19 April 2023. AI | Golf

What kind of business is the AI business? // Golf is the new golf, especially when working from home

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1: What kind of business is the AI business?

I’ve been following the noise since ChatGPT was launched a few months ago, and am struck by the way that it seems to have fired up some of the old tropes about the inevitability of technology that emerged much earlier in the long digital wave. I’ve also seen some excitable chat about how AI is, somehow, going to be the backbone of the next long technology surge. Both of these assertions seem unlikely to me.

Regular readers will know that I tend to go back to Carlota Perez’ model, or heuristic, of how the long technology surges work when I’m thinking about technology, and I do this because it has been a pretty reliable model.¹

One of my deductions from using the Perez model over the last 20 years is that by the time it reaches the end of its S-curve, the external costs of the technologies are all too visible, and as a result regulators are all over it and critics get heard.

We’re seeing this with AI, or more exactly Large Learning Models (LLMs) at the moment. I’m going to pass here on the much-trumpeted letter that called for a ‘pause’ to AI development, partly because it seemed to me to be a badly thought through piece of self-interested virtue signalling.

The more recent paper from the AI Now Institute, covered in Vox, seems to be closer to the mark, partly because it was written by a couple of former Federal Trade Commission regulators who understand how regulation works. (My thanks to my former colleague Andre Furstenburg for alerting me to it.)

Part of their argument is about market power:

To build state-of-the-art AI systems, you need resources — a gargantuan trove of data, a huge amount of computing power — and only a few companies currently have those resources. These companies amass millions that they use to lobby government; they also become “too big to fail,” with even governments growing dependent on them for services... “A handful of private actors have accrued power and resources that rival nation-states while developing and evangelizing artificial intelligence as critical social infrastructure,” the report notes.

Well, you can’t have it both ways, they suggest. If it is a critical social infrastructure, then private developers need to be able to demonstrate that there are not harms built in to their development:

(T)he report’s top recommendation is to create policies that place the burden on the companies themselves to demonstrate that they’re not doing harm. Just as a drugmaker has to prove to the FDA (Food and Drug Administration) that a new medication is safe enough to go to market, tech companies should have to prove that their AI systems are safe before they’re released.

This would, for example, be one way to deal with the current set of issues around bias in AI, which, famously, Google researchers were fired for pointing out.

And the comparison with the FDA is an interesting one, since—as we can see from the current waves of redundancies—when technology surge markets get saturated (typically somewhere around the beginning of the fourth quarter of the S-curve), they stop gaining ‘free’ growth from new customers and new applications, and become more like other companies which have to worry about margins and rates of return and optimising their product portfolios.

Part of the point here is that issues like market power are being talked about quite noisily; part of the point is that mainstream coverage, such as that seen in Vox, is now as likely to be critical as positive. Again, for obvious reasons, this happens at the end of the surge, not at the beginning. (The AI Now Institute report is here.)

This is one of the reasons why AI is unlikely to drive a new generation of rapid technical innovation—there simply isn’t enough money in it. Again, Perez doesn’t quite put it like this, but at the start of each of her five surges there is a significant innovation, sometimes only seen retrospectively, that creates a new form of abundance through radical cost reduction, which then opens up a transformative new market. It’s this that attracts the finance capital in. (Think: Crompton’s Spinning Jenny, or Ford’s assembly line, or the invention of the microprocessor.)

The money in AI? Well, on this topic, Byrne Hobart’s finance newsletter The Diff had an interesting piece in its outside-the-paywall coverage back in January.

Was the market in AI applications, he asked, more like the steel industry, or more like a software application like Visual Basic? He’s not denying that AI applications are getting simpler and cheaper, and that therefore they will create new use cases. In fact that is his starting point. But:

What if they're the next steel industry? Steel is a useful and ubiquitous product; this post opening an upcoming series on the steel industry notes that "Nearly every product of industrial civilization relies on steel, either as a component or as part of the equipment used to produce it."—but that doesn’t make it a great business.

Steel is a capital-intensive, cyclical business, with high fixed costs. Workers have the ability to negotiate good wages in the upcycle. Capacity is always higher than demand, because governments see it as a strategic industry. And because of this last factor, governments will also have a view on who AI businesses can sell to or buy from (see also Huawei and TikTok). And remaining competitive involves building ever larger LLMs, drawing on the same sets of data as your competitors:

So that's the pessimistic view for investors: AI will be as important and ubiquitous as a product, like steel, but AI companies will be relatively minor players in the economy they prop up.

And then there’s the optimistic version: AI as an analogue for Visual Basic. If this seems like an odd choice:

This case is compelling because large language models are a nice natural language glue between a) software products that don't have good APIs, or b) mixed software-and-human processes that are tricky to fully automate.... The world's many companies running some form of legacy software, with idiosyncratic levels of automation and organizations partly built around where they choose to have humans in the loop, will benefit from AI tools that connect these systems together. And what most of these businesses almost certainly have in common is that they're almost certainly running Microsoft software.

This makes AI products a high value niche, where software is valuable and humans are cheaper. And it is possible to see a market here which is more attractive than trying to sell advertising off the back of some kind of enhanced search product. It’s just not the kind of market that drives the next technology surge.²

Before someone misunderstands this article, I do believe that AI will have significant social effects. Again, Perez doesn’t write about this, but there’s a period after the end of the surge when there is significant socio-economic innovation around the now mature technology: think of the development of logistics and just-in-time business models in the 1970s. I think that’s the right analogy for AI.

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2: Golf is the new golf, especially when working from home

Impossible not to be distracted by a piece (paywalled, sadly, although this might work) in the Financial Times’ Alphaville section about a stern memo from JP Morgan instructing its senior people that they need to be in the office five days a week. Obviously this makes a change from badgering the juniors. Here’s an extract:

“Our leaders play a critical role in reinforcing our culture and running our businesses... They have to be visible on the floor, they must meet with clients, they need to teach and advise, and they should always be accessible for immediate feedback and impromptu meetings.”

But what’s more interesting is the reason why JP Morgan might be being stern. It seems that in the US working from home has been accompanied by a boom in playing golf, especially during the week.

Here’s the money chart on this, comparing golf trips in August 2019 and August 2022.

(Source: Finan and Bloom, ‘How Working from Home boosted Golf’, March 2023.)

Between August 2019 and August 2022, golf trips overall are up 52%, and peak weekday golf, at 4pm on Wednesday, up almost threefold. Craig Coben, a former banker who wrote the Alphaville piece, comments:

Now of course correlation does not always imply causation, but . . . c’mon.

And to think that I’m old enough to remember the moment in the mid-teens when cycling was the new golf.

The golf research is by Alex Finan and Nicholas Bloom of Stanford University. Bloom is better known as the lead author of an influential paper that says that research productivity is declining rapidly, so I’m imagining that researching weekday golf habits is more of a hobby for him.

I enjoyed the explanation of how they did the research, taken from their presentation deck. It seems to be designed to show off the capabilities of a company called INRIX, which Finan also works for:

Use AI analysis of Satellite images to identify the locations of golf courses across the US... Applying this we identify 3,400 golf courses across the US... Use anonymized vehicle and phone GPS data... This GPS and cell phone data can indicate golf course trips... Visits defined as 2 to 6 hours within the area of the golf-course and car park – example for a typical golf course.

There’s a slide with some qualitative snippets from interviews as well, which will bring a frisson of familiarity to anyone who has ever had to throw together a consumer insights presentation for a client:

(Source: Finan and Bloom, ‘How Working from Home boosted Golf’, March 2023.)

Given that the consequences of investment banking are almost entirely harmful for the general good, I suspect we might prefer that JP Morgan’s leadership cadres were on the golf course.

(OK, they’re harmful for the general good because they are largely extractive businesses. They create unnecessary merger activity which either reduces business value (in at least 50% of cases) or increases monopoly power (the other 50%). They structure deals around levels of debt that aren’t always sustainable. And they funnel wealth up the economic ladder, which has adverse social, economic, and environmental outcomes.)

Sadly, Finan and Bloom conclude, albeit speculatively, that weekday golf-playing may be good for productivity, stern memos notwithstanding:

(I)f employees make up the time later (as in Bloom, Han and Liang 2023) then this does not reduce productivity. Indeed, national productivity during/post pandemic has been strong.

And at the same time, this is also raises the productivity of leisure services such as golf (where productivity is up 50%).

This improved usage and productivity is likely true for other leisure activities like shopping, gyms, sports and personal services. So (working from home) may be improving national productivity by using personal assets – golf course, shops, gyms, hairdressers etc – more efficiently. This size of this may also be large... these “leisure activities” are a substantial component of GDP.

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j2t#447

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1. For new readers, Perez in a single paragraph: there have been five technology surges since 1771, each lasting 50-60 years. Each surge involves an installation phase, funded by finance or investment capital; then there’s a crash; then there’s a deployment phase, funded by production capital. She doesn’t describe it like this, but market share at the time of the crash is typically somewhere between early adopter and early majority.

2. I’m on the record as saying that the next Perez surge is likely to be driven by some form of synthetic biology or some kind of transformative materials technology.