usually assume, and arguably weaker than his own diagnosis warrants. If the automation/new-task mix is determined by firms’ choices under competitive pressure, exhortation is a strange instrument to reach for.

The obvious problem, unaddressed

The distinction may not survive contact with an actual product. A tool that lets one nurse do the work of two is simultaneously (a) expanding that nurse’s capability and (b) reducing headcount. Which is it? Acemoglu’s framework says the answer depends on whether total tasks expand — but that is only knowable after the fact and in aggregate, not at the moment of design.

Neither man raises this, and it is the question a working engineer would ask first.

From the book

Pro-worker AI is not a passing phrase in the conversation — it is the centre of Chapter 9, Liberalism in the Age of Algorithms, and returns as the fourth challenge of Chapter 10. The definition is the same one he gives Cowen: “Pro-worker AI explicitly aims to expand worker productivity and capabilities.”

The worked examples the interview only gestures at. He builds three, and is careful that two of them do not yet exist:

• The electrician tool. An AI trained on electrical-engineering knowledge, equipment specifications, and “past use cases where expert electricians dealt with different problems”, which diagnoses faults from sensors and photographs and then guides an iterative human search for the cause. He notes “this exact version… does not exist,” but that a global company is building related products for engineers and field technicians. The framing matters: “pro-worker AI can be pro-manual worker too.”
• The classroom tool. Trained on curricular material and past classroom experience, fed by short quizzes, recommending in real time that the class split into smaller groups and proposing strategies the teacher can modify. “This tool doesn’t exist at the moment either, but a prototype is being developed.” The test he applies is the sharp one: “While automated tests and other current instructional offerings attempt to reduce the need for teachers, a successful rollout of an AI tool of this sort would increase the demand for teachers.”
• Healthcare, more briefly — nurses “more actively engaged in diagnosis and care decisions,” checking drug interactions, with real-time guidance on when to involve a physician.

There is also an argument the interview never reaches: pro-worker AI as the answer to Baumol’s cost disease in exactly the sectors where public services are most expensive, which is what ties Chapter 9 to the public-services challenge in Chapter 10.

Why we don’t see more of it. Two reasons, given in order: an economic one and an ideological one. The economic reason is that “the business models for generating revenues from digital ads… and for automating work by selling software services to companies are well-established,” while “there is less of a track record in digital technology of monetizing new tasks” — and that whoever builds the first version usually isn’t the one who monetizes it. Then the tax code, which “subsidize[s] capital expenditures relative to labor outlays.” The ideological reason is stronger, and is the most quotable thing in the chapter:

“This approach produces excessive focus on AGI and automation, and may even put blinders on technologists, making them miss out on feasible and profitable pro-worker AI opportunities. Belief in AGI can also be self-fulfilling.”

Self-fulfilling twice over: within firms, because “more and more sophisticated-looking models are marketed and adopted for automation, even when they are not ready for prime time, with alternative technological directions remaining unexplored”; and between states, because if AGI is imminent and decisive then “US-China relations inevitably become more conflictual.”

And here the book and the interview do not say the same thing. Cowen’s regulator trap draws an emphatic denial — “I’ve never argued that, and I do not believe that” — and a retreat to narrative change with “public policy… in a supporting role.” Chapter 9 ends like this:

“Creating new aspirations for technology and enacting appropriate policies and regulations could ensure that the market process still functions, but prioritizes technological developments that differ from our current path. This calls for new regulations as well. … the aim should not be to create a completely deregulated economy… The point is to focus on the more important regulations, and in the age of algorithms, the regulation of social media and AI should be at the top of the list.”

Chapter 10 goes further and names the instrument: after arguing for cutting ex-ante rules generally in favour of ex-post accountability on the Federal Reserve model, it makes an explicit carve-out — “though for AI, social media, and certain issues relating to health and safety, ex-ante regulations would also be necessary.” Plus antitrust: “reducing the dominance of technology monopolies” on the grounds that “monopoly in the tech sector discourages new directions of innovation.” Plus the tax-code fix, plus union support.

Strictly, it isn’t a contradiction. What he denies to Cowen is narrow and specific — a regulator that inspects models, rules on which are pro-worker, and permits only those. Nothing in the book proposes that. But “narrative change, with public policy in a supporting role” is a poor summary of a programme containing ex-ante AI regulation, antitrust action, a rewritten capital-versus-labour tax treatment, and a reconstituted labour movement bargaining over technology choice. The interview makes his lever sound weaker than his book does. The criticism on this page — that exhortation is a strange instrument for a problem he diagnoses as structural — turns out to be a criticism of the interview rather than of the position.