> But again, what should we do with very strong, very stupid men? There used to be an abundance of jobs for them, some of which paid well. Nowadays there are no jobs for them.
Last summer I took a job in construction. Construction fits the bill of what you’re describing. There needs to be more construction in America
I agree, but it won't be possible to expand construction enough to cover all of the jobs that agriculture lost. Back in 1950 that was almost 5% of the work population. Construction might absorb 2% or 3% under ideal conditions. But some creativity will be needed to reabsorb all of what was lost.
"We compiled data on 250+ US public companies involved in corporate securities frauds identified in 1,000+ Securities and Exchange Commission filings over 2005–2013; we randomly selected a comparable control group of 500+ US public companies from Compustat."
I’m probably preaching to the choir, but yup that inherent uncertainty seems to distinguish biology from the other sciences. There is a huge stochasticity & serendipity in everything because there is no intentionality in the design of any biological component; any convergence toward a chemically or physically optimized component or behavior is driven by evolution by natural selection but remains imperfect. Warts, quirks, and all
It also explains how the folks on HN positing “Covid is just the flu” and “long Covid isn’t real” have been so confident yet so gravely mistaken. They are used to other sciences where there is much less room for uncertainty
Yep. For better or worse i've been up to my eyeballs in "hey, you know, biological systems are actually insane!" since high school, and now through a few advanced degrees :)
I've been working in tech more than normal for a few years now, and the endemic nature of "the andy grove fallacy" among my coworkers has ceased to startle me, but it's just kind of bothersome every time.
Despite the common trope that software/hardware engineers think they understand everything, automation can make exponential development possible in bio. I know someone who works at a biotech company, and the stories I hear lead me to believe that traditional thinking in e.g. medicine and insurance, and from colleagues, really is a bottleneck. Maybe exponential growth will never apply to the number of diseases cured, but it can and should apply to the hardware and software that facilitates the next iteration of biological discovery.
It's not that software/hardware engineers think they understand everything, it's that the absolutely common idea "my way of thinking about understanding things is appropriate in this other domain" breaks down very fast.
I'm not speaking about running hospitals or dancing with insurance companies. I'm speaking about much earlier in the pipeline: fundamental, blue-sky biological research is fundamentally different from software. The reason is that we are not studying designed systems; the effect is that there is so much that we don't know that we don't know that things that sound easy are in fact basically impossible because the prior knowledge is simply not established. (Until they aren't. When does that change? First slowly, then all at once.)
As a biomedical engineer, i'm on board with hardware and software improvements: it's kind of my job. The trick is knowing what you're doing, what you aren't, and what you can expect, and to balance confidence in the value of what you _can_ tightly constrain and design versus humility in accepting that the natural world simply doesn't care.
I think what bugs me the most is the common assumption that we just need better modeling tools to make a lot of the messy lab work go away. Everyone who raises this idea seems to think it's original and revolutionary and a no-brainer, but there's at least 40 years of bitter experience in biotech and pharma development saying otherwise. Even genuinely impressive feats like AlphaFold get inflated wildly in importance (and used to retroactively bash experimentalists for all the time they wasted by not listening to software people). Actually speeding up the entire process of biomedical research requires improvements across the board in many different fields, and it's not something that is magically going to be solved by computer science wizardry alone.
I primarily do modeling, and I am first in line to tell anyone who is willing to listen that the very first thing you should do to learn to be a good modeler is spend a year at the bench first in the problem area you want to model.
Wet lab is so informationally dense in terms of personal knowledge that it's not even funny.
Last summer I took a job in construction. Construction fits the bill of what you’re describing. There needs to be more construction in America