@adlrocha - The Model Race Hangover
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@adlrocha - The Model Race Hangover<br>A round-up of my recent theses, and a wrap for the season.<br>adlrocha<br>Jul 19, 2026
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I gave a talk earlier this year at CONVEX, in Madrid, in an actual cinema. The talk was my attempt to put everything I’ve been circling in this newsletter for the past few months into a single forty-minute argument about local AI , why the company everyone wrote off might win the infrastructure war, and why I think we should all be quietly building our own way out.<br>This is me rounding off the local-AI series (and “the writing season”) with it. No new arguments this week. This talk was the perfect excuse to try and put all the posts I’ve been exploring on the topic in the same place..<br>That talk happened in mid-June, so I could have posted at any point since then, but this week made the perfect timing for it. On Friday Apple briefly overtook Nvidia as the most valuable company in the world (again) for a few hours. Apple was the most valuable company in the world already a while ago, but with the advent of AI, many were seeing a loss in their lack of a frontier model. But think about who Apple passed to get there. Nvidia sells the shovels. And right behind them, Amazon, Microsoft, Alphabet and Meta are about to spend a combined $725 billion on AI infrastructure this year, up 77% on last year: roughly $200bn from Amazon, $190bn from Microsoft, $185bn from Alphabet, $125bn or more from Meta. Apple is spending a rounding error next to any of them, and for one afternoon it was worth more than all of them.
This same week, Moonshot shipped Kimi K3, a 2.8-trillion-parameter open-weight model that can be considered Fable-level, with its full weights out under an MIT-ish licence within the week (I can’t wait to get my hands into one of those GGUF, even if I can’t run it). So on the same day open-source got their first frontier-level model , the market rewarded the company that decided to sit out of the model race. As I mentioned a few times in this newsletter, intelligence is clearly commoditising.
My CONVEX talk
I received the invitation to talk at CONVEX in May, and since then I think I have had to update the slides at least five times. Things were changing every week, and every version went stale the day I finished it. I’ve spent two years telling myself not to write about anything trendy for exactly this reason, and then went and built a whole talk on the trendiest thing there is (never trust your own advice). My thinking has moved on since (so apologies in advance for potential inconsistencies), but the shape of it still holds better than any single post of mine does . If you’ve only got the patience for one thing from me this year, this is probably the one.
“Model Race Hangover: Why the AI Loser May Win the Infrastructure War”. CONVEX 26, Madrid. Roughly forty minutes, plus a genuinely good Q&A at the end that I’d not skip.
The posts this talk built on
Some people prefer to consume videos and audios rather than written content, and the opposite side also exists (I personally lean more towards the latter). So for those that like to read and haven’t been around for a while, here are a few of the posts that laud out the arguments shared in the post..<br>The spine of the whole thing is that intelligence has become a commodity, and context is the moat. Once you believe that, the company everyone called the AI loser stops looking like a loser: Apple didn’t join the model race and quietly ended up holding the unified-memory hardware and the personal context that actually matters. This may be what the market was starting to see on Friday when Apple became the most valuable company in the world again. And the layer above the raw models, the software you’d pay for, gets rebuilt too, which is the case I made for agents replacing SaaS.<br>The next big argument I’ve been sharing throughout these past months. I don’t think we’re in an AI bubble. I think we’re in an AI trap. The models are useful and they’re being sold to us below cost, which is a great deal right up until the day it isn’t, and the people who lose access first are the token-poor. And even with all that money going in, I still think an AI winter is coming, not a technical one this time but an adoption one, because we’ve genuinely useful technology and, coding aside, almost nobody has worked out how to use it beyond bolting a chatbot into a textbox. The trap has a bit of bubble-vibes in any case :)<br>The way out of the trap is to own your own stack, and this is where most of the practical talk goes. If you want to run models yourself, the number that decides everything is memory bandwidth, not the GPU everyone tells you to buy (they are definitely required, but may not be the critical one. I just realised than neither in the talk nor in any of the posts I talk about the difference between token throughput and prefill numbers, I’ll need to spend some time on...