Jerry Neumann: I invest in early-stsge startups; I teach entrepreneurship at Columbia U.; I write at


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Why is there no equivalent of the VC industry, but for patentable inventions instead of startups?

Interesting to think what changed in that time period. Ideas prior to 1900 or so would have been primarily mechanical, and the patent system was designed for this. Even though all patents represent ideas, ideas prior to the electrical age were about mechanical objects that could, perhaps, be instantiated in one best way. Ideas in the electrical age could probably be instantiated in many ways, making patents harder to enforce. And now, in the information age, the idea itself can be articulated in many ways, making patents somewhat useless for information-based products except as post-hoc bludgeons.

Why is there no equivalent of the VC industry, but for patentable inventions instead of startups?

It's called getting a job.

The returns to patents follows a power law distribution (PLD), just as VC-backed companies do, but it is even more extreme. The exponent for VC-backed startups is close to but just under 2*, it seems, while Nordhaus writes that the exponent for the return to patents is between 1.3 and 1.7**.

I don't have an answer to your question! But it has always struck me that venture capital returns should cluster just below the PLD alpha of 2. This value is important because when you calculate the mean of the PLD, it goes to infinity as alpha goes to 2. (This means that if venture capital follows a PLD, and if the alpha is less than 2, and if there is no tail-off, that the mean return on a company would approach infinity. Each single return is finite, of course, but the more companies you invest in, the higher the mean return you will get.)

There is also some evidence (Neumann, op cit) that VCs can choose their alpha. That early-stage investors choose a slightly lower alpha than later-stage investors and that private equity investors choose an alpha greater than 2.

Alpha, besides determining the average of the PLD, also determines its variance. The lower the alpha, the higher the variance. The variance, in turn, is a useful measure of investment risk.

So, a hunch: as you lower the alpha of the PLD you choose to invest in, you increase both risk and mean return. But when the alpha goes below 2, mean return goes to infinity. There's no point in going any lower.

Of course, a portfolio of patents should approach infinity faster than a portfolio of startups (meaning you would need fewer patents in the portfolio to expect a desired return than you would need startups) but this is misleading: the lower alpha PLD has a longer tail, offset by a bigger base...any single pick from that distribution is likely to be worse, while that one single homerun is likely to be much better. So, in theory, you should expect a better return with a smaller portfolio than you would with startups, if you are optimizing to a specific overall return (say, 3x) then you would have to have a larger portfolio. (That is, you have fewer hits, though the hits are bigger.)

You can see this if you look at press releases of university tech-transfer offices. They often brag about that one patent that has been 90% of their return over their history.

If you are managing someone else's money, you want to maximize your return, but you are also motivated to minimize your minimum return, so you can keep your job. In this case, you would need to build a really, really big portfolio of patents, much larger than the portfolio of startups you would need. You would also need more time for the patents to come to fruition. It's not a stretch to see why, under these conditions, patents would primarily be funded by entities investing their own money (universities, large corporates, individuals, etc.)


* Neumann, Power Laws in Venture,

** Several papers: Nordhaus, W.D., (1989). “Comment on Zvi Griliches’ ‘Patents: Recent Trends and Puzzles'”, Brookings Papers on Economic Activity: Microeconomics, pp.320-325; Scherer, F.M., Harhoff, D. & Kukies, J., “Uncertainty and the size distribution of rewards from innovation”, Journal of Evolutionary Economics 10, 175-200 (2000); Scherer, F.M., “The Size Distribution of Profits from Innovation”, Annales d’Economie et de Statistique, No. 49/50, (Jan-Jun 1998), pp. 496-516.