Cool to see this from Brian Hie, who was doing interesting computational bio research at Meta's FAIR before they axed it. Interesting that this is work on the more physical/testing/manufacturing level than the computational, but it seems very useful.

It's hard to quantify the impact of new foundational tools like this at launch. Most of the time it falls flat, but even the successes are difficult. For example, CRISPR has led to interesting experiments and treatments on the way, but the effect does feel muted compared to the initial predictions. But there are many other related techniques that can be pulled out of this original research (e.g. dCas9 which lets you operate without cutting).

Similar story with cellular reprogramming.

Eventually one of these things will surface that will be GPU/transistor type innovations.

> but even the successes are difficult.

Yeah, it feels like we need a phase transition in the speed and practicality of the process. But I don't believe we need a single concrete lab tech.

Years ago when I did research, my impression was that there was complexity galore. A researcher on Drosophila developmental signaling would have a very disjoint knowledge domain than that of a researcher in horizontal gene transfer and antibiotic resistance. Both would exist in a different planet altogether than a clinician prescribing a cancer treatment. And the three of them would generally lack the tooling that somebody doing systems biology was used to.

So, to me, the key thing we need is some sort of "domain cement", or a good way to pull operative knowledge and usable skills from everywhere.

> Eventually one of these things will surface that will be GPU/transistor type innovations

Why do you think that?