Hermann
Tribukait

Hermann Tribukait is not a chemist. He studied economics - summa cum laude at Mexico's elite ITAM, then a PhD at Harvard. Yet in 2017, he walked into a room of 55 of the world's top materials scientists and came out having co-coined the phrase that would define a field: "Self-Driving Labs."

That phrase, now a registered trademark, describes the closed loop between AI, robotics, and physical experimentation - where machines design experiments, execute them, learn from results, and iterate without human bias getting in the way. Tribukait didn't need a chemistry background to see what the scientists in that room couldn't quite articulate yet: that the bottleneck wasn't talent or funding. It was method.

Before founding Atinary in 2019, he spent years designing global innovation partnerships - North American and European public-private collaborations that directed more than $200 million toward accelerated R&D. He represented Mexico's Energy Innovation Funds at Mission Innovation, the global clean energy initiative, where he co-led the Clean Energy Materials Innovation Challenge.

The frustration is specific: researchers guided by bias, exploring familiar chemical territory, relying on intuition and Excel spreadsheets instead of systematic search. Tribukait frames it as a cognitive mismatch - humans trying to navigate a search space too vast for human brains to traverse efficiently.

His answer was to build software that does the navigating. SDLabs, Atinary's no-code AI/ML platform, lets a researcher define their experimental constraints and then sits at the wheel. No robotics PhD required. No code. Just faster science.

Tribukait is multilingual - English, German, Spanish - which tracks with a career built at the intersection of Mexico, the United States, and Switzerland. He's a nature enthusiast with a particular affinity for the sea, and frames Atinary's mission in terms that go well beyond profit: faster development of sustainable materials and catalysts as a path to addressing climate change and pollution.

He talks about R&D with the frustration of someone who sees an obvious inefficiency that the field has collectively normalized. The spreadsheets, the gut instincts, the trial-and-error - not as quaint traditions but as symptoms of a system that hasn't been updated in decades.

The long game is democratization. Tribukait wants self-driving labs to be as accessible as email - not just for well-funded pharmaceutical companies with robotics infrastructure, but for any research team anywhere. SDLabs' no-code design is the proof of concept: a tool built for working scientists, not for AI engineers.

The Boston physical lab is the next step - closing the loop between software and actual bench science, showing that the autonomous experiment isn't just a digital concept but a physical reality that can run in a real laboratory.

His stated goal: unlock limitless science and accelerate the transition to a healthier, more sustainable future. It's the kind of sentence that sounds large until you look at the specific technology underneath it - and then it sounds exactly right-sized.