Model Labs
topic on 3 shows · 8 statements across 7 episodes
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8 statements about Model Labs, every show
Atallah: No single layer can capture all AI memory context
“I do think that like, it's impossible for one layer to capture all valuable memory because the apps own so much important context that the model labs don't have. And they, the model labs in order to get this to work, they'll have to incentivize the apps to, li…”
Angelopoulos: Harvey's CEO views model labs as his biggest competitive worry
“Harvey, the CEO of Harvey himself is saying that, you know, his biggest competitive worry is the model labs.”
Evans: Agentic Coding Has Driven Token Demand Up Multiple Orders of Magnitude
“And as hopefully everyone listening to this will understand, like in the last six months or so, the sudden product market fit of agentic coding means that demand for tokens has gone up by many orders of magnitude, and the model labs have been sort of scramblin…”
Evans: AI Model Inference Alone Generates 40% to 50% Gross Margins
“And meanwhile, we sort of know that you have positive gross margins on inference alone of sort of 40, 50%, but you've got the cost of building the next model, and you don't know where the cost The cost will move, or the cost of the next model will move, and yo…”
Swix defines Agent Labs as routing and evaluating models without training
“One observation I have, this kind of massive thesis I've been pursuing, which is what I've been calling an agent lab. Where you're sort of different than a model lab in the sense that you never train your own models, but you are the router evaluation layer, su…”
Swyx: Agent labs will capture higher margins than model labs
“I think what I've been calling agent labs, which are people who build on top of all the other models. We'll probably have a better time with the margins because they price against the end user hours spent or like human labor. Whereas models get commodity price…”
Frontier AI model labs ignore application-level integration needs
“Like the model apps couldn't care less because they're not really building applications.”
Swix: AI engineering exists because labs crowdsource emergent capability discovery
“The reason that AI engineering can exist outside of the model labs is because the model labs release Models with capabilities that they don't even fully know because you never train specifically for it. It's emergent. And you can rely on basically crowdsourcin…”