AI is rapidly getting smarter. Now, humanity can too. Today, @nucleusgenomics is announcing Vitruvian, our newest set of genetic optimization models. Vitruvian’s intelligence model can optimize embryo DNA for 14 IQ points — nearly a standard deviation. The models were trained on 1,000,000+ people, validated across 40,000+ siblings, and used more than 7 million genetic markers. In Superintelligence, Nick Bostrom proposed genetic optimization as a key way for humanity to keep pace with rapidly advancing AI. Bostrom’s vision is no longer theoretical. Genetic optimization, like AI, has followed a scaling law: as datasets have grown, so have model capabilities. This trend will continue. Parents across the world now have the choice to substantially increase their child’s intelligence. And the next generation can choose to do the same. AI is no longer the only intelligence that will compound. Humanity can now direct its own evolution.

When I went to Penn, I began to be influenced by this guy named Paul Graham whose essays I started reading. Graham is one of the godfathers of contemporary Silicon Valley thought, who espouses precise yet substantive ways on how to found and run a startup. His essays revolve around software and programming. As I read them, I began realizing that the logistics of starting a gene editingcompany — not exactly a software company — would not be easy at all. I ruminated on how one could go about doing it, or whether it made sense to start a more trivial company that wasn’t rooted in biotech. I figured the best thing to do would be to learn computer science. Maybe I could combine it with gene editing and biology in some way? This led to two of the best decisions I made in college, for they would both prove to be pivotal in starting Nucleus: 1) taking computer science and bio-stat classes, and 2) reading CS183: Startup. The latter are notes I found online from another billionaire guy named Peter Thiel. Remarkably, in classic Thiel fashion, he had a brilliant little essay comparing biotech and AI (in 2012!) and evaluating which one to pursue. He actually said, in what may be truly one of the most prescient investment calls of the twenty-first century, AI is “contrarian and under-explored.” If you want a cool thing to read … go read that. He also remarks on genome sequencing, longevity, and computational biology where he writes: “One big question is the extent to which biological problems can be reduced to computer problems? The cost of DNA sequencing is falling rapidly. It cost $500 million to sequence a genome in 2000. Now that’s down to something like $5,000. Within a year or two, it will probably cost $1,000. The question is whether we can do as much as people have been assuming we can with all the information this will yield. The Human Genome Project was seen as incredibly revolutionary in the late 1990s. But it hasn’t quite lived up to the hype. Perhaps it was all too early or too costly. But the second cut may be that it’s because the main problem is not a sequencing problem at all. The biggest problem may be that we just don't know what to do with the data. Exactly how much of biology is computational is still an open question.”  This got me thinking. Is this the way to combine genetics and programming? It also got me thinking about what happened to genome sequencing. How does it make sense that reading the entirety of our source code has led to … nothing? What??! Interestingly, I remember also reading at the time Graham mentioning in his essay “How to get startup ideas”: “So if you're a CS major and you want to start a startup, instead of taking a class on entrepreneurship you're better off taking a class on, say, genetics.” He then continues: “The most successful startups generally ride some wave bigger than themselves, it could be a good trick to look for waves and ask how one could benefit from them. The prices of gene sequencing and 3D printing are both experiencing Moore's Law-like declines. What new things will we be able to do in the new world we'll have in a few years? What are we unconsciously ruling out as impossible that will soon be possible?” From my vantage point in 2018–2019, I felt Thiel was being too bearish in his essay about genomics. We could now engineer human life, I couldn’t imagine a technology more consequential than that. Between Graham and Thiel, what I had been grasping at coalesced: apply computation to genomics as a way to get started now. That is, harness the wonders and efficiency of software to get a genomics company off the ground. Then, eventually, incorporate the far more expensive, regulatorily complicated, and technical challenges required for a gene-editing company, which I viewed principally as a human optimization company. In other words, on Thiel’s computational processes to biological processes axis, start from the right, then move to the left. Read more in link —>