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[ # ] Science vs. Business
July 30th, 2011 under Computers, Corporate, OSS, Politics, rengolin, Science

Since the end of the dark ages, and the emergence of modern capitalism, science has been connected to business, in one way or another.

During my academic life and later (when I moved to business), I saw the battle of those that would only do pure science (with government funding) and those that would mainly do business science (with private money). There were only few in between the two groups and most of them argued that it was possible to use private money to promote and develop science.

For years I believed that it was possible, and in my book, the title of this post wouldn’t make sense. But as I dove into the business side, every step closer to business research than before, I realised that there is no such thing as business science. It is such a fundamental aspect of capitalism, profit, that make it so.

Copy cats

Good mathematicians copy, best mathematicians steal. The three biggest revolutions in computing during the last three decades were the PC, the Open Source and Apple.

The PC revolution was started by IBM (with open platforms and standard components) but it was really driven by Bill Gates and Microsoft, and that’s what generated most of his fortune. However, it was a great business idea, not a great scientific one, as Bill Gates copied from a company (the size of a government), such as IBM. His business model’s return on investment was instantaneous and gigantic.

Apple, on the other hand, never made much money (not as much as IBM or Microsoft) until recently with the iPhone and iPad. That is, I believe, because Steve Jobs copied from a visionary, Douglas Engelbart, rather than a business model. His return on investment took decades and he took one step at a time.

However, even copying from a true scientist, he had to have a business model. It was impossible for him to open the platform (as MS did), because that was where all the value was located. Apple’s graphical interface (with the first Macs), the mouse etc (all blatantly copied from Engelbart). They couldn’t control the quality of the software for their platform (they still can’t today on AppStore) and they opted for doing everything themselves. That was the business model getting in the way of a true revolution.

Until today, Apple tries to do the coolest system on the planet, only to fall short because of the business model. The draconian methods Microsoft took on competitors, Apple takes on the customers. Honestly, I don’t know what’s worse.

On the other hand, Open Source was born as the real business-free deal. But its success has nothing to do with science, nor with the business-freeness. Most companies that profit with open source, do so by exploiting the benefits and putting little back. There isn’t any other way to turn open source into profit, since profit is basically to gain more than what you spend.

This is not all bad. Most successful Open source systems (such as Apache, MySQL, Hadoop, GCC, LLVM, etc) are so because big companies (like Intel, Apple, Yahoo) put a lot of effort into it. Managing the private changes is a big pain, especially if more than one company is a major contributor, but it’s more profitable than putting everything into the open. Getting the balance right is what boosts, or breaks, those companies.


The same rules also apply to other sciences, like physics. The United States are governed by big companies (oil, weapons, pharma, media) and not by its own government (which is only a puppet for the big companies). There, science is mostly applied to those fields.

Nuclear physics was only developed at such a fast pace because of the bomb. Laser, nuclear fusion, carbon nanotubes are mostly done with military funding, or via the government, for military purposes. Computer science (both hardware and software) are mainly done on the big companies and with a business background, so again not real science.

Only the EU, a less business oriented government (but still, not that much less), could spend a gigantic amount of money on the LHC at CERN to search for a mere boson. I still don’t understand what’s the commercial applicability of finding the Higgs boson and why the EU has agreed to spend such money on it. I’m not yet ready to accept that it was all in the name of science…

But while physics has clear military and power-related objectives, computing, or rather, social computing, has little to no impact. Radar technologies, heavy-load simulations, and prediction networks receive a strong budget from governments (especially US, Russia), while other topics such as how to make the world a better place with technology, has little or no space is either business or government sponsored research.

That is why, in my humble opinion, technology has yet to flourish. Computers today create more problems than they solve. Operating systems make our life harder than they should, office tools are not intuitive enough for every one to use, compilers always fall short of doing a great job, the human interface is still dominated by the mouse, invented by Engelbart himself in the 60’s.

Not to mention the rampant race to keep Moore’s law (in both cycles and profit) at the cost of everything else, most notably the environment. Chip companies want to sell more and more, obsolete last year’s chip and send it to the land fills, as there is no efficient recycling technology yet for chips and circuits.

Unsolved questions of the last century

Like Fermat’s theorems, computer scientists had loads of ideas last century, at the dawn of computing era, that are still unsolved. Problems that everybody tries to solve the wrong way, as if they were going to make that person famous, or rich. The most important problems, as I see, are:

  • Computer-human interaction: How to develop an efficient interface between humans and computers as to remove all barriers on communication and ease the development of effective systems
  • Artificial Intelligence: As in real intelligence, not mimicking animal behaviour, not solving subset of problems. Solutions that are based on emergent behaviour, probabilistic networks and automatons.
  • Parallel Computation: Natural brains are parallel in nature, yet, computers are serial. Even parallel computers nowadays (multi-core) are only parallel to a point, where they go back on being serial. Serial barriers must be broken, we need to scratch the theory so far and think again. We need to ask ourselves: “what happens when I’m at the speed of light and I look into the mirror?“.
  • Environmentally friendly computing: Most components on chips and boards are not recyclable, and yet, they’re replaced every year. Does the hardware really need to be more advanced, or the software is being dumber and dumber, driving the hardware complexity up? Can we use the same hardware with smarter software? Is the hardware smart enough to last a decade? Was it really meant to last that long?

All those questions are, in a nutshell, in a scientific nature. If you take the business approach, you’ll end up with a simple answer to all of them: it’s not worth the trouble. It is impossible, at short and medium term, to profit from any of those routes. Some of them won’t generate profit even in the long term.

That’s why there is no advance in that area. Scientists that study such topics are alone and most of the time trying to make money out of it (thus, going the wrong way and not hitting the bull’s eye). One of the gurus in AI at the University of Cambridge is a physicist, and his company does anything new in AI, but exploits the little effort on old school data-mining to generate profit.

They do generate profit, of course, but does it help to develop the field of computer science? Does it help tailor technology to better ourselves? To make the world a better place? I think not.

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