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With AI demand outpacing supply, & domestic data center construction slowing, soon the US will consume more tokens than it produces.

At that point, the US will become a net importer of tokens, just like it is a net importer of rare earth metals. A basic model estimates this switch between 2028 & 2030, depending on how many planned facilities get cancelled.

Very roughly, the current systems, with new data centers coming online, can manage 2.5x annual token growth¹. Above that rate, chip & model efficiency gains can no longer offset domestic demand growth : power required in gigawatts equals tokens demanded divided by tokens per watt.

The cost of breaking into a corporate network with AI has fallen 99% in five months, & corporate security budgets are racing to catch up.

Goldman executives have discussed how the firm could soon be on track to spend as much or more on AI for security than it spends on AI coding tools.^1,2

Security has been a collection of layers of defense, each an insurance policy against a particular kind of attack.¹ As autonomous AI threats become the predominant risk, both attackers & defenders are competing on how much AI compute they can deploy.

The ceiling for AI in 2027 is about 80gw : doubling all the compute built in the history of the country in twelve months.

Yet even at this historic pace, the hourly cost of GPUs has doubled in the last six months : short-term rental rates have jumped from $3 to $12-13 per hour, pushing Anthropic’s blended GPU-hour cost up 82% in just two quarters.¹ Finished server racks are already sitting unpowered in staging warehouses because data centers lack the energized utility power to turn them on.

The essential ingredient to AI has more than doubled in cost in the last 6 months.

GPUs, the chips that power AI, have increased in rental price per hour from $3 to $12 to $13 on short-term contracts, pushing Anthropic’s blended cost per GPU-equivalent hour from $3.10 to $5.65 (within a $3.45 to $8.29 confidence band), according to data from the updated Anthropic S-1/A¹².

There are 4 layers in the AI stack, each with a fundamental question before it.

Application

Breakout hits appear overnight. The next model release can erase them just as fast. Thin wrappers die when the model absorbs their feature. To build a moat, app companies must decide whether to build a brand, disrupt incumbents through pricing, develop their own models, or expand faster than competitors.

Who will be the first $10t company? The one that first crosses the trillion dollar chasm : building a $1t AI compute engine before AI erodes the legacy cash cow paying for the GPUs.

In the last few weeks, two CEOs have put a $1t revenue target on their businesses : Andy Jassy & Elon Musk. We are tracking each company’s cloud & AI compute engine toward $1t in annualized segment revenue¹.

On Friday, November 6th at 10 AM Pacific / 1 PM Eastern, Office Hours will host Arvind KC, Chief People Officer at OpenAI & former CIO at Palantir, for a conversation on the future of the people function in the AI era.

We’ll talk about :

• Why efficiency is the wrong framing for AI when model capabilities double every six months.
• How the people, teams of companies, can democratize AI & amplify the most valuable capabilities in an organization.
• Span of control changes where an individual grows from being a manager of 7 people to 100, 200, 1,000 agents.
• The importance of emotional intelligence, or EQ, as a differentiator of leaders.

Office Hours is a 15-minute online conversation about a single topic with a luminary in the AI world, where we collect questions ahead of time, accept call-in questions live, in a candid conversation.

Anthropic filed their S-1¹ this week, the largest AI listing yet.

Founded in 2021 by Dario & Daniela Amodei, the company has grown from a research lab into the second-largest model developer, selling Claude to more than 11,900 customers who spend over $100,000 a year. The comparisons below set the reported OpenAI figures² against Anthropic’s derived gross margin³, adjusted operating margin⁴ & compute commitments⁵.

Anthropic generated $16.3B of revenue in the first six months of 2026, a fourteen-fold increase over the $1.2B of the first half of 2025. That half-year alone was more than three times all of 2025, when Anthropic grew 11.9-fold to $4.6B, more than four times OpenAI’s 253%.

Coding & customer support were the first two product-market fits in AI.

Black October, the industry’s term for the deluge of new security vulnerabilities, is the prelude to the third product-market fit in security.

Attacks on banks, certificate authorities & core open source libraries have never been greater.¹ Ransomware against financial firms rose 30% last year, & 76% again in the first quarter.² Compromised intermediate certificates let an attacker impersonate any website or service.³ A single five-day campaign stole 78,330 secrets from 2,186 organisations.⁴

Every major technology build-out requires two phases, according to Carlota Perez¹: the infrastructure to power it, & then the applications to pay for it.

Between 1996 & 2005, American telecom companies spent $1.5t building the internet in today’s dollars.² Meanwhile, applications riding on that infrastructure booked $180 billion of revenue.³

That’s an interesting ratio, isn’t it? Application revenue to infrastructure spend. It matters because the shorter the gap between the two, the sooner the infrastructure pays for itself. Let’s compare this to the AI era.