Latent Space: The AI Engineer Podcast
00:49:11 · Jamin Ball, Shawn Wang
[00:49:11] S0: Exactly. Exactly. And then let's let's find all these vulnerabilities like the PHP one. Let's go create the patches so that we can instantaneously release the patch by announcing the vulnerability. The challenge is attackers are using these models, using really powerful open weight models, they're finding these vulnerabilities themselves. [00:49:28] S1: Yeah. It's a race. [00:49:28] S0: And it's a race, and it used to be, you you had weeks, months from like vulnerability discovered to like exploit. I mean, now it's collapsed, and it's basically zero. You know, it's zero days. Like vulnerability announced or discovered, exploit available, like it's almost negative. And I think in OpenAI's announcement of the PHP thing, I think hidden in there was, I think they basically said like, oh, and we we... There are more serious vulnerabilities that we know about that we're working on patching right Yeah. Which, I mean, more more more more critical than, you know, something that touches 70% of the Internet is is is pretty serious. [00:50:08] S1: Like, the 70% of Internet is just a proxy for old. Yeah. Yeah. Yeah. Of course, it's older, and it's gonna be more vulnerable. That's right. Yeah. Of course. Yeah. You know, that's a good way phrase That's a... Yeah. I think, there's a couple of takes. I'm not that interested in, like, dwelling on on that stuff only because there's gonna be smarter people than me on that. [00:50:25] S0: Yeah. Of
00:50:25 · Jamin Ball, Shawn Wang
[00:50:25] S0: Yeah. Of [00:50:25] S1: course. The... There's one angle of this, which is kinda funny, which is like, well, we're spending a billion tokens finding all the vulnerability, and we're gonna spend a billion more to fix the vulnerabilities. What if we just didn't do either? And it... Which would go like net the same position as what we were at. Yeah. Yeah. [00:50:40] S0: Yeah. Well, I think cat's out of the bag. [00:50:41] S1: Yeah. That's out of bag. [00:50:42] S0: You can't put the toothpaste back at the tube. [00:50:43] S1: So that... But that is one argument people make, like, blame the AI instead of blame the... They they kill the messenger. Right. The the other part, which is interesting, is like, I I... And this might actually be an issue for basically nation state actors Mhmm. Who actually had all these exploits already. Yeah. And now, like, while, you know, civilians are finding them, goddamn it. Yeah. Yeah. Now I to work harder. [00:51:03] S0: Yeah. Exactly. [00:51:04] S1: Like, all my zero days are gone. Like, I I had them for ten years. Like Yes. Whatever. Yes. But anyway, so so these are fun anecdotes or consequences of this. If you want to really do super Straussian and Machiavellian, if you're a nation state actor, what do you do to to to preserve your edge? Because, like, you cannot have the channels that you're relying on lock up. Yeah. Then you then then you send a Saudi decoy Yeah. To put the thing behind your TV. That's crazy. So that people... So that get distracted by that, so that your real spy Yeah. Gets in.
00:51:04 · Shawn Wang, Jamin Ball
[00:51:04] S1: Like, all my zero days are gone. Like, I I had them for ten years. Like Yes. Whatever. Yes. But anyway, so so these are fun anecdotes or consequences of this. If you want to really do super Straussian and Machiavellian, if you're a nation state actor, what do you do to to to preserve your edge? Because, like, you cannot have the channels that you're relying on lock up. Yeah. Then you then then you send a Saudi decoy Yeah. To put the thing behind your TV. That's crazy. So that people... So that get distracted by that, so that your real spy Yeah. Gets in. [00:51:40] S0: That's just some war games. [00:51:42] S1: Yeah. I I I watch too much TV to to to think that what we are told is the full story. Yeah. Yeah. [00:51:48] S0: I think that's fair. Right? I think that's fair. Too much is at stake. [00:51:51] S1: Yeah. It's too much at stake. Okay. So coming back to the actual, again, finance, like, I really want to be a primer for everyone Yes. Who are are new to the economics of models and model labs and what what have you. One thing that have... That people have said is that the R and D per model. Let's say, like, what does what does Fable five... Sonnet five Opus five cost? Mhmm. Let's call it a billion dollars. [00:52:15] S0: Mhmm.
00:52:15 · Jamin Ball, Shawn Wang
[00:52:15] S0: Mhmm. [00:52:15] S1: That lasted me six months. Mhmm. Right? Because you just said the dep... The depreciation, it lasted last six months. Yeah. Yeah. And so isn't that worrying? Right? Like, about, like, well, these things are the fastest depreciating assets in history. Yeah. This is something that me and Saragol have have talked about before. Yeah. Yeah. Yeah. Yeah. And I think the the net is there's no, but, like, it's a qualified no Mhmm. Of, well, as long as my next model run is better than the than the than the current state of the art, we're good. Yeah. Yep. Yep. But it's a little bit of a house of cards if if the next one doesn't work out. Yeah. Which briefly affected OpenAI. Yeah. So a lot of people know by now that, you know, like, GPT four five, the the big the big run wasn't wasn't that successful. Yep. And that they, you know, they... They've they've since since fixed it. But, like, that slump Yeah. In a in a run or a failed run Yep. Can really, really screw with the the ROI on the thing. [00:53:08] S0: And look. I think there's a really good analogy to the trip industry. And I remember speaking to someone much older and much smarter than myself who did a lot of semiconductor investing in the eighties and nineties about all these chip companies. [00:53:22] S1: They've seen it all.
00:53:22 · Shawn Wang, Jamin Ball
[00:53:22] S1: They've seen it all. [00:53:22] S0: And they've seen it all, and they were highly skeptical. And sometimes you have industry vets that are highly skeptical, and they miss the next big opportunity because they never think the incumbent can be disrupted. But the point of view they had was it's very different to tape out a chip that is competitive with the leading chips at the time from building a machine that can continuously tape out chips that compete with, know, [00:53:49] S1: It's the Marvel Marvel factory. [00:53:50] S0: Yeah. The chip factory. And so he was like, this individual's point of view was maybe a chip startup can build one chip, but guess what? They're not gonna have the machine to constantly pump out a new chip every year and stay at the frontier from the chip standpoint. And that's why NVIDIA's king. It's not because they have the best chip now, it's they have the best factory. Process for chips. Exactly. Exactly. [00:54:09] S1: It's the machine that builds the machine that
00:54:09 · Shawn Wang, Jamin Ball
[00:54:09] S1: It's the machine that builds the machine that [00:54:11] S0: And so it's kind of one of these... I mean, this is the bear case on all these Neo Labs, which is, you and and you talk to many smart folks, what they might tell you is, hey. If you wanna really compete and be... Have like a soda model at the frontier, you can't you can't you can't go more than one order of magnitude of compute less than the labs. You just can't compete if you don't have, you know, one order of magnitude of compute of the big labs. And so as a neolab, you know, the question isn't can you build one model? It's can you attract compute and talent required to continuously pump out new generations of models every year to ultimately compete in your domain? And so I think for the labs, the reality is is I think it's very hard [00:54:55] S1: for