AI Investment Bubble or Not? Dot-Com Danger or "Only" Normal Overinvestment in a Major New Technology?

Nobody knows yet whether the investment boom in artificial intelligence we now see is a bubble, or not. Conventional wisdom seems to suggest AI is a bubble, but there is disagreement. 

And if some argue it is a bubble, there remains an argument that there is a significant difference between a dot-com style bubble and an “ordinary” investment bubble associated with introduction of any major new technology. 

To be sure, for some of us, there are hints to parallels of excesses akin to the excessive dot-com investment at the turn of the century. As I was writing one startup business plan, I was told “there’s plenty of money, make it bigger.” 

As it turned out, “this time is different” and admonitions that some of us “did not get it” were wrong. Economics was not different and normal business logic was not suspended. 

But some might note that there are important differences between AI investment and dot-com startup investment. Back then, many bets were placed on small firms with no actual revenue. 

Today, it is the cash flow rich, profitable hyperscalers that dominate much of the activity. Investment burdens are real, but so are immense cash flows and profits to support that investment. 

And by some financial metrics, valuations do not seem as stretched as they were in the dot-com era, though everyone agrees equity market valuations are high, at the moment. 

We also can’t tell yet what impact artificial intelligence might have on productivity and economic growth, much less future revenues for industries and firms. 

And that might be crucial to the argument that there actually is not an investment bubble; that there are real financial and economic upsides to be reaped; new products and industries to be created. 

There is some thinking by economists that AI impact could be greater than electricity and at least as important and positive as information technology in general. 

General-Purpose Technology	Primary Timeframe of Peak Impact	Estimated Annual Productivity Boost (Peak Rate)	Macro-Level Impact Metric
Steam Engine	Mid-19th Century (Decades after invention)	0.2% - 0.3%	Contribution to annual TFP* or Labor Productivity Growth
Electrification	1920s - 1940s (30+ years after initial adoption)	~0.4% - 0.5%	Contribution to annual TFP or Labor Productivity Growth
Information Technology (IT) / Computers	Mid-1990s - Early 2000s	~1.0% - 1.5%	Acceleration in annual Labor Productivity Growth (U.S.)
Artificial Intelligence (AI) (Current Forecasts)	Early 2030s (7–15 years after GenAI breakthrough)	1.0% - 1.5%	Projected increase in annual Labor Productivity Growth over 10 years

Study/Source	Projection Focus	Estimated Gain (Over Baseline)	Caveats
Goldman Sachs (2023)	Macroeconomic Forecast (Global/U.S.)	7% increase in Global GDP over 10 years; 1.5 ppt annual U.S. labor productivity growth	Highly optimistic, assuming rapid adoption and task automation.
McKinsey Global Institute (2023)	Economic Potential of Generative AI	$2.6 to $4.4 Trillion added annually to the global economy.	Based on value from 63 specific use cases across business functions.
Acemoglu (MIT)	Conservative Macroeconomic Model	0.7% increase in TFP  over 10 years (U.S. economy).	More modest, based on historical adoption rates and cost-benefit analysis of task automation.
Brynjolfsson et al. (Micro Studies)	Firm/Task-Level Productivity	10% - 40% increase in productivity for tasks like coding, customer service, and professional writing.	These are early, firm-level gains, which historically take time to translate into aggregate macro statistics.

Each of us has to make a call: bubble or not; big bubble or only “normal” overinvestment?