Thinking strategically about platforms

Digital platforms have been very much a focus of policy attention of late, with reports on the problems and challenges they raise published by the UK (several, including the Furman Review), Australia, Germany, European Commission & others. The platforms these various reports discuss are the big ones, and the concerns range from competition policy to employment practices to online harms.

A terrific new book by Michael Cusumano, Annabelle Gawer and Devid Yoffie, The Business of Platforms, points out though that most of the digital platforms that are not big are dead: four in five fail. The book is aimed at people running or starting platforms, offering advice on (as the subtitle puts it). “Strategy in the Age of Digital Competition, Innovation and Power).” The book very nicely links business strategy to the underlying economic characteristics of digital, and I think is probably in this respect the best tech business book since Shaprio and Varian’s (now old, 1998) Information Rules.

It starts by pointing out that there is nothing inevitable about network effects (direct and indirect) kicking in: they have to be nurtured: “Companies and governments have to make the right srategic and policy decisions in order to drive strong network effects.” These can include technical standards, for example, or ensuring competition thrives at the right times and points. The book also distinguishes between two types of platform, requiring different strategies (although there are a groing number of hybrids). Innovation platforms create value by enabling third parties to develop products or service on top of the platform, while transaction platofrms create value by matching different sides of a market.

Key challenges for all, though, involve solving the ‘chicken and egg’ problem (because different sides of the platform depend on each other) by appropriate pricing and cross-subsidy, and figuring out a business model. (And in my view the dependence of so many on advertising is a major weakness & can’t be sustained). The book uses the framework to explore the many platform failures. It also has a chapter on how non-platform incumbents can respond to the digital challenge (it’s tough…), and looks briefly at issues such as the use and governance of data, and also the importance of working with regulators rather than against them and recognizing the responsibilities that come with (market and other) power. “Every major company we cited in this book has been the subjject of government investigations, local regulatory oversight, and intense media scrutiny.”

All in all, highly recommended. If you know the economics, the case studies and management literature covered will be informative, and if you know the business details, the economic framework should be useful. I very much enjoyed reading it.

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The Technology Trap

Anybody interested in the economic impact of digital and AI, in particular on jobs, will want to read Carl Frey’s new book, The Technology Trap: Capital, Labor and Power in the Age of Automation. He is probably best known for his rather gloomy work with Michael Osborne (original pdf version here) highlighting the vulnerability of many jobs – almost half in the US – to automation in the next couple of decades. The book expands on the issues that will determine the actual outcomes, and is – as the title indicates – still quite pessimistic.

The structure of the book is historical, with sections on pre-industrial technologies, the Industrial Revolution (which saw widening inequalities), the mass production era (which reduced inequalities and created an affluent middle class), the recent polarization in the era of globalisation and digital, and future prospects. The key distinction Frey draws in between technologies which substitute for labour and those which complement it. Whereas the 19th century and the present seem to involve the replacement of people with machines, the 20th century innovations needed increasingly skilled labour to work with them.

Although I am probably not as gloomy about future prospects for work and incomes, I really enjoyed reading the book, which covers a wide range of technological applications in addition to the well-known historical examples. It leaves open two questions. One is about the present conjuncture: what explains the combination of seemingly rapid technological change and adoption with – in at least some OECD economies – very low unemployment rates? The answer might just be ‘long and variable lags’ but the question surely needs addressing.

The broader question, or set of questions, is really about the interaction between technology and labour market and other economic institutions. Although automation is likely to have the same general effects everywhere, the outcomes for workers will be refracted through very different national job markets, education systems, tax systems and so on. How much can any individual country lean successfully against the wind? Frey is not (unlike Robert Gordon) US-centric but does not get into these issues.

And beyond the response to technological change, what is it that determines the direction of technical change in the first place? The book treats the labour substitution or complementing as exogenous. But why were electric unit drives in auto plants and internal combustion engines created as complementary and yet automation in today’s car industry seems like it will substitute for labour? It seems to me this must be an institutional story too, but I don’t think it’s been told yet.

51VabazLy7L._SX327_BO1,204,203,200_The Technology Trap

 

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Digital arrivals and deaths of despair

There’s definitely a digital theme in the new crop of books arriving at Enlightenment Towers – the left hand mini-pile here.

IMG_0292On my recent trip to Washington (for a fascinating National Academies/Royal Society discussion on international co-operation on AI, culminating in this public symposium) I read the pile on the right.

The Economics of Artificial Intelligence is a terrific collection, edited by Ajay Agarwal, Josh Gans and Avi Goldfarb. It has sections on AI as a general purpose technology, jobs and inequality, regulation and the implications of machine learning for economics. The cast list of contributors is stellar. It’s far from the last word but a must-read as a starting point.

61bIH+8Vs2L._AC_UL872_QL65_The Economics of Artificial Intelligence: An Agenda (National Bureau of Economic Research Conference Report)

Tom McLeish’s The Poetry and Music of Science is a persuasive comparison between creativity in the arts and in the sciences, exploring the parallels between the creative process in music, poetry, art and fiction and the discovery process in the natural sciences. Well, I was persuaded. 51wNUley1XL._SX351_BO1,204,203,200_

The Poetry and Music of Science: Comparing Creativity in Science and Art

Matthew Desmond’s Evicted is a distressing piece of reportorial sociology (Pullitzer-winning), detailing through a handful of specific individuals in Milwaukee the reality of the human crisis and housing crisis in America. The book describes the knot of poverty, drugs, ill-health, appalling housing conditions, impossible for any individual to escape. I was shocked on my recent trip to San Francisco to see the desperate condition of its large numbers of homeless people, literally worse than I have seen anywhere in the world. The conditions described in Evicted are intolerable. I recently heard Angus Deaton talk about his and Anne Case’s work on the ‘deaths of despair’ in the US (and some foreshadowing of a similar if less pronounced pattern in UK data). Given the extreme social inequality in the US, its political disintegration is not surprising. The new Deaton Review here in the UK into inequality may uncover ominous similarities, and it would be good to know how other OECD countries compare/contrast.

41qhBahSGLL._SX323_BO1,204,203,200_Evicted: Poverty and Profit in the American City

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A digital crop

I spent the holiday weekend sitting in the sunshine reading digital economy books of varios types (in between cooking for the family and playing with the 10 week-old). First up was Steffen Mau’s The Metric Society, one of the slowly expanding genre of sociology of economic measurement books. The underlying theme is the use of metrics to quantify the qualitative, and the consequences of the appearance of objectivity: “By assigning a number to the thing observed, we take a step toward objectivizing it.” At the same time, measurement ‘disembeds’ phenomena from local context and knowledge. “Numbers not only isolate information from its original context but also place it in extended comparative contexts.” The added spice in this book is the ever-growing scope of the use of data as digitalisation marches on. And, like other similar books, The Metric Society is pretty pessimistic – this implies, it suggests, a panopticon society with entrenched structures of inequality. After all, “Categorical systems, once established, become extremely hard to overthrow.” However, I decided the power of numbers gives some reason to be cheerful. As Mau writes: “The nomination power invested in indicators, data and measurements can potentially restructure whole areas of society and impose new logics of action.” As Lenin said (quoted here): “We must carry statistics to the people and make them popular.” My new motto. While people might find an obsession with economic statistics a bit – nerdy – in fact it’s a revolutionary programme!

41sQCo09PuL._SX317_BO1,204,203,200_The Metric Society: On the Quantification of the Social

The second book was a proof copy of Democratic Capitalism at the Crossroads by Carles Boix, out next month. I probably shouldn’t give too much of a preview before its publication date, but this is about the interplay between the economics and politics of digital – as the subtitle puts it, ‘technological change and the future of politics.’ The first half of the book compares three modes of capitalism, the 19th century Manchester variety, the 20th century Detroit variety and the 21st century Silicon Valley one. The second part discusses the interaction between digital technology, especially AI, and the labour market. Quite a lot of this covers the economic literature on the issue of the skill bias of technical change, and the resorting of jobs into tasks in extended supply chains, so this is familiar territory. The polarisation of jobs and wages is linked to populist politics and the prognosis is somewhat gloomy – the author is a bit techno-determinist, taking the ‘half of all jobs’ to be taken by robots line as more of a forecast than a thought-experiment. The book ends with some rather generic recommendations – enhance skills, pay a universal basic income. I’m sure it’s right to draw the link between the economic and political polarisations, but I’m more in the territory of taxing multinationals, capping CEO pay, enforcing competition policy etc.

415Rzs1j8qL._SX327_BO1,204,203,200_Democratic Capitalism at the Crossroads

The third was How to Be Human in the Digital Economy by Nicholas Agar. It advocates ensuring there are ‘human’ jobs as more and more activities get automated – in effect, the book takes Baumol’s well-known prediction about the growing share of employment in the least productive sectors, and, labelling this the ‘social economy’, argues against seeking ever greater efficiency in these jobs. Although I agree – and hence it means interrogating what we mean by ‘productivity’ in different types of job – I found the book rather rhetorical. Eg, “AI is the digital superpower that thwarts traditional human responses to technological unemployment.” Whereas Boix has rather too many numbers and charts, Agar has too few. The latter’s suggestion for paying for the “less productive” social economy is the Lanier/Weyl data-as-labour idea, but otherwise it is not very specific about how to create the desired social economy.

51MF72+uFHL._SX336_BO1,204,203,200_How to Be Human in the Digital Economy (The MIT Press)

Anyway, it’s quite interesting to see this crop of books on AI/digital and the future of the capitalist democracies. No doubt there are many more to come.

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What counts?

After hating the book of the moment, Shoshan Zuboff’s much-praised Surveillance Capitalism, perhaps it underlines my contrariness if I tell you how much I loved my latest read, a book about classification. It was Sorting Things Out by Geoffrey Bowker and Susan Star, quite old now (1999). I can’t remember how I stumbled across it, but it absolutely speaks to my preoccupation with the fact that we see what we count & not the other way around.

The book investigates the confluence of social organisation, ethics and technologies of record-keeping as manifest in the establishment of systems of classification and standards. The examples it uses are medical systems such as diagnostic manuals, but the arguments apply more broadly. The point it makes about the role of record keeping technologies reminded me of a terrific book I read last year, Accounting for Slavery by Caitlin Rosenthal, which explored the role of commercially produced record books in the managerialism of large slave plantations in the US. The argument that a classification system lends the authority of something seemingly tehnocratic to highly political or ethical choices echoes Tom Stapleford’s wonderful book The Cost of Living in America.

As Bowker and Star point out, classification systems shape people’s behaviour. They come to seem like natural rather than constructed objects. They also fix perceptions of social relations, as a classification framework or set of standards, “[M]akes a certain set of discoveries, which validate its own framewor, much more likely than an alternative set outside the framework.” To switch frameworks requires overcoming a bootstrapping problem – you can’t demonstrate that a new one is superior because you don’t yet have the units of data on which it relies. People can’t see what they take for granted until there is an alternative version not taking the same things for granted.

And, although this book was written early in the internet era, the authors note that “Software is frozen organisational and policy discourse” – as we are learning with the burgeoning debate about algorithmic accountability. The essential ambiguity of politics is impossible to embed in code. The big data and AI era will force some of the fudged issues into the open.

 

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