The Legible blog

Outsized attrition risk

Mon, 20 Jul 2026 00:00:00 GMT

Do AI-supercharged employees represent outsized attrition risk?

Of course they do.

Any time you have an employee performing at 3x-5x-10x their peers, you want them to stick around.

In the olden days, we used to put retention plans in place for our “key resources” - the people who were so impactful that their departure would be a meaningful setback.

In 2026, these key resources tend to share something in common - they have red pilled on AI.

I spoke to a founder today who listed this dynamic near the top of the list of things keeping him awake; in his mind, the problem is exacerbated by the fact that his most red pilled employees are more often than not running their AI automations on their own machine, not visible to the team’s Claude instance.

This is the new attrition math. When an AI-powered employee walks out the door, you don’t lose one person. You lose the ten people they had become. Their agents, their prompts, their workflows, all of it walks out with them.

The old version of this problem was tribal knowledge. Painful, but survivable. Someone leaves, the team limps for a quarter, the knowledge gets rebuilt. The new version is worse because the leverage is bigger and the assets are invisible. Nobody knows what agents exist, where they run, or what breaks when the login gets deactivated.

The fix is structural:

Agents built on shared infrastructure, not personal accounts
Company workflows in a common repository, not scattered across individual tools
Model-neutral, so the work survives a vendor switch too

And of course, a concerted effort needs to be made to lift up your most AI-averse employees through training and enablement so that they look more like your superstars.

Individual AI adoption feels like progress. And it is, right up until your best person resigns.

The takeaway

The companies that get this right will treat agents like they treat code: built by individuals, owned by the company.

Are AI Chat Threads the New Transcripts?

Thu, 16 Jul 2026 00:00:00 GMT

One of my favorite sources of interesting things to write about is conversations that I have with my cofounders. Here’s a pretty cool insight that came from Brandon, Founding Partner at Legible, that suggests that mining AI chats between humans and AI might be as valuable as mining transcripts between humans.

“I have conversations with AI agents all day. What I share with the team is the result. It’s like sitting in meetings all day and only passing along the summary.”

His point: the journey that brought him to the summary gets lost. The reasoning, the dead ends, the ideas that went untouched.

So he built himself a workflow. Every evening, a job runs over all of his AI conversations from the day and summarizes them: what he worked on, what he learned, what might be worth sharing with the team. He’s mining his own chat logs.

Of course, we’ve seen this movie before. For decades, the knowledge in live meetings simply evaporated the moment everyone left the room. Then AI notetakers exploded, because companies realized how much thinking was buried in those conversations. Memorializing human-to-human conversation went from novelty to standard practice in about three years.

The same realization is coming for human-to-AI conversation.

Chat threads are the next transcripts.

The thinking that goes into a Claude or ChatGPT session is real work: framing problems, weighing options, making calls. Today almost none of it is remembered by anyone. Even the AI forgets it across threads.

At Legible we call the answer a Shared Brain: team memory that captures the important thinking wherever it happens, in meetings or in chat. Clearly, we need to be thinking more about pulling in the human to AI transcripts, and not just the human to human ones.

Your team is already doing its best thinking with AI. Do you have a plan to memorialize that knowledge?

Discretion as a Feature

Sun, 12 Jul 2026 00:00:00 GMT

Most people paying attention would agree that recording your meetings is AI 101. If you are not memorializing that context from when your people share their knowledge, you are already behind.

So I paid attention when the head of an AI training company emailed me this week with some insights from the field that suggested AI note-taking has not yet reached full saturation because of some feature gaps.

He works with legal advisors who refuse to let prospect calls be recorded at all. In their quest to mitigate risk, the firms lose the transcripts entirely. And on the calls that do get recorded, something trickier happens: enormously useful knowledge gets shared in the same breath as things that must stay confidential. You can’t share the transcript without exposing the sensitive parts. So nobody shares anything, and everyone misses out.

That’s the conundrum. The industries where institutional knowledge is most valuable, law, finance, healthcare, are exactly the ones where blanket transcript access is impossible.

The answer isn’t to stop recording. It’s also not to record everything and hope. It’s infrastructure that treats discretion as a first-class feature: access controls that map to who was in the room, retention policies with teeth, and eventually the ability to reliably separate the shareable knowledge from the confidential material around it.

Whoever solves that opens up a market the current generation of notetakers can’t touch. Sensitive industries don’t have an AI adoption problem. They have a trust problem, and it’s still unsolved.

Are Services Companies Venture Fundable in the Age of AI?

Tue, 07 Jul 2026 00:00:00 GMT

A few months ago, Y Combinator issued a call for startups naming a category it has avoided for decades: services businesses. Insurance brokerage. Accounting, tax, and audit. Compliance. Healthcare administration.

There’s a reason venture had traditionally stayed away. Services businesses have always been “body bound.” Add a client, add a person. Revenue and headcount move together, so there’s no operating leverage, and no operating leverage means no venture-scale return. A firm can be great and still cap out at a normal-business multiple.

Here’s what’s actually changed, per Gustaf Alströmer, the YC partner leading this category. A few years ago, if you wanted to build a startup around insurance brokerage, you built software that brokers used to do their job. More recently, you built an AI copilot to help them do it faster. YC’s new bet skips both:

→ Old playbook: build software the professional uses

→ Recent playbook: build an AI copilot that speeds the professional up

→ New playbook: replace the professional, AI does the labor directly

That third version is what makes services fundable. If a team of five can deliver what used to take fifty, revenue can finally grow faster than headcount. That’s the exact return profile venture capital has always required, showing up in a category built entirely on hours.

The categories YC named aren’t random either. Insurance brokerage, accounting, compliance, healthcare admin are all already outsourced. A company doesn’t have to develop a new habit to work with an outside vendor. They already do. The only question becomes who’s doing the work.

That’s the real headline. AI just made a category built entirely on hours look fundable, maybe for the first time in venture history.

If you run a services company and are interested in leveraging AI to achieve product-like software margins, the Legible team and I would love to talk to you.

Hourly Billing Needs to Die

Tue, 07 Jul 2026 00:00:00 GMT

For a hundred years, professional services ran on one assumption: an hour of an expert’s time was worth roughly the same amount, regardless of what tools that expert had. The assumption made billing simple. It also built in a trap nobody noticed until now.

AI breaks the assumption completely. If a senior analyst can now produce in one hour what used to take five, hourly billing punishes that gain instead of rewarding it.

Walk through what actually happens inside a firm that keeps billing by the hour once AI shows up:

The firm gets faster and earns less for the same outcome
Partners quietly discourage staff from using AI, because efficiency shows up on the invoice as lost revenue
Junior staff learn to stretch two hours of real work into five billable ones, because that’s what the model rewards

None of that is hypothetical. It’s the default outcome of pricing the input once the input gets cheap.

Here’s the part that should bother every owner of a services firm: hourly billing turns every productivity gain from AI into a pay cut you volunteer for. You do the hard work of getting faster, and the pricing model hands the savings to the client for free.

The firms that fix this make one change. They stop pricing the hour and start pricing the outcome.

Hourly billing was built for a world where speed and value moved together. AI just severed that link, and the billing model hasn’t caught up.