Automating Research

I started Transformer Lab because I wanted to dedicate my career to the work that could have the largest possible impact on the world. I would like to take some time to walk through why I believe accelerating the pace of research is humanity’s most important next frontier.

We have all just lived through a revolution. In a few short years, large language models went from a research curiosity to something that changed how we write, code, learn, and work. It felt like everything changed at once.

But I believe what comes next is bigger. Not because the models will get a little smarter, but because of who will be doing the inventing.

The revolution we just witnessed was created by people. A handful of foundational insights—the transformer, scaling laws—discovered by a small number of researchers, then built out by elite teams of human engineers. That is the left side of the diagram. Today we are in the middle: AI is already helping those teams move faster.

The right side is the part that changes everything. When AI can do the research itself, we are no longer limited to one OpenAI. We can create the insights behind OpenAI, and the labs that turned them into products, at the turn of a dial. The revolution we just lived through stops being a once-in-a-generation event and becomes something we can run again and again, on demand, in every field. This is what researchers call Recursive Self-Improvement (RSI), and it is the most powerful compounding technology we have ever conceived.

The Engine of the Trillion-Dollar Era

It is worth pausing on just how much human effort sits behind the current AI titans. The trillions of dollars in value generated by the leading labs were not produced by the foundational insights alone. They were produced by armies of machine learning engineers who spend their days tweaking architectures, managing datasets, diagnosing training runs, and slowly wringing out efficiencies. Those engineers are the engine.

The core claim of Recursive Self-Improvement is that we are astonishingly close to replacing that entire human effort with AI itself. An “AI Scientist” is not a glorified coding assistant; it is a system that can automate the hypothesis generation, the architecture design, the experimental coding, and the optimization. For the first time in history, the thing being automated is not the labor around discovery, but the act of discovery itself.

It is, essentially, an OpenAI in a box.

When an AI can autonomously build a model, evaluate it, make it more powerful, and rebuild itself without human intervention, the timeline of innovation fundamentally breaks. The development cycles that currently take a skyscraper full of human PhDs four years to accomplish could soon happen in a matter of months. Once AI takes over its own research and development, the progression of technology is essentially unlimited.

The Biological Speed Limit of Discovery

And this is not just about building better AI. The same loop that builds models is the loop behind every discovery humans have ever made, and it has always had the same bottleneck: us.

Every physical innovation we rely on today follows a strict loop: read the literature, propose a hypothesis, run the experiments, analyze the results, and publish. For all of human history, from Thomas Edison burning through thousands of filament materials one at a time in Menlo Park to Noam Shazeer and a handful of coauthors at Google seeing the shape of the transformer where no one else had—and then writing the code and running thousands of experiments to find out if they were right—this loop has been bound by biological speed limits.

Humans need to sleep. We read slowly. And before a single hypothesis can even be proposed, we have to train the person capable of proposing it—years of schooling, then a doctorate, then more years at the bench, all to produce one expert in one narrow field. Only then do we take months to synthesize literature, write code, and wait for results. A single turn of the scientific loop traditionally takes months, if not years.

An AI scientist removes the biological friction from the scientific method. It can read millions of papers in seconds, write the code, run the experiments on compute clusters, draw conclusions, and iterate. It breaks the speed limit of human discovery.

The First Sparks

None of this is a distant science fiction scenario for the 2030s. The first sparks are already here.

At Transformer Lab, we recently released Primus. I do not mention Primus because I believe it is the endgame—it is very much a first preview of what is possible. But it serves as proof that this autonomous loop actually works.

We ran Primus for 30 days. In that time, running 30 times faster than a human researcher, it hypothesized, experimented, and wrote over 30 complete, novel papers across domains like seismology, biology, and LLM architecture. It answered questions that had never been answered before, and its findings are already being cited by major labs.

Primus is merely the first turn of the loop. It proves that the friction can be successfully removed. To understand where this leads next, we are now working with leaders in diverse fields across quantum mechanics, silicon design, mathematics, and biology. Our goal is to demonstrate to the world exactly what becomes possible across these distinct disciplines when a system like this is fully scaled up.

The next step is the obvious one: ask a system which is self-improving to improve its own self-improvement loop. That work is already underway, and in the coming months we will share the results.

Unlocking Human Potential

A v0.1 system already runs the loop 30 times faster than a human. Now imagine it has optimized itself to run 100 or 1,000 times faster. The implications will not stay inside computer science. They will bleed into every physical science—from curing disease to materials physics.

This brings me back to why I started Transformer Lab, and why I believe this work is so vital.

I believe that accelerating the pace of scientific discovery holds the greatest possible promise for advancing humanity. When we give more people the tools to solve intractable problems—whether that is mapping the mechanics of a new disease, discovering cleaner energy materials, or unlocking new mathematics—we elevate our collective potential.

Of course, a technology this powerful brings profound challenges. The societal, economic, and safety implications of autonomous research will require immense care, humility, and collaboration to navigate. We do not take these risks lightly, and we are striving to be thoughtful and respectful in addressing them.

Ilya Sutskever famously noted that we are moving from the “age of scaling to the age of research.” But I’d like to suggest something even bigger happening—we are moving into a world of infinitely scalable research. We are entering an era where the power of an elite, full-scale machine learning research team can be placed into the hands of anyone with a hypothesis. We are automating discovery itself. And if we guide this transition with the care it deserves, this infinitely scalable research has the potential to be the most profound un-bottlenecking of human progress we will ever see.