From woolly to concrete liberalism

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I got round at last to reading Abundance by Ezra Klein and Derek Thompson, and enjoyed the book. It’s a good read and makes its case well. The pro-growth case has traction beyond the US of course. UK ministers have been on about planning regulations hampering growth ever since the July 2024 election, and a John Collison article in the Irish Times recently got the Irish chatterati talking about it.

Did I agree with the case? Yes and no. I do think restarting economic growth – in an environmentally increasingly sustainable way – is essential. Social and political phenomena have many causes so the electoral success of far right populism (anathema to me as an old fashioned woolly liberal) is not caused in a simple way by the absence of growth since the mid-2000s. But that absence is certainly part of the story. My views about this have long been shaped by my PhD adviser Ben Friedman, and his book The Moral Consequences of Economic Growth. (As for the sustainability component – of course that’s essential, but I can’t think of any far right party/government that is not claiming climate change is a hoax. So significant political change has to come first.)

So Abundance does identify some barriers to growth and describe the implications. But the book is also strangely non-political in the sense that I didn’t find anything about how to get from here to the sunlit uplands of there. The book says, “What we are proposing is less a set of policy solutions than a new set of questions around which our politics should revolve.” OK, nobody wants another list of 10 bullet point policy solutions in the final chapter. But what is the political economy of getting from today’s polarised and disgruntled world of concentrated power and authoritarianism to the Abundance-land of pro-concrete liberalism, where building new things is welcomed by communities and technology works for everybody?

Top marks for optimism to the authors, and I enjoyed the read, but it didn’t rattle my pessimism about the current moment.

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Reshuffle – or how productivity happens

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Term time is not conducive to doing a lot of reading, but I have managed a couple of interesting books recently. One was Abundance – I’ll jot down some thoughts about that later. The other was Reshuffle: Who wins when AI restacks the knowledge economy by Sangeet Paul Choudary, which I read and then listened to a presentation by the author organised by the Dynamic Competition Initiative.

I liked the book because it focuses on an aspect of the impact of AI that is underemphasised in public discussion and to some extent in academic circles. That’s its likely catalysing significant organisational change. So much discussion focuses on labour market change and the specific tasks within jobs that will be automated, and how tasks will be rebundled into new jobs. This is a rich literature, flagging up the interaction between the automatability of tasks and the level of expertise required in each task. However, less attention has been paid (though this is changing) to the consequent changes in processes, work flows and business models.

The core point in Reshuffle is that understanding AI’s impacts on the economy requires thinking about tasks as nested within organisations, which in turn sit within systems of production. The focus needs to be directed towards the broader structural architecture, the book argues. It has a construct of being ‘above’ or ‘below’ the AI – I think this means having or not having agency in decision-making – with implications for distribution. “Much of the value associated with a job is not derived from the task alone but also from the system within which the task is executed.”

I wholeheartedly agree with this perspective, that value creation in organisations has an essential social dimension. Firms are more than a collection of individuals. There was years ago an excellent book making exactly this point, Chasing Stars by Boris Groysberg.

The book also majors on the way AI will unbundle some knowledge tasks from humans – often described as codifying tacit knowledge – and the consequences. Such forms of knowledge are more flexible (there is no human or long-term contractual relationship involved) and can be more easiry reproduced or rebundled. So for these reasons I like Reshuffle.

On the other hand, the author wrote it as an airport-style business book, a perfectly valid decision but irritating for me – it’s somewhat repetitive and fond of diagrams that seem less clear than the words. More irritating is the econ-bashing. Yes, economists have been focused on task-based labour market approaches, but there is now a lot of  economic research taking an institutionalist, transactions cost perspective, building on Luis Garicano’s now-classic work, and the earlier tradition of institutional economics all the way back to Coase.

Nevertheless, Reshuffle is an interesting read, with some useful insights – and can indeed be read on a flight or train ride.

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The people’s AI?

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The title of Maximilian Kasy’s new book The Means of Prediction cleverly riffs off Marx’s concept of the means of production for th age of AI. These means, Prof Kasy argues, are data, computational infrastructure, technical skills and energy. The core argument is that these means rest in few hands, so the value being created by AI is concentrated in a relatively small number of technology companies and their founders and employees. What’s more, this concentration also skews the kind of AI systems being created: AI is designed to optimise some objective function, as I argued in Cogs and Monsters (and some recent as-yet-unpublished lectures).

This makes a powerful case for democratising AI, the book argues. Unless countervailing power from workers, consumers, and politicians is brought to bear, the technology will create further inequality and will not serve the public good, only private, profit-maximising interest. Kasy convincingly argues for collective means of control, rather than just individual protections such as GDPR: “Machine learning is never about individual data points, it is always about patterns across individuals.” Control over one’s own personal data does not protect privacy as long as similar people share their data.

The book starts with a section explaining AI, followed by a section explaining how economists understand social welfare – this being the approach (as Cogs explains) that is being automated by AI. These are very clear and useful for people who are hazy about either, although as they are so introductory it did make me wonder about the target audience for the book. Having said that, there is such a lack of knowledge among the public and indeed lots of policymakers and politicians that these sections are probably sorely needed.

The final two sections go on to regulatory challenges and the need for democratising the development and use of AI. As Kasy points out, policy choices have always been choices, with winners and losers, and decisions have often involved predictions; after all this is what policy economists have been doing for decades. The increasing use of AI to make decisions automates and speeds up choices – the danger being that it does so with embedded biases and implicit decisions hidden by the technology, and the all-too-common presumption that the machine must be right.

The optimistic take on where we are is that the use of AI to make predictions and decisions will surface some of the implicit assumptions and biases, and so force more public deliberation about how our societies operate and affect different people. The pessimistic take is of course that they simply become more deeply hidden and entrenched. Depending on my mood, at present I think things could go either way. But that open prospect makes The Means of Prediction a very timely book. And – having pondered who it was aimed at – probably one that every official and politician should be made to read as they chirrup about using AI in public services.

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When a nearly 30-year old textbook is still the best on the digital economy

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There’s a gap in the market. I’ve started developing a Digital/AI economics syllabus for policy MPhil students who won’t all be economics graduates; and of course there are loads of fantastic papers and resources online.

It’s fairly straightforward to write down a list of topics – recognising that one can’t cover anything.Here are my headings for an 8 week Cambridge term – so there’s nothing on crypto for example, as I don’t know enough about it (& am still pretty sceptical). This is the first half – the second 8-week term will cover competition, regulation and trade.

  1. Basics: Information and the economy, why digital is different from the old economy
  1. Implications for organisations: Business models, platforms, ecosystems; auctions
  1. Intangible assets: IP, copyright, data (& privacy), measurement
  1. Inputs: finance, energy and materials, chips
  1. Innovation: clusters and types, direction, policies
  1. The demand side: technology diffusions, ‘free’ services, household production, open source
  1. Impact on labour: task models, AI & jobs
  1. Policies for the digital/AI economy: skills/jobs, industrial policy, digital public infrastructure, ‘the stack’ & sovereignty,

The structure might well shift as I get into details. But naturally I wondered whether somebody had done much of the work for me and written a textbook covering a lot of this. And it seems the answer is no: nobody has done an update of Carl Shapiro and Hal Varian’s fantastic 1998 Information Rules. I just re-read it and of course the examples have dated but the principles have not. The brilliant Daniel Rock told me (via BlueSky) that he still teaches using it.

I wrote Markets, State and People a few years ago as a policy economics textbook because the ones available were unsatisfactory for the course I was teaching then – none took the perspective of the basic co-ordination problems policy seeks to tackle. So maybe it’s me. But I still think there is a gap in the market for a new Information Rules – somebody could surely do this in time for its 30th anniversary?

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Conference hinterland

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I attended recently an excellent NBER workshop at Stanford on the economics of transformative AI. It’s always interesting to hear what books/cultural references people cite in addition to the usual econ papers, and it was an unusually wide range this time – reflecting both the interdisciplinary crowd and the breadth of the issues. Here they are.

Books:

Deep Thinking by Gary Kasparov

Automation and Utopia by John Danaher

The Myth of Sisyphus by Albert Camus

Philosophical Writings by Charles Sanders Peirce

The Limits of Organization by Kenneth Arrow

Brave New World by Aldous Huxley

Movies:

Perfect Days Wim Wenders

Beauty and the Beast

I *loved* Perfect Days. There’s only one of the books I haven’t read (Danaher) although I don’t claim to have got to grips at all with Peirce – on his work, I found The Metaphysical Club very informative. But participants also cited papers from computer science and sociology that I feel the need to read. Sign of a good workshop – a lot of brain space opened up.