Distilling leadership wisdom

Get your very own Jeff, Larry or Steve.

This month, we’re going to explore a practical technique for learning from leaders you’ll never have access to: distilling their thinking into an AI role you can query on demand.

Throughout history, those who’ve succeeded the most have often had something in common: access to the best people, whether as mentors, advisors, or sounding boards. That access has always been scarce and unevenly distributed. But now there’s an opportunity to democratise it: you can create your own coaches and advisors from some of the smartest minds out there, using nothing more than their interviews, podcasts, and talks.

You’ve probably experienced this: you read an interview with someone whose thinking you admire, the insights resonate deeply, and for a few days you see your work differently, but then the ideas slip away, dissolved into the background of the day-to-day. This technique is about making that wisdom stick, not by memorising it, but by creating something you can return to whenever you need a different perspective on a problem.

To be clear, this isn’t about impersonation, and it’s certainly not about outsourcing your judgment to someone else’s thinking. It’s about having a different set of questions available when you’re stuck: the questions that someone you admire might ask, applied to your own context.

It also opens the door to something else: trying out your thinking with people you’d find challenging to work with, without the friction of actually being in the room with them.

We’ll start with how to choose who to distil, then cover how to gather the raw material from interviews and podcasts, and walk through the step-by-step process for turning it into something usable. Finally, we’ll look at how to build and use the role in practice, and I’ll hint at how the same technique works in reverse: extracting your own thinking from your own body of work.

If you’d like to dig deeper, here are some related articles from the archive:

• Councils of agents: group thinking with LLMs explores a lighter-weight version of this idea: using AI to simulate multiple perspectives in a single conversation.

• Use it or lose it covers the importance of not outsourcing your thinking entirely to AI, which is relevant here: this technique augments your judgment, it doesn’t replace it.

• Coaching is a reminder of what coaching looked like before AI: the push and pull of directive guidance versus helping someone work through their own problems. What we’re building here is a version of that, on demand.

I’ll be honest: this started as a bit of fun. I was curious whether I could make an AI channel the thinking of a founder I admire, and the initial experiment was more novelty than anything serious. But, actually, it turned out to be genuinely useful.

Having those questions available when I’m stuck on a decision, or when I want to stress-test an idea, has changed how I work through problems. What began as an experiment became a tool I actually reach for.

Let’s get going. Or, as Jeff would say: it’s Day 1.

Choosing who to distil

Not every leader is a good candidate for this. The technique works best when three conditions are met: there’s enough source material to work with (a few hours of interviews, ideally more), you genuinely want to apply their thinking to your own context (not just consume their content passively), and their frameworks are somewhat transferable (they think in principles, not just anecdotes).

Founders with extensive podcast appearances are often good candidates, as are authors who’ve done multiple interviews about their books, and executives who speak at conferences. The pattern to look for is someone who’s been asked similar questions from different angles, which surfaces their underlying principles rather than rehearsed soundbites. You want enough material to triangulate on how they actually think.

To give you a sense of what works: Claire Hughes Johnson has Scaling People plus hours of podcast interviews on Lenny’s Podcast and First Round Review, and her thinking is structured enough to extract clear frameworks. Charlie Munger’s “latticework of mental models” is perhaps the most codifiable leadership thinking available, spread across shareholder letters and his Acquired interview. Naval Ravikant has been interviewed extensively on Tim Ferriss and The Knowledge Project, and his ideas on decision-making are applicable to any field or domain.

They’re working in completely different worlds, but the pattern is the same: there’s plenty of material to work with, their thinking is rooted in principles rather than anecdotes, and what they’ve learned travels beyond their specific context.

Gathering the raw material

The simplest approach is YouTube. Most podcast interviews end up there, and YouTube has a built-in transcript feature: click the three dots below the video, select “Show transcript”, and you get the full text. I copied each transcript into its own file, one per interview, which made it easier to process them separately before synthesising across sources.

If you want to go deeper, there are more elaborate options. Some podcasts publish their own transcripts: Tim Ferriss has an archive of over 800 episodes, and Lex Fridman publishes full transcripts on his site. Aggregators like Tapesearch let you search across millions of podcast transcripts to find every appearance by a specific person. But for most purposes, YouTube and a few tens of minutes of copy-paste will get you everything you need.

Here’s what a raw YouTube transcript typically looks like when you copy it:

0:00
today I want to talk about something that
0:02
I think is really important which is the
0:04
idea of working backwards and so you
0:07
know at Amazon we we famously start with
0:10
the customer and work backwards from
0:12
there and I think that's that's
0:14
something that a lot of companies say
0:16
but don't actually do right they they
0:18
sort of pay lip service to customer
0:20
obsession but then when push comes to
0:22
shove they optimise for something else

It’s not pretty: timestamps interrupt every few seconds, there’s no punctuation, and sentences break mid-thought across lines. Don’t worry about cleaning this up yourself: we’ll do that next. What matters is capturing the full conversation.

The distillation process

The work now is to turn those messy transcripts into something structured: cleaned up, synthesised across sources, and organised into principles you can actually use. The good news is that this can happen in a single conversation. Feed your transcripts to an AI with a prompt like this:

I have transcripts from several interviews with [name]. Please:

1. Clean up each transcript: remove timestamps, fix punctuation, and mark who’s speaking (interviewer vs [name])

2. Extract the key principles, mental models, and decision-making frameworks that [name] articulates

3. Identify patterns that recur across multiple interviews — these are likely their core beliefs

4. Organise the output by domain (e.g., decision-making, people, product, strategy)

5. Include direct quotes where they’re particularly memorable

6. Output everything as a structured principles document I can reference later

The transcripts are below.

Within minutes, you’ll have a working principles document. Resist the temptation to have the AI research further or expand on each principle: the value is in capturing their thinking from their words, not generic material found online about the same topics. I’ve found it useful to keep the distillation specific: it makes the role you create from it more pointed and useful as a coach.

Building the role

Once you have a principles document, you can turn it into a role: a structured definition that tells the AI how to apply those principles to your questions. If you want to see how this fits into a larger system of roles, I covered my full daily driver setup in the April subscriber edition, but here’s the core structure you need.

A role typically includes:

• Description: Who this persona represents and why you’re using it

• Core questions: The questions this person tends to ask when evaluating ideas

• Mental models: The frameworks they apply

• Tone: How it should behave (direct, challenging, supportive, etc.)

• Reference: A pointer to your principles document so the AI has the full context

Here’s a short generic example to give you a flavour of what to aim for:

Founder Lens

Description: Applies the mental models of [leader name] to my current context. Not pretending to be them, but using their frameworks to surface angles I might miss.

Core questions:

• Is this derived from first principles or copied from others?

• Does this require courage? If not, is it ambitious enough?

• What would someone who genuinely cares do here?

• What are the unstated assumptions?

Mental models:

• First principles thinking starts from atomic building blocks

• Change your opinion when you get better information

• The best ideas often feel uncomfortable

Reference: See context/founder-lens/principles.md

Tone: Direct, challenging, focused on clarity over comfort. Asks questions rather than giving answers. Pushes for specificity.

If you’re using Claude Code, this role definition goes in your CLAUDE.md file, and the principles document lives as a separate file in your project. I keep mine in a context/ folder with subfolders for each role.

Activating a role just means asking for it: “founder lens on this decision” or “what would [name] think about this approach?” The AI reads the role definition and the principles document, then applies that perspective to your question. Other tools will have their own way of handling system prompts and context files, but the structure is the same: a role definition that points to a richer source of material.

Using it in practice

What it feels like in use is less “chatbot pretending to be someone” and more “having their guidance available when you’re stuck”. So I often find that it surfaces an angle you wouldn’t necessarily have considered on your own, because they may see the world differently to you. The value isn’t in the answers, it’s in the reframing: the questions force you to articulate assumptions you hadn’t examined, or to defend choices you’d been making on autopilot.

I find it most useful before committing to a significant decision, even when I have consensus with my own group, because I want to stress-test my thinking before it becomes expensive to change course. It’s also valuable when I’ve been circling the same problem for too long and need a way to break out of my own perspective.

With that in mind, it’s worth considering whether there are any founders or notable people out there who would have a very contrarian perspective to yours. Including them as part of this exercise can surface blind spots that a like-minded advisor never would. The role doesn’t tell you what to do, but it does make you defend your reasoning to a different persona.

Here’s what this might look like in practice. Imagine you’ve distilled Elon Musk’s thinking into a founder lens, and you’re using it to stress-test a timeline your team has given you:

You: Founder lens on this: my team says migrating our payment system to the new provider will take six months. I’ve pushed back but they’re confident that’s realistic.

[Founder]: What’s actually in those six months? Break it down for me. Not the project plan — the actual work. If you had to ship something in six weeks instead, what would you cut?

You: We’d probably skip the automated rollback tooling and do manual rollbacks if needed. And we’d run the systems in parallel for longer rather than building a full reconciliation dashboard upfront.

[Founder]: So the core migration is six weeks, and the other four and a half months are de-risking. Is that de-risking based on something that’s actually happened before, or is it fear of something that might happen?

You: Honestly, it’s fear. We’ve never had a payment migration go wrong, but the consequences would be severe.

[Founder]: Severe how? Quantify it. What’s the actual blast radius if you migrate and something breaks? Are we talking about lost revenue, angry customers, regulatory problems? And what’s the cost of moving slowly — four months of carrying two systems, delayed features, team context-switching?

You: I hadn’t thought about the cost of delay that concretely.

[Founder]: That’s the question. You’re insuring against a risk you haven’t priced, with a premium you haven’t calculated. Maybe the six months is right. But you should know what you’re buying with that time.

The lens didn’t tell you what to do, but it made you defend your reasoning to a different set of priorities.

The mirror

The same technique works in reverse. Instead of encoding someone else’s thinking into a role, you can extract your own: feed your writing, decisions, and documentation to an AI, and ask it to identify your patterns. The output is a principles document that captures how you actually think, not how you imagine you think.

I recently ran this process on my own writing: several years of newsletter articles, fed through the same extraction steps. The output was a set of principles organised by how often they recur. Some I expected to find: “constraints are superpowers“ shows up repeatedly, as does the conviction that managers should stay technical. Others surprised me: I hadn’t realised how consistently I return to the idea that autonomy and ownership are non-negotiable, or how often I argue recently for being in the details rather than delegating everything, which is a marked change from how I used to be when I first started out in management.

The value isn’t just in having the list. Your previous writing, whether internal memos or public articles, tends to represent your best self: considered, articulate, thoughtful. The messy day-to-day often pulls you away from that. Having your principles explicit means you can check whether you’re still aligned to what you actually believe, or whether you’ve drifted without noticing.

This is another reason to write, even if only to yourself. Writing has always been a tool for thinking, but now it’s also a tool for self-knowledge: the more you write, the more material you have to analyse, and the clearer the picture of who you actually are. This month’s subscriber edition goes deeper into the inward-facing version of this technique: how to gather your own material, what prompts to use, and what to do with the principles once you have them.

Your turn

The technique is straightforward enough to try this week. So why not give it a go?

• Find your candidate. Have a look through all of the podcasts and interviews that you’ve read, listened to, or watched recently, and see whether anyone that you found particularly inspiring could form a coach that can be your go-to in your day-to-day.

• Gather the material. Find two or three interviews with them and run them through the distillation process: clean the transcripts, extract principles, and organise them by domain.

• Build a simple role. Create a role definition with a handful of their core questions and mental models, and don’t worry about getting it perfect on the first pass.

• Try it on a real decision. Pick something where you’d value a different perspective and see what surfaces.

• Iterate. Refine the role based on what’s useful and what isn’t: the first version is never the final one.

Wrapping up

And remember: this isn’t about hero worship, and it’s certainly not about replacing your judgment with theirs. It’s about making the wisdom of people you admire more accessible to yourself, on demand, when you need a different perspective. The insights you read in interviews don’t have to fade anymore; they can become tools you reach for.

Until next time.