Microsoft recently announced major pricing changes for GitHub Copilot — changes that were drastic enough that a Reddit user said their company has started calling it the Tokenpocalypse.

On the latest episode of TechCrunch’s Equity podcast, Kirsten Korosec, Sean O’Kane, and I discussed what those changes might mean for the larger AI ecosystem. After all, as Anthropic and other big AI companies plan to go public, leading to awkward questions about profitability, we’re likely to see similar price increases for other AI products, and more usage restrictions as businesses try to keep costs under control.

“Can these AI labs collapse that cost [and] progress the tech enough in a way that it eventually meets in the middle with customers’ appetite for spending?” Sean wondered.

Kirsten, meanwhile, suggested that this also reflects “how quickly things are moving.” In just a few months, companies became obsessed with “tokenmaxxxing,” then turned against it due to the high costs. So as AI companies write their IPO filings, she asked, “How do you even write these risks in, because they are evolving before our eyes?”

Keep reading for a preview of our conversation, edited for length and clarity.

Anthony Ha: When we were planning for this, Sean, you called this the Tokenpocalypse. And I want to hear more about what you think about it, but there was an example of Microsoft deciding with GitHub Copilot that they’re going to start charging more per token [instead of a flat rate].

This whole ecosystem is heavily, heavily subsidized by investor money. And so stuff that seems like it has no cost is, in fact, incredibly expensive. And now we’re going to get to a point where more of that cost is going to get passed on to the end consumer, to the customer. How is that going to change behavior? I don’t think we know, but there’s going to be a lot of pain.

Sean O’Kane: I mean, how many token-related risk factors do we think are going to be in the Anthropic’s S-1? This is a big question. It’s something that I’ve mentioned a lot on this show and we seem to just keep running into it, where Uber has done like the full arc in the span of a month and a half of saying, “Boy, we kind of blew through our budget on this stuff way quicker than we thought this year.” And then, “Ooh, maybe this is going to be a little too expensive, we need to put caps on this, and we need to limit people’s usage inside the company.”

That’s just a little worrying. Imagine if you see that happen so quickly at a company like Uber, that is using this stuff a lot, and it’s just a question of: Can these AI labs collapse that cost [and] progress the tech enough in a way that it eventually meets in the middle with customers’ appetite for spending? 

A funny thing to think back on is, I don’t think there was really any strategy involved in charging $20 a month [for ChatGPT Plus] when ChatGPT originally came out. It was just sort of like, “Let’s spit out a number.” And we’ve all been reckoning with that ever since. Clearly, people pay more for the more advanced models, but even that still isn’t enough to close that gap to the true cost. So that’s clearly the biggest question here.

Kirsten: All of this, to me, illustrates how quickly things are moving. I mean, when you really think about it, the whole tokenmaxxxing thing has become a thing, peaked, and now is seen disfavorably, within six months. The scale of this, the whole pricing mechanism, to your point, was put in place before business models were really shaped and solidified around AI labs. 

And then, at the same time, you have the government trying to catch up. Also this week, President Trump signed an executive order — it is a narrow version, but this is designed to give the government a chance to review powerful AI models. So you have all this happening at a pace that I don’t think I’ve ever experienced.

That’s why I’m really looking forward to some of these S-1 IPO registration statements, because of the risk [factors]. How do you even write these risks in, because they are evolving before our eyes, and day by day?

Anthony: Uber is an interesting example, Sean, because you mentioned their AI spend, but they’ve also come up in the AI discourse because sometimes, people who think there’s this bubble, they’ll bring up just how wildly unprofitable these tools are, these companies are, and then people will bring up Uber as a response. People talked about how unprofitable Uber was, but eventually you get to scale and then you close that gap.

And I think that’s true. But also, for Uber to do that, it had to really transform itself as a company in a lot of ways. What Uber was at the beginning and what it is now, all the different areas of business that it’s had to expand into, the different ways that customers and drivers have gotten squeezed, those are things that had to happen to get to the point where it could be a profitable company. 

And I think you’re going to have to see similar transformations for a lot of these AI companies if they’re going to survive.

Sean: Is there any way that these labs can squeeze pennies like Uber has squeezed the drivers over the years? Is there something squishy enough there for them to do that? I don’t know. This seems like harder, more straightforward costs in a lot of ways, so it’ll be interesting.