There’s a lot buzz today about Netflix’s offer to pay one million dollars to whoever can improve the accuracy of predictions of its movie-recommendation system. And it looks like there are number of people eager to throw their hat in the ring.

To this I ask: the prize is only a million dollars?

Consider that this contest is coming from a company with $830M in annual revenue (ttm), $66M in net income, and $1.6B in market capitalization. It obviously has already had the means to employ a number of distinguished people working on the existing system (a few of whom are now the judges of the contest) and surely has spent in aggregate well over $1M to develop their current technology. In addition, with a number of personalization & recommendation technology startups in the market at various stages of development, there are plenty of companies out there to acquire that would presumably command (or at least desire) a higher price.

Yet the company chose to go the route of a contest, making strides in pioneering “prize outsourcing research and development.”

Obviously, Netflix has all of the leverage here with their distribution and existing customers to which it could apply any technology. So of course it can and has offered whatever bounty it wants (which I’d argue is largely for PR purposes).

In reality, what this move does is call into question the viability of startups out there working on personalization and recommendation systems. Without the leverage of a huge market presence (read: customers) that a Netflix has, I wonder how they are going to be able to adequately monetize their offering. Surely these systems will lead to an increase in media purchases, and I’ve long been an advocate for the power of personalized predictive media. However, there is a significant distinction between being able to create value and the ability to capture it, and the power of distribution in this case appears to overwhelm. If you’re a genius who can compete “with 15 years of really smart people banging away at the problem” and it’s only worth $1M to you, then what does it say for everyone else – individuals and companies – working on that very same problem?