The Structure of the Fastest Startups, and Dreams about Robots

now is the time to understand more.

Good morning

In today’s edition, among other things:

• The Structure of the Fastest Startups

• Dreams about Robots

• AI Engineers and Equity

• Why humans are AI’s biggest bottleneck

Onwards!

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The Structure of the Fastest Startups

For most of startup history, structure was something you worried about after product-market fit. You hired a few people, improvised roles, and promised yourself you’d “clean it up later.” That logic no longer holds. The fastest startups are designed to move quicker.

The fastest startups win because they treat structure as a throughput system for judgment, not a headcount plan.

NFX makes an argument for this shift in a simple idea: company design follows the constraints of the technology moment.

Founders often treat structure as fixed. In reality, org design is fluid. It bends to two forces: the technology moment you’re building in and the competitive pressure you face.

That’s a useful frame because it’s falsifiable. If your market rewards reliability and compliance, you will rebuild hierarchy. If your market rewards iteration, hierarchy becomes a tax.

Information travels instantly. Work gets executed instantly.

The essay points to “two pizza teams” and Atlassian “pods” as early answers to the same problem: move fast without bureaucratic drag. AI pushes this to the limit by making interdisciplinary work easier and by shrinking the minimum viable team.

AI has simply put this force into overdrive, with three main effects: 1. It has completed the flattening… 2. …teams can feasibly be smaller than ever before 3. This culture demands a different type of employee psychology.

The interesting claim here is not “teams are smaller.” Everyone repeats that. The claim is that coordination is no longer the reason you form a team. Innovation is.

And when innovation is the purpose, silos are self-harm.

It’s a new loop: diagnose → propose → execute.

A flat org is a way to compress the feedback loop until the market can’t keep up.

Flint argues early hiring logic flips: you don’t hire “Head of Sales” to build playbooks and manage process. AI will handle playbooks and repetitive tasks. You hire for results, taste, and insight.

Instead of designing roles around processes… you design roles around outcomes. AI handles the playbooks, and repetitive tasks. What you’re really hiring for is results, taste, and unique insight…

This lands because it matches what you see in breakout teams: the first non-founder hire is rarely a narrow specialist anymore. It’s a generalist with sharp edges. Someone who can write, ship, sell, and instrument. The role title becomes a promise, not a department.

You don’t hire for a role. The right person can be the role.

Your first job descriptions are culture documents. They tell the team what gets rewarded. The quote puts it cleanly:

“Broad roles are fine; vague outcomes are not.”

There’s another constraint NFX hints at: being in-person. They claim most early-stage teams slow down when they’re not together once markets shift. That’s probably directionally right, but it’s not universal. Deep research, commercialization, and distributed talent pools can beat co-location. The test is practical: do you lose days to misalignment, or minutes?

Two years ago, I would have disagreed. Today I agree in full.

What would change my view: clear evidence that remote-first teams consistently match in-person teams on cycle time during “unknown unknown” pivots.

So what’s the playbook?

• Write roles as outcome contracts. Define the one thing this person makes inevitable in 90 days. If it reads like a task list, you’re hiring a coordinator. If it reads like an impact statement, you’re hiring a builder.

• Design a team topology that minimizes handoffs. Keep the smallest unit able to diagnose, decide, and ship. If you need three people to run one experiment, you built a mini-bureaucracy.

• Screen for AI posture. NFX calls out “enthusiastic, informed humility.” In practice: watch how candidates use AI. Do they treat it like autocomplete, or like a junior analyst they can interrogate? Do they reject it on principle, or outsource thinking to it? Both are disqualifying.

The closing line of the essay is the right north star: in 1855, the job was coordination. In 2025, it’s creativity and speed.

The fastest startups look different because the constraint moved. The winners structure themselves around the new constraint before the market forces them to.

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AI Engineers and Equity

For most of the last decade, equity followed a familiar script. Early engineers got 0.5–1%. Exceptional hires might stretch to 1.5%. That playbook is now broken. In AI-native companies, equity has stopped being a retention tool and started being a value signal.