My husband has written a terrific book

When Rory was writing Always On: Hope & Fear in the Social Smartphone Era I was firmly shut out of the process. He required none of my constructive criticism, just lots of cups of tea and the occasional, “How marvellous, darling.” Well, the book is out in a couple of weeks, the copies arrived, and I’ve been allowed to read it. And it’s terrific – a front row view of the key developments, the business of tech, and the social trends for good and ill this technology (only with us for relatively few years) has unleashed.

Rory & I recently celebrated our 31st wedding anniversary, so I lived through all these stories. I well remember the holiday in Mallorca almost torpedoed by his obsesssive phone calls; the embarrassing Google Glass episode; the endless new gadgets; and since last March as we’ve all been working from home have been able to hear most of the BBC tech team’s online editorial meetings. “Leo again!” we chorus when the phone rings at 7.45 am, or over lunch. Anyway, I didn’t need the book to know the ins and outs. It’s still a terrific read. Do buy it! It will make him happy and bring great cheer to the family dining table.

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Baumol meets Marx

I read Jason Smith’s Smart Machines and Service Work: Automation in an Age of Stagnation because there was a positive discussion of it on Twitter. I’d describe it as a mash-up of Baumol (‘cost disease’) and Marx (‘exploitation’).

The first part of the book is a rant about technology and why today’s tech will not increase productivity. It channels Robert Gordon and criticises economists like Erik Brynjolfsson (or before him Paul David) for arguing there are delays between innovation and the productivity effects they produce.

I have the same problem with this as with Gordon’s magnum opus: it might turn out to be correct that today’s techs have no productivity impact, but focusing only on digital entertainment and communication devices is completely unpersuasive. Vaccines, hello? The wave of biomedical innovation like the development of mRNA vaccines has rested on the plunging cost of gene sequencing, enabled by computation applied to massive amounts of data. Lab benches, test tubes, and also computers. The transition to green energy supply will require large-scale computation to manage storage, networks and grids. Additive manufacturing has many potential applications including printing organs and tissues. These applications are genuinely slow to emerge: large additional investments in equipment are needed, the organisational and ethical hurdles are high, other discoveries might be required to make them economically viable. We’re lucky so much of the prior mRNA research had been done before 2020.

Anyway, the book halfway through then turns to the growth of the service sector, the automation of routine tasks, and the debate about the potential impact on jobs. It looks back, too, at the well-known decline in middle-income jobs and growth of the contingent workforce. Having introduced Baumol’s familiar ‘cost disease’, it then turns to a Marxist analysis. Having never learned Marxist economics I found this quite interesting but heavy going, as it has its own jargon. Still, it is surely right to consider the impact of automation in the context of power struggles, or class conflict.

The book has some sections where it pauses to ask what is actually meant by ‘productivity’, a question of evergreen interest to me. It touches here on the issue of time use and time saving in services, and on activities crossing the production boundary, making it hard to measure ‘true’ productivity. As it points out, many previously household (uncounted) activities became marketed during the 20th century (‘commoditised’), and are often low-pay and precarious. However, the book then veers back to the more abstract class struggle.

All in all, I found the book quite interesting for its novel (to me) perspective, and it is well written. But much of the (non-Marxist) economic literature it draws on will be familiar to many people enticed by the subject matter. What it adds to the technology debate is, quite rightly, the issues of power and deregulation of the labour market,  beyond discussions of gig platforms. But it didn’t tell me anything new about the productivity puzzle.

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Where *is* the internet?

I’m not sure ‘read’ is the right verb for Networks of New York by Ingrid Burrington, but I looked at it this week. It’s a sketchbook of various bits of internet kit from cables below ground to antennae on rooftops, with notes about the physical kit that makes up the system getting people online, and about the ownership of the kit. I’ve been growing  slightly obsessed by how little is known about the physical internet. So far, James Ball’s The System and Andrew Blum’s Tubes are the only entries on my list of books about this. I was pleased to find out about Ingrid Burrington, who has a number of articles on this and related subjects. But if anybody knows of other books or papers about the physical infrastructure of the internet and the ownership of it (especially for the UK) I’d love to know.

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Big Tech – the contrarian view

There’s a torrent of material to read about competition (lack of) in digital markets, which of course goes much wider than economics and law. Indeed, I’ve contributed to the many pages written, including in the form of being a member of the Furman Review team. The general theme is that Big Tech does indeed pose a challenge for competition policy, with the individual conclusions running from ‘some adjustment within current framework is needed’, all the way to ‘destroy them’.

What makes Big Tech and the Digital Economy by legal scholar Nicolas Petit so refreshing is its absolutely contrarian perspective. He has coined the phrase ‘moligopoly’ to describe Big Tech, and argues that while there has certainly been increasing competition in digital markets, there is also vigorous competition unremarked by all the commentators. I’d describe what he calls competition as oligopolistic rivalry, but the book does document the ways in which the GAFA and others compete with each other.

Some of the emprical evidence is rather interesting. For example, the book looks at what the big companies describe as the major risks facing them in their SEC filings and all but Facebook claim competition is their 1st or 2nd biggest threat – they would say that of course, but it intrigues me that Facebook doesn’t bother (number 4 or 5 in its ranking). The others do all see each other as their main rivals. Among the book’s other evidence is their high rate of spending on R&D – but I’d like to know about what it is they’re researching, though.

The ultimate question is not about current competitors, however, but about potential competitors. If you believe digital markets tend to winner-takes-all because of network effects, and you can live with concentration because of the large consumer benefits, then what matters is whether new rivals with great technology and products can take the current Big Tech markets. In that case, moligopolistic rivalry along various dimensions is not only fine but anyway inevitable.

The book didn’t win me over in the sense that I concluded there is no reason to be concerned about digital competition. Without intervention, it’s hard to see anybody rivalling Google in search, or Apple and Android in mobile operating systems. To be fair, the author doesn’t argue that there’s no cause for concern, quite. He is issuing a useful warning that we should think carefully and in detail about what harms we believe Big Tech is causing. This book is a distinctive corrective against the current tendency toward groupthink on this subject. As we said right at the start of the Furman Review, Big Tech has brought many benefits, and there is growing evidence about how much people value its products. Anyone certain they know Big Tech needs fixing should read this more nuanced argument with an open mind.

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Are humans or computers more reasonable?

This essay, The Long History of Algorithmic Fairness, sent me to some of the new-to-me references, among them How Reason Almost Lost its Mind by Paul Erickson and five other authors. The book is the collective output of a six-week stint in 2010 at the Max Planck Institute for the History of Science in Berlin. That alone endeared it to me  – just imagine being able to spend six weeks Abroad. And in Berlin, which was indeed my last trip Abroad in the brief period in September 2020 when travel was possible again. I started the book with some trepidation as collectives of academics aren’t known for crisp writing, but it’s actually very well written. I suspect this is a positive side-effect of interdisciplinarity: the way to learn each other’s disciplinary language is to be as clear as possible.

The book is very interesting, tracing the status of ‘rationality’ in the sense of logical or algorithmic reasoning, from the low status of human ‘computers’ (generally poorly-paid women) in the early part of the 20th century, to the high status of Cold War experts devising game theory and building ‘computers’, to the contestation about the meaning of rationality in more recent times: is it logical calculation, or is it what Herbert Simon called ‘procedural rationality’? This is a debate most recently manifested in the debate between the Kahneman/Tversky representation of human decision-making as ‘biased’ (as compared with the logical ideal) and the Gerd Gigerenzer argument that heuristics are a rational use of constrained mental resources.

How Reason… concludes, “The contemporary equivalents of Life and Business Week no longer feature admiring portraits of ‘action intellectuals’ or ‘Pentagon planners’, although these types are alive and well.” The arc of status is bending down again, although arguably it’s machine learning and AI – ur-rational calculators – rather than other types of humans gaining the top dog slot nowadays. As I’ve written in the economic methodology context, it’s odd that computers and also creatures from rats to pigeons to fungi are seen as rational calculators whereas humans are irrational.

Anyway, the book is mainly about the Cold War and how the technocrats reasoned about the existentially lethal game in which they were participants, and has lots of fascinating detail (and photos) about the period. From Schelling and Simon to the influence of operations research (my first micro textbook was Will Baumol’s Economic Theory and Operations Analysis) and shadow prices in economic allocation, the impact on economics was immense. (Philip Mirowski’s Machine Dreams covers some of that territory too, although I found it rather tendentious when I read it a while ago.) I’m interested in thinking about the implications of the use of AI for policy and in policy, and as it embeds a specific kind of calculating reason, thought How Reason Almost Lost its Mind was a very useful read.

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