OpenAI’s ‘Code Red’ Hasn't Revived ChatGPT’s Flatlining Growth
What Happens When Exponential Growth Suddenly Plateaus?
“Failing to Understand the Exponential, Again”
The viral article, written by Anthropic and ex-DeepMind researcher Julian Schrittwieser, captures the often smug dismissal that the pro-AI crowd directs at skeptics.
The current discourse around AI progress and a supposed “bubble” reminds me a lot of the early weeks of the Covid-19 pandemic. Long after the timing and scale of the coming global pandemic was obvious from extrapolating the exponential trends, politicians, journalists and most public commentators kept treating it as a remote possibility or a localized phenomenon.
Something similarly bizarre is happening with AI capabilities and further progress. People notice that while AI can now write programs, design websites, etc, it still often makes mistakes or goes in a wrong direction, and then they somehow jump to the conclusion that AI will never be able to do these tasks at human levels, or will only have a minor impact. When just a few years ago, having AI do these things was complete science fiction! Or they see two consecutive model releases and don’t notice much difference in their conversations, and they conclude that AI is plateauing and scaling is over.
It’s easy to write off the skeptics as not understanding how exponential growth works. Harder to wrestle with the substance of their counter-argument. It’s not a failure to grasp the mathematics behind the model, but rather a recognition that growth patterns are constrained by the systems they inhabit.
The tech echo chamber doesn’t often abide such heresy, which is why Big Tech keeps ramping infrastructure investment to reckless levels and why investors keep pouring in money.
This reflects an emerging technology upside bias, which is most prevalent among those with skin in the game. It’s understandable that exponential growth generates excitement, but when it’s extrapolated too far into the future it ceases to reflect real-world conditions. Most of the time, what starts as exponential growth curve evolves into an S-curve.
At the same time, the downsides of—or opposing forces to—this growth are neglected. And this has consequences.
OpenAI now finds itself in this predicament. The company plans to invest $50 billion in compute power this year, has raised exorbitant amounts of capital to do it, and requires similarly exorbitant revenue growth to justify the CapEx.
But that growth isn’t happening, according to the April 28 article “OpenAI Misses Key Revenue, User Targets in High-Stakes Spring Toward IPO” in The Wall Street Journal:
OpenAI recently missed its own targets for new users and revenue, stumbles that have raised concern among some company leaders about whether it will be able to support its massive spending on data centers.
Chief Financial Officer Sarah Friar has told other company leaders that she is worried the company might not be able to pay for future computing contracts if revenue doesn’t grow fast enough, according to people familiar with the matter.
New data reveals the extent of the issue. According to Sensor Tower, total sessions/visits on desktop, mobile web, and mobile app show that total US usage has come to a virtual standstill—in fact, down 2% since July 2025. (Globally, it’s up 20% during that period, but only up 3% since the beginning of 2026.)
