Solving Billion-User Problems With CTRL-labs

At 19 years old, instead of going to college, Thomas Reardon went to Microsoft, where he built Internet Explorer and helped architect the world wide web. After that, he made an unusual move: He left tech and went back to school, where he studied the classics and had his “ego utterly obliterated” while completing a PhD in neuroscience. (“That’s actually a good exercise, he says of the ego destruction. “I highly recommend it to other founders.”) Today, all of Reardon’s multidisciplinary experience dovetails into CTRL-labs, where he and a team of machine learning scientists are designing the next wave of paradigm-shifting technology: Gadgets that let humans control computers—with their minds.

Genentech, but for neuroscience

I had a long career in tech. I got to ride Microsoft from the beginning, when it was a 4,000-person company, to the world-dominating company that it was by the end of the 1990s. I got to bring to life something that went from zero to a billion users. That’s a once-in-a-lifetime thing. It got me focused on billion-user outcomes; if the problem isn’t a billion-user problem, I’m not interested in it.

But after that, I took a big, 12-year break from working in tech and I got an undergraduate degree in the classics, because I wanted to get as far away from tech as possible. I wanted to rewire my brain, and, frankly, I wanted to get bored. I thought if I were to have any creative breakthroughs on my own, I need to get bored first. That’s how I found my way to neuroscience. I had my ego utterly obliterated by doing a PhD in it. Actually, PhD programs are a good place for VCs to use as a litmus test for whether or not somebody might be a credible entrepreneur, by seeing whether or not they’ve gone through that kind of ego destruction-and-reconstruction process.

“What would be a truly disruptive technology that could move people from an existing technology paradigm to something totally new?”

There was this tremendous amount of neuroscience technology that had been developed over the last 50 years, but it was commercially unexploited. It felt like Genentech in 1975. Thirty years of crazy, academic science went into understanding DNA, but it took years before a company created a commercial model to exploit that science. And that had never happened in neuroscience. Science has built up a phenomenal understanding of the brain, and created a phenomenal number of tools for interrogating the brain, but nobody has exploited it. I felt like we had an opportunity to be Genentech, but for neuroscience and neuro-technology, and I expected an explosion of companies to follow us. There’s been a boomlet of that, but it’s not quite the explosion I expected.

A paradigm-shifting pitch

A lot of tech startups right now are based on a business model of economic arbitrage, where there’s no real new product—it’s just a different way of calling a cab or renting a house. I’ll give an example: We’ve had the QWERTY keyboard on our computers now for 170 years, even though nobody anywhere ever said that it’s the optimal way to have a textual exchange. Then the iPhone came out in 2007, and you have this breakout device that opens up a whole new way of using the internet. But right there on day one was that QWERTY keyboard—this deep tie to the past, because there was an assumption that we’d need this technology bridge. That’s at the heart of what I think of as this business model pitch that’s always anchored to the technology-translation problem.

“The way to exploit this is to do something much bigger than a translation of past technologies.”

There’s another way to go about this. I wanted to know: What would be a truly disruptive technology that could move people from an existing paradigm to something totally new? If you look at our start, the arbitrage opportunity would’ve been in medical biotech, where everything is relative to healthcare outcomes. If somebody’s lost a limb, or someone’s damaged something in the spinal cord, we can help them renew their lives. But that’s a million people, maybe 10 million. I was trying to flip that on its head and say, no, we’re going after the mass market—the way to exploit this is to do something much bigger than a translation of past technologies. We turned down opportunities to take money that would’ve made us into a biotech company rather than, ultimately, a consumer tech company—which is what I wanted to be. 

That’s how we pitched it to VCs. Mostly we heard, “This is interesting, but we don’t even know how to analyze it, therefore, it’s outside of our wheelhouse.” There was a lot of skepticism that this was an ego project on my part, or a flight of fancy. But people like Bill Gates and Paul Allen were hugely supportive, and that gave us some more confidence. They were deep-pocketed, long-range thinkers in our corner. As far as the VCs—as anyone who’s found a company knows, you mostly hear a lot of nos and very few yeses, and you just have to jump, go really hard at the yeses, increase their conviction, and realize that it’s hard for anyone—VC or founder—to know what the outcome will be. Hopefully, you get to an intellectually honest engagement. Even if nothing’s going to happen for four years, because that’s how long it’s going to take to build it, you’ve got to have an honest discussion about what those milestones along the way are going to be.

“Like splitting the atom”

We started as a team of neuroscientists, with no ambition to do hardware whatsoever. We thought we’d have some loose partnership with another company that did gadgets. But we very quickly realized we had to care about hardware, after one of our founders brought up the analogy of the Wright brothers. They succeeded, when other novice aeronauts and airplane-makers failed, because they created wind tunnels. They could model planes and prototype things under controlled conditions, before going out to live conditions.

“We very quickly realized we had to care about hardware, after one of our founders brought up the analogy of the Wright brothers.”

For the first couple of years, we just did wind tunnels. I studied motor neuroscience, which is the half of the brain that controls your muscles. We had an idea of how we could work with motor and neural signals, repurposing them so a person could control machines rather than just their own body. But these were just textbook-level principles. In the neuroscience world, to figure this out, we’d put sensors on somebody, collect hours of data, then go off and crunch that data for weeks. Instead, we said the real breakthroughs are going to happen when our wind tunnel—in this case, our algorithms—can operate in real time, and we can drive feedback to somebody to alter their neural output. We needed that closed loop system. 

If I could show our decks from the first few board meetings, you’d see us saying again and again, “Okay, we’re going to be in this invention-and-discovery phase for a little while, holding our breath, doing these wind tunnels,” which I think was probably terrifying for our investors because they were like, “Well, where are the customers?” And we were doing the complete opposite of that. Our goal was to get to the neural signal, not the muscular signal, because at that point we can completely change the rules of how people control machines. You can literally move beyond your five fingers to a paradigm of 20 imaginary fingers that you can still control, with the same excellence and fidelity with which you control your natural five fingers. 

They were like, “Well, where are the customers?” And we were doing the complete opposite of that.

When we got to single-neuron resolution, we had the ability to actually see the electrical activity from a single neuron. As neuroscientists, that was crucial—we hit a level of atomic information where we can’t reduce it any further. It took many years to get to that point, but we’re unbound now. We can listen to a single neuron, and see all the messages from that neuron. What specific product does that equal? I can’t tell you. But I can tell you it is like splitting the atom, and you just have to imagine a little bit what happens after that.

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