🔮✨ Should AI be left to the market? Rights for robots. Measuring productivity & how to treat data. Prime number magic ++ #161

Azeem's welcome note

This week's EV is guest-curated by Diane Coyle.

I’ve been reading Diane's columns and books about economics for a number of years, so I was delighted to discover she was a reader of Exponential View. She’s had a significant impact on the public understanding of economics, with a long-standing interest in the nature of digital economy. She was recently appointed Professor of Public Policy at Cambridge University.

Please enjoy her Exponential View.

About Diane

Diane Coyle is the Bennett Professor of Public Policy at the University of Cambridge (in fact, this week was the formal launch of the Bennett Institute of Public Policy at the University of Cambridge). She was previously Professor of Economics at the University of Manchester, and prior to that held a number of roles including Vice Chair of the BBC Trust, a member of the Competition Commission and of the Migration Advisory Committee. She has been researching the economics of digital technology since her first book The Weightless World (1997). Her latest book was GDP: A Brief But Affectionate History. Her current research concerns the implications of technology for measuring and understanding the economy, in the ‘Measuring the Modern Economy’ programme of the Economic Statistics Centre of Excellence (ESCoE).

Dept of the near future

⚡ Interesting report by mathematician and French En Marche MP, Cedric Villani, on how to make AI work for citizens. “Artificial intelligence is one of the keys to power in tomorrow’s digital world.,” it states. “Because of this, collectively addressing this issue is in the general interest” - and not to be left to market forces alone.

🤖 European lawmakers are exploring the issue of whether to grant robots “electronic personalities”. More than 150 AI experts, including law professors, CEOs and computer scientists warn against it. Doesn’t this confirm granting corporations legal personhood wasn’t a great idea?

🤓 Economists get exasperated by frequent criticisms of economics by people who manifestly have no idea what most economics consists of. Maybe we haven’t spent enough time explaining our work, although there’s now a Twitter hashtag #whateconomistsreallydo to try to challenge the misperceptions. It isn’t that there’s nothing to criticise, but I’d put lack of diversity in the profession near the top of my list. Here three brilliant Stanford economists discuss their work.

💯 A must-read article about how to communicate knowledge. Not by publishing conventional academic papers, for sure.

Dept of productivity in the digital age

One of the questions many economists are tussling with at present is why measured productivity in most advanced economies is flat when the impact of technology on economic and social life is plain to see. This is one of the questions motivating the ESCoE programme in the UK - maybe businesses are investing in the tech but we’re just not measuring it properly?

Scarcely a day goes by without some interesting new research on this question - and its counterpart, which is what will happen to jobs and incomes when new technology does start raising productivity.

Bank of England economists found the slowdown in productivity growth has been most pronounced among the most productive firms, which makes the puzzle even more puzzling. 🤔

In a recent report, the OECD debunked earlier headline-grabbing predictions that automation will destroy half of all jobs. (Summary here.)

While even a lesser amount of job turnover will be painful, history offers some nuanced lessons about the effects of technological change on work and incomes. This recent paper tracked the spread of the use of steam engines across France to document the way the high tech of its day raised the demand for skills and contributed to lifelong learning.

There has been a long debate among economic historians about when the Industrial Revolution started to raise living standards - early on, or not until the mid to late 19th century? The latest round goes to those arguing that people saw early benefits from industrial growth. As Dietz Vollrath notes in discussing the results this evidence supports the idea that high labour costs drive technology adoption and hence productivity.

It’s never plain sailing, though. Analysts think over-automation is the cause of problems at Tesla.

Dept of regulation in the digital age

Many people have noted Mark Zuckerberg’s frequent use of the reply “We’ll get back to you,” during his marathon Congressional committee hearings this past week. Ben Thompson pointed out on the Stratechery blog how open he seemed to be to the idea of regulation - and how unsurprising this is when you recognise that big incumbent firms often like regulation as a barrier to keep put newcomers.

Even before the latest privacy scandal engulfed Facebook, and set people questioning the entire business model of data-devouring digital giants, there had been a rising tide of concern about the market dominance of a few companies. Some question whether conventional policy tools can work at all: in a widely-cited article, Lina Khan, Director of Legal Policy at Open Markets Institute, argued that standard competition policy was not up to the task of policing the economic power of the GAFAM group (Google, Amazon, Facebook, Apple, Microsoft). But Columbia law professor Tim Wu, net neutrality advocate and antitrust expert, argues that competition can effectively topple Facebook. I agree with him - here’s my new working paper on how to update competition policy for the era of digital winner-takes-all markets.

This doesn’t mean governments shouldn’t bother to update other forms of regulation. For instance, peer-to-peer matching markets (the ‘sharing economy’) has been growing rapidly. There’s a lively debate about how to update economic regulation to guard against tax non-compliance and exploitative use while harnessing all the benefits, enabling people to earn money from under-used assets and giving users a wide choice at lower prices. Online matching platforms essentially came about because of the combination of the area of economics called market design, algorithms and smartphones. They have tremendous potential to enable the more efficient use of assets and the better matching of supply and demand  - including some surprising examples. And the model is widely expected to continue to grow.

However, as this VoxEU column describes, there is a huge range of issues policymakers need to consider.

At present, there is little consensus about the extent to which new regulation is needed, or better enforcement of existing regulation, or whether in fact, most platforms self-regulate effectively. Nor does any country have good statistics yet on the extent of their activity, how many people are involved, what prices people pay, or any of the other essential economic statistics. I’m mid-way through a project looking at UK sharing economy platforms to understand what statistics are needed to capture their economic impact - and enable better regulation.

The focus on Facebook has made people aware of just how much information the company has been gathering - described here for instance. So not surprisingly, one regulatory step many people agree on is the need for people to own their own data. Evgeny Morozov made this argument.

Some economists were already making the case that online companies should pay people for their work in creating personal data - that we should treat data as labour.

It’s an interesting idea, and for sure people should have more control over their data, but it is an individual solution to a collective problem. The real value lies in the aggregation of data. I’m more interested in whether there’s scope for a data commons. These are taking off in science research communities.

A new research paper suggests data sharing may be key to future innovation?

Interestingly, the Villani report (above) flags the issue too: “From the point of view of developments in artificial intelligence, we could even simply ask ourselves whether the concept of personal data still has any real meaning.”

Short morsels to appear smart at dinner parties

The surprising origin of price tags. 🏷️

Puerto Rico is privatising the collection of official economic statistics, in an act of complete folly.

What do the symbols painted on container ships mean? There is a rich language of signs few people know. ⚓

Prime numbers still fascinate mathematicians, 2300 years later.

End note

Please take a moment to thank Diane for doing a phenomenal job covering productivity and regulation in this week's _EV—_the best is to tweet, but forwarding the issue to a friend also means a lot.

In other news, I'm running a stream called The Cutting Edge, at the upcoming CogX event in London (June 11-12). This is a stream which will look at the key areas in the development of AI and machine intelligence over the next 3-5 years. We're looking at topics like: future of processors, data vs. algorithms, frontier algorithmic research; problems such as prediction, decision-making, SLAM and location; future of voice assistants, UX in the AI age, as well as areas like synthetic biology, future of energy and the like.

The schedule is nearly locked down but if you are interested in being on a panel or giving a talk, please let Marija know.

We also have a special discount for you. Enter code 25EV37d8 atcheckout for 25% off.

Have a great Sunday,

P.S. Thanks to the amazing founders and investors who have been in touch with their breakthrough businesses. As I mentioned, I'm investing more actively and interested in seeing AI firms at the early stage (Seed / Series A) and more mature stage (Series B). Do drop me a line.