Today, artificial intelligence companies are now being valued in hundreds of billions of dollars.

It's open AI. It's anthropic. It's all the value that DeepMind has added to Google over the past

years. But that force is a really important question, and it's a question that is being asked by

the mainstream, but also by specialists. do the economics actually work?

When you look at what it costs to train and run a frontier model and what you earn from it before the next model comes along and replaces it,

is that a profitable business?

Are we looking at something a bit like Uber, which lost money for 14 years

before turning a profit and is now handsomely valued,

or something that doesn't have an end in sight. Now, these questions really matter.

The stock markets, well, big tech had a lurching week this week and at one point more than a

trillion dollars was wiped off valuations. Wall Street's very linear investors were trying to digest the

$650 billion of capital expenditure commitments being made by Big Tech for 2026. Some of that $650

billion is going towards AI infrastructure. Does any of this make sense? Are there actually

going to be operating margins

to defend and is the revenue growth going to support this? Now, as a reader's exponential view,

you'll know that we've been asking these questions for months, if not longer. But most recently,

we partnered with Epoch AI. I'm sure everybody knows Epoch, but if you don't know,

they are really preeminent independent research organization tracking some of the trends

behind AI. You've probably seen their work on scaling laws and compute trends. So we

worked with their team to dig into the actual margins of Frontier AI, and the results

are really, really interesting. So whether or not you've had a chance to read our research yet,

and you really should have done, this conversation will give you a really clear picture of where

...