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Nobody will open Gmail five times a day in five years.

The average knowledge worker receives 121 emails per day.¹ That’s one every four minutes during working hours.

The inbox is a conveyor belt that keeps accelerating. You open Gmail. You read. You decide. You respond. One at a time. But the belt doesn’t wait. It just moves faster.

Today’s triage is generic : “This is from your boss. I need to work on that today. Next. Spam. Archive. Spam. Archive. Newsletter, read & archive.” Tomorrow’s is personal. User-defined skills & rules. Programming in English that encodes your priorities, your relationships, your workflow.

It’s time for the 2026 Annual Theory Go-to-Market Survey. This is a brief 25-question survey.

Our goal is to understand how startups have evolved their sales, marketing, customer success, and cash management over the last several years by comparing these results to our surveys from 2022 through 2025.

We will publish these results and answer questions about them at upcoming Office Hours.

This year, we’re focused on five key hypotheses. Each is designed to be rigorously testable with the survey data:

As demand for AI inference explodes, I’ll be asking a lot more of my little computer.

How much more?

Over the past five weeks, I’ve been using local models to see how much of my daily work I can accomplish without the trillion parameter models in the cloud. The answer is half.

If you classify these 1.4k tasks by category, half can succeed on a local 35B model. Email & Inbound, Scheduling, Summarization, & Admin total 618 tasks (41.8%). Market Research & Engineering split roughly 50/50 between simple tasks (data lookups, script fixes) and complex ones (multi-source synthesis, architectural decisions). That gets us to 50%.

Enterprises run on AI agents. So do the attackers.

What does it mean to build, secure, and operate AI systems when both sides - defenders and attackers - are automated?

Jonathan Jaffe, CISO at Lemonade, is one of the most forward-thinking security leaders in this age of AI, with more than 25 years of experience in technical security roles since 1997.

Anthropic grew from $1B to $30B in 15 months. So why does it trade at a discount to public comparables?

High-growth companies trade on forward (NTM) revenue. Anthropic’s $30B run rate implies $20B in actual TTM revenue. If they exit 2026 at an $80B run rate¹, we can estimate NTM revenue of around $50B. The EV/NTM multiple is 17x.

Anthropic commands a 65% discount to Palantir while growing nearly 3x faster. Four factors explain the gap.

What happens when a startup employee leaves on a Monday?

In a twenty-person engineering team, one resignation is a 5% headcount loss. The remaining nineteen absorb the work.

In an AI-pilled three-person team running twenty autonomous agents, one resignation is a 33% headcount loss.

The agents do not resign. They keep generating, reviewing, testing, and deploying. But one-third of the institutional memory that trains, prompts, validates, and debugs the agent fleet walks out the door.

One search ad every 40 minutes pays for a trillion-parameter model. One content ad every 3 minutes does the same. The math on ad-supported AI is better than you think.

When Anthropic pulled Claude Code from the $20 plan last month, it signaled an industry assumption : frontier intelligence requires frontier pricing. For open models, the economics flip.

A B200 GPU costs $4.50/hour on spot markets.¹ Google Search ads generate $38.40 CPM (cost per thousand impressions),² while Google Display runs $3.12.³

I remember growing up reading Sports Illustrated. There was a small column called “This Week’s Sign the Apocalypse Is Upon Us.”

With all the dire predictions about AI, it’s important to also spend time recognizing the tremendous pace of innovation & the impact AI is having broadly. Here’s what it fixed, taught, and discovered in the last two weeks.

In medicine

• Mayo Clinic validated an AI that detects pancreatic cancer up to 3 years before diagnosis on routine CT scans.¹
• J&J reported AI is halving the time to generate new drug development leads and cut clinical trial report preparation from 700 hours to about 15 minutes.²

In classrooms

Google Cloud grew 63% year-over-year in Q1 2026. Amazon Web Services posted 28%. Microsoft Azure hit 40%. All three are exceptional. Only one hit 63%.

The divergence is striking. AWS & Azure resell compute. Google bundles compute with its own models. Whether that explains the full gap is unclear, but the structural advantage is not : Google owns Gemini & TPUs top to bottom, with no licensing fees to OpenAI or Anthropic. Its growth may be more profitable too.