> ## Content Index > Fetch the complete content index at: https://hoeijmakers.net/llms.txt > Use this file to discover other available public pages before exploring further. # When AI enters the R&D lab - URL: https://hoeijmakers.net/when-ai-enters-the-rd-lab/ - Published: 2026-10-13T11:51:58.000Z - Updated: 2026-10-13T11:51:58.000Z - Description: AI becomes especially interesting in R&D when it enters a deep research environment: years of experiments, evidence, language and accumulated human reasoning. - Author: Rob Hoeijmakers - Tags: Artificial Intelligence, Quantum Computing, R&D For some time now, I have been trying to connect two subjects that increasingly occupy my attention: artificial intelligence and quantum computing. There is an obvious connection. Both are developments in computing, both are moving quickly, and both may change what computers can do. But that always felt superficial to me. Put two important technologies next to each other and it is easy enough to imagine how they might eventually interact. I was looking for something more concrete. Then I came across Microsoft's Majorana 2 quantum processor. Microsoft says AI played a role in developing the materials behind its latest generation of quantum hardware. The new material stack, which replaces aluminium with lead, was designed with the assistance of agentic AI. Microsoft says the resulting qubits have mean lifetimes of around 20 seconds, more than a thousand times longer than those in Majorana 1. There it was: a real connection between AI and quantum computing. But the more I looked at it, the less interested I became in the fact that two technologies had finally met. I became interested in what the AI had been given to work with. ## Twenty years of research Microsoft has been pursuing topological quantum computing for roughly two decades. The company has spent years developing materials, fabricating devices, running experiments, measuring results and trying to understand an unusually difficult area of physics. Majorana 2 is the latest result of that research programme. Microsoft's claims are substantial and remain contested. Researchers have continued to question whether the evidence fully establishes the topological states on which the approach depends. Microsoft disputes those criticisms, and its latest hardware is now also being made available to DARPA for independent testing and evaluation. That scientific debate matters, but it is not the part of the story that caught me. What interested me was the environment in which the AI was operating. Twenty years of R&D produces much more than a stack of scientific papers. It produces measurements, experiments, simulations, device configurations, failures, laboratory notes, hypotheses, code, interpretations, abandoned approaches and decisions about what to try next. Some of that material is highly structured. Some of it is numerical. Some of it is governed very tightly by physics. But a great deal of research is also language. Scientists constantly translate between the physical world and words. They describe what they observed, what they think it means, why an experiment failed, why one explanation seems more plausible than another, which assumptions they made and which question should come next. Over twenty years, that becomes an enormous record not only of what is known, but of how knowledge was created. And that strikes me as unusually fertile material for AI. ## More than a knowledge base I often argue that AI works best when it has proper knowledge to work with. For an organisation, that might mean giving an AI system access to reliable internal documents, data and context rather than expecting a general-purpose model to somehow know how the organisation works. That is already a powerful use case. But R&D introduces something different. The material is not simply a body of established knowledge waiting to be retrieved. It is the accumulated process through which knowledge is still being made. It contains things that turned out to be correct, things that turned out to be wrong and things whose meaning may still be unclear. One experiment may only become interesting when considered alongside another experiment performed years later. A failed approach may become relevant again because a material, instrument or theory has changed. No single researcher can keep all of that in active memory. AI does not solve that automatically. Twenty years of poorly organised research is still twenty years of poorly organised research. Provenance matters. Experimental conditions matter. The distinction between a measurement, a hypothesis and an interpretation matters enormously. But if that corpus is organised properly, and if the models and reasoning systems operating on it are good enough, this seems remarkably close to an ideal environment for AI. There is a huge body of information. There are relationships to find. There is language to reason across. And crucially, there is often harder evidence against which that reasoning can be tested. The model does not need to replace the physicist. It may be valuable because it can traverse the research history differently. ## AI inside R&D That is what I now find interesting about the Majorana story. Not that Microsoft used AI. “AI” has become such a broad category that the statement alone tells us very little. The interesting part is where the AI was placed. It was placed inside a mature research programme, surrounded by years of accumulated experimental work. That is quite different from giving a chatbot a difficult scientific question and asking it for an answer. It is closer to adding a new reasoning capability to an existing research system. And once you look at it that way, the relationship between AI and R&D becomes more interesting than the relationship between AI and quantum computing. Research already produces exactly the kind of material that increasingly capable AI systems may be good at working across: large bodies of evidence, language, relationships, uncertainty and accumulated reasoning. The AI can then become part of the research loop itself. Experiments produce evidence. Researchers interpret it. AI helps connect, search or reason across it. That produces new questions or possibilities. New experiments follow. Their results return to the corpus. The knowledge base is not static. It grows because the system is using it. Microsoft increasingly describes its quantum work in terms of combining AI, high-performance computing and quantum technologies to accelerate scientific discovery. I find the more concrete version more compelling. Twenty years of research created an extraordinarily rich environment. AI has now become another way of working inside it. That was the connection between artificial intelligence and quantum computing I had been looking for. But it also changed the question. Perhaps the interesting thing is not simply what increasingly capable AI can do. It is what it can do when we give it twenty years of serious human work to think with.