Is Venture Capital investment in AI Realistic or Out of Control?
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Anyone observing the news can see that artificial intelligence and machine learning are getting lots of attention. It goes without saying that startups are playing into this trend and raising more money than ever, as long as they have AI or cognitive technologies in their business plans or marketing material. Not only are startups raising increasingly eye-opening amounts of money, but venture capital (VC) funds themselves are raising skyrocketing levels of new capital if they focus their portfolios on AI and related areas. But are we in a bubble? Are these VC investments in AI realistic or out of control?
Why so much interest in AI funding?
AI is not new. As we have talked and written about many times, AI is as old as the history of computing. Each wave of AI interest and decline has been both enabled and precipitated by funding. In the first wave, it was mostly government funding that pushed AI interest and research forward. In the second wave, it was combined corporate and venture capital interest. In this latest wave, AI funding seems to be coming from every corner of the market: governments, especially in China, are funding companies at increasingly eye-watering levels, corporations are pumping billions of dollars of investment into their own AI efforts and development of AI-related products, and VC funds are growing to heights not seen since the last VC bubble.
AI’s resurgence started in earnest in the mid 2000’s with the growth of big data, cheaper compute power, and deep learning-powered algorithms. Companies, especially the big platform players (Google, Facebook, IBM, Microsoft, Amazon, Apple, and others) have tossed aside any previous concerns about AI technology and are embracing it into their vocabulary and business processes. As a result, entrepreneurs smell opportunity, forming new ventures around AI and machine learning, and introducing new products and services powered by AI into the market. Investors also smell opportunity and are taking notice. Over the past decade, total funding for AI companies, as well as the average round has continued to rise. For perspective, in 2010 the average early-stage round for AI or machine learning startups was about $4.8 million. However, in 2017, that round had increased to $11.7 million for first round early stage funding, a more than 200% increase!
In addition, AI investment is surprisingly global with startups raising large amounts of funding everywhere there’s a technology ecosystem. In contrast to previous technology waves where Silicon Valley was the undisputed champion of startup fund-raising, for AI-focused companies, no one location can be claimed as the nexus for investment or startup creation. By the end of 2017 venture funding in AI companies had reached $12 billion, with companies from the United States and China leading the way with the largest rounds raised. In fact, ten of the biggest venture capital deals of Q4in 2017 were evenly split between Chinese and US companies. And investment in 2018 hasn’t slowed down. Some of these massive rounds include SenseTime (China) round of $620M in May 2018, Dataminr (US) round of $391.5M in June 2018, Yitu (China) with a $200M round in June 2018 and another $100M in July 2018, Orbbec (US) with a $200M round in May 2018, Cylance (US) with $120M in June 2018, Pony.ai (US) with $102M in July 2018, and Cambricon (China) with $100M in June 2018 on top of a previous $100M raised less than 12 months prior. China now has the most valuable AI startup, Sensetime, after its recent raise valued the company at over $4.5 billion, and a reputed additional $1 Billionraise in the works with Softbank. Wow.
Rational Investment or Game of Musical Chairs?
If you want to see firsthand this latest surge of AI-related VC investment, check out the eye-opening results of this search on Artificial Intelligence companies funded within the past three months. As of August 2018, over $3.4B in capital has been raised by these firms just since May 2018! That’s both remarkable and concerning. Why is there so much money being pumped into this industry and will this sugar rush be followed by the inevitable sugar crash and pull back?
There are a few reasons why this investment might be rational. Just as the Internet and mobile revolutions in the past decades fueled trillions of dollars of investment and productivity growth, AI-related technologies are promising the same benefits. So this is all rational, if AI is the true transformative technology that it promises to be, then all these investments will pay off as companies and individuals change their buying behaviors, business processes, and ways of interacting. No doubt AI is already creating so-called “unicorn” startups with over $1 Billion in valuation. This could be justified if the AI-markets are worth trillions.
So, what is this money being used for? If you ask the founders of many of these AI companies what their gigantic rounds will be used for you’ll hear things like geographic expansion, hiring, and expansion of their offerings, products, and services. As we’ve written about before, the difficulty in finding skilled AI talent is pushing salaries and bonuses to ridiculous heights. Not only do startup companies need to compete with each other for great talent, but they need to fight against the almost unlimited deep pockets of the major technology vendors, professional services firms, government contractors, and enterprise end users also fighting for those scarce resources. A million dollars simply doesn’t go that far in hiring experienced AI talent. Heck, even $10 Million doesn’t go that far. So, an early-stage round of, say, $20M with almost half going to hiring and the rest to business development isn’t completely bonkers.
However, what about the billion-dollar rounds that are making headlines? Why would companies need to raise such ludicrous amount of money? The best reason that comes to mind: it’s a land grab for AI market share. The general rule in the technology industry is that the big winners are the ones who can command market share first and defend their turf. Certainly there’s nothing that unique about Amazon’s business model. Yet the reason why they are such an almost unbeatable force is that they aggressively expand and defend their turf. If you have a lot of money it’s easy to out spend the competition, or buy them. Companies that want to become global leaders need to “land and expand” — which means finding some easy way into a customer deal and then expanding on that deal later. This might mean losing money on the initial transaction, which quickly can burn lots of money. These unicorn startups also need a lot of capital to go up against the big established players like Amazon, Apple, Facebook, Microsoft, Google, IBM and others. Venture funds believe that these startups can be the new entrenched players of the future, and as such, need capital that will back them to the point where their dominance can’t be denied.
There are many other reasons why such high levels of investment and valuation are necessary. Many AI technologies, such as self-driving vehicles, are still in the research and development phase. It’s not simply a matter of banging out code and throwing servers and technology up to get these technologies working. This AI R&D costs a lot of money to create, build, and test. The downside to the need for all this R&D investment is that it pushes companies who have been funded under the promise of their AI technology, but unable to deliver on those promises, to succumb to the disturbing trend we talked about recently called pseudo-AI, in which humans are doing the work that the machines are supposed to be doing. Some of this capital could be needed to hire humans who do the work of the so-called “AI systems” until the technology is actually able to provide the promised capabilities.
Enterprises are also spending their money and time buying and implementing cognitive technology solutions from emerging technology firms and clearly want AI solutions that can solve their problems. The problem is that enterprises aren’t as patient as venture capital firms, and VC firms aren’t particularly patient either. They won’t put up long with fake AI or lack of market traction. If enterprises lose faith in the ability of AI to solve their problems and start rejecting “fakery”, there won’t be much opportunity for “makery”. And that’s the biggest danger of all this AI investment. If the AI solutions can’t live up to the hype, the bubble will rapidly deflate, taking with it all the energy, time, and money from the space. This could then deliver a major setback to AI adoption and growth in the long term, resulting in a new AI winter.
Keeping the AI Beast Fed or Suffering Withdrawal
There are really only two outcomes for these super-funded companies. Either AI proves itself as the great transformative technology that startups, established technology players, enterprises, governments, and consulting firms alike promise it to be, or it doesn’t. If it is in fact the next big wave then all these investments are indeed sound, and the investments will pay off handsomely for those firms that can the last person with the seat in the game of market share musical chairs. However, if the promise of AI fails to materialize, no amount of external funding and puffing can keep this bubble inflated. VCs firms are, after all, beholden to their fund limited partners, who demand a return for their investment. These returns are realized through company acquisitions or IPOs. Acquisitions and IPOs are in turn fueled by market demand. If the market demand is there, these exits will happen and everyone wins. But if these companies take longer to exit than investors like, or fail to happen at all, then the house of cards will quickly collapse.
Cognilytica believes that cognitive technology and AI has tremendous promise. We believe that rational investment on the part of enterprises will result in realistic ROI as long as expectations are in check. The amount of money that a company has raised is really immaterial to the amount of benefit that its solutions will provide a company. As such, maybe enterprises (and analysts) should really ignore how much companies have raised. For sure, startups that haven’traised any significant amount of money will easily and quickly be outcompeted by those that have. In fact, Cognilytica won’t cover self-funded or under-funded startups or companies that lack significant customer revenue for exactly this reason — why should we invest our time and energy on covering these firms if enterprises or investors won’t? And likewise, enterprises should focus on the real benefit these companies will bring to their bottom line. Ignore the fund raising metrics, unless the market share grab makes it impossible not to.