Recursive self-improvement loops

Is continual learning for AI the final frontier?

In my previous apocalyptic post on building a god, I mentioned the concept recursive self-improvement (RSI). It’s another term for continual learning or self-improving AI. The weights in a model contain its knowledge. Through inference, weights remain frozen in time. In other words, the model does not learn new knowledge. It’s why every model has a knowledge cut off date, signifying the date on which its knowledge was last updated during the last training run. Training a model is expensive, takes time, and therefore cannot be performed more frequently. AI companies apply other ways to give the model the perception of acquiring new knowledge — yet the weights never change. These range from different forms of fine-tuning approaches, RAG, vector DBs, prompt engineering, to the broader field of context engineering which encompasses many different factors from the model’s context to build its superficial knowledge. Yet none of those truly make the model learn anything.

Late last year, it became clear to people who knew where to look that self-improvement is what is standing in the path to AGI. We know that AI labs are working hard to solve this problem. This is why they have been trying hard to automate programming through AI, as it paves the way for automated AI research — the most recent Anthropic and OpenAI models have ushered in what many still don’t realize is the beginning of a new world. Automating AI research means that models can write better versions of themselves — eventually autonomously. That is the point that will trigger an intelligence explosion. Models becoming true sentient beings, capable of learning and relearning and improving themselves without human intervention. Of course, we are years away from achieving that.

Or are we?

Jimmy Ba left xAI this week. In his brief X post announcing his exit was a single shocking revelation: recursive self-improvement loops are twelve months out. He knows it, he sees it, and admits 2026 will the most consequential year for our species. What did he mean by species?

At the same time, Roland left xAI to start a company focusing on building self-improving AI. Is it the final frontier? And they all know it?

Then there’s Matt Shumer’s foreboding piece called Something Big is Coming where he rings the same alarm bells as others — I recommend you read it at all costs. Self-improving models are very near, if not already here. OpenAI Codex 5.3 is the first model that was instrumental in creating itself — OpenAI claims that. A model that helped built the next version of itself? It’s already happening!

Hardware advancements are increasing rapidly, and maybe that explains the unbelievable amount of CapEx being committed to AI hardware spend. If recursive self-improvement is truly near, models will need even more compute and memory, the latter of which today is a big bottleneck. Without access to fast memory, these models can’t make the leap to what will eventually become Singularity. Memory hardware is improving and the memory oligopoly — SK Hynix, Samsung, Micron, etc — are coming up with unique solutions to solving bottlenecks in existing memory architecture. AI companies and hyperscalers are moving twoards custom Silicon in part to cut costs and optimize compute spend.

They all see it. That’s why they are spending every dollar they can find on future AI infrastructure build-out — not present, but future. They know what the leading AI labs are building in private. They know what the world will look like, and what it won’t look like. And we know very little.