The ROI on science is increasing.

We've been trained to think about scientific research as a public good. According to the standard line of reasoning, the direct return on investment in science is low, even though the long-term social benefits are often very large. As a result, firms and individuals underproduce research, unless the state provides subsidies in the form of research grants, patent protections, etc. By this reasoning, science is beneficial for everyone, but profitable for no one—voila! A classic public good.

But what if the ROI* on science increases? "Innovation is accelerating" is a mantra among scientists, business gurus, and technologists. The raw ingredients of science—especially data and the capacity for experiment—are more widely available today than at any other moment in history.

If the ROI on science increases, we should expect an increase in science at the margins. More and more, we should expect to see direct, private investment supplying the money, data, and talent required for scientific progress. But we should not expect the new institutions of science to look and act like the old ones, because their incentives are fundamentally different.

An optimist might call this a "Scientific Renaissance": the scientific method flowering and flourishing throughout society.

A pessimist might call it "the Privatization of Science": corporations, governments, and individuals selfishly hoarding new knowledge as sources of personal wealth and power.

I'm convinced that both views are at least partly correct. Many of the old constraints will no longer bind, and many of the old rules will no longer apply. The foundations of science are shifting.

I've been watching this evolution for several years now. As a lifelong "data guy" with a deep love of the scientific method, the shape of science in society is a matter of keen professional interest. More broadly, I'm human being with a lot wrapped up in personal relationships, choices, and health—that are increasingly being measured, mined, and monetized by "innovative" companies and government agencies.

Over the next few weeks, I plan to blog my observations on the evolving shape of science. At a minimum, I plan to explore the logic of increased scientific ROI within four centers of research: corporate R&D, grant-supported labs and research universities, hobby scientists, and business analytics. My goal is to lay out the likely outcomes, with the best available illustrating examples and supporting data.

On top of that, I hope to start a conversation. I've found the concept of "increasing scientific ROI" to be a powerful unifying idea—one of the big forces shaping the future—but haven't heard about it talked much. We should be talking about it. The incentives behind these emerging sciences are going to shape us for a long time to come. If we're going to shape them, the time to do it is now, before the wrong interests become too entrenched and powerful.

Sneak peak
Here are my in-a-nutshell ideas for the way increased scientific ROI will play out in four major centers of research. Each institution is structured differently, so each institution's response to the accelerating pace of science is unique. Work with me here—this is the time to make connections and explore implications. Let's fit this puzzle together.

  • Corporate R&D: More research and more patents, by smaller companies with shorter time horizons. More companies making strategic leaps on the assumption that science will catch up. Possibly, a shift towards (permanently private) trade secrets, rather than (eventually public) patents.
  • Grant-supported labs and research universities: Increasing dependence on computational infrastructure, along with increasing difficulty in training and retaining computationally skilled researchers. In some (not all) fields, data and talent will become bottlenecks for university research. Collaboration with corporations creates new opportunities, but opens up a legal and ethical can of worms, too.
  • Business analytics: Applying the scientific method to improve a single business model spawns whole analytical "disciplines" within corporations. Business intelligence, operations research, and data science teams become more influential within companies, making data-literacy and lean methodology essential skills for almost everyone.
  • Hobby scientists: Low capital costs encourage significant research by amateurs—hackers, makers, quantified selfers—generating a lot of buzz, occasional breakthroughs, and a climate of skepticism that will help keep the big players honest. Personalized science—coaching, "life hacking," personalized medicine, etc.—becomes increasingly common.

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