Andrew on the BEAR case. Now the obvious cavaet is Andrew may be motivated to talk the book around data needs given he is launching a reinforcement-learning data company and benefits from the view that scaling alone will not solve model weaknesses. That does not make his argument wrong.
The strongest point imo is that capability remains highly spiky outside a small number of digitally native domains, especially coding. Models can look extraordinary on well-represented benchmarks while remaining unreliable in messy, contextual workflows that require judgment, exploration and adaptation. <-- This is the whole diffusion debate as you need knowledge graphs + lots of context that lives in org culture and people's heads. Can't operationalize in a valuable way without all of that. It's know just a raw intelligence problem.
If these conditions persists, many costs currently treated as temporary may prove recurring. Labs may continually need new domain-specific datasets, evaluation environments, expert supervision and post-training work to unlock each additional capability. Frontier training then looks less like a one-time platform investment and more like a recurring competitive obligation.
This destorys the valuation case around the positive flywheel running away with it all that Dwarkesh laid out in his thought experiment. High inference gross margins on the current model do not necessarily translate into high steady-state profitability if much of that gross profit must be reinvested to prevent the product from commoditizing. This is the bear case and why people arguing speed-running to RSI is just leading to more reckless financial behavior.
Andrew’s nowhere suggests AI lacks value or dunks on Frontier labs but he's saying that useful capability may arrive more narrowly and require more bespoke, recurring investment than aggressive frontier-lab valuations assume.
Andrew Ho (@andrewho03)
I'm actually fairly bearish on frontier lab valuations. I've never seen the reasons articulated to my satisfaction, so before I go to sleep, I wanted to quickly jot down my thinking here.
The basic issue is that the labs are highly unprofitable. This may seem like a simple point, but private market valuations can be relatively irrational; however, like with $SPCX, post-IPO pricing will likely be much more punishing, especially as the standard 6-month lockup period expires and selling pressure intensifies.
Many people claim that the labs have high margins. Yet even with high margins, a valuation of $1T would be justified only if the labs were doing nothing aside from serving inference (thus reducing costs only to those relevant to inference) and posting annual revenue numbers in the $100-200 billion range assuming ~80% gross margin and a 20x earnings multiple.
This assumption is obviously not true, because the frontier labs have to continually spend money training the next generation of models. This is because of market competition from runner-up firms. For example, if OpenAI had paused model development last year, there would no longer be any point in paying GPT-5 API prices when you can just use Qwen or Kimi instead for much cheaper. Thus, the labs are forced to invest ever-increasing amounts of money in model training, in a way such that at any given point of time, the amount you're forced to invest in the next model is dramatically higher than the amount of money you're actually making, because even if your revenue goes up with higher model capabilities, so do your future training costs. This is a profoundly punishing dynamic which severely penalizes frontrunners.
(There is also a related subpoint where frontier labs claim they can distill their leading models to win out at lower intelligence levels as well. This makes no sense because the revenue numbers involved are far too low when taking into consideration the rather low margin of such inference.)
Frontier lab valuations appear largely to be based on the assumption that as you scale up, the capabilities which emerge will be sufficiently general and profound that we'll see explosive growth (epoch.ai/publications/explos…) from things akin to AI agents starting and autonomously managing entire companies of subagents. But it's not clear to me that this is the case; indeed, as I mentioned in my previous post (nitter.net/andrewho03/status/2082…), I believe that capabilities growth will be slower, spikier, and more data-limited than people currently assume. It may be the case that eventually we will see explosive growth of this nature with full automation of the economy, but at the very least my viewpoint implies much longer (multi-decade) timelines until we reach this point. It is not clear to me that the frontier labs will be able to operate unprofitably for so long, although I suppose maybe this foreshadows some sort of inevitable nationalization.
I also want to make a broader point about technological diffusion. The reason why technological diffusion is slow isn't just because, e.g., old people take a long time to learn how to use technology (although this is of course a contributing factor to some degree). In my view, it's because when a new, revolutionary technology comes along, the ways to incorporate that technology into subsequent developments are not always obvious, and in fact they cannot necessarily be arrived at through the application of pure reason. If they could be, then perhaps frontier models, at a certain point, would have a perfect understanding of how the LLM application layer should be developed, and they would then autonomously code, deploy, and sell such a layer.
But it seems more plausible to me that this diffusion is limited moreso by the hard problem of economic calculation--that is to say, the Hayekian notion through which the price system gradually promotes efficient allocation of resources and which cannot be simulated through central planning--and that even if we froze current capability levels at today's levels, it would take well over two decades to fully integrate in LLMs into our lives. Such a view is consequently rather bearish for the continued profitability of labs as it reduces their prospects for finding, say, something else comparable in profitability to coding agents, which seems to have been a somewhat lucky discovery by Anthropic to begin with. That is to say, even if you spam FDEs you aren't necessarily going to be able to just figure out the "correct" product shapes fast enough.
Overall, I don't think that people have clearly reasoned through their mental models for why lab equity should be worth as much as it currently is, and that if you actually bother to write down such a model, you may not arrive at the conclusion that you want to arrive at. This isn't to say that I don't expect AI to experience a huge (industry-wide) boom in the coming decades, but just that I'm not entirely sure I would buy OpenAI or Anthropic stock at latest valuations if I were given the opportunity to do so.
Of course, as an ex-lab employee, arguably this is talking against my own book; I should really be giving people more reasons to be bullish. But in the end, my influence is so small that it doesn't make a difference, so why not have some fun?
Link
Explosive growth from AI: A review of the arguments
Our new article explores whether deployment of advanced AI systems could lead to growth rates ten times higher than those of today’s frontier economies.
epoch.ai
— https://nitter.net/andrewho03/status/2082786931419812338#m