Term Sheet Rigetti — Wednesday, October 25
TERM SHEET: You led Rigetti Computing’s Series A round. The quantum computing company has raised nearly $70 million in venture funding. Why did you decide to invest? PANDE: @Rigetti has a full-stack operation where they create their own chips, they build their own computers, and they write their own software and applications. That full-stack approach will be critically important in this juncture of quantum computing where we are still working to design the first machines. Many companies, like @Google and @Intel, feel that there is huge opportunity for #quantumcomputing right now to be able to do certain tasks dramatically faster than the way traditional computers could work. People have been talking about quantum computing for at least 20 years, but I think we’re now seeing a big shift. For the last two decades, we had been trying to work out the fundamental science of quantum computing, and right now, a lot of the scientific advances are done. The next steps are to figure out engineering advances. In other words, we’re asking questions about how we scale up chips, rather than how we build the fundamental devices themselves. More and more companies are working on building a brain-computer interface, which would allow the mind to connect with artificial intelligence. Facebook is building a BCI that would let people type with their mind, and Elon Musk launched Neuralink to create devices that can be implanted in the brain. What do you think about the future of these innovations? I think it’s a super exciting area. This is another example where tech is meeting biology in stride. There are so many more advances than our understanding of neuroscience and the brain that happened just in the last five or 10 years. It’s very natural to apply machine learning to this brain-computer interface because the computer will have to understand and decode our thoughts — and that’s something that would be very hard to achieve without machine learning. This is very much the topic of science fiction past, but like any other technology, it will start off simple and evolve from there. The simple things will have a huge impact on human health. A brain computer interface could dramatically change the lives of quadriplegics and paraplegics, for example. As the technology gathers some scale and as machine learning gets stronger, these innovations will get closer and closer to your science fiction dreams. The cancer immunotherapy market is projected to reach $111.23 billion by 2021 and there’s been plenty of VC activity there lately. Why are we seeing more of that now? The oncology area is super interesting to me because many of us have felt that drugs don’t really kill or cure people — your immune system cures you. Even something like an antibiotic, which will kill a bacteria in a Petri dish, is largely administered in doses designed to weaken the bacteria so that your immune system can go after it. The immune system is really key. Machine learning will come into play more and more, and we’re seeing biology companies add a layer of tech that will help them accelerate innovation. What are some interesting innovations going on in the biotech space that Term Sheet readers should know about? There are a couple of different spaces that are getting interesting. One is diagnostics. We’re seeing diagnostics companies use AI to create new tests that have much higher accuracy, much lower cost, and typically diagnose things much earlier. In a sense, the genome sequencer is almost like a smartphone. With that one piece of hardware, you can run many different types of tests. That trend is very much blowing up. What do the next 10 years look like? Will we all have brain implants and be able to edit our genes as we wish? I think a lot of medicine will become like dentistry. Dentistry is a great example of doing things preventively. When you get an X-ray, for example, maybe you have a cavity and you just treat it. It’s not like you’re waiting until you’re 80 years old to finally see the dentist. I think in 10 years, we’ll have cancer tests such that you take them once or twice a year, find out you have early-stage cancer, take care of it early, and move on. The big trend is that we’ve got all these new data sources and machine learning to take advantage of and the ability do something actionable.