Upload Yourself for Better Tech Transfer

This week’s post is a cross-post with Reinvent Science (thanks for writing with us).

This post touches upon one idea from our series on “Dark Matter: Harnessing the lost innovation of deep tech wind-downs”, where we discuss how IP is lost during startup shutdowns and opportunities to rescue it. You can check out our earlier posts on Substack (part 1, part 2, and part 3) or read our complete white paper on our website.

Only 1 in 4 of the inventions developed at U.S. universities are ever licensed,1 and fewer still reach a market. We spend billions generating innovation — academic research expenditures topped $109 billion in 20242 — and then fail to move most of that science the last mile into a product.

The usual explanation is a lack of money or market fit. Another deeper problem is what we call dark matter—the tacit know-how, the failed experiments, the undocumented intuition that lives only in the inventor’s head. A patent is the visible tip; the dark matter is the invisible mass that makes the thing work. Strip it away, and you are left with a document nobody outside the original lab can operationalize.

The data backs this up. Across 50 years of spinouts at Stanford University, the top-earning patents were licensed by the inventor’s startup, and self-licenses were 3x more likely to generate >$1M in royalties3. Inventor-led startups have the greatest commercial success because the inventor is the nexus of the invention’s dark matter.

Capturing this dark matter was once thought to be like catching lightning in a bottle, but with accelerating AI, we are beginning to have the right tools. Imagine a digital twin of the inventor: an AI model built by combing through lab notebooks, experimental dead-ends, and structured interviews, then made queryable for the next team. Not a novelty chatbot — a working interface to the reasoning behind the invention. Why did you abandon that pathway? What did you do ‘that one time’ it worked? What would you try next? These answers are available in conversations around the water cooler and lab bench but vanish the moment a team disbands (or a student graduates).

Building a digital twin of an inventor differs considerably from the omnipresent ambitions of building an ‘AI scientist’. Peer review4 is a far lower threshold to cross than true tech commercialization. Breakthroughs often come directly from mistakes,5 unmeasured variables,6 and subtle unwritten choices.7 A classic example includes the Nobel-winning discovery of quantum dots, which could not be replicated from a new batch of reagents.8 Further investigation revealed a single bottle was responsible for all prior successes; it contained trace amounts of oxidized reagents that were essential for producing the successful reaction.9 Real innovation lives in the fringes of the wet lab, in the recesses of dark matter in a scientist’s mind – in the undergrad intern’s subconscious preference for a specific reagent bottle.

The opportunity is largest exactly where the loss is worst: in hard tech startup wind-downs, where we estimate 60% of informal IP simply evaporates. A digital twin captured before the lights go out could let the next founder pick up where the last one stopped — instead of repeating a decade of mistakes.

Somewhere right now, a PhD candidate who knows exactly which bottle to reach for is about to graduate — and no one is writing it down. Multiply that by every lab, every wind-down, every “that one time it worked,” and you’re looking at the largest untapped reservoir in deep tech. A digital twin of the inventor is how we finally bottle the lightning before it leaves the room.

We don’t need to wait for an AI that can do science to rescue the science we’ve already done.

Virginia Emery, PhD, has worked in the advanced biology and agriculture sector for over 10 years. Virginia was the founder and CEO of Beta Hatch, a first-of-a-kind, sustainable waste-to-value agtech company developing technology ranging from CRISPR to robotics. She is now a Partner at Gliding Ant Ventures, where she works with companies in biotech, marine energy, machine learning, and space technologies. She earned a PhD in Biology from UC Berkeley.

Jared Silvia, PhD, has worked in the energy and materials sector for over 15 years, as a consultant at McKinsey & Company, Director of Product Management and Marketing at Doosan GridTech, and CEO and Co-founder of BlueDot Photonics. He is now a Partner at Gliding Ant Ventures, where he co-builds deep tech companies with technical founders from science and engineering backgrounds. He earned a PhD in Chemistry from MIT.