Built in a weekend. OpenAI paid a fortune. Here are the lessons.

Hi, and happy Tuesday.

🔔 I'm meeting with some of you on Friday 6th March, 10am PT / 12 CT / 1pm ET, to demo what we’re building with OpenClaw. Reply “INVITE" if you want to be added to the invite or to get the recording.

On a weekend this past November, Peter Steinberger opened his laptop and started tinkering.

He had a simple thought: what if AI could actually do things? Not just answer questions, but take action?

Book the flight. Fill out the form. Update the CRM. Join the meeting. Handle the thing.

Though the name required a few iterations, OpenClaw was born.

Then the internet found it. And everything changed.

Within weeks, it became the fastest-growing GitHub project ever.

(Github is a platform that hosts software projects, allowing anyone to contribute to them).

More tech-inclined business operators immediately recognized what they were looking at: A new kind of employee.

One that doesn't need dopamine hits to stay motivated, a ping-pong table, a quarterly review, or a 1:1 to feel heard. One that just... works: Clicking buttons, filling forms, navigating software - and doing it all without burning out, rest room breaks, or morale boosts.

Peter Steinberger recently spent a week in San Francisco meeting with every major AI company.

On February 15th, Sam Altman, CEO of OpenAI, posted:

No compensation terms were disclosed - but OpenAI recently paid over $6 billion for Jony Ive's startup. They are not writing modest checks.

One person. One weekend project. Three months. A life-changing outcome.

This is not a story about AI replacing humans.

It’s a story about leverage. It shows that one person with good judgment plus AI tools can create large amounts of value fast.

The value that OpenClaw created - and why it has been a sensation - is because OpenClaw itself creates leverage for the rest of us.

We can apply the lessons from this story to how we use AI, through tools such as OpenClaw, to our own work.

1. Pick a leverage point, not a pain-point. Peter didn’t build to solve a singular pain point. He went after a leverage point: “What if one person could coordinate a whole swarm of digital workers that click, type, and handle your computer for you?”

We can apply the same type of thinking to our own work. Where in your world does a single improvement unlock 10x leverage across everything else? Is it:

• Agents that watch your inbox and systems.

• Tools that cut away entire layers of coordination and data entry.

• Systems that turn institutional knowledge into something searchable and reusable.

2. Use AI to replace tasks, not expertise. The magic wasn’t “AI did everything.” AI handled the repetitive parts; Peter supplied the expert judgment - what the agent should do, what good UX feels like, and what constraints matter in the real world.

Two questions for us to keep handy:

• What am I still doing that a smart agent could do 90% as well?

• Where does my judgment, domain knowledge, or expertise actually matter?

AI can tackle the first list, but we need to staying glued to the second.

3. Ship to the network, not the org chart.  OpenClaw didn’t roll out through persuading stakeholders; it spread through GitHub, dev chats, and social feeds.instagram.

We can ask:

• Can a single user get value in 10 minutes?

• Can they share it with someone else in one click?

• Does using it make them look smart or early to their peers?

4. Think “AI agents & humans,” not “humans vs AI agents”. The endgame here isn’t replacing everyone. It’s about how to redeploy them - moving humans up the value chain and letting AI handle the work that was never a great use of human potential to begin with – the work that requires judgment, expertise, relationships, and genuine creativity.

Which brings us to what we're building.

What we're doing with all of this at PreScouter.

A few weeks ago, we set up OpenClaw and quickly realized that the “out of the box” configuration wouldn’t work for us, because it’s designed as a single user experience.

As such, most people setting up OpenClaw are setting it up as an assistant - an AI that helps you with your personal tasks, such as writing emails or making calendar bookings.

For us, this has some value, but not enough value.

We’ve instead been working on setting it up as a caretaker - an AI employee whose job is to tend to the operational infrastructure of our business.

Our team manages a huge amount of “metadata” - client names, deadlines, assignments, Salesforce updates. These are all necessary, tedious and a quiet tax on hours that could go toward client work. We're building OpenClaw to handle this, so our people handle what they're actually at PreScouter for.

But that's just the beginning. The longer-term vision is an always-on AI employee that soaks up documents, transcripts and other knowledge to function as institutional memory. When someone leaves, their knowledge doesn't leave with them. 

We could ask "Why did we recommend that technology to this client?" and actually get an answer.

We're building something that will change how our company operates and remembers.

Want to see it in action?

🔔 I'm meeting with some of you on Friday 6th March, 10am PT / 12 CT / 1pm ET, to demo what we’re up to. Reply “INVITE" if you want to be added to the invite or to get the recording.

Dino