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Enterprise AI differentiation increases as models improve
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The big bitter lesson question in the applied AI layer is that as models get better and better, what is the best way to diffuse that intelligence into an enterprise. Ironically, I’d argue that as models are getting more capable, and can handle longer running tasks and better tool use, the ability to differentiate on a domain by domain basis goes up instead of down. When models were quite limited in what they could do, ironically all you could be literally was a “wrapper” because you were…
signüll@signulll5d ago
the more intelligent the underlying model becomes, the less intelligence a founder needs to build into a product in some sense. if you imagine some end stage of ai, it’s simply just intelligence without any fluff around it. today building ai software is much more about adding guardrails & specifying direction more than creating capabilities. this may be why there is a fuck ton of convergence with slight deviations everywhere because the guardrails are sort of universal at least for general purpose stuff. i’m not saying this is a bad thing btw but it’s certainly relevant.