Most of the biotech companies coming out of YC are what I'd characterize as total fliers -- unlike the company highlighted by the Forbes article, these startups aren't being founded by a collection of gray-haired biotech executives with a collective 200 years of research experience in a single disease. And that's fine. One of the biggest problems in biotech right now is that everything is too stodgy and conservative. When I got out of grad school, there was no way of starting a biotech company without putting in 20+ years in academia or at drug company. You wouldn't get funding.

If funding a few grad students for oddball biotech startups leads to a single success, it has the possibility of really opening up the field. And in the worst case, very little money will be lost. I think it's a good thing. If nothing else, maybe it'll turn the attention of smart, ambitious people to something more important and meaningful than photo-sharing apps and YuppieFoodDelivery.com v87.0.

Let a million flowers bloom...as long as they all don't try to crowd into San Francisco, that is.

...most startup companies aren't doing anything new. How many different food-delivery services has YC funded? Photo-sharing services? Video startups? AirBnB wasn't anything new in 2007, either. Dropbox was criticized because everyone knew that file sharing had been done before.

This goes back to what I was saying about biotech being stodgy: the one thing the software industry has going for it is that it's willing to fling seed capital at re-tread ideas. If success is mostly about being in the right place for luck to occur, then the secret is putting enough companies in a place to experience luck. I think that's basically what YC does.

So-called "biotech investors" tend to want to wait around for commercially ready tech to just fall out of academic labs, so that they can avoid putting any investment at all into R&D. There are probably a lot of inexperienced teams that could make a business in biotech if only they had access to cash.

There's a fundamental difference between trying something again where the customers weren't receptive and trying something again where the product couldn't be created. Customer reception can change over time, often quite rapidly and drastically, making previously failed business models successful (ex: Webvan -> Instacart). But nature doesn't just change (at least not over short timescales).

Moreover, as cge explains elsewhere in this thread, you cannot really iterate in biotech:

> Good ideas in software might not catch on, but good ideas in science more often than not turn out to be entirely wrong. Do a closed beta of your software, too, and while your users might not end up liking the software much, it's very unlikely that it's just going to fundamentally fail to work at all. Clinical trials can often end up that way.

Contrast this to business model-based innovation in tech, where a slight tweak can be the difference between a unicorn and a dud.

Another thing I think is different about the industries is that while there are tons of food delivery and photo-sharing startups, there's no really effective/disease modifying treatments for common diseases such as Alzheimer's disease, frontotemporal dementia, ALS, etc, so I think there's plenty of space for any number of new approaches to be tested in the field. I feel it's more worthwhile than pumping billions more into failed antibodies against beta amyloid, as seems to be the trend nowadays.

This is a flawed analogy, because Google's success is largely attributable to business model innovation (AdSense), rather than any core technology, such as PageRank. After all, Robin Li published Rankdex two years before Brin/Page published PageRank, and we all know how trivial it is to engineer around software patents.

> Another thing I think is different about the industries is that while there are tons of food delivery and photo-sharing startups, there's no really effective/disease modifying treatments for common diseases such as Alzheimer's disease, frontotemporal dementia, ALS, etc, so I think there's plenty of space for any number of new approaches to be tested in the field.

That's because the bottleneck in tech is (a) identifying problems actually worth solving and (b) a business model that will lead to a sustainable company. Achieving success means creating a business and iterating to a position of dominance. There was never any question regarding the technical feasibility of Airbnb's site or Uber's app. It was (and still is) all about things like whether Airbnb could create trust between hosts and guests, and whether Uber could navigate the regulatory landscape.

The biomedical space could not be more different. Everyone with a half a clue knows that neurodegenerative diseases are a problem. Nobody who knows the industry is questioning that tons of customers for your (potential) product exist, or that they'll pay boatloads of money for a treatment or cure. The gigantic elephant in the room is whether your product works. Nothing else really matters.

That's something that is strongly influenced by luck. I can point to famous researchers who have made entire careers out of this sort of momentary serendipity, actually.

That's not iteration, that's banging your head against a wall and praying for a miracle. Iteration implies methodically and continuously moving towards an optimal solution, not heading back to the RNG after each failed attempt.

It doesn't help that here, I think, we're seeing a community and media largely centered around software development trying to deal with a field that is far, far riskier, and has far less certainty at every step. Good ideas in software might not catch on, but good ideas in science more often than not turn out to be entirely wrong. Do a closed beta of your software, too, and while your users might not end up liking the software much, it's very unlikely that it's just going to fundamentally fail to work at all. Clinical trials can often end up that way.

> A smart software developer can build and launch a web or mobile app and get paying customers for under $2,000. The same will soon be true for biotechnology.

Moore's law is a unique phenomenon, and it most certainly does not apply in biotech.

2. Hyping up cheaper sequencing the way you are is nothing more than what Sydney Brenner referred to as "low-input, high-throughput, no-output biology" (video source: http://thesciencenetwork.org/programs/reading-the-human-geno...). Also see: http://wavefunction.fieldofscience.com/2015/03/cancer-genomi...

3. Here's a more useful Moore's law derivative that's seen in the life sciences: http://www.nature.com/nrd/journal/v11/n3/fig_tab/nrd3681_F1....

You bring up another good point about "Eroom's Law" in which pharma productivity is actually decreasing, and that article was actually one of the things that got us interested in trying to do something new. What we are trying to do is to help reverse that trend by doing something different than what large pharma companies are already working on - if we keep doing the same thing despite ever-decreasing productivity, then I don't see how we can expect the trend to change.

You can sequence as fast and as cheaply as you like, but the big-O on biological research is not our ability to generate DNA sequences.