Five AI Startup Predictions for 2017

MARCH 03, 2017


- Bots go bust


- Deep learning goes commodity


- AI is cleantech 2.0 for VCs


- MLaaS dies a second death


- Full stack vertical AI startups actually work


With AI in a full-fledged mania, 2017 will be the year of reckoning. Pure hype trends will reveal themselves to have no fundamentals behind them. Paradoxically, 2017 will also be the year of breakout successes from a handful of vertically-oriented AI startups solving full-stack industry problems that require subject matter expertise, unique data, and a product that uses AI to deliver its core value proposition.


Bots go bust


Over the past year a mania has risen up around ‘bots.’


In the technical community, when we talk about bots, we usually mean software agents which tend to be defined by “four key notions that distinguish agents from arbitrary programs; reaction to the environment, autonomy, goal-orientation and persistence.”


Enterprises have decided to usurp the term ‘bot’ to be mean ‘any form of business process automation’ and create the term ‘RPA’, robotic process automation.


While business process automation will of course continue to play out for decades to come, the current mania around ‘bots’ defined as conversational interfaces over voice and chat will begin its collapse in 2017. Here’s why:


- The social vs. personalization wars in consumer internet provide a good guiding light. Ultimately the winning personalization platform was facebook, which was the winning social platform. People still like to interact with other people for most things, and i suspect that many of the chatbots will go the same way as the non-social media platforms that tried to bet on personalization without social curation. A lot of the thinking around bots is naively utilitarian and lacks the social intelligence to recognize the range of human needs being met by person-to-person interaction. For this reason, most bots will fail to retain users even if they can attract them initially.


- There are a lot of misguided signals being drawn from the global messaging app boom, the rise of slack, and the success of certain interactions on platforms in china like weibo. A lot of folks have extrapolated from these trends to bet on platforms like AI-powered digital personal assistant. Per #1 above, these social platforms are solving for both utilitarian and emotional needs, and it’s not clear that we can extrapolate from this setting and apply it to pure utility AI-driven chatbots.


- Conversational interfaces are often very inefficient to accomplish tasks as compared to other more visual solutions. Conversational interfaces are interesting and have been around in the HCI community for decades. There are certain applications where conversational interfaces are awesome, but in reality i think we’ll see that for the vast majority of applications, there are far more efficient interfaces to get things done.


- Note that none of my reasons for the bot bust state that ‘the AI isn’t good enough yet.’ The issue with most systems like siri is more that they’re poorly implemented. We can build many interesting bot interfaces using modern techniques, the bigger issue in my mind is that its not clear humans want to use them.




Deep learning goes commodity


Deep learning is in full mania right now. For those without much of a sense of what various AI terms mean, deep learning is part of machine learning, which is part of of AI. Deep learning is not a different thing, its just a cool body of work that’s yielding state of the art results for lots of important problems, and so people are rightly availing themselves of it. If you want to understand the longitudinal picture here and how deep learning fits into the ever-evolving AI landscape, I wrote about this last fall.


Deep learning startup acquihires have replaced the iOS mobile apps startups of 5 years ago. A bunch of companies were blindsided by the ability of deep learning, especially for computer vision, to generate superior results and tackle new problems. As a result, we’ve witnessed a major wave of Google, Facebook, Twitter, Uber, Microsoft, and Salesforce running out an aggressive M&A strategy to fill the gaps.


So if this is so important and highly sought after, why do i think it’s going commodity this year? NIPS 2016 and the overall conference circuit of 2016. It’s very clear that deep learning is everywhere now. There are so many grad students coming out now with these skills. Four years ago the story was dramatically different. The market has adjusted to create more supply.


Now, all this being said, i need to make a clarifying statement. I am suggesting that deep learning will become more commodity among machine learning people this year, but i am not suggesting that machine learning itself will become commodity. The premiums on machine learning talent will still be incredibly high. The premiums on deep learning startup acquihires that we’ve seen in past few years will collapse after the second tier of tech companies and those outside tech (like the folks in detroit) finish their current wave of acquisitions. I expect a steady flow of late adopters this year coming in with dumb money, but that later in the year we may see that this wave of m&a deals starts to slow.




AI is Cleantech 2.0 for VCs


Let’s recall the salient properties of the recent cleantech bust that I think apply equally to AI.


- Cleantech isn’t a market, it’s a cross cutting concern. Issues of climate change and sustainability are very serious issues and incredibly worthy ones to think about both as causes and for profit businesses. A cross-cutting concern isn’t a business though, a business is something that sells a product or service that customers want to buy. Tesla and solar city are arguably success stories for cleantech, but note that they are both ‘full stack businesses’ -- a car company and a solar energy company respectively. So when cleantech is an element of a full stack company selling a real product into a real market, it works, but cleantech for cleantech’s sake doesn’t work because it doesn’t start from the premise of a customer need. Great businesses start with customer need. Great missionary businesses start with a vision defined by customer need, and incorporate a mission that aligns to satiating the need. An organization with a societal mission but without a customer-centered vision is at best a moderately effective philanthropic organization. Great business put customer needs first, not a cross-cutting technology trend, even if its a missionary one.


- Green energy isn’t a market, energy is. Solar is king and growing fast -- because now it works economically. When Warren Buffett and Elon Musk are competing over a market, that’s likely a sign that it makes good business sense. Both view sustainability as an important mission, but also understand that it has to make sense as a business and for the customer first, and the mission much be achieved in service of the needs of the businesses customers and employees. Nothing is more ironic than an unsustainable business with a mission of sustainability.


- Self-important save-the-world mentality. In cleantech, there was a lot of the hubristic knight-in-shining-armor attitude that is characteristic of tech manias. In AI over the past couple years, we’ve started to see self-aggrandizing AI ethics committees and the like, people talking about what to do when the robots take all the jobs, and so on. It’s the attitude that those working in and around AI are now responsible for shepherding all human progress just because we’re working on something that matters. This haze of hubris blinds people to the fact that they are stuck in an echo chamber where everyone is talking about the tech trend rather than the customer needs and the economics of the businesses. This toxic reality distortion field is what allows the mania to draw large numbers of smart but self-important people into the impending web of doom.


- Cleantech and AI are both deeply technical problems, and a startup and VC community increasingly trained up on consumer internet and trivial SaaS services is increasingly incapable of adequately evaluating investment opportunities in deeply technical domains. Driven by the state of hubris outlined in #3, people dive in after reading a few blog posts and hearing a few pitches. Linked profiles are duly updated, and an era of ephemeral experts are born.


So how does this play out?


I have a theory that the information era of the economy fundamentally changed the mania-panic cycles we’ve experienced throughout human history. As a former hedge fund guy, I have read all the great books on financial history and market psychology. It’s been interesting to track how things have evolved differently since the mid-90’s.


I think that the rapid increase in social interaction and spread of information online created a self-heisenberging effect that pulls manias up to the front of a business cycle before it even really begins. Consumer internet is a great example, where the 90’s pre-mainia lead to a 2000 crash just as the actual business cycle was getting started. Two years later in 2002, Google, which had started in 1998, was hiring up all the talent at the bottom of the bust and defining the real business cycle for consumer internet.


Four years after cleantech was pronounced dead by wired, solar is cleanest and cheapest source of energy, Elon and Warren are all over it. Tesla and solar city are becoming a full stack cleantech empire.


So I think we are in this pre-mania for AI startups right now. Most of what I see out there right now is going to fail in the same ways that AI startups have been failing for 10 years now. There is a very tiny community of folks that have been doing AI startups for 10 years or more, and the batch that are diving in at the top of this pre-mania are making the same mistake that cleantechs did -- they are diving into AI instead of diving into a customer need.


AI startups right now are mostly hammers looking for nails. As this becomes more evident over the next 12-24 months, and the bigcos exhaust and ramp down their appetite for AI acquihires just as they did for mobile app dev shops, I suspect that we start to see potential founders and VCs realize that something is off. At that point, I will get fewer AI startup pitches on linkedin from people who have decided to get into AI in the past 12 months.


MLaaS dies a second death


Machine Learning as a Service is an idea we’ve been seeing for nearly 10 years and it’s been failing the whole time.


The bottom line on why it doesn’t work: the people that know what they’re doing just use open source, and the people that don’t will not get anything to work, ever, even with APIs.


Many very smart friends have fallen into this tarpit. Those who’ve been gobbled up by bigcos as a way to beef up ML teams include Alchemy API by IBM, Saffron by Intel, and Metamind by Salesforce. Nevertheless, the allure of easy money from sticking an ML model up behind an API function doesn’t fail to continue attracting lost souls.


Amazon, Google, and Microsoft are all trying to sell a MLaaS layer as a component of their cloud strategy. I’ve yet to see startups or bigcos using these APIs in the wild, and I see a lot of AI usage in the wild so its doubtful that its due to the small sample size of my observations.


Whether services from the big cloud providers or from startups, the end will be the same, as they go sideways this year. Cloud providers will leave the services on but they won’t be big money makers, the MLaaS startups will start meeting their demise this year as growth goes sideways and appetite to double down on them dries up.


The problem here is a very practical matter; the MLaaS solutions have no customer segment -- they serve neither the competent nor the incompetent customer segment.


The competent segment: you need machine learning people to build real production machine learning models, because it is hard to train and debug these things properly, and it requires a mix of understanding both theory and practice. These machine learning people tend to just use the same open source tools that the MLaaS services offer. So this knocks out the competent customer segment.


The incompetent segment: the incompetent segment isn’t going to get machine learning to work by using APIs. They are going to buy applications that solve much higher level problems. Machine learning will just be part of how they solve the problems. It’s hard enough to bring in the technical competence to do machine learning internally, and its much much harder to bring in the ‘data product’ talent that can help you identify the right problems and means to productize machine learning solutions. the incompetent segment includes everyone outside of tech companies with established strong machine learning and data product teams. yes, that means the entire global business world across every industry. Its quite a large segment. If you buy into the “software is eating the world” thesis, then you think that every company in every industry more or less has to become a tech company at some level. The same will be true for becoming a data company. There’s already a very wide gap in technical competence between top tech companies like google and facebook and the top companies in each industry outside tech.  This gap is dramatically wider when it comes to data competence.




Full stack vertical AI startups actually work


I have been working with AI for nearly 20 years, and building silicon valley AI startups for nearly 10. I’m a cofounding partner of DCVC, a leading AI and data focused VC. My experience makes me both broadly excited and soberly focused on full stack vertical AI applications.


I’m broadly excited because I think that every industry will be transformed by AI. I’m soberly focused because low level task-based AI gets commoditized quickly. I think that if you’re not solving a full stack problem that’s high level enough, then you will be stuck in a commoditized world of lower level AI services, and you are going to have to be acquired or wind down due to lack of traction.


Vertical AI startups solve full-stack industry problems that require subject matter expertise, unique data, and a product that uses AI to deliver its core value proposition.


While most of the machine learning talent works in consumer internet giants and related general tech companies, massive and timely problems are lurking in every major industry outside tech. If you believe the ‘software is eating the world’ hypothesis, then every company in every industry will need to become a tech company.


When you focus on a vertical, you can find high level customer needs that we can meet better with AI, or new needs that can’t be met without AI. These are terrific business opportunities, but they require much more business savvy and subject matter expertise. The generally more technical crowd starting AI startups tend to have neither, and tend to not realize the need for or have the humility to bring in the business and subject matter expertise required to ‘move up the stack’ or ‘go full stack’ as I like to call it.


New full stack vertical AI startups are popping up in financial services, life sciences and healthcare, energy, transportation, heavy industry, agriculture, and materials. These startups will solve high level domain problems powered by proprietary data and machine learning models. Some of these will hit 100M in ARR in 2017-2018. These full stuck AI startups will be to AI as Tesla and Solar City were to cleantech.