Forefront Communications

MarketBrains Report: Startups Ride the Hype Cycle in the “Year of AI”


Sam Belden

Sam Belden

In a conversation with astrophysicist Neil deGrasse Tyson, “Singularity” theorist and author Ray Kurzweil talked about what can and cannot be predicted in the development of the internet and AI.

In the 90s, Kurzweil reckoned that there would be a need for search engines because there’d be so much knowledge on the web that it would be impossible to find anything.

What could not be predicted, he noted, is that of 50 projects doing that, “these couple of kids in the Stanford dorm” would win, he said, referring to Google.

“I wouldn’t have to work very hard if I had made that prediction,” Kurzweil noted.

Today, it seems obvious that far more sophisticated machines are required for big data and automation, but the big question remains: who’s going to figure that out in which industries?

Who is going to make money in AI?

In a recent article, Simon Greenman, co-founder and partner at advisory firm Best Practice AI, tried to create a framework for tackling this question from a start-up perspective, comparing this year’s investment boom with a gold rush in a recent article titled: Who is going to make money in AI?

“What we’re seeing here is the startups come rushing in funded by venture capitalists (and) the corporates come rushing in to try and adopt it. So, there is a mass of companies trying to effectively mine the gold,” said Greenman speaking to MarketBrains.

Specific to finance, he named firms like Zest and Affirm as contenders to watch, pointing to characteristics such as level of investment (fundraising reached some $300 million and $700 million respectively).

“For that level of investment, it means that they have significant customer traction …those companies that can get scale are going to do well,” he added.

AI investment by the numbers

There is a tsunami of research trying to pin down how much money has been invested in artificial intelligence and how much that investment might pay off.

Estimates range from billions to trillions of dollars in terms of how much AI could increase economies in the next decade or more.

In the US, AI had another big quarter: funding to US-based artificial intelligence companies rose 21% in Q2 2018 to $2.3 billion, after a 37% rise in Q1, according to research from CB Insights and PwC.

But AI hedge funds haven’t been doing so well so far this year: AI hedge funds posted losses for the third consecutive month, down 0.60% in June. This loss brought their year-to-date return down to -3.11%, placing them behind all of the primary strategic mandates, according to Eurekahedge data.

In 2017, AI hedge funds were outperforming their peers.

AI (media) hype cycles

One of the most cynical assessments so far comes from Riot Research’s tech unit in a report titled, AI: show me the money.

Riot thinks that the AI market will reach $39 billion globally by the end of 2023, compared to investment of $100 billion in 2018 alone, and predicted that the field will be “littered with corpses” on the way.

Despite this cynicism overall, finance and insurance remain poised for gains because an “AI bubble burst will clear the air for sustained growth in key sectors”.

Indeed, finance and insurance are described in the report as an “area where some of the hype in terms of growth and threat to jobs is justified.”

On the latter point, there’s another set of numbers that industry professionals are warning on: how many people are actually qualified to “do” AI.

In a report from earlier this year, Canadian firm Element AI found that there are just over 20k PhD-educated researchers globally that are capable of working in AI research and applications, with some 3,000 candidates actually looking for a job.

The US had the highest concentration of researchers at 9,000, followed by the UK with just under 1,900, and Canada came in third with just over 1,000.

New graduates, narrow focus

There are plenty of startups founded by people who only understand neural networks, or deep learning, because that’s what’s being taught right now, said Pierre Haren, CEO of Causality Link, a US-based startup focusing on explainable AI in research.

Haren’s background with AI goes back some 40 years, and a company he founded, ILOG, was acquired by IBM for $340 million in 2009.

The narrow focus on one popular AI technology, deep learning, is “disastrous” he added, but somewhat typical based on AI waves that have come before.

“There will be a lot of people falling flat on their face, because there is a discrepancy between what marketing says and what the technology can do and therefore, as usual, this is long-term going to create downdrafts for the excitement,” he said.

But that won’t change industry demand for a higher level of “smart, explainable automation”, Haren added.