“Why can we solve Millennium Prize problems in math, but not automate anything touching accounting? That should be really weird to a lot of people. And that is still our North Star, even if people get distracted by cost and speed.”
Unsupervised Learning with Jacob Effron · AI Builders · November 2026
Context
Diogo was explaining why his company builds 'system one' decision models and stresses their weaknesses. His point is that most of what software needs is reliable, boring decision-making, not the flashy capability milestones the field chases.
Transcript
…of what you can extract from these models. The reason we call them system one models, and among other things, and system one actually, people did not want us to name that it either, because system one thinking tends to be a bad thing. Like people use that as kind of like an insult, right? Yeah,
it's like the part that just naturally happened from evolution, you know, from back in the day before makes mistakes, et cetera.
Exactly. But and like people often like say that that is the mistake making side. And I believe that the shape that actually AI gets compressed to happens to be like very system one. And therefore the honest thing to do is to communicate its weaknesses because I think that on its own can be incredibly, incredibly valuable for software. And most of what people need actually is system one stuff. You know, like, why can we solve Millennium Prize problems in math, but not automate anything touching accounting? Like that is, that should be really weird to a lot of people. And like, I think that that is like still our North Star, even if people get distracted by like cost, speed, you know, like, oh, it's, it's, it's good for like really fast decisions or whatever else. Like I see these as like distractions compared to what is really needed for AI to meet its potential. And that is my, my North Star.
And so obviously you put the models out there and you have a lot of people using them in different ways. How much does like the real world usage of the models come and feed back into the roadmap versus just like the stuff you were you all planning to do?
I would say it's almost none real world feedback. I think people should be complaining to us so much more. I think that like this the field is still in a Stockholm syndrome of like, oh yeah, the model providers just do stuff in their one track path and we happen to be like the victim is the wrong…
Yeah.
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