Can a startup afford to pay market rates for PhDs and other qualified individuals? Probably not. That's always been the case though, and I don't really see that as being new for computer vision and machine learning talent.

So you need to look at what else you can offer them. A significant amount of equity seems like a big one. It may be more than you wanted to give, but that may be the reality of the market. Beyond that, since risk is significant and fewer and fewer liquidity events seem to be occurring, perhaps a straight profit-sharing model might be more appropriate and more immediate in terms of its ability to balance the risk equation of startups. If you start getting customers and someone knows they'll stand to make money as you grow vs. waiting for some increasingly unlikely IPO, that could be more favorable.

There's also other less tangible things like flexibility that comes in the form of remote work, flexible hours, etc. that can have very real value to people. Taken to the extreme, you may be able to land some awesome talent remotely in other countries where maybe what you can offer them is still a lot of money.

It would be interesting to know what you are trying to do to remain competitive in these other areas. Money solves a lot, but depending on what someone is looking for and where they are in their life, it may not be everything.

It's true, however the difference in talent is staggering between someone with experience in CV/ML and someone who has only studied it. So I suppose that just stating "PhD" is too vague.

Your point is correct though about giving other benefits, and that's what we (and others in the field) do - equity, flexibility etc...

Understand though that the key issue here is that most of the people with these skills are rabid about working on big projects with grand vision, and rightfully so. Part of the problem however is that there are only so many data sets for these problems and without those data sets the pace is slow.

The frontier today requires much more infrastructure than previous breakthrough projects. I was just watching the "rise of the nerds" and it was illuminating how "simple" even back then it was from a scale perspective to make great progress. The microprocessor really opened up this whole ecosystem because it was something that anyone could buy. That's just not possible with machine learning because you have to have so much data and that kind of data you can't buy, you have to build it.

I was talking with a major heavy hitter in this field (one you've heard of) about his work options this year. He was debating leaving a very well funded billion dollar company to go to one with some of the best people in ML in the world. He didn't leave because the company he was working for already had terabytes of vision data to work with and infrastructure that made his work possible. The newer company, which had amazing talent and some great funding, would have to start gathering data and it would take time to set up their GPU clusters etc... So it was because the bigger company was already at scale with data and could gather data faster, that he didn't leave. And these are the cream of the crop people with a lot of money we are talking about here.

So it's not so much compensation, it's that the biggest companies have the scale needed already to do the breakthroughs in tasks like reinforcement learning and machine vision - the tasks that are going to dominate the landscape going forward - that are impossible for startups to get to.

An example of a truly distributed purely technical power group would be something like Debian GNU/Linux. One of the reasons it's possible, and getting easier by the day to run desktop software that isn't locked down by one of the big corporations is the existence of distributed projects like the kernel and Debian.

What you say is anathema to everything else written on this site about how to build good products with good people that have impact.

Otherwise you wait around till the next gauss/perelman shows up that's only working for passion.

With small amounts of data and/or unconventional learning situations, there should be options for incorporating domain expertise in interesting ways. I might be wrong, but I think the fraction of problems that can be solved with deep learning/large data set analysis is a small fraction of the set of AI problems as a whole.

For example, at a statistics symposium at MIT last year Stanford Professor Robert Tibshirani mentioned a healthcare (cancer diagnosis) problem, done in collaboration with healthcare people: http://www-stat.stanford.edu/~tibs/ftp/canc.pdf (these slides are different from the ones used at MIT, the content is similar though). He mentioned how they talked to some deep learning experts at Google, who told him that the data set was too small for effective deep learning usage. Another reason is mentioned on pg 30 of the above, basically sparsity being more important than nonlinearity here.

In healthcare, people care a lot about "interpretable" models. The term is ill-defined, but in a sense it is something where the trained model is easily explainable, and easily written down. A linear model is a simple example of such a thing, though usually insufficient on its own. Deep neural nets arguably do not meet this, hence a reluctance (among other reasons) to shift in certain fields.

Your point may still be valid though - I do not know whether there are enough viable economic opportunities. Computer vision in particular is dominated by DNN/large scale analysis these days, and I sympathize with your difficulties here.

http://www.ibm.com/watson/health/

Your point is valid though about there being silos of industries that don't have a lot of data, or the data not being accessible.

I think a great example of this is in Agricultural Tech. There is just incredible amounts of data that is totally siloed and fractured. But if you pay attention Google Ventures is right there, ready to fund or acquire whatever is happening in the space.

It's personal interest, I don't have a degree at all yet, but I understand convolutional neural networks fairly well. Stanford has a good number of open classes on such subjects.

Otherwise, you probably have to give them something they can't get at Google; autonomy, environment, time off, whatever.

Not to harp on what we do too much, but that's all part of what we offer. Remote, minimum vacation (meaning we set a mandatory 10 day minimum but no higher bound on vacation day), equity grants (not options) etc... to make up for not having 400k salaries.

As I mentioned elsewhere though, with most people money is what tips it over, but not they key thing. It's access to data that is important - it's why LeCun went to Facebook and Ng to Baidu. That's not something you can get as a startup without a product at massive scale already (though we have some staggeringly large proprietary vision data).