Why "AI" is Not Like the Internet or Dot-Com Bubble

For some of us, analogies between the internet bubble around the turn of the century and a potential AI bubble often emphasize excesses of investment, but also questionable accounting practices and, in a few notable cases, outright fraud (Enron, Worldcom). 

If the basic AI market danger can be stated as overvaluation leading to overinvestment, creating financial stress that encourages aggressive accounting that then slips over to illegal actions, the main danger right now is still overinvestment. 

But accounting assumptions seem to raise some issues.

At least some observers of the high-performance computing industry and neocloud providers worry about possible financial excesses such as off-balance-sheet financing of graphics processing units; infrastructure overinvestment; circular financing and GPU depreciation assumptions. 

The legitimate concern is that demand will not ultimately support the supply, leading to a bubble collapse of firms and significant financial losses for investors. 

But accounting assumptions are among the contributing issues. The concern is that what is lawful might not be wise, at scale. 

But there might be some new information on GPU useful lives that allays some of the concern. Secondary market values of Nvidia H100 GPUs, an older generation, seem to be quite strong. 

NVIDIA H100 GPU prices in 2026 suggest that the cost of refurbished units is in the mid-80-percent range of new units. 

That is a  narrower discount than buyers expect from the refurbished category, where 30- to 50-percent discounts are typical. 

A mid-80-percent floor on a three-year-old accelerator suggests demand is strong enough that even second-hand units hold most of their value.

Older GPUs remain useful for operations other than frontier model operations. Even if the highest value for the latest generation of chips is to support frontier language model training, inference operations can still use older GPUs. Beyond that, many batch operations can be completed using processors that are five to six years old. 

So depreciation schedules embodying assumptions about six-year useful life are not an accounting trick. 

There are other users of such devices and chips as well. 

Still, there is some evidence that used GPU prices for the latest generations might depreciate faster than did older generations, as new generations are released faster.  

All that matters because depreciation assumptions bear directly on reported profits. 

If a GPU's true economic life is three years but that asset is depreciated over six years, the company understates depreciation expense and also overstates net income for years one to three.

It also then will take an accelerated depreciation later, which lowers reported income. 

Secondary market values for H100s seem to provide reassuring evidence that a six-year deprecation schedule is grounded in reality, and does not distort earnings. 

Still, some might worry about Enron-style excesses, but Enron’s accounting practices were not simply unwise, but unlawful. The same might be said of Worldcom.

Still, the main problems with the dot-com bubble relate to mistaken assumptions about demand, and subsequent oversupply. 

Question	Dot-com/Enron-era warning	AI equivalent
Is demand real?	Internet traffic was real, but forecasts became extreme	AI usage is clearly real—but is ultimate willingness to pay keeping pace with compute investment?
Does revenue come from outside the ecosystem?	Telecom companies sometimes effectively sold capacity to companies whose own economics depended on the same boom	Are AI companies buying from each other in ways that make industry revenue look larger than end-user demand?
Is infrastructure earning its cost of capital?	Fiber existed, but often couldn't generate adequate returns	Are GPUs/data centers/power assets generating sufficient cash flow over their useful lives?
Are accounting profits turning into cash?	Enron's mark-to-market profits could precede cash realization	Are AI-related profits accompanied by operating cash flow?
Are assets fairly valued?	Enron used models to value difficult-to-price assets	Are assumptions about GPU useful lives, residual values, utilization and AI infrastructure returns realistic?
Where is the debt?	Enron obscured liabilities through SPEs	Are AI infrastructure obligations sitting on balance sheets or in partnerships/project-finance structures?
Who ultimately bears the risk?	Financial structures redistributed risk	Who owns the downside if AI demand disappoints—AI developers, hyperscalers, chip companies, landlords, lenders or investors?
Is growth organic?	Acquisition and financial engineering could sustain reported growth	Are customers independently generating AI revenue, or is capital circulating among AI companies?
What happens if growth slows?	Small reductions in demand could make enormous infrastructure investments uneconomic	What happens if inference demand grows 30% instead of 100%?
Does valuation require perfection?	Dot-com valuations incorporated extraordinary future growth	What assumptions about revenue, margins and AI productivity are embedded in today's valuations?

To be sure, one resonant concern is the use of special purpose vehicles to move capital investment off balance sheets. 

To be fair, other capital-intensive industries, such as airlines, have used SPVs to finance aircraft. Power utilities use them for power plants. 

But it’s an area of concern. 

Circular transactions between value chain participants also are familiar issues. When the same $100 billion can show up as a chipmaker's revenue, a lab's funding, and a cloud's backlog, actual demand can be obscured.

On the other hand, Enron and Worldcom were guilty of outright fraud. Enron's core energy-trading business was dependent on accounting assumptions and actions. 

Nvidia, Microsoft, Amazon, Alphabet, and Meta have enormous real, profitable, non-AI-dependent businesses generating current cash flow.

The hyperscalers are unlikely to be in danger of an Enron-style collapse. But some neocloud providers without the existing cash flow and profits from other lines of business are at greater risk.

And that is why depreciation assumptions matter, especially for neocloud providers. 

But again, those assumptions ultimately matter only if demand does not develop as many expect. Yes, there are timing issues. 

Ideally, revenue scales in line with investment.

But it is ultimate demand that matters most, even if gross investment levels and payback timing also matter.