It is summer in the Northern Hemisphere, and we are attempting an experiment that may not please everyone. With fewer eyes during the peak holiday period and a diminished appetite for the usual bullish narratives in these bearish weeks, we risk a discussion of the contradictions between the beliefs people hold and the evidence sitting in front of them.
There was a time when the emerging market equity industry had a popular bellwether chart that placed US BBB+ bond spreads on one axis against an EM equity index on the other. For many, the know-all credit market direction was the primary thing to decide for investments in the developing world. The chart was flawed from birth. Bond spreads are bounded. An equity index, in the long run, is not. Neither the numerous times when the chart did not work nor its theoretical impurity ever deterred the followers. We confess we admired its tidiness ourselves at times, although by now it is largely relegated to footnotes. This is not history bashing, of which we have done plenty elsewhere. We simply note how comfortably two inconsistent ideas can share a desk.
They still do. So many of us now worry that memory demand may peak soon, and we do this worrying while waiting minutes for our models to finish an answer. The waits are the early DOS and Windows era all over again, the hourglass on the screen quietly announcing how much more computing the world still has to build and improve before one cries saturation or excess supply. The rest of this letter is a walk through such popular pairings, held with a straight face, by intelligent people, all over the investment world.
The library closed. The writing improved.
Our umpteenth repeat: LLMs are heuristical. There is no human logic that says where they are headed, any more than there is in quantum physics or in the study of DNA. Still, two camps formed early. One we file, loosely, under the scaling law: the belief, rarely stated so bluntly, that as humanity runs out of fresh high-quality data to feed the machines, the models will stop improving. The other camp, of “singularity,” is decades older than the technology and was never written with it in mind: intelligence, once it begins building on itself, keeps building, until it runs away. The two beliefs were always in conflict, though remarkably so many believed in both.
The first camp had its moment in December 2024, when one of the field’s most celebrated researchers announced from a conference stage that peak data had arrived, comparing data to fossil fuel. The forecasters penciled the exhaustion window to begin around 2026. The window arrived on schedule. The models did not notice. The tasks the best systems can finish unaided have stretched from minutes of human work to most of a working day, and the doubling keeps compressing. The fuel, meanwhile, is increasingly home-brewed: One Chinese laboratory disclosed that its latest model practiced inside more than 1,800 artificial environments of its own, the way a chess player improves by playing herself.
A human analogy explains what the arithmetic cannot. A young person who reads exhaustively eventually meets nearly every word he will ever use, and one more dictionary adds nothing. Yet nobody concludes that his intelligence has peaked. Beyond a point, internal regurgitation – or call it creativity – will make him write like Dickens one day. Something similar appears to be under way with the models. They generate their own material, grade their own homework, and improve on the results. Call it synthetic data or self-play; the label matters less than the fact.
The early fear deserves a respectful burial. Serious people once warned that machines trained on machine output would degrade, hallucinate more, and drift into bias, the photocopy of a photocopy fading toward gray. The observed record runs the other way: each generation hallucinates less than the last, not more. And, so far, with each generation of synthetic data, the models keep getting better.
No celebrated theory is given a quick burial; expect some theoreticians to keep flogging the scaling law – which, by the way, has utility in specific circumstances, just like the statistical parrot or run-away-bias tendency followers. Meanwhile, as we finish drafting the section, another model is likely to have been released with more extraordinary features.
Somebody has to own the machines
Imagine living among calculators when the spreadsheet arrives. It quickly becomes clear that the new tool wants a different machine, and for years afterward the story of that machine is penetration, not saturation. Every year more desks get one; every year somebody declares the last desk reached. The same film is running again. The hardware the new models need is very different; most of the world accepted only recently that AI is not a hype or about to stop improving on some scaling law. These are early chapters wearing a late-chapter costume.
The twist last time, during the mainframe era, was that the new hardware was personal. Now, the new hardware is collective. Almost no company, and certainly no individual, can buy a useful share of it alone. Somebody must own the machines and lease out their use, and that somebody collects the rent of the era. The entire buildout, stripped of its vocabulary, is a leasing business for cutting-edge hardware, and the tenants are standing in the lobby.
Every lessor of this hardware business keeps repeating how they are unable to meet the demand. To the armchair pessimist, the announced investment numbers have gone up multiple times in three years since they have been forecasting a bust, and every demand argument appears flaky. The hyperscalers and other infrastructure builders are putting money where their beliefs are in the face of demand evidence they have not refuted and cannot refute.
The fear of a competitive rat race has validity. For every major technology player, here is a new business with massive long-term potential that also carries the risk of overinvestment. Yet the parallels with earlier technology cycles are instructive. Just as the Bank of Japan's monetary experiment showed how long-held assumptions can be upended, the AI buildout may defy the saturation forecasts that have been wrong so far.
In Asia, the implications are particularly acute. Chinese laboratories are already pioneering synthetic data approaches, while Japanese and South Korean semiconductor firms are racing to supply the specialized chips these models require. The region's technology giants are placing their bets, aware that the fog of contradictory beliefs will eventually lift – but not before separating those who placed the right wagers from those who did not.
