Tag Archives: AI

Still utopian

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The month has been a bit of a blur but on one trip I read People’s Republic of Walmart by Leigh Phillips and Michal Rozworski. It has one of those long, chatty subtitles that try to save you the trouble of reading the book: How the world’s biggest corporations are laying the foundation for socialism. They wish – the authors are out of the Jacobin stable, and so explicitly (sort of) Marxist.

The premise is revisiting the socialist calculation debate, mashed up with Coase and Simon, in the light of big data and AI. So it’s similar territory to Bastani’s (2020) Fully Automated Luxury Communism. People’s Republic begins with recent examples of large coprorations planning their internal economy using digital technologies, data, and investment in logistcs – Walmart and Amazon. It expands from there to index funds and common ownership, the NHS, the space race and the examples of the Soviet Union and Allende’s Chile. The point hammered home in each chapter is that there are so many examples of managed rather than non-market exchange that technological advances mean planning (or bureaucracy) can replace markets in many more areas of the economy.

There’s certainly an interesting debate to be had about the extent to which AI can potentially substitute for prior modes of organisation, whether market arrangements or bureaucracies, by the new affordances for summarising and organising a lot of information. Henry Farrell and co-authors propose AI is indeed a new social technology.

But People’s Republic fails to distinguish between planned economies – Walmart may be described in this way, in line with Herbert Simon’s well-known observation – and actual (as opposed to ‘free’) markets. The fully free market is as mythical as the unicorn. All actual markets are structured by regulation, other government intervention, standards, customs and other institutional arrangements; but this is not ‘planning’. The book underestimates the huge amount of physical and software investment needed to make Walmart’s logistics possible; organising information is neither entirely intangible nor easy even in the big data era.

It’s also slightly weird, to me at least, to position Walmart and Amazon (even with a benign approach to human labour assumed) as demonstrations of the possibility of socialist utopia in the 21st century. Even before its ‘enshittification’ Amazon couldn’t distinguish between me buying economics books for me and young reader books for my 7 yr old grand-daughter.

I think it likely that AI will reshape economic structures, just as earlier digital technologies did, making Walmart and Amazon possible, along with many other platforms – how dramatically depends on what you think the limits of computability are. It would be nice to think governments will respond strategically in their interventions in the economy and how they structure the operation of markets, though this isn’t something I’d confidently expect. I hope the conditions of labour improve through policy and organisation, doing away with terrible, precarious jobs. But the 1920s/30s lens of (idealised) planning versus (idealised) markets won’t be the best way to analyse what happens. Having said that, this was a very jolly read, and anything debunking the myth that markets are ‘free’ is welcome.

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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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Leviathan, supersized

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My dear husband gave me a Daunts book subscription for my birthday so I get a lucky dip new paperback each month. A recent one was my colleague David Runciman’s The Handover: How We Gave Control of Our Lives to Corporations, States and AIs, first published in 2023. As David writes too many books for me to keep up with, I hadn’t already read it. The core argument is that human societies have already ceded many decision-making powers to non-human entities, namely states and corporations.

I read most of the book thinking, ‘Yes, but….’, as it’s a neat argument but not watertight. It starts with Hobbes, and the idea of non-human persons as it developed in different institutional forms. A key difference with decisions made by machine agents seems to lie in their autonomy or lack of openness to change or redress; and changing that requires them to be part of states and corporations rather than separate entities.

The book does, though, sort of acknowledge this towards the end: “If the machine decides what happens next, no matter how intelligent the process by which that choice was arrived at, the possibility of catastrophe is real, because some decisions need direct human input. It is only human beings whose intelligence is attuned to the risk of having asked the wrong question.” He goes on to link this back to the claim that the state is a ‘political machine’ or ‘artificial decision-making machine’ so there is no difference really between states and AIs – but this, again, makes the use of AIs in political domains part of the state machine.

He concludes: “For now the bigger choices is whether the artificial agency of the state is joined with human intelligence or artificial intelligence.” Will AI crowd out the humanity? Looking at the US now, this seems like a question from another era, a gentler era, though. The new regime there has merged state, corporation and AI in a behemoth that dwarves Hobbes’ Leviathan.

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