- Deep Learningはコモディティ化
- AIはVCにとって新たなcleanTech (アメリカで盛大に盛り上がり散った)
- MLaaS(Machine Learning as a Service)はまた死ぬ
- 1, Botは崩壊する
- 2, Deep Learningはコモディティ化
- 3, AIはVCにとって新たなcleanTech
- 4, MLaaS(Machine Learning as a Service)はまた死ぬ
- 5, フルスタックな垂直型(業界を絞った）AIスタートアップだけが実質的に機能する
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
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.
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.
2, Deep Learningはコモディティ化
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.
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.
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.
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.
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.
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.
4, MLaaS(Machine Learning as a Service)はまた死ぬ
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.
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.
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.
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.