Eiso Kant

speaker

1,158 appearances 1 recordings 1 series first heard Jul 2026 last heard 23 Jul

Eiso Kant’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Jul 2026 with 1.

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Latent Space: The AI Engineer Podcast · Inside the Model Factory — Eiso Kant, Poolside AI · 23 Jul 2026 podcast

And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous benchmarks had shown for any model remotely this size or even larger, at least on coding tasks, that it's because of the behaviors.

And so now the question I have, and I don't have an answer to it, is,

I know at the limit, so infinite model size, extremely large model, and the cost of that model is going to be very expensive to run.

We know this, right?

So larger model, ROI.

So I know that at the very limit, I'm not going to use the world's largest model one day, quadrillion parameter, whatever crazy skill we scale up to do a basic coding task.

Already today, I'm starting to size down for certain tasks.

So it means that there is an optimal.

It means there's some curve that goes as we go up to model size for knowledge work.

At some point, we're at the peak.

And after that, the return on investment of using a bigger model just doesn't make sense.

Now, I think the question is, before I would have thought that peak was very far away.

This model for me is the first sign that maybe that peak is at a trillion, five trillion, 10 trillion.

Maybe we can just squeeze way more out of these models.

I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to solve problems.

knowledge work, the accounting, the legal, the code that we write.

And so if that holds true, it is an argument for the commoditization of models.

It's an argument that open source can win and like succeed in this world.

And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works, we just have to go discover in the next couple of years of how much more we can squeeze out.

Now, I do want to put a big asterisk.

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