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In 2025, we predicted that 2026 would be the year agents would earn as much as a person.

It’s already happening.

In markets where there’s a labor shortage and an urgent need to hire people, we are seeing agents command 75%, 85%, even 100% of a human equivalent salary. This is faster than we were anticipating.

The first-order benefit is completing the work.

But there are second-order benefits that are now starting to appear. Training agents is significantly faster since all materials can be presented at once & in parallel to the AI.

I set up a race today between two robots.

My Mac on the left vs Claude Code on the right. Both tasked with building a payment app on Stripe’s new Tempo blockchain. Same prompts, same task, side by side.

Opus 4.5 is about 20% smarter than Qwen 35B on benchmarks. And it’s likely 50x larger. The hare should have won. It didn’t.

The local model finished in 2 minutes. Claude took over 6. I asked Claude to score both outputs : local model 6.5, Claude 4.5.¹

For every dollar hyperscalers earn from AI today, they’re spending twelve dollars to build more capacity.¹ That’s the bet embedded in $575 billion of capital expenditure this year.²

How fast does AI revenue need to grow to pay back this data center mortgage?

From 2020 to 2024, hyperscalers issued an average of $20 billion in bonds annually.³ In 2025, that jumped to $96 billion. In 2026, it will reach $159 billion.³ Morgan Stanley projects $1.5 trillion over the next few years.⁴

What happens when your AI doesn’t answer?

In September 2024, Hurricane Helene flooded Baxter International’s plant in Marion, North Carolina, which produced 60% of the nation’s IV fluids. Within a week, more than 80% of U.S. healthcare organizations reported shortages. One plant, one flood, one week.

That disruption made headlines. Most don’t. Eighty-five million packages arrived damaged in the U.S. in 2024, up 30% from the prior year, costing businesses $4 billion.

Sean McCarthy saw those failures accumulate during his years at Amazon Shipping, where he was one of the early hires. The investigation process never varied. Query the warehouse management system, often two decades old. Cross-reference the carrier portal. Call the driver, who doesn’t pick up. File a claim: seventeen fields. Four hours pass. Sometimes the problem gets solved.

AI eliminates the marginal hire.

Tech job openings are down 45% from the 2022 peak, but up 16% since the start of 2026 - from 227k to 264k. Why the narrative violation?

Companies are hiring again, just fewer people than before. A reset to a lower baseline.

“Since last November, 100% of my code has been written by Claude Code. I have not manually edited a single line, shipping 10 to 30 PRs per day.”

Boris Cherny, creator of Claude Code, ships 20-30 pull requests per day. Major code changes, not typo fixes. He runs five parallel AI instances, each on a separate branch.¹

Compare that to a traditional engineer : 3 PRs per week.² Cherny isn’t 10% more productive. He’s 30x more productive.

“Hi, Tomasz or Tomasz’s agent.”

I’ve started receiving emails that begin this way. A byproduct, I suppose, of having written so much about AI. People now assume my inbox is monitored by robots.

Which raises an odd question : what does it mean to write to someone when you expect a machine to answer?

Gmail suggests my reply before I’ve thought it. “Sounds good!” “Thanks for sending!” “Let’s circle back next week.” The machine knows what I’d say. Sometimes I click it. Sometimes I wonder if the person on the other end can tell.

GitHub Copilot pioneered AI coding assistance. First to market. 20 million users. Then Claude Code & OpenAI Codex launched in mid-2025. Within six months, Copilot’s daily installs peaked & declined while competitors surged past 100,000 combined.¹

The sword didn’t fall on a laggard. It cut the early leader. If Microsoft can lose share in six months, no one is safe.

I burned 84 million tokens on February 28th. Researching companies, drafting memos, running agents.

That’s running Kimi K2.5, a serverless model via API. At Claude¹ or OpenAI² rates, roughly $9 per million tokens blended, equivalent usage would cost $756 for a single day’s work. My peak days hit 80 million tokens. My average days run 20 million. Cloud inference at frontier-model pricing adds up fast.