Cal Newport

What can we say?

What if we say it's bug, let's focus on this bug finder.

We haven't talked about that in a while.

Oh my God, we built that ultimate bug finder.

Nowhere before mythos were they saying that was their goal or this was one of the big uses of LLM.

So to me, that was a red flag.

So what I think they did is they were hoping to play us both ways.

Say this thing is really terrifying, get a lot of terrified columns written.

get everyone really worried about it, wait a month and a half, and then release it and try to ride on that sense of

this is a super powerful model that when we then release a version to the public, they'll be willing to pay the much higher token prices that these larger models command.

So they're trying to play us both ways, terrify us and still just go right ahead without restriction with their plan of releasing and trying to make money off of that software.

So I think that was just pure marketing.

Again, this doesn't say that LLMs are not good at finding security vulnerabilities.

This is not the saying that the new coding harnesses that took off starting last fall aren't good at writing exploits if you explain to it

But what I am arguing is that we never had evidence that mythos was some sort of revolutionary leap in these capabilities that would merit the way that Anthropic talked about it or it uniquely being put on an export control list as compared to other cutting edge frontier models.

All right.

The second relevant question is, of course, should the government...

be more involved with AI.

So if we are putting aside politics, like, is there a way we could imagine a more hands-on regulatory regime that would make sense?

Well, in order to understand what role the government should or shouldn't play here, let's look a little bit closer on the question of why the Trump administration did what it did with mythos, because there's three different explanations that are out there right now floating around.