I’ve spend the last few months digging into the intersection between the on-going deep learning revolution and the world-wide opportunity for startups. This little exercise has highlighted both how the startup funding world is evolving, and some of the unique issues and opportunities for deep learning-based startups.

Looking at some basic funding trends is a good place to start. Pitchbook as just published an excellent public summary of key quantitative trends in US startup funding: http://pitchbook.com/news/reports/2016-annual-vc-valuations-report

These show the growth in the seed funding level and valuation, the stretching out of the pre-seed stage for companies and the a reduction in overall funding activity from the exceedingly frothy levels of 2015.

Let’s look at some key pictures – first seed funding:

That’s generally a pretty comforting trend – seed round funding levels and valuations increasing steadily over time, without direct signs of a funding bubble or “irrational enthusiasm”.   This says that strong teams with great ideas and demonstrated progress on their initial product (their Minimum Viable Product or “MVP”) are learning from early trial customers, getting some measurable traction and able to articulate a differentiated story to seed investors.

A second picture on time-to-funding gives a more sobering angle – time to funding:

This picture suggests that the time-line for progressing through the funding stages is stretching out meaningfully. In particular, it says that it is taking longer to get to seed funding – now more than two years. How to startups operate before seed? I think the answer is pre-seed angle funding, “friends-and-family” investment, credit cards and a steady diet of ramen noodles ;-). This means significant commitment to the minimally-funded startup as not a transitory moment but a life-style. It takes toughness and faith.

That commitment to toughness has been codified as the concept of the Lean Startup.  In the “good old days” a mainstream  entrepreneur  has an idea, assembles a stellar team, raises money, rents space, buys computers, a phone systems, networks and cubicles, builds prototypes, hires sales and marketing people and takes a product to market.  And everyone hoped customers would buy it just as they were supposed to.  The Lean Startup model turns that around – an entrepreneur has an idea, gathers two talented technical friends, uses their old laptops and an AWS account, builds prototypes and takes themselves to customers.  They iterate on customer-mandated features for a few months and take it to market as a cloud-based service.  Then they raise money.   More ramen-eating for the founding team, less risk for the investors, and better return on investment overall.

Some kinds of technologies and business models fit the Lean Startup model easily – almost anything delivered as software, especially in the cloud or in non-mission-critical roles.  Some models don’t fit so well – it is tough to build new billion-transistor chips on just a ramen noodle budget, and tough to get customers without a working prototype.  So the whole distribution of startups has shifted in favor of business models and technologies that look leaner.

If you’re looking for sobering statistics, the US funding picture shows that funding has retreated a bit from the highs of 2015 and early 2016.

Does that mean that funding is drying up? I don’t think so. It just makes things look like late 2013 and early 2014, and certainly higher than 2011 and 2012. In fact, I believe that most quality startups are going to find adequate funding, though innovation, “leanness” and savvy response to emerging technologies all continue to be critically important.

To get a better idea of the funding trend, I dug a bit deeper into one segment – computing vision and imaging – hat I feel may be representative of a broad class of emerging technology-driven applications, especially as investment shifts towards artificial intelligence in all its forms.

For this, I mined Crunchbase, the popular startup funding event database and service, to get a rough picture of what has happened in funding over the past five years. It’s quite hard to get unambiguous statistics from a database like this when your target technology or market criteria don’t neatly fit the predefined categories. You’re forced to resort to description text keyword filtering which is slow and imperfect. Nevertheless, a systematic set of key word filters can give good relative measures over time, even if they can’t give very good absolute numbers.   Specifically, I looked at the number of funding deals, and the number of reported dollars for fundings in embedded vision (EV) companies in each quarter over the past five years, as reported in Crunchbase and as filtered down to represent the company’s apparent focus. (It’s not trivial. Lots of startups’ descriptions talk, for example, about their “company vision” but that doesn’t mean they’re in the vision market ;-). The quarter by quarter numbers jump around a lot, of course, but the linear trend is pretty clearly up and to the right. This data seems to indicate a health level of activity and funding climate for embedded vision.

I’d say that the overall climate for technologies related to cognitive computing – AI, machine learning, neural networks, computer vision, speech recognition, natural language processing and their myriad applications – continues to look health as a whole as well.

In parallel with this look at funding, I’ve also been grinding away at additions, improvements, corrections and refinements on the Cognitive Computing Startup List. I’ve just made the third release of that list. Take a look!