Is AI a bubble? In this episode, I unpack a new five-gauge framework for understanding the biggest question in tech. Drawing on lessons from past manias – railways, telecoms, the dot-com boom – and grounding our analysis in fresh data, we examine economic strain, revenue growth, valuations, and the quality of capital fueling AI’s ascent. This is our effort to cut through hype and fatalism to provide a clear dashboard: where today’s AI build-out looks like a genuine boom, and where early warning signs of bubble dynamics may be emerging. Whether you’re an investor, policymaker, or executive, this framework offers a disciplined way to navigate the noise.Jump to the best parts:(00:29) Echoes of the past (01:31) The 5 gauge framework (01:54) Gauge #1: Investment intensity(03:45) Gauge #2: Monetization level (04:48) Gauge #3: Revenue trajectory (06:23) Gauge #4: Valuation level(07:24) Gauge #5: Quality of capital (10:10) Overall assessmentProduced by EPIIPLUS1 and Supermix. Thanks to my team: Nathan Warren, Hannah Petrovic, Chantal Smith & Marija Gavrilov Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Full Episode
Are we in an AI bubble? As I speak, hundreds of billions of dollars are being poured into artificial intelligence. Many say it's the investment the technology deserves, the push towards an abundance of intelligence that could change humanity forever. Others are calling it a bubble, a repeating economic story of excess, untethered belief and ultimately collapse.
In the 1600s, a bubble led to tulips trading for more than houses. For the price of one bulb. And in the 1800s, bubbles led to thousands of miles of unused railway tracks being built.
All of these ended in disaster with money down the drain, investors losing their shirts and shocks to the economy. The narratives seduce us, but ultimately that rhetoric turns into reality and prices collapse. To some extent, the current AI wave resembles past manias. And so there's a key uncertainty. Is this sustainable growth or are we leading to a bubble?
It's complex because bubbles look a lot like booms while you're in them. A boom also involves rapid investment and optimism. But eventually fundamentals like cash flows and productivity catch up. So I spent hundreds of hours figuring this out. I dug through data. I talked to investors and economists. I made a number of models. I compared what's happening today to the bubbles of history.
So all of this led me to a five-gauge framework that helps us understand AI bubble risk in much the way that a pilot uses a number of different gauges to figure out if the plane is flying safely. These gauges can give us a sense of the path we're on right now and help us understand what we need to look out for in the future. A key gauge is the investment intensities.
So what is the scale of capital expenditure relative to GDP? And is that getting unhinged? When one sector attracts an enormous amount of capital, it bends the entire economy. Capital moves there, labour moves there, supply chains move there. And one problem is that any kind of reversal transmits quickly. If assets are short-lived, that can really compress the payback window.
If we look historically... The US build-out of the railways in the 19th century had a number of bubbles, and at one point, the annual capital expenditure in building that railway network and the trains approached 3.6% of GDP, right before one of the busts.
In the late 1990s, investment in telecoms infrastructure as we started to digitize telecoms and communications reached about 1% of US GDP level. And it left behind many, many conduits of dark fiber that we still use today. When we look at artificial intelligence in 2025, we're going to see about $400 billion go into build-out data centers.
Not all of that is in the US, but roughly speaking, given the majority is, that's about 1% of US GDP. And it seems to be rising. Maybe it'll get towards 1.5% by 2030. Now, hardware like GPUs doesn't have the same lifespan as iron rails on a railway. They depreciate over six years.
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