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The fastest growing market in the world isn’t AI ; it’s in crypto.

¹

Real world assets (RWA) means enabling stocks & commodities like oil & copper to trade on blockchains, handling nearly 100% of the public price discovery when the commodities exchanges close.

RWA powers IPO pricing weeks before public listing for Anthropic, OpenAI, SpaceX, & Cerebras,² using instruments called perpetuals on stocks;³ & RWA provides international access to important companies.

Like two SailGP boats in San Francisco Bay, Anthropic & OpenAI are vying to be the next multi-trillion public company & adding complexity to their strategies beyond technical one-upmanship.

Technology innovations marked the pre-2026 era : thinking models, bigger models, RL environments, agents, harnesses.

GPU prices have doubled in the last six months, from $4.40 to $8.08 per GPU-hour. But AI prices are falling. How can that be?

It is not a simple answer. Higher GPU costs are likely to remain while the industry races to build out new data centers. Every component of the buildout is increasing in cost, from concrete to copper to credit.¹ Above all, electricity remains the limiting factor, which Oracle is experiencing : last week it invoked force majeure on its New Mexico campus after the natural-gas pipeline feeding it slipped by six months.²

“Writing code by hand is no longer an economically productive enterprise for the vast majority of programmers working at the vast majority of companies. That’s today. By the end of the year, it will be virtually all domains, virtually all programmers, virtually all companies.”

David Heinemeier Hansson, CTO of 37signals · Rails World, Austin · 23 September 2026

Software engineering have evolved to systems architecture. That was always the destination.

Yesterday, Anthropic released a new model & cut its price. Ninety minutes later, OpenAI did the same.

Most business AI use is the messy middle: multi-step workflows that need a smart enough model at a price a company can afford. It is the most important part of the market today, & it is where the competition is fiercest. The price cuts are the evidence.

In June, Anthropic set the frontier price at $10 & $50 per million tokens with Fable 5. In July, OpenAI answered with GPT-5.6 Sol at $5 & $30, matching that capability at a third of the cost per task.

Working in the back of a grocery store has its own set of rules : if the food is a banana, send it to produce ; if it is a cookie, the snack aisle ; if it is cumin, shelve it with the spices.

But what if the load of bananas has spoiled, the cookies have crumbled & the cumin is caked? These rules & exceptions govern every grocery store & neighborhood mart. At the beginning they are rigid ; over time, more exceptions are discovered : is that a plantain? Dubai chocolate : dessert or baking supply?

The better the harness, the better the business.

Berkeley published a study this week showing harnesses, the systems that control AI agents, set the price of an answer. The right harness cuts the cost of the same result by 71% without a loss of accuracy.¹

The data points to the opportunity for the next generation software applications : harnesses. Yes, we can use AI to do almost anything we want at work. But no, we cannot afford to provide everyone access to state of the art models for every task.

Vercel took its inbound sales development team from 10 people to 1.25.

Jeanne DeWitt Grosser, COO of Vercel, shared this on our Information interview together¹ :

“We had 90% automation of sales development for inbound. And our support agent that we’ve home-built handles 93% of all support cases…

In both of those cases, the total annual infrastructure bill is in the single-digit thousands…

Dario Amodei asked the industry to pace itself on a Saturday¹. But what does that word mean?

The proposal promises auditors examining AI closely, collective action in the industry to meter the pace of innovation, & international coordination across allied countries. Pacing is a policy question. Five camps priced the consequences & none named a speed.

1. The interpretability camp wants time. Evan Hubinger, who leads alignment science at Anthropic, puts the odds that AI goes catastrophically wrong for humanity above 10 percent within a decade & says there is no plan for it². Interpretability, the science of understanding what happens inside a model, “doesn’t always produce clear & reliable results,” Amodei wrote. “We still only understand a tiny fraction of what goes on inside these models.”¹
2. The labor camp wants time for workers, not for researchers. Bernie Sanders has introduced a federal moratorium on new AI data center construction until safeguards are enacted.³
3. The economic camp sees AI’s contributions to GDP as the path to managing the national debt.⁴⁵ “The world is awash in debt,” Bessent said at the G20, “& the only way for us to get out of this is to grow our way out of this… This CapEx will turn into productivity, & that will be extremely disinflationary,” he said. “I would guess that in the next six months we will start seeing the benefits of that.”⁶ It is the only dated prediction any camp has made.
4. The geopolitical camp seeks to sustain strategic advantage in the AI race. “We’re leading China in AI,” Trump said Sunday. “Whoever wins AI wins.”⁷
5. The regulatory capture camp wants no new rules at all. Write the rules & your rivals pay to obey them. David Sacks, who chairs the President’s Council of Advisors on Science & Technology, put it to Amodei & Altman directly : “Stop pretending you need anyone else’s permission. Stop pretending antitrust law has to be suspended so you can form a cartel.” If the labs will not slow down on their own, he wrote, “we’ll know this was just another bid for regulatory capture.”⁸ Lina Khan, who chaired the FTC until 2025, arrives at the same place from the opposite direction. “We shouldn’t let discussions about new legal regimes distract from the fact that there’s no AI exemption from laws already on the books.”⁹

These perspectives still do not answer the question : what does pacing mean?

The market is telling us that we should be 3x more productive with AI.

What if that productivity gain is just an AI working 24 hours a day while a human works eight?

OpenAI published the math behind its 3x claim. In mid-August, its research staff logged 3.14 agent-workdays¹ for every 8-hour human shift.² The typical researcher ran four agents in parallel.

That machine shift comes with an industrial price tag. In late March, the median OpenAI researcher spent $14 a day on inference. By mid-August, that bill climbed past $600 a day : a 40-fold surge in under five months.² At the top end, the 90th percentile researcher burns through more than $7,000 a day, an annualized run-rate of $2.5m.