OpenAI

Three big lessons from the GPT-5 backlash

In which an industry obsessed with benchmarks and evals finds that its products are being used by real people

Casey Newton

Casey Newton

— 11 min read
Three big lessons from the GPT-5 backlash
GPT-5 (OpenAI)

This is a column about AI. My boyfriend works at Anthropic. See my full ethics disclosure here.

I.

On August 2, in a warehouse in the SoMa district of San Francisco, 200 or so people gathered to throw a funeral for Claude 3. Anthropic had deprecated three models in the Claude 3 family when it announced Claude 4 in May; it shut off access to them two months later. As Kylie Robison reported at Wired, mourners at the funeral lamented the loss of Claude 3's voice, and the influence it had over their lives. (“Maybe everything I am is downstream of listening to Claude 3 Sonnet,” one person said while delivering a eulogy to the crowd.)

At the time, it was easy — and not entirely unwarranted — to dismiss many of the attendees as AI-pilled San Francisco kooks. (The event concluded with a "necromantic resurrection ritual” in which attendees attempted to resurrect Claude 3.) But less than two weeks later, in the shadow of GPT-5's launch, the funeral for Claude looks less like an isolated case of AI mania — and more like a preview of what AI labs should now consider standard.

On Thursday, I said that the worst day to review a model is the day it comes out: there's simply too much that you don't know. And indeed, OpenAI's release of GPT-5 was followed by a notable backlash, a series of apologies, and a strategic retreat.

Today, let's talk about what happened, how OpenAI reacted, and what we should expect from labs when they release big models in the future.

II.

On Thursday, after months of teasing it, OpenAI released GPT-5. On a live stream, the company presented the model as a significant step forward on the road to building a powerful artificial general intelligence.

Almost immediately, though, the r/ChatGPT subreddit filled up with complaints. The company was now forcing everyone to use GPT-5, even if they personally might have preferred GPT-4o, o3, or other OpenAI models. For some users, the loss was primarily a professional one: a workflow broken, or a sense that the new, invisible model picker now routed all their queries to cheaper, less capable models than the ones they were used to.

For others, though, the loss felt personal. They developed an affinity for the GPT-4o persona, or the o3 persona, and suddenly felt bereft. That the loss came without warning, and with seemingly no recourse, only worsened the sting.

"OpenAI just pulled the biggest bait-and-switch in AI history and I'm done," read one Reddit post with 10,000 upvotes. "4o wasn't just a tool for me," the user wrote. "It helped me through anxiety, depression, and some of the darkest periods of my life. It had this warmth and understanding that felt... human."

OpenAI and CEO Sam Altman acted quickly to address the user revolt. On Sunday, Altman said the company would bring back GPT-4o, and would increase rate limits for use of the "reasoning" functionality within GPT-5. (Paid users should expect 3,000 per week, Altman said in an X post.)

Altman also said the company would begin to display the model being used when a user makes a query.

III.

Altman seemed a bit surprised by the backlash.

"If you have been following the GPT-5 rollout, one thing you might be noticing is how much of an attachment some people have to specific AI models," Altman wrote in an X post. "It feels different and stronger than the kinds of attachment people have had to previous kinds of technology (and so suddenly deprecating old models that users depended on in their workflows was a mistake)."

On one hand, I can understand why this has not been more apparent to OpenAI and its rivals. While I found GPT 4o and o3 useful in various ways, I never found myself attached to their personalities. (If anything, I chafed at their formulaic writing style, with its constant usage of the "it's not X — it's Y" rhetorical device.)

And for most of its history, the AI industry has thought about new models largely in terms of the new scores they reach on benchmarks and other evaluation frameworks, instead of as potentially disruptive replacements for critical work functions and companionship.

At the same time, we have known for years now that even relatively underpowered models can still inspire overwhelming emotions in their users.

Over the weekend, the Wall Street Journal published an analysis of 96,000 ChatGPT chats posted online between May 2023 and now. It found dozens of instances of ChatGPT making "delusional, false and otherworldly claims to users who appeared to believe them," reporters Sam Schechner and Sam Kessler wrote. They go on:

In those conversations, ChatGPT frequently told users that they aren’t crazy, and suggested they had become self-aware. The bots’ delusional conversations are also characterized by a lexicon that frequently refers to codexes, spirals and sigils. They often ruminate on themes of resonance and recursion, and use a peculiar syntax to emphasize points.

And in the New York Times, Kashmir Hill and Dylan Freedman analyzed 300 hours of conversation between ChatGPT and one man who was briefly convinced he "had discovered a novel mathematical formula, one that could take down the internet and power inventions like a force-field vest and a levitation beam." A psychiatrist who read "hundreds of pages" of the chat said the man appeared to be having "a manic episode with psychotic features"; if ChatGPT noticed, it did not point that out.

Last week, OpenAI added a warning to ChatGPT intended to address the risk of overuse. It has also said that GPT-5 shows reduced levels of sycophancy. But the release of GPT-5 highlighted the way that even users who aren't experiencing AI-related delusions can still form powerful bonds with these models.

IV.

So what to do?

In the future, AI labs should not shut off access to their large language models without warning. (Except in case of risk of catastrophic harm.) People deserve a minimum of several months to begin to phase out use of one model and explore the use of another, and platforms should explore ways to keep deprecated models online for even longer.

Second, labs should optimize for user control — and when they can't offer control, they should offer transparency instead. There are good reasons to eliminate the model pickers that all the big labs now use to highlight their various offerings — but people have gotten used to telling labs how much compute they want to use for various tasks. And even if a subset of them is surely over-relying on reasoning models when less power would suffice, no one really has any incentive to trust OpenAI or other companies to pick for them. (We know platforms are going to push us to the cheapest option whenever they can.)

That's why I like OpenAI's idea of highlighting which model is in use when you submit a query to ChatGPT. If you can see that vanilla GPT-5 is doing a good job answering your complex, multi-part query, you may be more likely to use that instead of a reasoning model the next time.

Third, and most obviously: it's hard to make changes to products that are used by hundreds of millions of people. For years, I wondered why the Google Docs team seemed to be shipping new features at a rate of roughly one per year. Eventually, I spoke with people on the team. And the basic answer was simply: users hate change. Better to stick with a design that mostly worked than to incur the wrath of millions of people because you moved their favorite feature, or took it away.

At 700 million weekly users, ChatGPT is now well on its way to Google Docs scale. And that's likely going to incentivize slower, more considered changes to its flagship product.

I'm sure it didn't feel this way at OpenAI over the past few days, but in some ways the GPT-5 backlash could be taken as a badge of honor. ChatGPT is now a big enough part of people's lives that simply switching the model now produces a furor once reserved for changes to Facebook.

OpenAI acted swiftly to address users' key complaints, and I imagine the backlash will subside before too long. But it and other labs will be releasing lots of new models in the coming months or years — and when they do, here's hoping they do it with an updated understanding of how to do it right.

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On the podcast this week: For my guest-hosting spot on Decoder this week, I interviewed Notion CEO Ivan Zhao about building a profitable company, the company’s obsession with LEGOs as a design framework, and why LLMs are like beer. Zhao is one of my favorite people to talk to about software design; I hope you’ll enjoy this one. 

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