Richard Hanania writing on his Substack:
We kind of keep the train running in terms of these technologies, but I would also argue we keep the economy dynamic. Without venture capital, you would have all of that activity consolidated over time to a very small number of big monopolies or oligopolistic companies. There was a big fear in the American economy in the 50s and 60s that that was exactly what was happening. Then venture capital in its modern form burst onto the scene in the 70s. It’s really kept the American economy much more vital and fresh than it would have been otherwise.
Marc takes many liberties around historical contexts. I understand this is a podcast (and what follows is transcription) but the context gets lost and it can’t be strictly “academic”. Therefore, read it with your blinkers around how monopolies were allowed to fester with insufficient regulatory oversight. There has been an increasing clamour around “reversing” the decision and breaking up technology companies, as they did for the railways in the US – this is far-fetched wishful thinking. It is definitely beyond my purview to discuss the legal nuances around what constitutes monopolistic behaviour, but when it comes to healthcare, these arguments hold more water – especially when it is related to “data concentration”.
This is interesting insight from Marc:
The way to think about what we do is we sort of advance research into development productization. The timeframe our companies can operate on between inception and when they ship the viable product is sort of five to seven years. What you tend to find is if there’s something where it’s like, “ok there’s a new technology which I know is going to result in a viable product but it’s going to be 10 or 15 years out,” it’s very hard to do that from a venture capital mindset, and the reason is because it’s hard to do that from a company building standpoint because if you staff a company with employees, you need a team, and if there’s nothing to show for it after five to seven to eight to nine years, at some point you lose that team because these are talented people who can work at other companies. So it’s very hard to make time scales match for 20-year intellectual journeys.
(emphasis mine)
Marc also spoke about replication crisis in science:
There’s this other amazing study that maybe we can link to for your listeners. It was a study of medical trials funded by a branch of the federal apparatus, heart and lung research. It’s this great chart that shows that new medicines stopped working at the point when the funders of the research require the researchers to register their experiments ahead of time. It’s basically this chart that shows for 30 years or something you had all of these new medicines coming out and the research results were like “wow this really works we should give it to patients,” and then there’s this point where the people running medical trials were forced to pre-register their hypothesis with the funding agency. They were forced to basically say upfront “this is exactly what we’re testing for; here’s exactly how we’re going to measure the results.” Subsequent to that point, new medicines have stopped working.
Anyway, science right now is in an existential crisis. This is a real, real issue, and there’s now a generation of scientists who specialize in pointing this out and analyzing it. Andrew Gelman and others. I’ll give you an example: I had a conversation with the long-time head of one of the big federal funding agencies for healthcare research who is also a very accomplished entrepreneur, and I said, “do you really think it’s true that 50-70% of biomedical research is fake?” This is a guy who has spent his life in this world. And he said “oh no, that’s not true at all. It’s 90%.” [Richard laughs]. I was like “holy shit,” I was flabbergasted that it could be 90%.
(emphasis mine)
Rest of the transcript is related to his other investment activities and general ideas around venture capitalist companies. However, the leeway around these conversations got me to think about the model of science funding, and need to have “academic criteria” or “publications” where the metric of citation index is routinely flogged and abused. Everyone needs job security. However, the current model of funding is flawed too. For countries struggling to break through the publication (and replication crises), it is essential to direct research that impacts end users. Basic science has no meaning, unless it serves as a funnel for monetisation or end users benefiting from it.
China, for example, has a state policy around getting articles published in “high impact journals”. As such, there is a stream of publications around nasopharyngeal carcinomas (each almost like what’s been published earlier). I struggle to understand the idea behind publication of same results in the first place and their key takeaway around its application in other domains. Genomics of Asians is completely different from Caucasians (including the disease phenotypes and henceforth presentations). Yet, there continues to be constant advocacy around “practise changing guidelines”, which attempt to have “one-size-fits-all” and is increasingly being pushed out with disclaimers.
Collectively, we need to question the direction of science, funding models and those projects with the quantitative estimates (measurable outcomes). I was actually aghast during one conference when a $50,000 kitty was “awarded” to a team of “researchers” doing something like what was accomplished, while other compelling ideas were sidelined.
RadOncNotes 