AI: Is it "Different This Time"?

The phrase "it's different this time" has often been uttered when financial markets seem to be behaving in dangerously inflated ways. It refers to a belief that past trends or patterns won't hold true in the present situation and that the current situation is unique and exempt from the lessons learned from historical events.

Some of us last encountered the phrase during the dotcom era, when some argued historical data and traditional methods of analysis were not relevant due to unique and unprecedented circumstances.

As a practical matter, it meant some investors justified continued investment despite unsustainable valuations; without clear revenue models or business models. 

It might also be seen in periods of industry merger and acquisitions frenzy, when high valuation multiples are justified on the basis of asset scarcity. 

For the most part, we are not hearing that about firms in the artificial intelligence area. Investors see higher valuations, of course, but are not saying traditional criteria are irrelevant. That is helpful. 

On the other hand, 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.

So is AI in an investment bubble? To be sure, many believe AI is the "next big thing," as was the internet. And despite the dotcom excesses, we generally now recognize the internet as a general purpose technology capable of enabling lots of innovation and industries and firms. 

So excesses aside, any 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 investment capital was wasted. 

To be sure, there is a strong likelihood of over-investment in AI as well. On the other hand, 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.