The Summer I Turned Pretty

[upbeat music] All right.

Good morning, everybody.

Uh, this is the July Eye on the Market podcast.

This one's called The Summer I Turned Pretty, which I'll explain.

Um, and it r- and, uh, earlier this year on my TikTok feed, I was bombarded with all sorts of clips from a sh- a TV show called The Summer I Turned Pretty, which I never saw.

But, um, uh, I used it for a tagline here because this is, this is the summer that I turned pretty cautious on two pretty important topics, and that's what we're gonna talk about on today's podcast.

Um, cautious about zero-day cyberattacks resulting from the latest frontier AI models, and that's gonna be the bulk of the discussion on today's podcast, and also cautious about some market technicals and some growing challenges to the frontier labs from both new open and closed weight models.

And I want-- Let's start with some of the market technical issues.

And there's a bunch of people that have been writing similar things.

This is, um, this is some work a colleague in the investment bank, Jason Hunter, has done.

Uh, if you look back to the late 1990s, there was a point at which the, the communication stocks flatlined even though the infrastructure stops, stocks kept on rising.

And as a proxy for that, think about, you know, Juniper Networks and, and Verizon and things like that.

So you had this period in the market where the front end, which were the communication services stocks, started to flatline, but the infrastructure stocks kept going, and it was a bit of a head fake to the market.

We're having a little bit of a parallel right now because the hyperscalers, which are the front end of this AI boom, are seeing their stock prices stagnate and their free cash flow fall even as the semiconductor and other infrastructure providers and optical networking companies are continuing to go up.

So there's some parallels here that I think we need to think about because you always want the caboose going fast, slower [chuckles] than the front of the engine, and that's not what's happening.

There are also some serious challenges to the frontier labs, and by that I'm r- obviously referring to Anthropic and OpenAI, coming from a whole bunch of new models.

And for those of you consuming this, uh, in video form, you can see this really colorful chart that's become somewhat standard in discussions about these different models.

On the Y-axis, you have some kind of measure of their productivity and performance.

In this case, it's a, it's a benchmark focused on math and reasoning and coding tasks from this group Artificial Analysis.

And the X-axis is typically in log scale.

And importantly, it's not cost per token, it's cost per task, 'cause models can differ on the intensity of how many tokens they use and need, so it's better to just look at net of that efficiency, what's the overall cost of a task? And this curve used to be such that OpenAI and, and, um, Anthropic frontier models were way better than everything else.

Now you can kind of slide to the left on this chart and, um, Meta has a new model, um, SpaceX AI has a new Grok model.

There's a new Chinese model where all of a sudden you're looking at models whose performance isn't that different from the best OpenAI and Anthropic models at either a third or a tenth of the cost.

And so I can imagine there's a lot of CFOs, and I've spoken to some of them already, that are starting to think about, "Hey, maybe these models can work for us on certain tasks," not all of them.

But, uh, some of them are having a bit of a sticker shock on the [chuckles] token, uh, bills that they've been paying.