Tomasz TunguzModify

2026-03-12 08:00:00

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.

The obstacle was fragmentation. A single shipment can touch 40 to 60 processes across multiple vendors. Connecting them would mean hundreds of bespoke integrations. The project never got funded.

Sean partnered with Henry Ou, who led ML teams at Apple and built ranking systems at ByteDance. Together they founded BackOps, which deploys AI agents that read emails, click through portals, call drivers, and file claims. When a customer reports a problem, BackOps traces it across every system involved, escalating to a human only when a judgment call is required.

Watch the video

We’re leading BackOps’s $26 million Series A.

The product works in two stages. Employees record their screens while solving problems; BackOps converts those recordings into automated workflows. Then Relay, the automation engine, runs continuously: filing claims, initiating reshipments, responding to customers.

Customers report 93% faster response times and 60% time savings. BackOps files 100% of eligible carrier claims automatically. The platform serves a top global automaker, a leading retailer, major grocery chains, and industrial suppliers.

Sean and Henry are targeting a $3.5 billion market growing 13% annually. The bet: AI agents can connect systems that were never designed to talk to each other. So far, the connections hold.

If you’d like to learn more, reach out to Sean.

Read more from Sean, Theory partner Andy, and Axios.

2026-03-11 08:00:00

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.

A team that would have added two engineers to hit next year’s roadmap now ships with the headcount they have. Cursor, Claude Code, Copilot close the gap. The job postings never go live. The offers never extend.

Inside most organizations, headcount stays flat. No layoffs. No restructuring announcements. Just fewer new hires than planned.

Block slashing 40% of its workforce showed what happens when a company acts on this logic all at once. Jack Dorsey explained : “Intelligence tools we’re creating & using, paired with smaller & flatter teams, are enabling a new way of working which fundamentally changes what it means to build & run a company.”

Most companies won’t restructure so dramatically. Until an economic shock, a missed quarter, or pressure from the board forces the question. What AI made possible, AI makes necessary. The restructuring that might have happened gradually over five years happens in one quarter.

The seismic shock isn’t coming out of nowhere. It’s building invisibly, one unposted job at a time.

2026-03-10 08:00:00

“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.

That productivity gap compounds at the company level. Anthropic generates ~$5 million per employee.³ Cursor, $3.3 million. Midjourney, $2 million.⁴ Traditional SaaS considers $200-300k strong. A 10-20x difference.

One explanation : communication overhead. The math follows Metcalfe’s Law.⁵ Each new team member adds n-1 new connections. Coordination drag doesn’t grow linearly. It explodes.

Now consider what AI does to this equation.

A traditional 150-person organization runs four layers deep. The org chart creates 11,175 potential communication channels. Meetings multiply. Alignment decays.

An AI-enabled team producing equivalent output might need 30 people. Communication channels drop to 435. A 96% reduction.

This is one reason AI-native startups are pulling ahead, and why building AI companies feels fun. The advantage comes from organizational structure. Fewer humans, fewer channels, faster iteration, compounding speed.⁶

R&D adopts this fastest. AI writes the code. Human communication becomes the bottleneck. The span of control debate shifts from “how many people can one manager oversee?” to “how many AI agents can one human orchestrate?”

Small teams have always paid less coordination tax. AI cuts it further.

2026-03-08 08:00:00

“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.

Every customer support call is now with an AI agent. The voice sounds real. They are infinitely knowledgeable. The responses are fast. Does it matter that it’s not a person?

A friend sends voice memos instead of texts now. “So you know it’s actually me,” he said. But how do you know? ElevenLabs can clone a voice from thirty seconds of audio. The ums, the pauses, the little laugh—all reproducible.

Does it matter?

But maybe the people writing “Hi, Tomasz or Tomasz’s agent” have it right. They’re not being rude. They’re being realistic. They’ve adapted to a world where the answer might come from either side of the curtain, & they’ve decided not to care which.

The polite thing now is to assume the robot. The intimate thing is to be surprised when it’s not.

2026-03-07 08:00:00

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 analyzed 374 quarterly NDR observations from 25 public software companies. For years, the decline looked gradual. Net dollar retention fell from 125% in 2022 to 112% in 2025. Quarter by quarter. No cliff when ChatGPT launched. No acceleration when enterprises adopted Copilot.

Then came 2026.

The 25th percentile fell from 106% to 101% in a single quarter, now touching the breakeven line.² The weakest companies are bleeding first. Zoom sits at 98%. Asana at 96%. The bottom quartile is now contracting.

The companies in the bottom quartile face different threats, but they share one trait : products simple enough to replace. Bill.com (94% NDR) serves SMBs depressed by macro conditions.³ Zoom (98%) faces near-free alternatives in Teams & Google Meet. Asana (96%) offers task workflows that competitors & AI agents can replicate. With 96% NDR, they lose 4% of existing revenue annually. Growing 9% requires 13%+ new customer acquisition just to tread water.⁴

Macro pressure. Commoditization. Competition. AI. Each blade cuts differently. The bottom quartile will see accelerating losses. Some may tip into outright contraction. The sword of Damocles hangs by a single horsehair. For simpler products in competitive categories, that horsehair is fraying.

2026-03-05 08:00:00

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.

This week, Alibaba released Qwen3.5-9B³, an open-source model that matches Claude Opus 4.1 from December 2025. It runs locally on 12GB of RAM. Three months ago, this capability required a data center. Now it requires a power outlet.

A $5,000 laptop — a MacBook Pro with enough memory to run Qwen locally — pays for itself after 556 million tokens. At my usage rate, that’s about a month. At 20 million tokens per day, it’s four weeks.

After payback, the marginal cost drops to electricity.

It isn’t an intelligence compromise. Reasoning, coding, agentic workflows, document processing, instruction following : the 9B model matches December’s frontier across the board.

What changes when frontier intelligence runs locally? Everything I send to cloud APIs today — drafting emails, researching companies, writing code, analyzing documents — stays on my machine. No API logs. No third-party retention. No outages. No rate limits.

The tradeoff is parallelization. Cloud APIs handle thousands of concurrent requests. A laptop runs one inference at a time. For simple tasks — summarization, drafting, Q&A — that’s fine.

Queue them up. Let them run overnight. For complex agentic workflows that spawn dozens of parallel threads, local inference may not be worth the wait. The economics favor depth over breadth : fewer tasks, run longer, run cheaper.

Three months from data center to laptop. The buy-vs-rent math just changed.