OpenAI’s much‑anticipated GPT‑5 rollout was supposed to showcase the company’s AI dominance. Instead, it triggered a storm of user complaints, quick fixes, and a broader reckoning about whether OpenAI still holds the lead in the AI race.
What went wrong? How does it affect ChatGPT users? And where do we go from here?
I got the scoop from Marketing AI Institute founder and CEO Paul Roetzer on Episode 162 of The Artificial Intelligence Show.
The launch on August 7 immediately frustrated many users. OpenAI abruptly removed legacy models like GPT‑4o, forcing everyone onto GPT‑5. The move sparked outrage among devoted users who had grown attached to the older models’ style and personality.
Plus subscribers also bristled at surprising new rate limits, and early interactions left many questioning whether GPT‑5 felt as capable as promised.
CEO Sam Altman responded quickly. On August 8, he promised doubled rate limits for Plus users, the return of GPT‑4o as an option, and fixes to an auto‑switching system that had made the model seem “dumber” than intended.
By August 12, OpenAI went further, offering users a choice between Auto, Fast, and Thinking modes within GPT‑5, while expanding access to legacy models like O3 and GPT‑4.1. Altman also acknowledged criticism about GPT‑5’s “colder” tone, pledging to make its personality warmer.
In an interview with The Verge, OpenAI's head of ChatGPT, Nick Turley, admitted the company had miscalculated, saying: “In retrospect, not continuing to offer 4o, at least in the interim, was a miss." He said that the company was surprised at users' emotional attachment to specific models. Going forward, he promised that OpenAI will no longer retire models without warning.
Despite the turbulence, usage of ChatGPT has actually increased since GPT‑5’s release, Turley said. But the backlash highlights how fragile user trust can be when sudden changes disrupt established workflows.
For Roetzer, the GPT‑5 saga looks like a live business lesson.
"There's business, marketing, and product lessons to be learned by everyone here," says Roetzer.
"When you're doing things fast, you're not always going to get it perfect. You can judge however you want the decisions they made and whether the models rolled out properly, but at least they're stepping up and saying 'We kind of screwed up.'"
Roetzer sees parallels to crisis communications. Launching to 700 million users means mistakes play out on a global stage. The ability to admit errors and adapt in real time may ultimately serve OpenAI better than a flawless launch that never encounters user friction.
Still, he argues the bigger story is strategic.
"My biggest takeaway from all this is they don't have a lead anymore," he says. "It does not appear to be a massive leap forward."
For enterprises and individuals building on top of OpenAI APIs or relying on ChatGPT for mission-critical workflows, the chaos here should serve as a warning. If a model changes, underperforms, or disappears, entire workflows and software products can break overnight.
Companies should start contingency planning, whether by testing prompts across multiple models or running smaller open‑source systems locally as backup.
"People are going to be very dependent upon this intelligence," says Roetzer. "You have to start thinking about the contingency plans for that."
Bloomberg noted that while GPT‑5 scores higher than competitors on some benchmarks, it lags in others. Geoffrey Hinton, often called an AI “godfather,” even joked that GPT‑5 might be a “small backwards step” toward AGI. To many, GPT‑5 represents an incremental improvement rather than the revolutionary leap they were promised.
That gap between hype and reality may be the real risk for OpenAI. Each new release carries sky‑high expectations, and when the results are incremental, users notice.
For the time being, OpenAI says it will preserve old models going forward, refine GPT‑5’s tone, and expand customization options. But the rollout underscores a new reality: AI models are converging in capability, and leadership in the AI race will depend less on raw performance and more on reliability, trust, and ecosystem strength.
"The most significant thing about all of this is that the frontier models have largely been commoditized and the game is changing," says Roetzer. "It's no longer who has the best model for a year or two run. It's now all about other, all the other elements of this."