The Great AI Boom Ahead
Because the real-world experience of Boomers, Xers, and especially Millennials is limited to the speculative frenzy and transition stages of a Techno-Economic Revolution, we reflexive see every boom developing into a bubble which ultimately bursts with major collateral damage. But not all bubbles have negative consequences for the economy like we saw during the housing bubble, or even the dot-com bubble.
And an objective analysis indicates that an “AI bubble” is more likely to generate enormous value than it is to wreak havoc.
Today, investment in artificial intelligence is rising rapidly, especially in China and the United States. AI-related opportunities and threats are just becoming visible to most decision-makers. And many of these are likely to be mirages. Yet, “getting this right” is crucial for today’s investors, managers, and policymakers.
That raise two key questions
“But unlike the housing bubble,” BCG argues that, “the effects of a bursting AI bubble wouldn’t cause great harm. Indeed, this bubble has far more in common with the dot.com bubble, which helped finance the internet backbone, than with the housing bubble, which wreaked havoc on the household finances of millions of homeowners.”
Consider the facts.
Bubbles occur when the market value of assets decouple from their intrinsic value and expectations of rising valuations generate investor demand. In typical bubbles, both the volume and valuation of investments expand rapidly. We are seeing both trends in AI.
From 2013 through 2018, both the number and size of AI deals soared. Overall, investments rose by 75% annually. While the Chinese government and the U.S. Department of Defense have played a big role, private investment in all regions has also surged. As a result, AI investments are rising, both in absolute terms and relative to other categories of technology. For example, between 2012 and mid-2018, investors poured $110 billion into 9,800 rounds of financing for AI startups. This dwarfed the $12 billion invested in 1,500 blockchain startups during that time period, as well as the $700 million invested in 60 quantum computing startups. The average deal size, which is a rough proxy for valuation, almost tripled over the past five years.
The AI era is different from the dot.com era in at least one key respect. The biggest new dot.com companies began selling their stock on the public market quickly, whereas AI companies typically remain private; this makes direct comparisons difficult.
Importantly, there is no clear path for any significant number of these companies to become profitable. Most AI algorithms are available for free or at low prices. To be effective, algorithms must be trained on specific sets of data. But startups usually don’t own the data, that belongs to potential customers. And the more valuable it is, the less likely customers are to share it with suppliers.
Normally, a bubble is easy to spot because you can track the prices of widely traded assets like houses or dot-com stocks. Unfortunately, there aren’t many public, pure-play AI companies to evaluate. However, according to BCG, indirect evidence exists to suggest that we’re already seeing the frothiness of a bubble. Within public companies, the number of mentions of artificial intelligence during earnings calls with analysts and investors began to skyrocket around 2015. At IT companies, these mentions increased ten-fold between 2015 and 2017, a rate of increase that exceeds the growth of AI applications at these companies.
These data and trends suggest that “AI technology companies are in a bubble.” But, as mentioned earlier, not all bubbles are created equal. Many ambitious infrastructure projects that produced canals, railways, and telecom networks were fueled by bubbles. In the later stages of these projects, “greater fools” often invested at inflated prices. But an investor’s poor timing does not detract from the salutary impact of a worthy project. For example, the Erie Canal, which tied together New York City and the Great Lakes in the 1820s, opened a significant new avenue for east-west commerce and helped secure New York City’s place as a financial capital.
Yet, as we saw in the 2008 financial crisis, many bubbles are bad for a wide range of players. Indeed, without massive government intervention, the 2008 bubble would have wiped out large swaths of the global banking system. More than 10 years later, many households are still struggling to regain their footing.
Economist and venture capitalist William H. Janeway developed a useful framework for understanding bubbles by understanding the role of innovation and credit in their formation. The first thing Janeway looks at is whether a bubble is financing technological transformation or simply enabling speculation on existing assets, such as tulips, gold, or housing. The second thing he examines is whether credit is helping to magnify the size of the bubble. In his view, good bubbles are unleveraged and they finance innovation; bad bubbles, on the other hand, enable raw speculation and threaten financial stability by supplying excess credit.
By these standards, the internet and telecom bubble of the late 1990s was productive and played a crucial role in building out the infrastructure and technology needed to allow the internet to flourish; and it did so without undermining the financial system. The mortgage crisis of 2008 was dramatically different. It was fed by lax underwriting standards and complex securities that were difficult for investors to understand. What’s more, it created a moral hazard that required the emergency intervention of central banks. And, most importantly, it had nothing to do with innovation. The focus was on building houses the same way it had been done for at least 60 years.
So how does the AI bubble fit into this framework? BCG argues that it closely resembles the bubble of the dot.com era, not the one that led to the housing crisis. By definition, AI is a general-purpose technology that will provide benefits to all companies, not just speculators fortunate enough to get in early. Already, AI is playing a role in automating factories, speeding the development of new drugs, and helping retailers personalize offerings to customers. Like the Erie Canal, it will likely alter economic trajectories of entire industries. Furthermore, because AI is primarily being financed by equity investors rather than bank loans, there are no signs that it will have negative effects on the banking system.
What’s the bottom line?
In light of these dynamics, investors should assume there will be an AI technology bubble and we’re probably already in it. Therefore, those who choose to invest in AI technology must recognize they will largely be betting on the behavior and timing of other investors. Just as we’ve argued regarding the cannabis industry, it’s largely a matter of speculation and gamesmanship.
Companies, for their part, should be wary of acquiring AI technology companies that aren’t closely tied to what they know. The key is to only buy AI talent and technology that will enable them to leap-frog barriers to developing their own business-specific capabilities. This will enable them to participate in the critical benefits of AI without being exposed to the vagaries of the financial markets.
Given this trend we offer the following forecasts for your consideration.
First, AI will unleash an unprecedented wave of productivity finally ending the malaise that started in 2001.
Accenture and Frontier Economics concluded that AI could drive U.S. economic growth to a 4.6% average annual rate in the 2020s. The McKinsey Global Institute estimates that AI could contribute $13.5 trillion annually to worldwide GDP, (in constant dollars), by 2030. And, as explained in Ride the Wave, AI represents “the magic ingredient” that enables the other key technologies of the Fifth Techno-Economic Revolution to unleash their full potential.
Second, few AI-based startups will be economic winners; the rewards will go primarity to consumers and to the companies that deliver value to those consumers via AI-enhanced products and services.
In the deployment stage of a Techno-Economic Revolution, the big payoffs lie in the exploitation of a technology rather than in the technology itself. Those companies that have the big data sets to train AI-based systems to serve customers will reap huge benefits. For an analogy, think back to the deployment phase of the Mass Production Revolution: the winners were consumer goods companies that produced products that exploited the assembly lines, logistics, and mass media. Free or low-cost AI algorithms running on enormous “cloud services” will let companies monetize their cumulative treasure-trove of data, but the developers of the tools will seldom reap huge profits. And,
Third, we won’t see a big wave of “AI unicorns” hit the IPO market.
AI-based companies will go pubic only once they are generating real revenues and have the promise of a defensible market position. This will primarily represent a way for founders and venture capitalists to cash out. The best companies will be acquired, pre-IPO, by public companies which can integrate the proprietary AI-based assets into their existing operations to gain a competitive advantage. Most will remain private, either surviving as niche service providers or being liquidated. In that sense, the AI bubble is likely to resemble the multiple “bio-tech bubbles”, much more than the dot-com boom. That is, the big risk will be limited to sophisticated investors, who can effectively manage it. Cataclysmic crashes like history saw in housing and tulip bulbs, typically occur when unsophisticated investors take on excess speculative risks. That won’t happen this time.