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Roula Khalaf, Editor of the FT, selects her favourite stories in this weekly newsletter.
The writer is co-founder and co-chair of Oaktree Capital Management and author of ‘Mastering the Market Cycle: Getting the Odds on Your Side’
My recent exploration into large language models has taught me not to think of an AI model as a search engine that retrieves data and regurgitates it. Rather, it’s a computer system that is capable of synthesising data and reasoning from it.
Sceptics question whether AI can ‘‘think’’ for itself, given that it ultimately relies on reconfiguring what people have already figured out and applying it to new data and other fields. But isn’t that also how the human brain expands its capabilities? Is AI’s way of growing, learning and “thinking” really different from ours? In terms of economic output, this philosophical distinction may not even be relevant.
The pace at which developments in AI are occurring is unlike anything we’ve seen before, and this has implications that have never existed. In the past, infrastructure was built for a new technology and it often took years for that infrastructure to be fully employed. Demand for AI already exists and is growing rapidly, and I’m told it’s supply constrained. AI also has an element of autonomy that no other technology has ever had. I believe AI will take on tasks we didn’t imagine it doing, and perhaps even tasks that didn’t exist before AI dreamt them up.
The coding-model business has been growing at warp speed for a year or two. So why didn’t investors recognise and price in AI’s potential to impact the software industry prior to the sector’s recent stock market rout? This question highlights humans’ recurring failure to incorporate new information into their thinking, perhaps because of things like cognitive dissonance or other biases. And it hints at implications of AI for the investment process.
AI has the ability to absorb more data than any investor, remember it better and do a superior job of recognising the past patterns that preceded success. On the other hand, it’s missing a few things. Great investors are much more than fast, unemotional processors of data: they deal with novel developments, make subjective decisions and apply intuition. Because a lot of the investing process comes down to speculation, and because of AI’s less-than-total reliability, I think it’s unlikely that AI will be infallible as an investor.
Now, moving on to the big question everyone wants answered: is AI a bubble? I can say with conviction that the technology itself is a very real thing, with the potential to vastly alter the business world and change much of life as we know it. Also, the technology is already in demand and being applied on a large scale.
The question remains whether the magnitude of spending on AI infrastructure is excessive, and it requires more discussion than I can cram into this column. It’s important to note that more money is going into so-called inference capex to deploy models rather than train them. Whereas training capex was speculative — undertaken to build AI models — inference capex is taking place in response to actual demand for AI capacity. This demand is already translating into massive revenue growth, validating the capex.
Some AI revenue is currently “circular” in nature, derived from AI companies buying from each other. The chain of revenue has to ultimately rest on end users paying for real economic value, and while that’s increasingly the case, the question of how much revenue is circular remains an open one.
That just leaves the small matter of assessing the appropriateness of the prices of AI assets.
It’s worth making a quick distinction here. The so-called hyperscalers, for whom AI is one important part of a great business, may be overvalued or undervalued, but it’s unlikely that today’s prices for enormously profitable companies such as Microsoft, Amazon and Google are going to turn out to have been ruinously excessive. On the other hand, the start-ups to which multi-billion-dollar valuations are being assigned — some of which have yet to describe their strategies or announce products — can only be viewed as lottery tickets. Most people who participate in lotteries end up with worthless tickets, but the few winners get very rich.
If I had to guess, I’d say AI’s potential is more likely underestimated today rather than overestimated. However, that’s not the same as saying AI investments are on the bargain counter or even fairly priced. Thus, I’ll end with some advice that may seem typical for me: a moderate position, applied with selectivity and prudence, seems like the best approach.
