OpenAI's DevDay

This is speculation, not a leak. I am just trying to connect a few public dots that seem interesting.

OpenAI announced in April that DevDay 2026 will happen on September 29 in San Francisco. DevDay is not always just a normal developer event. Historically, OpenAI has used it for major releases. For example, DevDay 2023 gave us GPT-4 Turbo with a 128K context window, which was a pretty big deal at the time, plus the Assistants API and other major developer-facing updates.

Now put that date next to Sam Altman’s public statement about OpenAI’s internal goals. He said: “We have set internal goals of having an automated AI research intern by September of 2026, running on hundreds of thousands of GPUs, and a true automated AI researcher by March of 2028.”

That September 2026 target lines up suspiciously well with DevDay 2026 being on September 29.

There is also the IPO angle. Reuters recently reported that Altman told staff OpenAI expects to go public “within the next year,” but also that recursive self-improvement could weaken the case for rushing an IPO. The key idea reported was that the faster a possible RSI takeoff looks, the more advantageous it could be to delay an IPO. So OpenAI seems to be taking the possibility of near-term AI-driven AI research seriously enough that it may affect major corporate strategy.

Now I want to talk about Łukasz Kaiser, because he is kind of a big deal in modern AI.

Kaiser is one of the co-authors of “Attention Is All You Need,” the paper that introduced the Transformer architecture. He worked at Google Brain, contributed to major deep learning infrastructure and sequence modeling work, and is now a researcher at OpenAI working on reasoning models. So when he talks about the plausibility of an AI research intern, I think it is worth paying attention.

In an interview on YT called "AI Research Legend’s Honest Assessment of Where We Are" ( https://www.youtube.com/watch?v=N1geOimmdDo ), around 17:21, the interviewer asks him: “How close is an AI research intern?”

His answer is interesting because he says maybe we don't need a totally new post-Transformer architecture or a huge breakthrough in continual learning. Instead, he points toward a much more practical and "ugly" path.

For years, long context was treated as a deep machine learning architecture problem. But with agents, the practical answer may be much more like writing this into files, create index files, let the agent search through them with "grep" or "ripgrep" and RL on the loop.

For non-programmers: grep is a classic tool for searching text inside files. ripgrep, often called rg, is a faster modern version. This matters because an agent does not need to keep everything inside the model’s context window if it can store useful information externally, search it, and retrieve the right pieces when needed.

The key term Kaiser uses is “compaction.”

In this context, compaction means taking a long messy work session and compressing it into a useful state representation: goals, hypotheses, experiments tried, bugs, failed paths, partial results, decisions, next steps, and maybe even higher-level lessons. Not just a generic summary, but a compressed research state that lets the agent continue without losing the plot.

Kaiser says that if there is one reason he likes Codex over Claude Code (he said that before Fable 5), it is compaction. Codex is apparently good at continuing the thread because it is good at compacting the work state. And he suggests this may not require something mysterious. Maybe "people prompted it well, added some RL, and trained it to use tools, search files, and summarize well enough to keep the work going".

Then he connects this to the AI research intern question.

Maybe you have month-long conversations with Codex. Then you prompt it to go over those conversations, find meta-patterns, write them into files, and think about how to use them. Then imagine doing that over thousands of internal OpenAI research trajectories, with reinforcement learning on top.

At some point, the system might start behaving less like a coding assistant and more like a junior AI researcher.

Now back to the speculation.

My guess is that before the September 2026 target, OpenAI may launch a more ambitious pretraining run than the usual incremental GPT-5.x style updates. Not necessarily because pretraining alone is expected to magically create the research intern, but because they need a base model with enough raw capability to respond really well to the next stage of posttraining.

And that posttraining could include the usual RL and incremental improvements, plus a special layer trained on internal AI research work done through Codex.

If that works, the resulting model or system could be a serious qualitative jump.

If OpenAI actually hits or partially hits its September 2026 automated AI research intern goal, DevDay on September 29 would be a perfect place to announce or preview it.

Would they call it GPT-6? Obviously I do not know. It could stay an internal Codex research-agent system. But if the capability jump is big enough, and especially if it puts OpenAI clearly back ahead in the current race, GPT-6 would make sense as the public brand.