Cloud computing and biotech are the two most important nonlinearly-growing economic sectors. These two sectors intersect in Seattle in a unique way that has important implications for all involved. Small changes now will make big changes in what our lives are like decades from today, and Seattleites will have a ringside seat.

For now, there are only three organizations with the resources and outlook to be cloud providers: Amazon, Google, and Microsoft. Two of the three are headquartered in the Seattle area, and the third, Google, has a research presence here. Only Microsoft and Amazon appear interested in supplying cloud services per se, and really are setting the cloud agenda. This leaves Seattle with a planet-wide dominance it enjoys in no other economic area except perhaps global health. Such dominance shouldn’t be taken for granted (see: aerospace), but for now if you want to drive the cloud agenda in research, development, startups, or bizdev, you are going to spend time in Seattle.

While Seattle is a top-tier biotech hub, there is no area of biotech where Seattle predominates the way it does in cloud computing. However, it happens that Microsoft’s and Amazon’s cloud groups take disproportionate interest in biotech. Amazon’s cloud leadership includes people with strong biotech backgrounds, and Amazon’s new South Lake Union campus is literally surrounded by cutting-edge biotech research. Health IT is a key sector for Microsoft, and the Azure group has reached out to genomics researchers and others.

Compared to big cloud users like FarmVille and Netflix, biotech isn’t a big cloud consumer, and biotech probably never will be the biggest. Conversely, recent events make the cloud very important to biotech. The most important such development is next-gen DNA sequencing, which has used new chemistries to produce lower-quality DNA sequences very, very cheaply. At the same time, lower read quality increases the computational task of assembling reads. The result is that computational analysis costs are often higher than the wet chemistry costs; sometimes many times higher.

Consider why this situation might cause DNA sequencer makers to have a collective forehead-slap. Instruments are inexpensive with ever-increasing capacity. Reagents are cheap and bound to get much, much cheaper. Yet computation costs are going up. What’s the one part of the business the instrument makers don’t have a big piece of? Computational analysis. Oops.

As computation needs rise, cloud computing can make a big difference. Cloud computing promises systematically lower computation and storage costs, and frictionless scalability. Local companies like Geospiza and Labkey (as well as my company, Insilicos) exploit these advantages to offer computational services that biotech instrument vendors may now be wishing they owned and controlled.

Until recently, biotech researchers typically didn’t track computing expenses because the expenses were usually small and many researchers didn’t pay for computing out of their own budgets anyway. Those days are over for DNA sequencing. Other areas of biotech, such as protein folding, routinely have big computation problems, and in proteomics and many other areas, computation problems are growing faster than computer capacity. In future, much of biology research will have to plan and budget for computation as an integral part of most experiments.

Inevitably, most of this complicated computing will be done using cloud computing. Cloud computing has such economies of scale that it ultimately wins for a lot of things, particularly where computation demand fluctuates heavily over a period of hours to days. Scientific computing fits this profile: big computation, varying drastically over the course of an experiment. Consequently, scientists who use cloud computing will have an edge of those who do not, and in due time computational biology will largely be performed in the cloud.

But what kind of cloud?

Microsoft and Amazon have different things in mind … Next Page »

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Yesterday, I talked a little about balancing short and long-term effort at a startup. Today, I want to discuss “cranking,” which I’ll define as a short-run effort to achieve an extremely important short-term goal.

How hard can people possibly crank? For a sufficiently motivating goal, people can work well in excess of 70 hours per week for months. With occasional short “vacations” that normal people would call a “weekend,” this level of effort can be productively sustained for much longer, although not indefinitely. I know this for a fact because I’ve done it, and I’ve seen it done.

Just because this is possible does not imply it is the best course of action in many (or, in fact any) cases. We ought not to plan on effort levels greater than what we are likely to be able to achieve. Planning on working 160 hours per week for even one week is nothing but foolish bravado.

I’m going to use a personal story to illustrate my point. This isn’t a typical startup, but it exemplifies maximum short-term effort. Moreover, I happen to have good contemporaneous estimates of my and other people’s effort, that are well documented in government reports and this article from Wired magazine.

In 1994, I worked as a programmer for the South African election commission during that county’s historic elections. I was one of the last people added to the organization, 20 days before the election. In the following 25 days, I worked approximately 400 hours (an average of 16 hours a day). This level of effort was not universal, but it was typical in many departments. Some people had been working at this level of effort with brief 2- or 3-day breaks for several months. The organization was only five months old, and a few people had been working pretty fiercely that whole time.

The hours were not just long, they were hard. To illustrate, consider my diet. I ate three big meals a day, and consumed an additional 1000 calories a day in soft drinks and snacks. Over the course of the project, I still managed to lose five pounds. The only explanation I have is that I was thinking that hard. I was also consuming about half a gram a day of caffeine, which significantly increases … Next Page »

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Xconomy has seen a vigorous discussion of work/life balance in Seattle. Is Seattle slack compared to the San Francisco Bay Area? If so, does the difference matter? Janis Machala of UW TechTransfer brought up this point during a recent panel discussion, and it has stirred debate. I personally don’t think work/life balance is the most important question facing the Seattle startup community. But, it’s a very important question to entrepreneurs. Everybody should know that startups are hard work, but is there an optimal level of hard work? And anyway, is hard work more than just long hours?

During the ’90s dot-com boom, it became popular to talk in terms of all-consuming, total-commitment startups. When many companies were going from pitch to liquidity in less than two years and when every idea seemed to have five well-funded companies chasing it, there was an unprecedented and understandable emphasis on speed of execution. People worked like mad for a year or two, and the company made it or didn’t.

But even if Total Commitment was appropriate for that environment, it isn’t best for other places and times. Most biotech startups have to persevere for many years. Even today’s web companies have to be ready for the long haul. So while there may be periods of all-consuming effort, almost all startups now last too long for sustained effort at the human maximum. Nobody sprints a marathon, after all.

So when we talk about the intensity of startups, we need to distinguish between what people may do for a few months, compared to what they do for the longer haul.

It usually makes sense to work quite hard in a startup. If everyone works hard, then the team can stay smaller for longer. This means more upside for the early players, but a more important benefit is that a small team can communicate more efficiently than a larger one. A small team is also easier to manage, and startups are often short on management experience.

But when a small team is strained to the limit of human effort, quality of judgment declines, along with attention to detail and the sheer ability to care enough to do one’s best. The point is not to work hard. The point is to win. Depending on the situation, you might best win by drowning your adversaries in your own sweat. But even so, a good night’s sleep will sharpen your wits and might allow you to formulate a better plan, or else just appear more cheerful and personable to a potential collaborator.

I think most people who work in startups do so because they want work that feels important, and adds meaning to their lives. We work hard because we like working hard at things we care about. We work for startups because they can be about big ideas worth caring about, and they can disproportionately reward hard work. No matter where I’ve been, I’ve worked hard, but I generally find the hard work of a startup more satisfying than hard work in other kinds of organizations.

Tomorrow, I plan to expand on these ideas a bit more with some lessons I learned about “cranking” through a short-run effort to meet an extremely important short-term goal. It was when I worked in South Africa in 1994 on the election commission during that country’s historic transformation from the era of Apartheid to a new period of free elections.

Continue to Part 2

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