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.

Back in April, when we published what we called the Trump Great Reset Strategy, we described the grand realignment we believed President Trump and his acolytes were embarking on in three phases.

At the time, it read like a conceptual map. As the months passed, it began to feel like a set of operating instructions written in advance of turbulence.

As you can expect, any grandiose plan would get all kinds of blowback… but this year exhibited all manner of Trump Derangement Syndrome on top of the difficulty of steering a sclerotic empire clear of the rocky shores.

The “phases” were never about optimism or pessimism. They were about sequencing — how stress surfaces, how systems adapt, and what must hold before confidence can regenerate. And in the end, what do we do with our money?!

Relative to GDP, the net international investment claim on the U.S. economy was 20% in 2003. It had swollen to 65% by 2023. Practically every type of American company, bond, or real estate asset now has some degree of foreign ownership.

But it’s even worse than that. As the federal deficit has pumped up the GDP figures, and made a larger share of the economy dependent on government spending, the quality and sustainability of GDP have deteriorated. So, foreigners, to the extent they are paying attention, are accumulating claims on an economy that has been eroded by inefficient, government-directed spending and “investments.” Why should foreign creditors maintain confidence in the integrity of these paper claims? Only to the extent that their economies are even worse off. And in the case of China, that’s probably true.

As global government interest expense climbed, gold quietly followed it higher. The IIF estimates that interest costs on government debt now run at nearly $4.9 trillion annually. Over the same span, gold prices have tracked that burden almost one-for-one.

Silver has recently gone along for the ride, with even more enthusiasm.

Since early 2023, Japan’s 10-year government bond yield has risen roughly 150 basis points, touching levels not seen since the 1990s.

Over that same period, gold prices have surged about 135%, while silver is up roughly 175%. Zoom out two years, and the divergence becomes starker still: gold up 114%, silver up 178%, while the S&P 500 gained 44%.

According to the American Association of Individual Investors, the average retail investor has about a 70% allocation to stocks. That’s well over the traditional 60/40 split between stocks and bonds. Even a 60/40 allocation ignores real estate, gold, collectibles, and private assets.

A pullback in the 10% range – which is likely in any given year – will prompt investors to scream as if it’s the end of the world.

Our “panic now, avoid the rush” strategy is simple.

Take tech profits off the table, raise some cash, and focus on industry-leading companies that pay dividends. Roll those dividends up and use compounding to your overall portfolio’s advantage.