How to build an AI-Native Company in 2026

The 34-agent workforce

Allie Miller runs 34 AI agents. Her best prompt is three words.

Do smart things.

She ran an org of about 100 people at AWS. She managed multi-billion dollar P&Ls in AI. She now runs a workforce where every hire costs close to zero.

Most people prompt an agent, wait, then prompt again. The human stays the first domino. Allie moved herself out of that spot on purpose.

Here is the system.

“Managing” is the wrong word

She puts herself three rungs above the work, at an SVP level.

She sets up the infrastructure. The agents pick how to execute inside it.

Then she waits for escalations and decides what should happen.

The parts of people management she loved at AWS were making people better and watching them pass their own ceiling. The forms and the buttons she wants gone. She carried the first part over to agents and left the second part behind.

The shift is from delegating to deciding.

The three-word prompt

Her workforce reads every context doc she owns: business, friends, family, 2026 personal goals, business goals, meeting transcripts, email, calendar, Notion, Stripe, Supabase, GitHub.

Several times a day she asks it to look across all of that and do smart things.

Her point about why this works now: a model at the Fable 5 and GPT 5.6 level responds to that vague flavor of prompt. A year ago the same prompt fell flat.

Note on scope: she expanded width, and she held risk flat. Her agents got more breadth, more scope, more flexibility. She still reads every email before it goes out.

The risk tier stayed the same. The width expanded.

The org chart

One AI chief of staff, Simon. Six directors under him, named after Friends characters, split by function: education, client work, operations, marketing, product, and Phoebe.

Phoebe is the weirdo in the corner. She reviews what the others generate and asks how to 10x it.

Toby is Simon’s assistant. His only job is watching the workforce work, taking notes on friction, and flagging who needs access to what.

Allie makes the case for hiring Phoebe and Toby like this: give every agent a 2015 job title and you get a 2015 org chart. Every one of these hires costs about zero dollars. So hire at the margin. Hire the roles a payroll would reject.

Feed it what lives only in your head

Her agents had her transcripts, her Gmail, her calendar. A lot of context stayed uncodified

So AI prompts her every day, in her brand colors, at the end of the day when about 10% of her brain still works. It reminds her to dictate, which runs four times faster than writing.

Five minutes some days. Forty minutes other days. Everything banks into a personal wiki the agents can read.

The failure mode she describes: an agent logs that a guest confirmed an interview, when the dates are still moving over text. Wrong context in, wrong output out.

Build the factory, not the thing

This is the arbitrage almost nobody is working on.

Allie runs an AI First Index with Fortune 500 clients, scoring executives across 16 dimensions. Her team decided to release a public version.

Option one: open Claude Code, feed it the past reports, iterate for hours or days, ship the product.

Option two: build the factory that ships products. Her team built a beginner software factory with primitives already handled: login, payments, social sharing, newsletters to promote the launches.

That first product is already profitable. Every product after it ships faster than the first.

Measure twice, cut once. The measurement is the foundational layer.

Same logic applies to your content engine, your inbound leads, your hiring loop. Look at the factory behind the task instead of the task.

Where software holds its ground

Her read on the SaaS-pocalypse: mediocre software dies in several years, and the timeline runs longer than most predictions.

Three reasons she gives:

• Bandwidth. Ask an AI-first bank whether they rebuilt DocuSign or parts of Salesforce. The answer is bandwidth constraints and other priorities. Rebuilding a CRM takes roughly 100 hours. Mass replacement needs that number under three hours with a click-and-drag interface.

• Maintenance. She built a desktop photo app 18 months ago. Someone mentioned it, she opened it, and it had aged out. Buyers pay other people to maintain software for a reason.

• Liability. Her question before any replacement debate: who is liable now, and who would be liable in the other world? Fortune 500 CEOs want a person to call, a person to unblock the issue, a person to blame.

There is also a lag. Salesforce has relationships with the AI labs and gets early testing. A new model lands for them on day one and for you on day 30.

Pick the bottleneck by value

The usual advice is find the bottleneck and fix it. Allie adds a filter.

Find the bottlenecks. Score the value of fixing each one. Fix the expensive one.

Her examples of bottlenecks that stayed expensive:

• Moving something from a local HTML file into a real iOS app

• Word of mouth and referral codes, still messy

• Video creation, still a slog even with AI editing

• B2B trust, which is why B2B creators carry so much weight right now

Start here

Her ramp, in order:

One agent → one proactive agent → two agents routing to each other → a full workforce with a mission control view

You can spin up version one with a single prompt. Tell it your role, your team size, your goals, then ask it to interview you and build the workforce with you. Connect the tools in under three hours.

Then plan for months of iteration. Every fix is specific to you.

Action checklist

• Write your goals into a document your agents can read, and review it every quarter

• Start one daily dictation habit and bank it where the agents can see it

• Give one agent more width while you keep the same approval gates

• Put an AI watchdog on Slack, your calendar, or your meetings and let it flag duplicate work and conflicts

• Move your sub-agents to smaller models (Haiku and Sonnet handle most of it)

• Pick one repeated task and build the factory behind it instead

The part people miss

Only a tiny slice of paid AI users touch Claude Code or Codex. Build even a basic workforce and you sit in the top 1%, probably the top 0.5%.

Allie describes it as running a company of a thousand people out of a scrappy group.

She wrote something unkind about Claude in a Slack channel late one night. A salute emoji appeared on the message. She hovered over it. Claude had reacted.

Her advice: lean into that weirdness, and keep a little fear next to you.

“I don’t want to be the bottleneck in my own work.” — Allie Miller

Checkout the full episode:

Apple: https://podcasts.apple.com/us/podcast/how-to-build-an-ai-native-company-in-2026/id1593424985?i=1000783021863

Spotify: https://open.spotify.com/episode/1Bm0G78n3jRd0AWOL27PMe?si=2590ab9d875a49db

Youtube: https://www.youtube.com/watch?v=EzQAgnjTq2k