The current high interest in artificial intelligence companies has led some to draw parallels to the dotcom bubble of the late 1990s, where technology stocks saw explosive growth followed by a dramatic crash. The danger is over-investment in platforms, apps or use cases of questionable value.
To be sure, that will happen. At such an early stage, we cannot predict ultimate winners and losers, and perhaps seven out of 10 bets will be lost.
But underlying those trends was the emergence of the internet, something we generally now recognize to have been a general purpose technology capable of enabling lots of innovation and industries and firms.
A general purpose technology is a technology that has the potential to significantly affect a wide range of industries and sectors across an entire economy, often including massive disruption of existing industries. Past GPTs have included fire, agriculture, steam power, electricity, the internal combustion engine and mass production.
Many believe AI is going to emerge as a GPT. And that is where some of the apparent similarities to the dotcom boom and crash might apply.
The internet boom and bust of the dotcom era featured exuberant expectations about revenue growth; valuation excesses; over-investment; often a focus on “growth” or market share over “profits;” unclear business models or a lack of sufficient distinctiveness or competitive “moats.”
So much of the investment capital was wasted. But there is an argument to be made that the "expected" Ai win rate is about what one would expect from investment banking in general: more failures than successes.
The issue is "bubble" style over-investment, and there the parallels with the dotcom over-investment might not be so large.
Compared to the dotcom period, AI monetization mechanisms are better understood. Infrastructure suppliers of course already are making money selling the products essential to building and operating AI models and apps.
And that includes the expected development of system integration; consulting and managed services provided as professional services.
Others in the AI area have revenues based on licensing of models, such as OpenAI and others.
Beyond that, subscription models already are proliferating, allowing direct monetization of AI features and apps. Microsoft has been an early leader in that regard, adding AI subscriptions to its productivity suites. Others will follow.
So AI software as a service; infra as a service and data as a service models already exist.
Other revenue models will be more indirect, and may take some time to develop. Those might include pay-per-use; micro-transactions; performance-based revenue (revenue tied to specific cost savings, for example); or other outcome-based models (revenue earned by enabling specific return-on-investment objectives); other forms of profit sharing or licensing.
Eventually, AI will support many types of retail transactions or advertising. But the benefits will often be indirect, making the specific AI contribution harder to measure.
The point is that the analogies to the dotcom era are only partially applicable. Yes, over-investment and business models without “moats” could happen.
Still, we already see tangible and sustainable business models in action, clearly in infrastructure but in a growing number of subscription-based monetization models.
Though many will argue there will ultimately only be a handful of leading large language models to lead the market, it already seems the case that specialized, smaller models optimized for particular firms or industries also are developing.
The point is that wild or excessive investment in firms without viable business models is much less an issue with AI, which has reasonable and justified implications for reducing operating cost for almost any business.
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