Codex

Last week, OpenAI and Anthropic simultaneously launched their new AI models specialized in programming: GPT-5.3 Codex and Claude Opus 4.6. Beyond the improvements they represent in performance or speed, which are truly amazing, both companies also stated something that completely changes the rules of the game: AI models are actively participating in their own development. Or put another way: AI is improving itself. Why does this change matter?. Generative artificial intelligence tools are reaching a high level of efficiency and precision, becoming in a few years from being co-workers for simple and specific tasks to being able to be involved in a good part of a development. According to the technical documentation of OpenAI, GPT-5.3 Codex “was instrumental in its own creation,” being used to debug its training, manage its deployment, and diagnose evaluation results. On the other hand, it is worth highlighting the words of Dario Amodei, CEO of Anthropic, who in his personal blog affirms that AI writes “much of the code” in his company and that the feedback loop between the current generation and the next “gains momentum month by month.” In detail. What this means in practice is that each new generation of AI helps build the next, more capable one, which in turn will build an even better version. Researchers call it the “intelligence explosion,” and those developing these systems believe the process has already begun. Amodei has declared publicly that we could be “just 1 or 2 years away from a point where the current generation of AI autonomously builds the next.” Most people use free language models that are available to everyone and are moderately capable of certain tasks. But they are also very limited, and are not a good reflection of what a cutting-edge AI model is capable of today. In a brief session with 5.3-Codex I was able to draw this same conclusion, since the AI ​​tools that big technology companies use in their development are nothing like the most commercial ones that we have freely available in terms of capabilities. The code-first approach. Initial specialization in programming makes more sense than we think. And the idea of ​​companies like OpenAI, Anthropic or Google that their systems were exceptional by writing code before anything else is linked to the fact that developing AI requires enormous amounts of code. And if AI can write that code, it can help build its own evolution. “Making AI great at programming was the strategy that unlocked everything else. That’s why they did it first,” Matt Shumer, CEO of OthersideAI, said in a publication that has given us something to talk about these days on social networks. Between the lines. The new models don’t just write code: they make decisions, iterate on their own work, test applications as a human developer would, and refine the result until they are satisfied. “I tell the AI ​​what I want to build. It writes tens of thousands of lines of code. Then it opens the app, clicks the buttons, tests the features. If it doesn’t like something, it goes back and changes it on its own. Only when it decides it meets its own standards does it come back to me,” counted Shumer describing his experience with GPT-5.3 Codex. What changes with self-reference. Until now, each improvement depended on human teams spending months training models, adjusting parameters and correcting errors. Now, some of that work is performed by AI itself, accelerating development cycles. Just like share Shumer and referring to METR dataan organization that measures the ability of these systems to complete complex tasks autonomously, the time that an AI can work without human intervention doubles approximately every seven months, and there are already recent indications that that period could be reduced to four. And now what. If this trend continues, by 2027 we could see systems capable of working autonomously for weeks on entire projects. Amodei has spoken of models “substantially smarter than almost all humans in almost all tasks” by 2026 or 2027. These are not distant predictions, since the technical infrastructure for AI to contribute to its own improvement is already operational. And these capabilities are what are really turning the technology industry on its head. Cover image | OpenAI and Anthropic In Xataka | We have a problem with AI. Those who were most enthusiastic at the beginning are starting to get tired of it.