Microsoft’s decision to overhaul GitHub Copilot’s pricing structure — moving from a flat monthly fee to a per-token model — has sparked a debate that goes far beyond a single developer tool. On a recent episode of TechCrunch’s Equity podcast, hosts Anthony Ha, Kirsten Korosec, and Sean O’Kane explored what they’ve dubbed the “Tokenpocalypse,” a term a Reddit user coined to describe the sudden shift in cost dynamics.
The change is significant. Instead of paying a predictable subscription, enterprise customers will now be charged based on the number of tokens their developers consume. For heavy users, this could mean a dramatic increase in monthly bills. The move has already prompted some companies to reassess their usage, with one Reddit user claiming their organization has started calling it the Tokenpocalypse internally.
Also read: Reid Hoffman leaves Microsoft board to go 'founder mode' with AI drug startup Manus
Why this matters beyond GitHub
The Equity team’s conversation quickly moved beyond Microsoft. Sean O’Kane pointed out that the entire AI ecosystem is “heavily, heavily subsidized by investor money,” and that the GitHub Copilot price hike is just the first of many expected cost pass-throughs. “Stuff that seems like it has no cost is, in fact, incredibly expensive,” he said. “Now we’re going to get to a point where more of that cost is going to get passed on to the end consumer.”
Kirsten Korosec noted the breakneck speed of change. “The whole tokenmaxxxing thing has become a thing, peaked, and now is seen disfavorably, within six months,” she said. This rapid cycle creates a unique challenge for AI companies planning to go public. Anthropic, for example, recently filed for an IPO, and the Equity team wondered how its S-1 registration statement will address the volatility of token pricing and AI costs.
Also read: Anthropic Files for IPO as Amodei Defends AI Spending Against Skepticism
The Uber comparison and the path to profitability
O’Kane drew a parallel to Uber’s early days. “Uber has done 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,'” he said. “And then, ‘Ooh, maybe this is going to be a little too expensive, we need to put caps on this.'”
Ha pushed back on the comparison, noting that Uber eventually achieved profitability by transforming its business model — expanding into new areas and squeezing both customers and drivers. The question, he said, is whether AI labs can similarly transform themselves. “Is there any way that these labs can squeeze pennies like Uber has squeezed the drivers over the years?” O’Kane wondered. “This seems like harder, more straightforward costs.”
The path to profitability for AI companies remains uncertain. The initial $20-a-month price for ChatGPT Plus, Ha noted, was essentially arbitrary. “Let’s spit out a number,” he said. “And we’ve all been reckoning with that ever since.” As AI companies prepare for the scrutiny of public markets, the reckoning is only just beginning.
