This is .. surprisingly good tech and strategy analysis for free content on the internet, thank you.<p>A couple of thoughts — as you note hard infra / training investment has slowed in the last two years. I don’t think this is surprising, although as you say, it may be a market failure. Instead, I’d say it’s circumstance + pattern recognition + SamA’s success.<p>We had the bulk of model training fundraising done in the last vestiges of ZIRP, at least from funds raised with ZIRP money, and it was clear from OpenAI’s trajectory and financing that it was going to be EXXXPPPENSIVE. There just aren’t that many companies that will slap down $11bn for training and data center buildout — this is out of the scale of Venture finance by any name or concept.<p>We than had two eras of strategy assessment: first — infrastructure plays can make monopolies. We got (in the US) two “new firm” trial investments here — OpenAI, and ex-OpenAI Anthropic. We also got at least Google working privately.<p>Then, we had “there is no moat” as an email come out, along with Stanford’s (I believe Alpaca? Precursor to llama) and a surge in interest and knowledge that small datasets pulled out of GPT 3/3.5/(4?) could very efficiently train contender models and small models to start doing tasks.<p>So, we had a few lucky firms get in while the getting was good for finance, and then we had a spectacularly bad time for new entrants: super high interest rates (comparatively) -> smaller funds -> massive lead by a leader that also weirdly looked like it could be stolen for $5k in API calls -> pattern recognition that our infrastructure period is over for now until there’s some disruption -> no venture finance.<p>I think we could call out that it’s remarkable, interesting and foresighted that Zuck chose this moment to plow billions into building an open model, and it seems like that may pay off for Meta — it’s a sort of half step ahead of the next gen tech in training know how and iron and a fast follower to Anthropic and OpenAI.<p>I disagree with your analysis on inference, though. Stepping back a level from the trees of raw tokens available to the forest of “do I have enough inference on what I want inferred at a speed that I want right now?” The answer is absolutely not, by probably two orders of magnitude. With the current rise of using inference to improve training, we’re likely heading into a new era of thinking about how models work and improving them. The end-to-end agent approach you mention is a perfect example. These queries take a long time to generate, in the ten minute range often, from OpenAI. When they’re under a second, Jevon’s paradox seems likely to make me want to issue like ten of them to compare / use as a “meta agent”.. Combined with the massive utility of expanded context and the very real scaling problems with expanding attention into the millions of tokens range, and we have a ways to go here.<p>Thanks again, appreciated the analysis!