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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.

I hate to micromanage & I’ve been micromanaging AI.

A few months ago, I’d use Claude for a familiar workflow : capturing notes from a meeting, drafting a follow-up email, updating the CRM, writing the investment memo. Micromanagement at 10x speed. The agent would finish a step, then wait. I’d scan the output, type the next instruction, wait again. Prompt, response, prompt, response. I was the bottleneck in my own system.

Kirkland ibuprofen is the same molecule as Advil. Same dosage, same FDA requirements, same therapeutic effect.¹ It costs 80% less.

AI has its generic drug moment. DeepSeek V3 matches GPT-5.2 on most benchmarks.² It costs 90% less. OpenAI & Anthropic generated $22 billion in 2025.³ Chinese AI labs generated $1.8 billion.⁴ The ratio : 12:1.

Pricing explains the gap. Chinese AI API prices collapsed 90% in 2024.⁵ US frontier models average $3.38 per million input tokens. Chinese models average $0.48.

Databricks started later. It built a more complex architecture. It focused on unstructured data; images, documents, logs, audio. Though vast within the enterprise, this data had historically produced little insight. Too hard to process. Too messy to query. Too expensive to store in formats that mattered.

Snowflake took the opposite bet. Structured data. Clean tables. SQL queries that ran fast & returned answers executives could read. The market agreed. Snowflake went public at a $70 billion valuation. Databricks raised private rounds at half that.

Could you operate your company with half the people?

Jack Dorsey’s announcement yesterday¹, reducing Block’s headcount from 10,000 to 6,000, should provoke this question in every management team. The stock surged 24%. Dorsey’s memo framed it as inevitable :

Within the next year, I believe the majority of companies will reach the same conclusion & make similar structural changes. I’d rather get there honestly & on our own terms than be forced into it reactively.