Tag Archives: review

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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Great power transitions and the role of technology

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I have mixed feelings about Jeffrey Ding’s Technology and the Rise of Great Powers. On the one hand, it’s an interesting and persuasive hypothesis. He argues that great power transitions such as from the UK to the US around the turn of the 20th century are driven not by the new great power commanding the technological frontier but rather by the extent to which new general purpose technologies have diffused across the whole economy. He contrasts this with the idea – apparently dominant in political science – that it is the country with control of the leading sectors that predominates.

Thus for example it was the US, not Germany, which overtook Britain because Germany commanded the chemistry frontier but the US educated chemical engineers en masse and the new chemical-enabled manufacturing techniques permeated widely. The book looks at the first Industrial Revolution – the UK’s emergence as dominant after the earlier reign of the Netherlands – as well as this 2nd industrial revolution; and also at the failure of Japan to take over from the US in the late 20th century. The book focuses on the importance of developing skills institutions that enable widespread uptake, citing for example the Mechanics’ Institutes in 19th century Britain and the spread of engineering through universities in 20th century America.

This analysis is backed up by detailed case studies – very interesting – as well as empirical work. While economists will characterise transitions involving new general purpose technologies as involving both a period of leading sector change and then diffusion across the economy, it seems very plausible to me that geopolitical transitions depend on the latter. Military and strategic strength depend on robust engineering and production capabilities; leading edge R&D is necessary but not sufficient.

The ‘other hand’ is the writing style. The text is rather repetitive and written in academic-speak. I guess the book is based on the author’s PhD dissertation, but it would have benefited from a rewrite in order not to read like a series of academic journal articles. This is a bit of a shame, as of course the argument is relevant to the relative roles of the US and China now. The book was written before the US started shooting itself in all the feet it could find in terms of sustained technical and economic progress; but in any case the author recommends the US switch its focus to developing the broad skill base needed to enable AI use across the economy if it’s serious about winning the geopolitical contest.

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The end of progress?

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Carl Frey’s How Progress Ends: Technology, Innovation and the Fate of Nations is the kind of book that is exactly my cup of tea. I did have the opportunity to read it before publication and part of my blurb comment was: “How Progress Ends is a fascinating account of the way culture and institutions interact with new technologies.” The major part of the book consists of the history of some major technological advances along with some significant innovations in societal models (the American Revolution, Soviet central plannng) exploring exactly this interaction.

The thing that makes institutions and culture matter is that general purpose technologies – printing, steam, electricity, telecommunications – is their disruptive character. The affordances of the technologies enable challenges to the established economic or political order. Sometimes the incumbents can resist successfully – as in China’s ‘reversal of fortune’ following the formation of the Qing dynasty, or in the Soviet elite’s resistance to reform until it was too late. Sometimes the character of technology means political competition enables it to advance faster than if there were political centralisation – and sometimes the other way round.

States can therefore play a decisive role in whether their societies experience and (eventually) benefit from technological progress. The book ends with some reflections about the present. Frey is pessimistic about both the US and China (a bit of an echo of Dan Wang here). In the US he sees the incumbent AI companies and their relationship with the government as freezing out innovation: “Reaping the benefits of technological change requires institutional support to make space for exploration.” In China he sees future innovation as falling victim to cronyism and the assertion of control by the central government. “The decline of either China or the United States is by no means inevitable,” he writes, although one senses he thinks it is.

Who knows. What does seem clear is that the path taken by technology cannot be divorced from the politics, which is highly uncertain everywhere. The historical lessons are well worth pondering. How Progress Ends is well worth reading alongside for example Carlotta Perez (Technological Revolutions and Financial Capital) and Bill Janeway (Doing Capitalism in the Innovation Economy) to reflect on the current moment.

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Building vs stopping

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I read Dan Wang’s Breakneck: China’s Quest to Engineer the Future, which some people I follow (like Henry Farrell and Richard Jones among others) have already reviewed. It is,  as they have said, a very interesting read – much more about China than about America, although the contrast he paints between the two is clear. The core of that contrast is that China’s leadership consists of engineers, and they focus on getting things built, while America’s consist of lawyers, who focus on stopping things.

However, the point Wang makes is subtler than ‘engineering good, lawyering bad’, although China’s success in building infrastructure and developing manufacturing industry has delivered astonishing increases in living standards. For, as a couple of chapters emphasise, the engineering mindset can go badly awry when applied to social issues – the one child policy and the covid lockdowns are the examples.

Nevertheless, there are lessons for the US (and the rest) in China’s approach. He writes: “[China] embraced a vision of technology radically different from Silicon Valley’s: the pursuit of physical and industrial technologies rather than virtual ones like social media or e-commerce platforms. In China. technology is not represented by shiny objects; rather, it is embodied by communities of engineering practice like Shenzen, where technoogy lives inside the heads and hands of the workforce.” This seems spot on in identifying a relative weakness of the US and UK models, both of which overlooked the importance of keeping those communities rooted at home. One of my favourite papers is Gregory Tassey’s in the 2014 Journal of Economic Perspectives, making this point. It also reminded me of Dan Breznitz’s point about the different pathways for industrial policy, and Silicon Valley not being the obvious model to emulate.

Another lesson for both countries lies in their contrasting but equally unappealing urban forms – China with its soul-less new mega-block developments and America with its bland suburbs. Neither seems to manage dense, walkable, attractive neighbourhoods on the whole – in China, Wang lived in the attractive French Concession area of Shanghai, an exception to the rule. Here is one dimension where many European countries do better than either superpower, as Chris Arnade often points out.

My other reflection on Breakout – which is a must-read – is that it already seems dated by recent developments in US politics. There are now many things the lawyers are not stopping, although still much that the engineers are not doing either. In the long run the lessons of both countries’ recent past will be relevant for economic growth and technological advance, but the short run seems much murkier than it did even a few months ago.

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