Who Isn’t Using GPT 5.5

It’s been one week since OpenAI’s last big release, GPT 5.5. Today, we ask the team if they still feel as enthusiastic about the model, discuss the unusual career step that unicorn CTOs are making, and tell you exactly how Kieran Klaasseen, creator of the AI-native compound engineering methodology, hit a personal PR record in a day.—Laura Entis

Signal

The unicorn CTO-to-Anthropic IC pipeline

The prestige career ladder in tech used to run one way: Start as an engineer, become a manager, and eventually join the C-suite. AI has scrambled the equation. The new flex is quitting a high-profile chief technology officer job to become an individual contributor at Anthropic.

What happened: Six former CTOs at companies valued north of $1 billion—including Instagram, Workday, and Box—have made that exact career move, according to one of those CTOs on X. And the leadership-back-to-IC trajectory isn’t unique to Anthropic: PostHog is recruiting technical ex-founders, and Ramp says it has attracted 70 ex-founders by looking for “super ICs.”

Why it matters: AI has upended engineering workflows so dramatically that many managers who don’t ship code frequently anymore don’t have a clear sense of how their teams are using these new tools or which ways of working are the best. Anthropic’s models, talent, and growth trajectory make it one of the few places big-name CTOs can get their hands dirty and experience how engineering is changing—while not worrying too much about a pay cut.

Pulse check

We settle in with GPT-5.5

GPT-5.5 came out last week, and our first impression was that it was a faster, steadier, and easier-to-trust model for everyday professional work than Opus 4.7. A week later, we’re still bullish on GPT-5.5—but for people with Claude-specific agent workflows, skills, and tool integrations, making the switch to Codex is a barrier.

Cora general manager Kieran Klaassen, who initially didn’t think he’d use GPT-5.5 as a daily driver, has changed his mind. What won him over? GPT-5.5’s speed and “workhorse” ability to follow clear directions. GPT-5.5 isn’t perfect—it’s worse at multitasking and planning than Opus 4.7—but his work is now evenly split between Codex and Claude Code.

Every head of growth Austin Tedesco thinks GPT-5.5 is enough of a step change that he’s been telling friends to make the switch from Claude Code to Codex. They mostly don’t want to hear it. Austin says the response has been, “That feels like a lot of work; ‘do I really have to? Is it that much better?’”

Every’s consulting team is wrestling with the same dilemma. They have a good thing going with their Claude agent, Claudie, and migrating to GPT-5.5 in Codex requires time and testing. Head of consulting Natalia Quintero had GPT-5.5 and Claudie draft head-to-head sales proposals; Claudie’s won handily. Getting the most out of GPT-5.5 will likely require that the team optimizes Claude plugins for Codex.

Every head of tech consulting Mike Taylor doesn’t have the time to do that right now. He has gripes with Opus—it recently messed up some PowerPoints—but, “I already have my Claude set up the way I like it, and there are some things that are different about Codex,” he says. When work dies down a little, he’ll experiment, but until then, he’s sticking with the devil he knows.

Write at the speed of thought

That gap between your brain and your fingers kills momentum. Monologue lets you speak naturally and get perfect text three times faster, and your tone, vocabulary, and style are kept intact. It auto-learns proper nouns, handles multilingual code-switching mid-sentence, and edits for accuracy. Free 1,000 words to start.

Download Monologue for Mac

Want to sponsor Every? Click here.

Data point

24

That’s the number of pull requests Kieran merged in a single day last week, a number he thinks is a personal record. A month ago, he’d average two or three.

Kieran hit that pace because he’s automated most of the implementation process. His workflow:

1. Upload screen recordings of people using and reviewing Cora into Codex.
2. Have his agents watch the recordings, identify product fixes, and open pull requests against Cora’s repository overnight.
3. Review the pull requests when he wakes up.

Initially, he worried he’d have to clean up agent-generated gobbledygook. Not the case. “So far, everything works great, and nothing breaks,” he says. “It feels like cheating.”

Jagged frontier

We’re all one prompt away from perfection

We’ve spent years talking about the addictiveness of social media algorithms, dopamine drips expertly designed to keep us scrolling. Engineers, being engineers, like to believe we’re above this, or at least better attuned to the mechanism behind our compulsion. But now it has come for us too: LLMs have become the social media feed for people who make things.

Coding feels like playing the slots.

It used to be that you could code something exactly to your specifications, but that required time, hard-worn expertise, and design skills if you wanted to make it look halfway decent. Now, I can throw an idea at Claude Code and get something close. I spend my days toggling between sessions, waiting to hit the jackpot and receive the perfect version of whatever I’m looking for —the perfect API design, the perfect bug fix. I tweak my prompt and pull the lever again. And again. And again until it’s somehow 3 a.m.

It’s that sense of being almost there—but not quite—that’s so intoxicating.

I ask Codex for five ways to structure a new feature and decide that I like option three, but want to keep the data model from option two. In its next turn—the next roll of the dice—it might magically marry the two to create the result needed. Or I might need to roll again. Each pull has the potential to patch the bug, or perfect the copy, or reveal a better plan. It feels like productivity and gambling got wired together, each turn a workspace lotto ticket.

This is not only a coding problem. Writers feel it when they ask for one more way to structure an article or sharpen a sentence or revise a draft. Product managers feel it when they ask for one more onboarding flow, roadmap, or way to sequence a launch. We are all always one prompt away from perfection.

I do not have infinite hours. So at some point, I have to choose a path and stick with it, even though there are better ones. I accept that if the main shape of the solution is right, the edges can stay a little fuzzy.

The most important skill isn’t choosing the right model or prompt engineering. It’s knowing when to take your winnings and move on.—Willie Williams

One last thing

Behind OpenAI’s goblin ban

Starting a few releases back, OpenAI models developed an affinity for including references to creatures (sometimes visually, but mostly textual) in their outputs—raccoons, trolls, ogres, pigeons, but most of all, goblins and gremlins. “The goblins were funny at first, but the increasing number of employee reports became concerning,” the company said yesterday.

When OpenAI tested GPT-5.5 in Codex, there were so many goblin references that it added developer-prompt instructions forbidding creature-based chat unless “it is absolutely and unambiguously relevant to the user’s query.”

The culprit: A specific personality setting rewarded responses that included goblin and gremlin-based metaphors, a learning that spread to influence the training data for the entire model—including GPT-5.5.

If you want to welcome creatures back into the conversation, OpenAI shared the following command to unlock Codex Gringotts mode.

instructions=$(mktemp /tmp/gpt-5.5-instructions.XXXXXX) && \
jq -r ‘.models[] | select(.slug==“gpt-5.5”) | .base_instructions’ \
~/.codex/models_cache.json | \
grep -vi ‘goblins’ > “$instructions” && \
codex -m gpt-5.5 -c “model_instructions_file=\”$instructions\“”

Laura Entis is a staff writer at Every. You can follow her on LinkedIn. To read more essays like this, subscribe to Every, and follow us on X at @every and on LinkedIn.

For sponsorship opportunities, reach out to [email protected].