The dual PhD problem of today’s startups

Obviously that is not sustainable. If society is to continually innovate, you need to stop building innovative systems and start growing them. One approach to this is true AI; not improving your NLP algorithm by 10% with 3x the math complexity IE the transformer model (quote taken from Michael Stonebrake, although he was referencing database research), but by building math that can grow math.

Hell even math is becoming a road block (try integrating THAT Bayes!). The point is as long as we must learn to build, we will run into a wall as humans have finite lifespans and don't scale horizontally (nor do they want to). If we build something that we can feed or point to un-wrangled, raw information into such that it can learn on our behalf, we might have a shot.

Now BACK to pumping out small improvement papers, innovators!

The ones that do succeed end up spending what money they made to keep the deck stacked their way and crush opposition. It's not so much that there's no barriers to entry. They exist, it's all the competitors that have VC money ready to burn to keep you out of the game.

Example: EV wouldn't have really taken off without Tesla battering the living shit out of it. Now the other manufacturers are starting to play catch up after suppressing it for decades. It's not like we miraculously discovered the technology for EV drivetrains a decade ago. It's been there all along, and every single one of those fuckers has been stomping on any and every initiative with a warchest of money to make sure it doesn't happen.

Looking back after all these years, "invest in people not in products" seems like nothing more than glorified lip service.

I want to agree with the article but I have no skin in the game. The only VC tier stuff I was involved it was F&F angel investing and it has worked out quite well, but the scale of money and time needed for "hard sciences" is beyond my level of expertise, and, I imagine, beyond the expertise of most VCs out there.

In short, I suspect most VCs do not know what they are doing when it comes to investing, given the paltry ROIs for most of them. So the article is really restating that in a different sort of way.

For a pure biology undergrad who is probably med school bound, learning ML is superfluous so you don't see it in the curriculum at the undergrad level, unless there are specific concentrations offered for computational biology. A bioinformatics program may even just have you take these ML classes from the statistics or CSE department rather than offer some bioinformatics-specific section within their department.

When I was in grad school I knew a bunch of grad students who worked on biotech/bioengineering experiments. They would have to take care of their experiments like they were pets, nurturing them and making sure they were well taken care of, because if they died on you, that's months of effort down the drain. Vacations had to be carefully planned, and people had to be delegated to keep those little critters alive.

Whereas folks running experiments with non-living things could actually work 9-6 and take vacations. Computational folks could run their experiments while sitting on a beach in Hawaii (with an LTE signal of course).

Bio is just a different beast.

The worse thing is? Many of these biotech graduates actually struggle to find well-paying jobs after, despite how hot the field seemingly is.