The beauty is that this applies to a number of circumstances, from companies like x.ai that help people to schedule meetings faster to computational genomics companies like Phosphorous that work with hospitals to help them run genetic testing labs.

In a B2B context, it’s often a bit harder to get data network effects going because corporations are particularly (and rightfully) protective of their data and are pretty uncomfortable with the idea that you could be commingling their data with that of other companies in their industry.

But creative solutions are appearing to address this issue. Google Research had this really interesting publication on Federated Learning a few months ago, where the idea was to enable collaborative machine learning without actually pooling the data. That would address all those concerns around data privacy and open the door to all sorts of data network effects.

Regardless, it’s worth bearing in mind that it can take years for data network effects to kick in because startups need to build a customer base to collect enough data from which their models can learn. But it feels like a very interesting competitive moat once you get there.

Complexity of machine learning slows sales cycles

Sam: Do these startups lend themselves to one type of go to market strategy over another?

Matt: I think ultimately, most AI startups will end up looking very much like any other startup. For example, enterprise AI companies will mostly look like any other software or SaaS company, with a variety of different go to market strategies available to them, depending on industry, customer size, price point, etc.

For any AI product to work well enough, startups need to capture significant amounts of usage data, and then they need to use that data to train the algorithms and customize the product.

But I don’t think we’re quite there yet. For now, the complexity around building a machine learning product itself requires a lot of R&D, and training the algorithms requires a lot of time, effort, technical resources, as well as a lot of data, as we just discussed.

To take the example of x.ai once again, it’s taken several years, dozens of data scientists and machine learning engineers and millions of venture capital money to build an AI backend offering high levels of automation and reliability.

As a result, at least for now, it’s much harder for a machine learning company to be a “lean” startup.

For all the hope (and hype) around TensorFlow and other machine intelligence open source libraries, I think it’s trickier for these machine learning companies to build a truly AI-driven minimum viable product, and iterate from there. For any AI product to work well enough, startups need to capture significant amounts of usage data, and then they need to use that data to train the algorithms and customize the product. None of this is quick and easy, and we’re still very much in the “deep tech” world.

This has important implications on go to market strategy. In the enterprise world, for example, I haven’t seen many machine learning startups follow a “bottoms-up” sales strategy, at least not successfully. I’ve seen the opposite much more often: AI startups going after larger customers with larger budgets, sell with a top down approach, and essentially follow a partnership strategy where they build a lot of the early product in close collaboration with a handful of customers.

Often those companies, initially, solve their customer’s problems with a lot of services, and not a lot of software. The hope is that they can build the software on the job, find repeatable use cases, and over time turn their service offering into products. That’s a long process with long sales cycles, it’s certainly tricky, but I’ve seen it work quite well.

But that’s probably just a temporary phase of the market. As machine learning becomes democratized, and you get more open source datasets and algorithms, as well as more trained engineers floating around, you’ll see ML-first companies looking increasingly like every other company, with the opportunity to be more nimble.

Then we’ll all think it was quite quaint that we ever called a company a “machine learning startup”. As any successful technology, machine learning will go from novelty to ubiquity to eventually disappearing in the background.

AI must make product performance 10x better than alternative

Sam: Are investors focused on artificial intelligence startups as quick acquisition targets, or do they actually believe they can succeed as massive standalone businesses?

Matt: Given the economics of venture capital, you have to be very much a believer in the latter.

Obviously we’ve seen a feeding frenzy of large companies snapping up all sorts of small AI companies. That’s what happens when everyone agrees more or less at the same time that that AI is the next big thing, and there’s not that much machine learning talent around.

So you’ve seen many companies that were closer to research labs than actual startups get acquired quickly, sometimes for pretty meaningful amounts. Those were great outcomes for founders, occasionally truly life changing money. But from an investor’s perspective, those outcomes were singles rather than homeruns — not how you achieve great venture returns. I think we’re slowly reaching the end of that phase, though.

This is exactly why investors like me are so interested in vertical AI startups. With a vertical positioning, AI startups can focus very heavily, position away from all the giant companies, and may have time to build a significant business before those come sniffing.

With the right positioning, I do think that AI companies have a window of time ahead of them, during which AI can be a true differentiator and accelerant against any company that is not a heavy user of machine learning. Of course, you want to pick a use case where AI truly makes a huge difference to product performance, and is not just an add on. For the right use case, AI startups can offer a product that is 10x better than existing alternative.

There are lot of use cases where this is not true, but there is a whole range of companies where machine learning does have the potential to make product that’s 10x better. It can create the opportunity to build a company that is a true market leader.

Whether you’re a founder or an investor, it’s all about building companies that leverage the next big market inflection point. A few years ago it was SaaS. Now machine learning is the next evolution. Eventually, that window will close, but for now a lot of those AI-first companies have a real shot at leading their categories, or creating new ones.

Sam: Matt, this was fantastic. Thank you so much for your time.

Matt: My pleasure, Sam!