What If the “Scaling Cliff” Pops the AI Bubble?

“History as well as life itself is complicated — neither life nor history is an enterprise for those who seek simplicity and consistency.”

-Jared Diamond, Collapse

September 10, 2025 — Artificial intelligence is this decade’s tech success story. And that sector’s stocks — led by the almost supernaturally powerful chip maker Nvidia — are primarily responsible for the S&P 500 and Nasdaq being at record highs.

In just the past five years, nearly a trillion dollars have been thrown at AI data centers, chip plants, and model training. And the spending curve continues to steepen, as pretty much every tech firm and most governments enter the AI arms race.

Early AIs improved in line with the amount of computing power and new data they were fed. This led to the assumption that AI investment had a predictable rate of return (which investors absolutely love).

But with the most recent iterations of name-brand AI, that relationship has broken down. They’re not improving in line with the money being spent on them, leading a growing number of analysts to voice doubt about whether the return on this investment can be predicted going forward. This is known as the “scaling cliff.”

As Chat GPT explains the problem:

The entire LLM arms race assumes smooth scaling. If we’re close to a cliff:

Simply making models bigger stops being productive.
Labs must pivot to data curation, architecture changes, or reasoning-focused designs.
Many researchers suspect we’re nearing this cliff.

In short:

The AI scaling cliff is the point where bigger no longer means better — when scaling laws break because of data, optimization, or cost bottlenecks. It marks the boundary between “brute force scaling” and needing new approaches to intelligence.

Here are two video deep dives into the scaling cliff concept:

Could the AI bubble burst?

Bubbles, while they’re inflating, take on the aura of inevitability. In the 1990s, the Internet was going to rule the world, and the leading dot-coms would, as a result, grow exponentially forever. In the real estate bubble of the 2000s, home prices would always rise, so no price was too high for a nice house.

Those bubbles popped, catastrophically. That’s the nature of bubbles, and it would be a denial of history to expect the frantic money pouring into AI to return consistent profits. And to expect the broader markets elevated by this bubble to keep rising when the bubble pops.

By every historical valuation measure, US stocks (other than the commodities miners) are well into bubble territory. So it’s wise to build crash protection into today’s portfolios. Long-dated put options on the S&P or Nasdaq are just basic common-sense insurance at this point.

John Rubino
John Rubino’s Substack & Grey Swan Investment Fraternity

P.S. from Addison: We love AI. Specifically, the LLMs ChatGPT, Claude and Perplexity.

Over the weekend, we drafted a 67-page outline and publisher’s treatment of a future book using our own “AI Clone” (as our buddy Chris Daigle would call it).

At the very least, LLMs can collect, organize and describe data that took hours, days and weeks only two years ago when we updated Demise of the Dollar, Financial Reckoning Day and Empire of Debt in 2003-04 for their post-pandemic third editions. That was time spent in purposeful drudgery I would have preferred to be using to actually think.

We can see how LLMs and other advanced computational platforms will free a myriad of occupations from equal drudgery.

That said, AI doesn’t think for you.

Nor is it any more immune from market forces than routers in the Cisco bust of the 2000s tech wreck or radio transmitters in the great RCA boom and bust of the 1920s.

Grey Swan events – those which you cannot time, but can identify through current trends and historical examples – will pock the innovation cycle as much during the Age of Intelligence as any other age.

Thanks to John Rubino for sharing the growing challenge of AI scalability today.

Grey Swan Live! this week: Mark Jeftovic joins us tomorrow at 2 p.m. ET for “Shadow Fed & the American Dream” — how a September rate cut could hit the dollar’s purchasing power, where the money-market flood might go next, and why “control of money” is migrating from central banks to code, corporates, and courts.

If you’d like, you can drop your most pressing questions right here: Feedback@GreySwanFraternity.com. We’ll be sure to work them in during the conversation.

“History as well as life itself is complicated — neither life nor history is an enterprise for those who seek simplicity and consistency.”

-Jared Diamond, Collapse

As Chat GPT explains the problem:

The entire LLM arms race assumes smooth scaling. If we’re close to a cliff:

Simply making models bigger stops being productive.
Labs must pivot to data curation, architecture changes, or reasoning-focused designs.
Many researchers suspect we’re nearing this cliff.

In short:

Here are two video deep dives into the scaling cliff concept:

Could the AI bubble burst?

John Rubino
John Rubino’s Substack & Grey Swan Investment Fraternity

P.S. from Addison: We love AI. Specifically, the LLMs ChatGPT, Claude and Perplexity.

Over the weekend, we drafted a 67-page outline and publisher’s treatment of a future book using our own “AI Clone” (as our buddy Chris Daigle would call it).

We can see how LLMs and other advanced computational platforms will free a myriad of occupations from equal drudgery.

That said, AI doesn’t think for you.

Nor is it any more immune from market forces than routers in the Cisco bust of the 2000s tech wreck or radio transmitters in the great RCA boom and bust of the 1920s.

Thanks to John Rubino for sharing the growing challenge of AI scalability today.

If you’d like, you can drop your most pressing questions right here: Feedback@GreySwanFraternity.com. We’ll be sure to work them in during the conversation.

Alternative assets like gold and bitcoin are trading off their highs right now, but rising levels of debt and an ongoing political impasse will likely mean much higher price moves for these assets in dollar terms.

U.S. government debt is edging closer to the $38 trillion mark — now well over 120% of GDP. That puts the U.S. in the same league as basket-case economies like Venezuela, Sudan, and Lebanon. Not exactly the kind of company you want to keep.

But it makes sense: history shows that once a country crosses this threshold, things start to break — and it’s rarely just one thing. I’ve talked a lot about that in the past.

An admission: we’re mildly obsessed with the private credit markets.

When there’s a bull market in everything — AI stocks, financials, rare earths, gold and silver — it helps to keep an eye on the plumbing.

One chart tells the tale: since 2015, bank loans to non-depository financial institutions (NDFIs) — think private equity and private-credit funds — have soared nearly 300%.

Consumer loans, residential mortgages, commercial real estate? Flat as Kansas. Post-2008, Basel III and Dodd-Frank made leveraged and middle-market lending so capital-intensive that banks stepped back.

Gold dropped nearly 2% yesterday. But with the massive increase in fiat currencies globally, that’s an opportunity to buy more cheaply.

With that much cash sloshing around the system, the “debasement trade” is a go.

But it makes sense: history shows that once a country crosses this threshold, things start to break — and it’s rarely just one thing. I’ve talked a lot about that in the past.

An admission: we’re mildly obsessed with the private credit markets.

When there’s a bull market in everything — AI stocks, financials, rare earths, gold and silver — it helps to keep an eye on the plumbing.

One chart tells the tale: since 2015, bank loans to non-depository financial institutions (NDFIs) — think private equity and private-credit funds — have soared nearly 300%.

Gold dropped nearly 2% yesterday. But with the massive increase in fiat currencies globally, that’s an opportunity to buy more cheaply.

With that much cash sloshing around the system, the “debasement trade” is a go.