There is another incredibly interesting meta-discussion implicit in that article. How does someone very intelligent like Peter Thiel make investment decisions

There is another incredibly interesting meta-discussion implicit in that article. How does someone very intelligent like Peter Thiel make investment decisions in an area where he lacks a huge amount of background knowledge? He can obviously evaluate the team, he can ask experts he trusts to give him their opinion about the value of the approach, but... biotech start ups, far more than software start ups, live or die by the strength of their technology. Transplants have far fewer false positives than working with cell lines (especially HeLa!) - but they are also several orders of magnitude more expensive. Is the trade off between decreasing false positives versus increase in experiment costs worth it? That company obviously think so, but it's not a no brainer, and it's very difficult to seriously evaluate. Mice models are very expensive - and they are better than HeLa, but they aren't foolproof. In cancer biology, there's a continuum between how expensive a technique is, and how much it recapitulates the actual biology of the disease. The sweet spot also varies hugely from disease to disease. Another point which I find fascinating, is that more or less everyone who has worked on cancer biology knows that HeLa are not really a realistic model... yet people keep using them, why? Because a lot of researchers are interested in publishing papers in prestigious journals, not discovering robust findings. A pharma company instead has a lot more incentive in getting things right - because a splashy result that gets falsified at phase 1 is an incredibly expensive failure, while it's still a Nature paper for a professor. I don't think that academia should exist to serve pharma companies, but the problem of false positives is very real, and all the incentives around the publishing/grant system make it much worse. We should be rewarding researchers that get moderate but robust results that hold up under scrutiny, not people who make incredibly splashy discoveries that later turn out to be incredibly narrow and difficult to reproduce.