GPT-5’s bumpy start: what I saw, and what it means for your team
From missing 4o to misrouting: what went wrong and how OpenAI course-corrected
I woke up to a dozen texts from clients asking the same thing: “Where did 4o go, and why does 5 feel weird?” That was the first sign that this launch was going to be messy, not just technically, but emotionally as well. GPT-5 arrived with big promises around reasoning and speed, plus an automatic system that chooses the “right” model for each request. On paper, great. In practice, the first 72 hours were choppy.
What actually broke
Two things collided. First, OpenAI pulled several familiar models from the picker in consumer ChatGPT, including 4o. If you opened an old chat, it silently switched to a GPT-5 variant. No deprecation window, no bridge plan, just gone. Power users noticed fast. Workflows snapped. People who liked 4o’s voice and pacing felt like the rug got pulled out from under them.
Second, the new “router,” intended to automatically select a lightweight or deep-thinking path, misrouted. In plain English, a lot of prompts that needed the heavier reasoning brain got sent to the quicker, shallower one, so answers felt thin or wrong. That split the experience: some chats were great, others were off by a mile. If you were drafting a sensitive client note or reviewing an analysis, this hurt.
Layer on rate limits that felt tighter, or at least unfamiliar, and you had the perfect storm. For many, the first impression of 5 was not that it was smarter, but rather that it was different and less predictable.
The backlash was about more than features
The internet did what the internet does, but the tone was telling. A vocal group didn’t just miss accuracy, they missed 4o’s warmth. That was the headline for me. When a tool becomes part of how you think, how you write, how you manage your day, changes in “feel” land like changes in policy. Consistency is not a nice-to-have; it is a fundamental part of value. When that goes missing, trust drops, and people go searching for the old switch.
How OpenAI course-corrected in the first days
Credit where due, the response was quick and concrete.
Model choice returned. The model picker returned a way to surface additional models, allowing teams to select what they rely on. That one change reduces the risk of in-flight work.
Clear modes, clearer intent. GPT-5 added Auto, Fast, and Thinking. If you need depth, you can say so. If you need speed, you can say that too. Less “mystery router” energy.
Higher limits, less thrash. OpenAI raised message caps for paying users and continued to tune as the load stabilized. Fewer surprise walls mid-task.
Acknowledged the trust issue. The company said they would not yank older models without warning again. 4o reappeared for paying users. Predictability matters.
Under the hood, the router is supposed to learn from real signals, like when users switch models or rate responses. That should improve results over time, but the launch showed how sensitive production work is to early misrouting. Small mistakes at scale feel big.
What this means for business leaders
If your org depends on ChatGPT for research, drafting, client communication, or coding assistance, treat model shifts like any other platform change. Capability is only half the story. The other half is control.
Freeze critical workflows for a beat. For revenue-touching work, lock your team to known-good models this week. Turn on the setting that shows additional models, keep 4o available, and document when to pick Thinking versus Auto. Less guesswork, fewer surprises.
Publish a simple routing rule. My rule of thumb: Fast for quick editing and summaries, Thinking for logic, analysis, and anything that could embarrass you if it goes sideways. Write it down in your internal guide. No one should have to remember this by heart.
Run a side-by-side bakeoff. Take three of your standard tasks, run them in 5 Auto, 5 Thinking, and 4o. Compare output quality, tone, and cleanup time. Keep score. Pick defaults per task, not just per team. Share the results so people see the why behind the choice.
Plan for personality drift. Yes, tone is product. If your people write briefs or emails with ChatGPT, the “feel” of responses matters. Give teams an approved instruction block that coaxes the tone you want. Revisit after each major update because voices shift.
Create a rollback muscle. Have a one-pager that says which model to revert to if a change hits quality. Make it easy to find. When things wobble at 3 pm, no one should be Slacking for a policy link.
My take
I like 5’s direction, am genuinely impressed with a number of the enhancements, and I also understand why the first days felt rough. Auto-routing is the right idea in the long term for most users, as it reduces cognitive load, but only if the routing is trustworthy. And trust grows slowly. The quick restoration of choice, higher limits, and a public promise on future removals were the right moves. Now the job is to let people settle in, keep the picker stable, and show that Auto picks the same choice an expert would have made most of the time.
If you lead a function, give your org a week of structure: documented defaults, a fall-back model, and a place to report weirdness. After that, bring 5 into the center of the workflow where it shines, especially for complex reasoning. That balance, not hype, is what turns a choppy launch into real value.
One last note from the field. The teams that navigated this best were the ones that treated AI like a system, not a toy. Clear guidance, quick tests, shared results, calm comms. No heroics required. Just good habits, written down, and followed.
Looking for help to drive adoption of AI in your organization? Reach out to me at steve@intelligencebyintent.com.
Images were created with Imagen 4 Ultra in Google’s AI Studio.