Cal Newport

This really got a lot of people scared.

The problem is you can go back to one of their earlier models, one of their earlier Opus models, and if you read, like I did, a report that Anthropic released on their blog the same day as that earlier Opus model came out, they said, we found hundreds of zero-day vulnerabilities, some of which had been around for decades.

So it wasn't some brand new thing that mythos could do that earlier models could not.

Then we got multiple independent security researchers that said, okay, well, we took some of the marquee bugs that

Anthropic said they had found with Mythos and we gave that same source code to other models, smaller models, pre-existing models, cheaper models, said, you know, do a bug search on this.

And they found the bugs as well.

Then we got other sort of independent benchmark testing of Mythos once it became a little bit more available.

And it really fell into this pattern of like,

evolutionary, you know, incremental increases on these type of capabilities.

And of course, maybe the biggest sign that Mythos was not this world-changing bug finder is that Anthropix's own software remains very buggy and has security vulnerabilities, even post-Mythos.

So I guess it hasn't been able to fully find all of their bugs.

So what was really going on here, my contention was the original Mythos scare campaign was marketing.

One of the other biggest pieces of evidence for that is bug finding pre-mythos was not what the AI companies were bragging about.

These were not the capabilities that they were touting to try to emphasize the power of their software and all of its possibilities going forward.

Bug finding is what we were doing with like GPT-2.

This is not exciting.

So the fact that that is what they emphasized by mythos, in my mind,

was, uh-oh, we trained this new massive model and it got like a little bit better at everything.

That's not exciting enough.

We need headlines.