Tomasz TunguzModify

2026-03-17 08:00:00

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

Amazon, Microsoft, Alphabet, Meta, & Oracle will spend 90% of their operating cash flow on AI data centers in 2026, up from a historical average of 40%.²

Alphabet issued a century bond, the first by a tech company since Motorola in 1997.⁵ The debt matures in 2126. Who knows what AI will look like then, or whether Alphabet will exist to repay it.

What assumptions justify this borrowing?

The depreciation schedules encode the bet. Most hyperscalers depreciate AI infrastructure over five years.⁶ At 60% gross margins & 5% borrowing costs, a 5-year payback on $431B in AI capex requires $180B in annual revenue.⁷ Current AI revenue is $35 billion.¹ They’re underwriting 5x growth in five years.

Nvidia’s stated goal is to release new GPU architectures every twelve months, which will compress depreciation cycles. If chips become obsolete in three years rather than five, the required annual revenue jumps to $276B, 7.9x current levels.⁸

As Michael Mauboussin writes, there’s information in prices. The depreciation schedules tell us what hyperscalers believe : AI revenue will grow 5x within five years. The debt markets are betting alongside them.

2026-03-15 08:00:00

“What happens when a new employee brings their agent to work?”

An executive asked this recently. Imagine a few years from now : a student graduates, having trained their own agent through university. It knows everything they’ve learned, every paper, every problem solved. Day one, they bring it to work.

It’s like bring your own device circa 2009. The iPhone launched & nobody wanted corporate Blackberries¹ anymore. IT scrambled to adapt.

But a rogue phone couldn’t sign contracts. A rogue agent can.

Amazon just learned this at scale. $6.3 million in lost orders. 99% order volume drop across North America. Four severity one incidents in one week.²³

Amazon’s AI coding assistant contributed to at least one major production incident. The response : a 90-day safety reset with mandatory two-person review for all code changes.

An internal memo admitted what everyone implicitly knows :

“Best practices and safeguards around generative AI usage haven’t been fully established yet.”³

Companies can’t hide behind hallucinations. Utah’s AI Policy Act⁴ eliminates the hallucination defense :

“It is not an affirmative defense to assert that the GenAI tool made the violative statement or undertook the violative act.”

Newer and larger models are smarter and more reliable.⁵ But they fail unexpectedly. There is no relationship between size and how failures change over time. AI-generated code creates 70% more issues than human code.⁶

The TRUMP AMERICA AI Act would create explicit liability pathways - allowing the US Attorney General, state AGs, & private plaintiffs to sue AI developers for defective design & unreasonably dangerous products.⁷

That new hire’s personal agent? The company bears liability for its mistakes. The contracts it signs, the code it deploys, all of it lands on the company.

Like a dog or a device, you are responsible for your agent.

2026-03-13 08:00:00

What happens when your AI doesn’t answer?

Everything is in short supply. It’s no longer just GPUs. It’s power. Data centers. Memory. CPUs.

If there’s no relief for six more quarters, perhaps it’s time to plan for a world where inference isn’t freely available on-demand.

Inference prices, which have been static, will rise. Subsidies will be harder to justify.

Enterprises will need to rationalize workloads, deciding which teams receive state-of-the-art models & which don’t. Not every CRM update requires a trillion-parameter frontier model.

Inference rationing normalizes. Marketing receives this much, sales receives that much, software engineers probably receive a lot more.

Constraint will be the mother of invention. Companies will optimize what they have, adopt open source where they can, and likely move to smaller models for many workloads.

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.