Credit: Wyss Institute
In 2018, 16 different companies formed from the George Church laboratory at Harvard, encompassing a range of technologies from new life forms to dating. This was out of the ordinary, even for Dr. Church, whose laboratory has produced over 30 companies during his more than 30 years at Harvard. We looked into the factors that might explain this, by speaking with some of the founders---mainly students and post-docs from the lab----of this year’s cohort and a few from years past. The January issue of Nature Biotechnology (https://doi.org/10.1038/s41587-019-0369-7) has their stories. Here we spoke at length with Dr. Church to get his thoughts on what is going on.
NBT: If you look over time, there were occasional blips in start-up activity, but in 2018, there was a bolus. Did you see this coming? Do you have any explanation?
GC: I don’t have scientific explanations or much data. My guess was that some of the blips that happened earlier were noticed by people in the lab. In particular, Francois Vigneault. started AbVitro, which was acquired by Juno which was acquired by Celgene. That was a nice exit. And then Luhan Yang just had a nice entrance. She started eGenesis and got $39 million for the first start-up and then $20 million equity free, dilution free money in China for a second [Qihan Biotech], and she just got $100 million. Going back to the concept of time, Francois had a nice exit and Luhan had a nice entrance, and I think they [people in the lab] just said, “I’m as good as they are, they are just post-docs.” So the post-docs started trying. As each one tried it, as each jumped in and discovered there were no killer whales, more people jumped in and it became a positive feedback loop. Also I think they are all very nice to each other. I select for nice people.
NBT: So you do exercise some selection. You did a pod cast that you did with your former colleague Jorge Conde –“What’s in the water in the Church lab?”—he asked you about your selection process, you said they have to be nice people. You said there are lots of bright people around.
What happens is that even if they aren’t, they suddenly feel inspired to try to be and they are surrounded by people who are. It works out. It’s not an instant gratification thing, but now after dozens of years, it’s starting to payback, because you have this whole community that is being supportive.
NBT: That came out in the interviews. There’s the Church ecosystem. And then there’s the ecosystem around Cambridge.
GC: These are all positive feedback loops.
NBT: One thing that that I was told by Grant Zimmermann, your business development officer at Harvard, is that this new group of inventors are taking on being CEO at their companies, whereas in the past they would more likely be Chief Technical Officer or Chief Scientific Officer.
GC: I don’t necessarily encourage that. Most scientists aren’t very good as CEO, although being a professor is kind of like being a CEO, but anyway, it’s something you need to be cautious about.
NBT: You have built up a critical mass of people with the skill set to push technologies out.
It’s kind of a rare mix. Most labs don’t want to do technology at all. There are a few labs who do technology in the form of early adoption, then there’s a rare set that will do radical, disruptive technology and then there’s a rare subset of those who are willing to take it to the finish line---meaning developing an instrument that is ready for market, or molecular technology that doesn’t require an instrument.
NBT: Surely, there is some risk involved. Can they all success or maybe a better question is what might success be?
GC: That’s an interesting question. I can tell you what the venture capitalist definition is. They have to make more money than they put in. They have an expectation of about 80% failure, 20% success, by that definition. That’s kind of an industry norm with a lot of variation.
My definition or my people’s is that if the technology makes an impact on society and the people who brought it to society stay employed, then it doesn’t really matter if the investors make the money back. Now this is not something that you want to advertise to investors, because we are honestly trying to help them play their game, but they realize there is an 80% risk of failure and we’re just saying we know that too. We’re saying that there is a second way to succeed---not undermining their game at all.
I think there are very few that totally fail, where the technology disappears, and the people disappear and they become insurance salesmen or pencil salesmen or something. At least for our group.
NBT: Have there been any disappointments and what have you learned from them?
The few that have happened, where they have had a big loss---it almost never happens---at least inspires competition that does well. That’s okay with me too. If the idea gets out there, I don’t really care who gets credit for it as long as it has a positive impact. So there are some cases where we have been out competed, sometimes by a second or third or fourth team to the game. Sometimes being first to the game isn’t necessarily the right timing. It’s funny how academics fight over being first, it’s only business where being second is better on average. I’ve seen plots of this. That’s one way [to fail]. The other way is a CEO mismatch. Very often CEOs don’t have skills in the particular domain and that’s why it’s refreshing to have post-docs who want to be CEOs, but it’s only scary because they often don’t have the complementary skill set. People with the complementary skill set sometimes just don’t understand the culture. We had one CEO who will remain nameless, who never worked with fewer than 600 employees. All of his business was with big companies, so he immediately wanted to turn our little company into a big company. So he hired a bunch of sales people and they were all incredibly good at selling stuff, but we were losing a penny on every dollar so the more we sold the quicker we burned through our VC money. It terminated. And we started another company, recovered all the IP and just started again. In the worst case, it’s still not so bad.
NBT: One of the images I took from Jorge’s podcast- you say you ask “What if we try this?” You try it and if it doesn’t work, you put it on the shelf. Does that explain some of what has been happening? Did you take a lot of things off the shelf in 2018?
GC: I think my point was a lot of people reject ideas without even trying them. Other people will try them and then throw them in the trash. We put them up on the wall and glorify our failures as challenges for the future. We don’t necessarily have to stick to them, and bang our head against the wall every day for thirty years. We can put them on the wall for a while. That was the point of that. I don’t think it explains everything. Many of our ideas take off immediately or right away—next generation sequencing or CRISPR—we barely had introduced the idea and it stuck.
I think the thing is that it wasn’t so much that they had been incubating on the back burner for a long time. It’s more like what we have a certain number of balls in the air that we are juggling, many of them are in public view, people see them but they don’t trust them. We trust them because we were very close to them—we either developed them or co-developed them. So we can collide them in the air and they make new balls. You just keep generating a bunch of stuff that is barely working, but you keep using them as if they were working while everybody else is waiting. They are doing the wait and see. We don’t do the wait and see because we trust them. The more you have hidden in plain sight---everybody sees it but they are not using it—the more advantage you have. A lot of good inventions are collisions of previous inventions.
NBT: I do see some parallel lines or through lines. For instance, we were interested in Nebula- as you look backwards from there, you see PGP, and going back further, then Knome. Would you put a through line these groups?
GC: Absolutely. We do all kinds of experiments. We do the obvious experiments at the bench, we do different business models, social engineering. Each of these is an experiment. So we’ve done almost every way of doing sequencing. We’ve been involved with thirty different next gen sequencing methods---single molecules, fluorescent in situ, nano-force—and we’ve done almost every way you do the business models. We were involved with 23andme, Knome was started at the same time, opposite ends of the spectrum in terms of medical relevance and comprehensiveness. We were involved in Genos, the first marketplace idea, Veritas, which was the first one with under a thousand dollar direct to consumer genome, but with a physician involved, and with whole genomes. In almost every stage, I’ve been fairly consistent with whole genome with a few exceptions. But they are all experiments, and I knew pretty early on that there would be a paradoxical time when this amazing technology would be sitting there facing everybody and people would say, “No thanks.”
It’s like when I was introduced to computers, it happened at a very young age, and I just loved them, from 1963 onward. I just could not understand why everybody else didn’t love them. It seemed to be meteoric when we look at 1993 when suddenly it went from zero websites to millions but it was mostly a slow process. [It took] figuring out what the right tipping point event was. For the internet it was a web browser. Just a simple web browser on a Macintosh. I think for genomics it might be, I’m guessing, my latest guess is that it’s dating. You just need something that is de-medicalized but very impactful. I don’t think it’s gene therapy, but I could be wrong, unless it’s gene therapy for aging reversal or some gigantic market.
Are there any generalizations that would pertain to labs other than your own?
Sometimes it’s the exceptions rather than the generalizations.
These positive feedback loops, they reward concentration. They reward having MIT and Harvard almost in the same place, having almost all the pharma companies in the same place. And having all the crazy interdisciplinary people in the same lab. There’s something to be said for high concentration. We have one of the highest density of people in the university. They give us space, but not that much space. People bounce ideas more, when they are close to each other. So the generalization is that there’s something to be said for concentration, but it’s not a generalization that you can immediately impose. You can’t just suddenly say my town is going to have Harvard and MIT and 23 pharma companies, and 100 smart people in one of those universities.
The other generalization is creating an environment where risk is okay. The opposite of NASA model where failure is not an option. My motto is try to make yourself uncomfortable once a day, try to fail once a month, really if you’re not failing you’re not trying. That’s a culture where people don’t feel embarrassed when they fail. That’s important. Being nurturing and nice, that’s a generalization. Part of it is if you take one step and the one step is good, then take 10,000 steps and see how that feels. It’s like reduction ad absurdum. When people come to my office with a plot and they optimize something and they have three points on the plot and they say, okay, we optimized something because it’s better at this point. But, it could be 10,000 times better once step over. Just take it to where it’s ridiculous, even if you’ve satisfied your original goal. What if we did a million times more of it, what happens. Not by throwing money at it, by automation and parallelism. Just think about doing whatever you are doing more efficiently.
That’s another theme. Rather than trying to raise a lot of money we try to make things cheaper for everybody’s benefit. If you raise a lot of money then people get intimidated, and say, “Oh I can’t compete with that lab.” But if you make everything cheaper then it’s in a way, you are undermining your dominance, but we don’t care about dominance. It’s sort of the idea of open source. By giving stuff away you actually make a better product, and have a better reputation. We’ve been radical open source, one of the first open source medical devices we did in 2007, my genome and my medical records being open source. Radicalizing every dimension.
Frankly I really don’t understand what is going on, what’s in the water, but I’m glad that so many people care. It’s forces me to think about it more carefully.