In case you're wondering, my definition of "hard" is the CS definition: there are "trivial" problems ("send this structured text over a network connection"), there are "straightforward" problems ("scale this website to X requests per second with a median latency of Y"), there are the "hard" problems ("translate english text to another language"), and there are the problems we suspect are intractable ("solve the halting problem"). Pretty much all of the wealth generation in software over the last 30 years has come from the "trivial" and "straightforward" problem sectors. Even Google, when you back out a bit, was the result of the careful solution of straightforward problems. It's not like they "solved" information retrieval or machine translation -- they just took existing research, and scaled it.
In contrast to CS, biology is loaded with stuff that shows potential, but that we just don't understand very well. The money isn't in making bacteria glow, it's in making bacteria glow in a way that provides a reliable biosensor, or making those bacteria produce things that are safe to put inside people. The limiting step here isn't access to robots -- it's our horrible understanding of the underlying biological systems. There's very little (if any) biotech stuff out there that is a "straightforward" problem away from commercial success.
And, while the design cycle for biology is longer than CS, it's not that bad, especially now. Given that I have decades of computer programming and wet biology in my background, I guess you can 'trust' that I'm not one of these guys that thinks progamming a cell is as easy as programming a computer. But I do also frequently lament that the approach to biology is often the 'script kiddie' approach.
Among academics and in industry, I OFTEN see that the operator's understanding lacks depth (not the field). We were having a problem with DNA shearing once, and I asked all of my coworkers if they could explain to me the chemical mechanism of DNA shearing and not a single person could even postulate a testable experiment that could have a positive effect on both what we were trying to do and our global understanding.
In another case, I asked around if anyone knew the mechanism of electroporative transformation into E. coli. Looking it up (the answer had been known for about two decades) resulted in a small change in our transformation procedure (at no extra cost!) and a reproducible 20x improvement in the transformation efficiency of 500+kbp DNA.
Yeah, maybe "we just don't understand it well", but it's often because the scientists don't understand it well, not the corpus of humanity as a whole. Now: I'm not saying we have a comprehensive understanding of biology, I'm saying it what we the culture have is enough that simple, useful, garage operations are really in striking range.
Yeah, I know. I was (unclearly) trying to draw the comparison to the research into correctness. It's impossible for the general case, but people still try to make useful progress with the specific cases.
"A large reason why these have not been commercialized is because of the strong IP culture that permeates biology, and the chilling effect that has had."
I sort-of agree with this. I think one major difference between biology and CS is that CS has a long history of public knowledge about systems that tends to build on in ways that become more abstract (and more powerful) over time. A lot of biological knowledge is public, too, but most of the commercially relevant knowledge is locked up.
"This also creates a bias towards intractable problems. The more difficult your problem is, the less likely it is to have been patented, so there is an incentive to work on those problems."
I totally disagree with this. I've never encountered anyone in the research world who has shied away from a problem simply because there were patents involved. There are a lot of patents in software too, and in my experience, we treat them the same way: we ignore them, and deal with the consequences later.
"Among academics and in industry, I OFTEN see that the operator's understanding lacks depth"
Yeah, OK. I grant you this (I've encountered it, too), but I still don't see how this is meaningfully affected by the stuff we're talking about. Bio hacker-spaces and robot-rental startups do not make this problem go away.
"I'm not saying we have a comprehensive understanding of biology, I'm saying it what we the culture have is enough that simple, useful, garage operations are really in striking range."
Yeah, well...I'm still waiting for examples. ;-)
I tend to believe that the market for good biotech ideas is efficient: there are enough extremely talented people working in the space, and enough money chasing plausible outcomes, that if there's an idea out there that doesn't take decades of basic science to find product-market fit, it has a company working on it already.
More to the point, though, I definitely do not believe that the rate-limiting factor for the creation of new biotech startups is access to machinery. It's access to knowledge, and therefore, this kind of "we're on the cusp of a biotech revolution!" stuff is, well...hyperventilation.
I mean, I'm not a complete idiot here. I do have a PhD in biophysics/chemistry/biology and four years of postdoc experience. I've designed expression constructs nearly from whole cloth; I've done spectroscopy on metastable peptides; I've produced, characterized and sent for testing a mutasynthesis product; I've improved an enzyme four-fold. Of course all of those things were done in 'real labs', but the last two were done under budgetary pressure where there was a strong emphasis on getting things done cheaply (UMD is not very well-funded, J. Craig Venter Institute was making a painful transition from hard money to soft money). A lot of those lessons I learned under budgetary pressure translate to doing things in the garage, except, more extreme.
I think the primary obstacle to people doing garage biology is not that the biology is hard, but that there are generally two classes of operators, those who have little biology experience, and those who have worked under coddled environments. The real trick is to find things that are maybe a little hard, but not insurmountably so, but with one or two difficult pain points. And which are also interesting. The first class of operator is not generally capable of identifying those qualities, the second class of operator is out of the habit of identifying those qualities.
Biology really does mostly work, you're just looking at a biased system where people are just throwing money at problems and also looking for reasons to justify continuing to throw money at them (so they have to do hard things). That also creates silly folkways like the 'biology is hard' trope which makes the operators miss obvious things because it's just easier to say 'biology is hard' than to think about the problem and figure out how to solve it cheaply and effectively.