I recently took Stewart Brand’s advice and started limiting my news consumption to a handful of science blogs. Science reporting is much more uplifting than its mainstream cousin. No gossip. No disasters. No rumours of impending economic doom. Just discoveries: Genuinely new stuff that we didn’t know before. While there’s a lot to absorb, my first thought was “How can we get more?”

More Scientists = More Science?

My second thought was that in order to increase knowledge output, we simply need to increase the number of scientists.

What if every human being saw themselves as a scientist, or an engineer, having had the flame of curiosity sparked and stoked from an early age? Math is a skill that can be taught. It’s like riding a bicycle, or swimming, or drawing. That any child reaches adulthood believing that math is hard and that the gods of arithmetic passed them by when calculus genes were being distributed is a testament to the failure of our educational culture. Establishing a beachhead of math competency in all students would open up a world of possibilities in the sciences.

But investing in education alone would not necessarily lead to more science. Already the United States generates more scientifically qualified individuals (PhD’s and postdocs) than it knows what to do with. Competition for academic tenure-track university posts is fierce, sometimes with hundreds of applicants vying for a single position. Researchers themselves compete for limited grant money, ensuring that only the most promising (and policy-aligned) research gets funded.

More $$$ = More Science?

It is tempting to conclude that the bottleneck results from insufficient investment from governments and private foundations. Of course governments could invest more in R&D but in a world of scarce resources, a line needs to be drawn somewhere.

Disruptive change—the kind of change that would see a rapid doubling or tripling of scientific output—will not likely come from incremental changes in funding regimes that track slower-growing (or not-growing) economies.

Technological Change = More Science?

What we need is technology that increases the productivity of individual researchers and lowers barriers to entry. We need disruptions that are analogous to what digital cameras did for photography. While it is harder to make a living as a professional photographer, the world has been flooded with more—and better—photos.

The challenge and opportunity lies in the digitization of science. As long as we are tied to real-world bricks-and-mortar labs that don’t scale easily we will be limited in how quickly science can progress. Computer modeling, on the other hand, is progressing rapidly and in silico work could eventually replace much of what we do in expensive-to-build and expensive-to-run concrete spaces.

The world of stuff does not scale, but the world of bits—information—does. A lot of processes that are laborious and expensive in the real world are trivial to automate in the digital domain. Any sufficiently educated individual with an enquiring mind would be able to do meaningful and productive science with a laptop and an internet connection.

It’s an appealing vision, but digital modeling has a long journey ahead of it before becoming the central paradigm of scientific research—or does it? Even at this early stage, digital R&D has the capacity to scale free of physical constraints. Anyone with a computer can contribute meaningfully to the field’s development.

We may not need to wait too long before witnessing more technological revolutions.