Check out the conversation on Apple, Spotify, and YouTube.

Brought to you by

Mobbin - The internet’s best library of design patterns

Check Out Mobbin

Get a full year of Mobbin Pro, along with 8 other AI tools, at bundle.aakashg.com.

Today’s Episode

Billions of people have played Mark Pincus's games. He founded Zynga, and for years I walked past the Zynga building in San Francisco every day.

Mark just published Life at the Speed of Play. The book's subtext is how to launch products people love.

So I asked him to go to the whiteboard and teach his Proven Better New framework from scratch.

I use this framework myself when I think about building products. But I'd guess only 10% of you know it well.

By the end of this post, you will know:

• Why every great product starts as a copy

• Where the line is between stealing well and getting it wrong

• How to sort your roadmap into proven, better and new

🎁 Win a free copy of Mark’s book

I’m giving away 10 copies of Life at the Speed of Play! All you gotta do is watch the episode on YouTube and comment I watched till the end. I’ll reach out to 10 of you.

Go Comment!

Want to see an AI agent get built from scratch?

This Wednesday, Oct 28, I’m building one live in Claude Code and explaining MCP in simple language.

Register Now!

Newsletter Deep Dive

1. How to use Proven/Better/New to Build Better Products

2. How to tell better from new

3. When to ship less new

4. How to test 100 ideas before you build one

Subscribe now

1. How to use Proven/Better/New to Build Better Products

The big online poker sites used to make you download before you play a single hand.

Mark noticed that the moment you ask for a download, you lose at least 80% of your audience. That's his number, not a benchmark.

So he copied the leading poker tables down to the chip colors and removed the download.

Then he added the one new thing: your Facebook and MySpace friends could sit at your table.

Mark's most controversial idea is that copying is okay.

All music, movies, stories and games are derivative. Every artist builds on the artists before them.

But there is a line between taking inspiration and copying blatantly. That line comes down to taste.

Steve Jobs was a master of copying and appreciated it deeply. Bill Gates copied from the same place, Xerox PARC.

Then the two copied each other. Gates was worse at the trick, so it looked like he was just copying Apple.

“can you learn from other people's proven success and emulate it perfectly, but have it feel new to people?”

Proven Better New lets you act on both at once.

Proven is any part of the experience that already works for this audience on this platform, so you copy it click by click and pixel by pixel.

New is your novel idea and your innovation zone. Your job is to isolate it so your launch measures that idea and nothing else.

If you want the first-session side of copying what works, my Ultimate Guide: Activation (paid) and The Ultimate Guide to Onboarding (paid) go step by step.

2. How to tell better from new

Would 10 out of 10 people who use the proven product today choose it? If they all agree, call it better.

If only you think so, call it new. This test isn't always easy, and most mistakes go in one of two directions.

The first is calling your own idea better. Take Mark's family assistant built to compete with Instinct, the AI assistant that works over text. I tested Instinct for AI by Aakash.

Putting the whole family in one thread feels like an obvious improvement to the person building it. Under the test it is new.

Mark left the Better column empty:

“I have no ideas today that I could say 10 out of 10 Instinct users would call better than Instinct.”

The second is calling a removal better. Rejecting something that looks like an issue feels like progress.

But if it already works, removing something proven is a bet like any other. Zynga copied the neighbor bar from FarmTown, where the other player has to accept your request.

It looks like friction. Mark says calling its removal better “would have made our product less viral.”

The loop behind that is in my Ultimate Guide: Growth Loops (paid).

People at Zynga still repeat his mantra that all new fails.

If you expect that from the start, you won't be disappointed or let a failing idea linger too long.

But when new does work, we need to have a reverence for it.

So discount the hope you have for your own new idea. And pay close attention to new that's working out in the market, because that new just became proven.

Consumer AI didn't feel investable to Mark back in June. With Instinct at a $10B valuation, it feels a lot more investable today.

3. When to ship less new

Mark's proof is Hay Day. Supercell's game looked like Zynga's FarmVille 2.

In his words, “it was 0% new.” What changed was the better: it loaded faster, it had shorter crop cycle times and it had touch.

Late in a cycle it flips. Nobody wants a new farm game now, and you'd need “a lot of new.”

AI is early. So a lot of low-hanging fruit is always going to be there.

It is best to be disciplined about it and look for where you can be better because of AI.

Write down which part of your AI product is actually new. If it's more than one thing, you're running two experiments in one launch.

My guide to AI strategy is How to Build Product Strategy in the Age of AI (paid).

4. How to test 100 ideas before you build one

AI lets you make an MVP in months instead of a year and a working version in minutes.

Mark thinks that speed is a trap, because it tempts you to skip the top of the funnel and go straight to the whole product.

So you end up failing too slowly.

He had just argued about it with Eric Ries, whose Lean Startup gave us the MVP: “the problem with MVP is the word viable.”

Once you call something viable you start thinking it could be your launch product. I made a similar point in 2022 in “MVP”: the most misused term in tech.

The MVP is the version that tests your most critical assumption. Mark's funnel test goes one step earlier and checks demand before there is a product at all.

I go deeper on scoping in Should your MVP be Embarrassing? (paid).

His proof was a post by Rohan Nayak, CEO of Pocket Entertainment, the company behind Pocket FM.

Pocket FM runs a 90-second video trailer of an audio drama as its ad.

Mark's reading is that once they hit 2.25% in any country, they could grow to $10 million in ARR in three months.

Rohan's own numbers are narrower: France to $10M in 3 months, and 8 months to figure the engine out.

Mark says he could “vibe code something at least good enough to be the website behind an ad” and send an agent into France, Italy and Saudi to see if it gets a 2.25% click-through rate.

Here is how to run it:

1. Write the teardown. Fill in the proven slots for the closest competitor at click-level detail.

2. Generate concepts. A batch of names and one-line value props for each bet. Ask the agent to flag where it needs you to brainstorm.

3. Vibe-code one page that's just good enough to catch an ad click.

4. Run web ads. Mobile ad platforms may not let you advertise without an App Store listing.

Pocket FM's own benchmarks from Rohan's post are 2.5% CTR and 55% 3-second through-play on a 90-second trailer. Pick yours from your own funnel.

Get Transcript

Try this first

Open your roadmap and write proven, better or new next to every item. Anything you can't point to a competitor doing is new.

If one launch carries more than one new, you've found your first cut.

Where to Find Mark Online

• X: @markpinc

• Life at the Speed of Play: Launch Products People Love!

• Read the full transcript

Related Content

1. The Ultimate Guide to Onboarding (paid): the first clicks of a new user

2. Ultimate Guide: Activation (paid): getting users to the good stuff

3. Ultimate Guide: Growth Loops (paid): the loop behind the neighbor bar

4. How to Build Product Strategy in the Age of AI (paid): what to build as the platform shifts

PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!

Amazon and Google are at the top, as you'd expect. Amazon made 691 outside PM hires worldwide in the 12 months to April 2026, and Google made 507. Right after them is Capital One.

Then Microsoft. JPMorgan Chase and Mastercard both sit ahead of Meta, and Visa is just behind Walmart.

On the other hand, if you look at job board data (what most reports look at), you'd see something different. TrueUp, for instance, shows 7,267 open PM jobs at tech companies this month, up 77% from the 2023 low.

This report doesn't mislead you with postings. It looks at actual hires: people who started a PM job at a new employer. Hires, not ads. Look at the difference between postings and hires. It's a totally different trend.

That's just one of the things I cover in today's deep dive.

I teamed up with Alex Hamilton and the team at Live Data Technologies, who track job changes across about 126 million professional profiles.

I also teamed up with Zuhayeer Musa and the team at Levels.fyi, who bring the pay data.

And this isn't only a Levels report. 276 of you answered my pay survey, so every pay cut in section 3 sits next to what Product Growth readers told me they earn. You won't find that anywhere else.

I went through all the other information out there. Every source was missing something:

• Too focused on postings

• No hires

• No compensation

• No global perspective

So I put together the report I would have wanted if I were weighing the PM market, whether as an aspiring PM, a hiring manager or someone job searching.

I've published on this before. Last November, I published the definitive report on the AI PM market.

This is the update. And this time it covers the whole PM market, globally, with all the data you need.

First - About the Data

Everything in this report is worldwide unless I say US or name a region. The one exception is pay. The Levels.fyi data is mostly US, so pay is US unless a line says otherwise.

The reader survey is 276 PMs who read Product Growth, mostly in India and the US.

A few more notes on the data:

• The AI PM career-path panel is 23,642 people worldwide who started an AI PM job since January 2021, with their full job histories. Section 2 uses a random 45% sample of it.

• AI PM means the title. Some AI PMs are left out: people who do the work with "PM" in the title. So every AI PM share is a floor.

• Recent months fill in late. Live Data's newest months arrive with a lag, so 2026 is an early read, even though we're publishing in October.

With all those data notes out of the way (you're a good analyst!), let's get into the report.

Today's Post

I've put together the definitive guide to the PM market in 2026.

1. The state of the PM job market in 2026

2. The AI PM deep dive

3. How much PMs are paid

Subscribe now

Really quickly, I'm running two free webinars over the coming week:

1. How to build your PM interview and portfolio secret weapon with Claude Code, to land more of these jobs: Tuesday, October 27, at 2 PM PT

2. AI agents and MCP for PMs: Wednesday, October 28, at 9 AM PT

And if you want help landing the job itself, apply to my Land a PM Job program.

1. The state of the PM job market in 2026

The craziest trend in the PM job market is something you probably already knew.

PM hiring is down. We've been talking about it for years now.

The end of the ZIRP era in 2022 was a great time to be job switching. Since then, the market hasn't been as good.

But it's not all doom and gloom. Even though PM hiring is down 45% since 2022, it's mostly been flat through 2023, 2024 and 2025.

In 2026, the numbers are looking down, but I wouldn't read too much into it, because it's just February to June. It's an early signal, though, that 2026 is down 19% from 2025. That is a little bit concerning.

On the AI PM side, there are no concerns at all. AI PM hiring is up 2.5x since 2022, and up 10% from 2025 as well.

But there are six additional insights I put together that I think are really worth knowing about this market.

Insight 1 - The 2023 reset is still holding

As we covered, the big drop was in 2023. Man, was it a bad year to be in the PM market.

We saw layoffs, we saw a drop in postings, and we saw a drop in hiring:

• Layoffs: 262,735 tech employees laid off

• Postings: open PM jobs fell 59%, 10,062 to 4,115

• Hiring: outside PM hires fell 33%, 52,664 to 35,483

Layoffs: 2023. Postings: August 2022 to March 2023. Hires: February to June, 2022 to 2023.

Since then the market has barely moved: down 3.8% in 2024 and up 5.2% in 2025. The 2026 early read is down about 19%, with May and June still filling in.

When you look closely at 2023, the drop happened earliest and hardest in the US, which was down 41%.

Since then, the US has actually held up better: down about 10% in 2026, against 23% everywhere else. (I include more on the full regional trends in Insight 6.)

As I mentioned earlier, hiring is just one thing. The cleanest way to really look at the market is hires per exit. That is to say, how many PMs does a company hire from outside for every PM who leaves?

This helps us understand how the PM field is doing at a specific point in time, and how a subset like AI PM is doing too.

So let's look at the numbers.

Back in 2022, when PM was growing, we saw 1.16 outside PM hires for every PM who left. That's 16% more people coming in than going out.

Since then, it has come down: 0.94 in 2023, 0.92 in 2024 and 0.92 in 2025.

And here you can see that AI PM is the title that's been growing like crazy. Its outside hires ran 89% above exits in 2022, 74% in 2023, 72% in 2024, and 47% in 2025.

Now, this isn't a perfect metric either. It doesn't mean PM teams shrink 8% a year, because promotions and internal moves still fill seats.

What it does mean is that the outside door is narrower, and that's what matters when you're searching for a job.

So if your search feels harder than the one you ran in 2022, this is why.

Insight 2 - Though PM hiring is down, PM pay is up

You might think that with PM hiring dynamics changing the way they did, and hiring down, pay would be down too.

That's not the case at all.

I looked at all of the PM compensation submitted to Levels.fyi by year. Median compensation has been going up: $220K in 2022, then $210K in 2023. Yes, there was a drop there. But it was $224K in 2024, $236K in 2025, and now $250K in 2026.

So PM pay at the median is up 14% from 2022. Meanwhile, US PM hires are down 48%.

Here's what it looks like when you put pay and PM hires on the same chart, indexed to 100 for where they were in 2022.

As you can see, AI PM hires are up to 215 and pay is up to 114. Meanwhile, all PM hires are down to 52. In people, US AI PM hires went from 389 to 835.

That's the real story of the market. It's what you see when you look at the three variables that matter, and it's what most of the prior hiring reports you'd find online miss.

So what's going on with that 14% increase in pay?

Part of it is level mix. If you hold the level fixed, the jump is smaller. At Levels.fyi's L3, for example, the median went from $306K to $319K, up 4.5%. I left that line off the chart because it would crowd it.

So how much of it is real? That takes a closer look at levels, and it's the next insight.

Insight 3 - The PM field has gotten more senior

That's right: the fear everybody had is kind of true.

The share of PMs who reported as super junior, which is coded as L1 in the Levels.fyi data, is down from 17% in 2022 to 11% of reports in 2026. That's pretty wild.

Now, Levels.fyi is trying to synchronize levels across all companies. So here's what those levels look like, for US PMs over the last 12 months:

• L1: 5 years of experience at the median, median pay $175K

• L2: 7 years, $228K

• L3: 9 years, $296K

• L4: 12 years, $385K

• L5: 16 years, $500K

So you can think of L3 as a senior PM, and L5 as a principal-level PM.

Companies have different ladders on top of these. For instance, a Google L5 PM is an L3 on Levels.fyi, and a Google L6 is an L4. A Meta L5 PM is an L3, and an L6 is an L4.

If we look across the levels, we see some interesting trends. Across L3, L4 and L5, the share of PMs went up, from 50% to 61% of reports.

So PMs have gotten more senior. The people with PM experience are progressing, and fewer people are entering PM at the bottom.

And in terms of pay, interestingly, the most junior and the most senior PM pay is down:

• L5 pay is down 18%, from $511K to $419K

• L1 is down 16%

On the other hand, the middle of the range is up: L2, L3 and L4 are up 5.0%, 4.5% and 5.8%.

Weigh the five levels by how many PMs reported at each one, and pay is up about 6%. Nearly all of that comes from the shift to higher levels.

About 3 in 10 submissions don't carry a level, so I can't split the full 14% exactly, and L1 and L5 are the smaller, noisier groups. Read this as directional.

Insight 4 - Don't let anyone tell you AI PM is everything

At the top, we've been talking about how AI PM is growing. That's the positive spin on the PM hiring market. But it's not everything.

Let's take a look at the share.

Back in 2022, there were 1.5 AI PM hires for every 100 PM hires. Now it's 6.8, about 4.5x more. But that doesn't mean it's everything. It's still just 6.8.

There's core PM, regular PM and other titles in PM that still matter. The AI PM title is only 7 in every 100 PM hires.

Of course, I also looked at PM postings. In February 2024, AI PM was 2% of open PM jobs. Today it's 46%.

That's the thing with the Live Data numbers: they're looking at AI PM in the title. AI PM in hiring may be a bit higher than 7, but I doubt it's anywhere near the share of postings.

A lot of the hires are in fields outside AI PM.

Insight 5 - Who hires the most PMs

One of the numbers I was most keen to look at is who the top hirers of PMs are, and how their hiring has changed over time.

The results were fascinating.

Most of big tech made fewer outside PM hires this year. Meta is down 30% (366 to 256). Microsoft is down 31% (625 to 431). Amazon is down 21% (870 to 691). Google held level. It was pretty brutal there.

On the other hand, the financial companies actually increased their PM hiring: JPMorgan Chase is up 17% (345 to 404), Visa is up 32% (165 to 217) and Mastercard is up 55% (219 to 339).

So there's almost been a shift from tech companies to financial companies in the PM market over the past year. That's one of the most significant things I learned in this research.

Now zoom out. These are only the top 10, and together they made 3,756 of the 79,164 outside PM hires worldwide.

I put the full worldwide top 25 in a downloadable target list: ranked, tagged by sector, with each employer's change over the last year and its PM pay range where Levels.fyi has one.

That's five of the six insights. The sixth is just past the paywall: outside the US, 2026 is harder. I break it down by region: the US, Europe, India and the rest of the world.

And that's not all you get in the paid section. I cover all the cuts of the market you want to see.

Plus:

2. The AI PM deep dive. Where AI PMs come from and how they break into the top labs like OpenAI and Anthropic, whether to stay and move internally or switch, the titles to search for (agent PM, forward deployed PM and the rest), and where AI PMs go when they leave.

3. How much PMs are paid. All the information you need to negotiate your next offer: pay by level, geography, company size and employer.

Plus 3 downloads:

• A 71-page PDF covering everything you need to know, in the classic market-report style

• The top 25 PM employer target list, worldwide

• A Claude skill that reads your LinkedIn and places you on a route into AI PM

Don't forget: if you become an annual subscriber, you get access to my bundle. And if you become a founding subscriber, you get access to all my paid products, including PM OS, Content OS and more.

Read more

Check out the conversation on Apple, Spotify, and YouTube.

Brought to you by

• Fingerprint - Identify good and bad visitors with industry-leading accuracy

• Ariso - Ship AI agents and features faster, with fewer regressions

• Customer.io - Send smarter messages using your product data

• Amplitude - The market leader in product analytics

• Product Faculty - Get $150 off their #1 AI PM Certification via code AAKASH150

Today’s Episode

Claude Code can be the most powerful system in the world.

I’ve now built you several Operating Systems you can run out of it: for content, for PM, your team, and for your company.

The missing piece was a Growth OS. So today, I present you one. And I haven’t just ported over what works for me.

I’ve brought on James McAulay who worked on growth at ElevenLabs and grew his latest business, Agent Accelerator, to £100K a month in 95 days, without hiring a single FTE.

He’s open-sourced everything. And it works.

Halfway through our recording, James ran a Claude Code skill on my site. A few minutes later it returned posts with no SEO description, long titles, crawlability issues, duplicate content…

(Which I’ve now fixed, with this growth OS).

Free Webinar

The technologies that make a growth OS possible are AI agents and MCP. I’m giving a full masterclass on them for free next Wednesday:

Join the Free Webinar

Newsletter Deep Dive

I appreciate all the support you give my work. So here’s a written deep dive if you prefer it:

1. How the Growth OS Works

2. How to Set Up Your Growth OS

3. How to Run Growth Experiments with No Engineer

1. How the Growth OS Works

You open Claude Code inside it, and it has three layers: tools with APIs, everyday skills, and routines that improve the setup.

Tools with APIs

James only adopts a tool if it has a good API or CLI, like Stripe, PostHog, Attio, Firecrawl, DataForSEO, vidIQ, Kit, Apollo

Everyday skills

Morning standup, weekly and monthly retros, PRD skill, SEO audit, tone-of-voice, YouTube packaging, competitor scan

Routines that improve the setup

The system improves itself from your usage, as weekly scheduled jobs look at how you work. It even suggests new skills and other connections you might want to add.

The repo is growth-agents on his GitHub. It installs with a couple of commands and runs in Claude Code, Codex, Hermes and OpenClaw.

It’s built for a full-stack growth person or a company’s first growth hire. If you’re a specialist (a product marketer or SEO lead), the onboarding asks about your goals and adjusts the system to your role.

2. How to set up your Growth OS

So you don’t actually need a new folder for this. If you’ve got a second brain already, the folder where all your notes live, just put the OS in there.

Clone the repo and run the install commands from the README. Then open Claude Code. Run the onboarding and Claude goes through everything you’ve already got in there before it asks you a single question.

It’ll ask if you want rapid or full setup. James makes everyone he teaches do the full two hour setup. We did rapid on camera because we were short on time. Try the full one on yours if you can.

What to wire into Claude Code

Connect everything as fast as you can.

The two most important are analytics and Git:

1. We did Posthog via MPC.

2. Read-only access to Git is key. Your team might not let you ship code, but it helps you get the right PRs ready.

The OS will help you so never forget PostHog events again. Every event says when it fires, where it fires, and what to measure with it:

Pre-AI, you’d only see data that clean under a god-tier PM. Now, it comes with the OS.

The core loop: actually learning from experiments

Running a test is easy but concluding it with definitive learnings is a feat unknown to many. I know it may sound harsh but shiny-object syndrome pulls us onto the next big idea, and at year’s end we are left with a pile of PostHog experiments with no recorded learnings.

The OS solves for the exact problem:

• Every experiment gets a file with these fields before anything ships.

• A playbook for A/B tests is set up, and it stops you from reading a result early. The weekly retro and the daily standup both read the list of running experiments and tell you when one is ready to review. They also catch stale tests.

• Experiments only build on each other when a template forces the baseline and confounders up front and a weekly loop forces the learning at the end.

3. How to run growth experiments with no engineer

The way growth was done before, you’d wait on an engineer before any test could happen. Now growth people can run experiments themselves. That’s how James thinks about it.

We no longer need to wait for engineers to build growth experiments.

Gate 1 - PRD

Say you want a new page on the marketing site. What the system does is help you have the discipling to write a PRD where you answer key questions like: What metric will it move? So use the PRD skill to write a draft.

Gate 2 - Multi-agent refine

Then refine it with the multi-agent refine skill. This is cool. It launches subagents to critique your PRD, each using a different angle. Then a lead agent creates a master critique and priority list of action items to do.

Then - Ship

THEN ask Claude Code to build your feature.

Example: SEO

Here’s the whole loop in action with SEO.

Start with the SEO audit skill:

It’s a remix from Corey Haines’s Marketing Skills collection. But James added a few tools that really upgrades them:

1. Firecrawl which turns any website into Markdown, because when you ask Claude to scrape a site, it often hits Cloudflare blocks or misreads the page. Firecrawl fixes that.

2. DataForSEO is one of my favorite finds for this episodes. It’s a super cheap SEO API. Once you give Claude Code the key, it can research any term for you, just for a few cents.

3. The Lighthouse CLI runs Google’s page speed tests from the terminal. Before, you’d open PageSpeed, run the test, export it, save it and hand it to the agent yourself.

What it found on my site was that my search traffic was about 25K visits in May and has dropped to about 10K.

Then, it can help you ship the changes. I had it execute on this whole action plan:

Try it yourself! It’s pretty magical.

Final Words

How autonomous are growth agents, really?

Is the ‘autonomous’ part overhyped? Mostly, yes. A lot of the people on LinkedIn and X claiming autonomous agents are either burning tokens on low-value work, or running an agent that does most basic stuff.

But, on the other hand, people also don’t realize an agent can improve itself. That part is autonomous, and really cool.

So go use the Growth OS, push on its autonomy, but don’t expect it to work without you.

Get Transcript

Where to Find James Online

Please, let’s follow him everywhere and hit his businesses with a flood of Aakash’s podcast listeners!

• LinkedIn

• X

• YouTube

• Website

• Agent Accelerator

• GitHub

That’s how we’ll keep getting bigger and better guests. Talk soon.

P.S. An annual membership gets you a year of paid plans for my favorite AI tools at bundle.aakashg.com.

Model PM has become the most lucrative sub-field inside of product management, with a 60% premium over PMs outside AI.

Lest you think this is just a level effect, actually it’s the opposite. The gap is similar at the mid, senior, and staff levels.

But how did it acquire such a hefty premium?

There’s A Skill Shortage

It’s basic economics. The skills are in short supply.

Madhu Guru led product for Gemini and Veo, plus Nano Banana, and he's now a senior director of AI at Meta.

He estimated <75 people globally have the depth needed for this role:

You need a unique combination of model and PM knowhow. This is good news for you, though. With the right work, you can acquire the skillset, and nab a model PM job.

Today’s Post

In today’s post, I’ll give you all the “knowledge” you need to acquire those skills. Then it’s up to you to do the work:

1. What is a model PM

2. Analyzing the backgrounds of model PMs

3. Who is hiring model PMs and what they’re looking for

4. What the job is actually like in the top labs

5. Let’s learn the key skills practically

6. The model PM toolkit

7. How to get the job

Subscribe now

PSST… I’m Speaking at a Free Conference!

I’m speaking at a FREE conference. I’ll be giving a talk on the “The Rise of the Full Stack Product Builder”:

I’m also moderating the product panel:

Join Us

1. What is a Model PM

AI PM is a broad term. And under it, it holds at least 4 different types of PM jobs:

Model PMs are the PMs actually building the models. They sit alongside the model research team which is mainly AI researchers and engineers.

They need to be able to win respect from engineers who may have billion dollar pay packages (rumored for folks like Nat Friedman and Andrej Karpathy)!

This means they can’t lose the thread in a conversation on pre-training v post-training or gradient descent vs deep learning. They are not only tracking those terms but using them correctly themselves.

AI Platform PMs are also building AI tools, but not models. They are building APIs, agent tools, eval tools that other teams build on.

AI-Native PMs are building products where a model is the engine. They are at AI companies like Sierra, Glean, and Decagon.

Applied AI PMs are building AI features inside a product that existed before AI. This is the biggest bucket by far, and they are at companies like Freshworks and Amazon.

2. Analyzing the Backgrounds of Model PMs

Let’s look at a real model PM, Barkley Dai:

Barkley works at Luma AI, which, valued at $4B, has one of the best video models. He broke in after working AR and GenAI at Tiktok, and before that being a PM at Tencent and Kuaishou. And before that, he went to Yale.

That’s a typical path into model PM: tier 1 University → $100B market cap (Kuaishou) → $500B market cap (Tencent) → $500B valuation (Bytedance) → Model PM.

Notice that he doesn’t have a Ph.D. He wasn’t an engineer. He studied computer science at Yale, and he’s had a PM career since then. Three and a half years before breaking in as the 1st product hire at Luma.

And he progressively moved closer to models at each step. At TikTok, he started in AR. Then, he moved into GenAI effects (AI feature). Then he started in Luma on Growth projects. Then, he got closer to the models.

That is a very common path for PMs to break in, though it’s not the only one. I went through all 316 model PMs on LinkedIn and here’s what I learned:

There’s a lot to unpack here.

Let’s give each insight some space.

Let’s start with the 5 headline stats at the top and walk through what we learn from each:

• 85% of model PMs were PMs before. Model PM is a sub-specialty of PM. It’s not a separate field altogether. You go on needing PM chops, not just model chops.

• 64% of model PMs have a technical degree. Model PMs are on average technical. That’s because, even if they aren’t engineering themselves, they spend their day with researchers and engineers more than say a consumer UX PM. At the same time, one third of model PMs don’t have a technical degree. So it’s not a requirement.

• 28% of model PMs were founders. This seems to be one of the most reliable signals to a lab that you’re not just a PM, but a builder. But it’s not everyone!

• 24% have an MBA. Having an MBA can be a signal that you might make a good PM, but it’s clearly not a requirement.

• Only 2% have a Ph.D. This one really stuck out to me. I expected many more model PMs to be former researchers, but that’s not the case at all.

Second, there’s a really broad mix of backgrounds of where people landed after college.

PM is by far the largest at 24%, and, surprisingly data or research is the smallest at 6%! There’s a healthy mix of other backgrounds represented, as well: 22% in Consulting / VC, 17% engineering, 15% marketing/sales/ ops, and 7% their own startup.

Third, the real path is going to be unique to you:

• Yonatan Wolf was an APM through GPM at Google. Then he became a model PM at… Google.

• Matteus Pan was an associate at BCG, then a GPM at Dropbox, then a head of product. Then he became an MTS at Thinking Machines.

• Steven Pecht went from McKinsey to BizOps at Google, to PM at Google, to Head of Product outside Google, and then back to Google as a Model PM.

• Grant Gardner spent time as an Eng Manager, then CPO/CTO, then was a Staff PM at Meta Horizon before joining Meta Superintelligence Labs.

For paid subscribers, I share the whole database of all 316 model PMs below.

3. Who is Hiring Model PMs, and What They’re Looking For

Read more

Check out the conversation on Apple, Spotify, and YouTube.

Brought to you by

• Fingerprint - Identify good and bad visitors with industry-leading accuracy

• Ariso - Ship AI agents and features faster, with fewer regressions

• Customer.io - Send smarter messages using your product data

• Amplitude - The market leader in product analytics

• Product Faculty - Get $150 off their #1 AI PM Certification via code AAKASH150

Today’s Episode

On February 28, Pieter Levels posted that for the first time in his life, he thought he’d outrun his to-do list. With Claude Code doing the building, the bug board his users post to was empty.

I’d say this is about 10x my normal output, he wrote about the week’s shipped list. Over 1 million people viewed it. His last line:

The real bottleneck is becoming myself and my creativity, not how fast I can ship.

John Cutler ran into the other side of it. He wrote that CEOs now claim their teams are 100x more effective. He asked one, who runs a collaboration tool, whether there was 100x more demand for their product. The answer: “Crickets.”

That’s where most product teams are right now.

Output is up, and the open question is whether any of it made a difference. Or as today’s guest put it on the show: When features get cheap, not having chosen the right feature, funnily enough, gets more expensive.

So today I brought in the strategy guy, Tim Herbig (10+ years as a PM and Head of Product, now coach to product teams), to spill

• How do you tell a good strategy from a list of priorities

• What role should AI play in your product strategy

• And which metrics prove it’s working

He walks through all of it on a live board, with one made-up company that stands in for many of his clients.

Two FREE Resources!

The podcast is, of course, totally free (as is the newsletter deep dive below). I have two more resources for you, as well:

1. A Free Webinar on the Job Search

Want more interview callbacks without sending more job applications? Join me for a free live class this Friday, October 16, at 9:30 AM PT / 12:30 PM ET:

Join Webinar

2. A Free Conference to Up-skill in AI

I’m giving a talk on the “Future of the Full-Stack Builder” and moderating a panel on the future of product in two weeks, Thursday, October 29, at 9AM PT / 12PM ET:

Attend the Conference

Everything I do is for you. I hope these free resources help you in your career.

🎁 And Contest to Win a Free Book!

I’m also giving away 5 copies of Tim’s book, Real Progress. It’s just 3 steps:

1. Subscribe on YouTube and comment on the episode

2. Follow the show on Apple or Spotify

3. DM me a screenshot of both on LinkedIn

The first 5 people get a FREE copy!

Newsletter Deep Dive

Now… to our main event. As always, I’ve written up the podcast as well. That’s how much I value being in your inbox. Here’s what we’ll cover:

1. What product strategy is actually for, and where AI fits

2. How to test whether you made the right choice

3. How to choose differentiators that survive AI

4. How to know if your strategy worked

Subscribe now

1. What product strategy is actually for, and where AI fits

A few years ago, the limiting factor for most PMs was time. Building took a long time, so you had to prioritize ruthlessly. Today, even established companies get stuff out the door much faster.

But you still have to get people to use what you ship. Tim says many of his clients told him a year ago that feature adoption was already a problem.

So sooner or later, every team amping up its output has to ask: did any of it make a difference?

Our mutual friend Ravi Mehta (former CPO at Tinder) says difficulty prioritizing is often a strategy issue, not an execution issue.

So you need to start there.

Who is it for, and how would you know it worked? It’s for your teams and stakeholders. And it works if it helps them intentionally say yes or no to opportunities over a set horizon, say 6 to 18 months. You know it’s working if most of your team’s energy goes to work connected to the strategy.

As a product leader, I was once handed a strategy with 14 priorities. So what makes a strategy able to say no?

• Strategic narrative

  Why should we act, and why now?

• Playing field

  Who are we solving problems for, which problems, and what do they use today instead of us?

• Winning moves

  What value do we provide, and how will we stand apart?

Your answers have to be opinionated enough to enforce a no.

Tim’s view of canvases and templates: they’re windows into the actual work of strategy. The fundamental questions don’t change. Answer them first, then assemble whatever shape your boss wants.

Where AI fits (and where it doesn’t)

It would be stupid if we didn’t use AI as a tool for making strategy. Researching markets and competitors; synthesizing customer evidence; generating and comparing scenarios; pressure-testing your bets. But you’re still accountable for the consequences.

Treating AI as the value proposition is wrong. Almost no technology in history has been one on its own. AI is a resource you use to amplify the core value customers already come to you for.

Everyone has access to the same frontier models, but you can’t compete on that alone. I pushed back with 2 counterexamples. Pieter Levels’ Photo AI, which does $83K a month in revenue as a one-person company. And ChatPRD, which Claire Vo built while she was a CPO. Isn’t AI the whole product there?

Look at what’s layered on top. People root for Levels because of his personal brand, which keeps him out of a head-to-head with the big companies. Tim’s bet is that people pick ChatPRD for Claire’s product judgment built into it. That matches what I wrote in my AI product strategy guide in March.

2. How to test whether you made the right choice

A favorite pressure test comes from Roger Martin, who I had on the show for a strategy masterclass. Once you see it, you can’t look at strategy the same way.

If the opposite is stupid on its face, you haven’t made a choice.

Tim’s example: “make salespeople more efficient.” The opposite is slower salespeople, which is stupid, so that isn’t a choice. “Make salespeople more efficient by being in person with clients” passes, because virtual contact is a real alternative. One of Tim’s clients calls this the stupidity flip.

For example, sellers and dealers want qualified buyers who buy fast. Buyers arrive from Google and AI search. Many are first-timers who don’t trust a car they haven’t seen. Fair Mile (a hypothetical used-car marketplace) steps in to handle inspections and build trust.

Its default strategy is to paste AI everywhere. That’s an all-yes strategy that says no to capital, margin, and quality.

Of the seven things Tim flips, only 2 are real choices. And they’re the operational ones, where the company picks a mechanism (human call-center verification vs. automated, a static start page vs. an AI-personalized one). The headline phrases fail because no company would argue the opposite.

When I was at Apollo, the strategy from our CEO was to only focus on paid users. But as a product leader, I couldn’t just ignore free users. So how myopic should a strategy be?

First, a strategy doesn’t have to cover everything you do. Focusing on paid users doesn’t mean turning the product off for free ones.

Second, the choice has to fit the narrative. Why paid users? If the narrative is to grow revenue by improving retention for paid users, then a push to acquire more free users is a contradiction.

That was exactly our reasoning at Apollo. Retention was the biggest lever.

Strategy isn’t a top-down thing. You can start anywhere. Someone hands you a segment, and you ask why. Someone hands you a differentiator, and you ask who it’s for. The answers don’t have to be right, because no one knows what’s right anyway. They have to reinforce each other.

Once the audience is set, everything downstream narrows. Tim borrows the audience tree from Andy Johns, a former growth leader at Facebook, who said your target audience should be comically narrow. Tim’s guardrail: land somewhere between very broad and comically narrow.

The audience tree works like this. Start with the broadest audience and a comically narrow one, then list the ways you could slice it: region, income, car value, first-time or repeat buyer. For Fair Mile, Tim lands on repeat buyers of cars worth more than 100K in Germany. Germans love cars and often buy new ones. Repeat buyers might be easier to convince to buy online. And above 100K, every transaction carries a big margin.

This is where AI helps. Ask it how many car owners there are in Germany, how often they buy, what they pay, and how many cars competitors list above and below 100K. Tim’s illustrative answer was about 50,000 people you could reach every year. If the number is too small to fund the company, widen it. Germany becomes Germany, Austria and Switzerland.

3. How to choose differentiators that survive AI

Tim calls differentiation the trickiest component, and the one AI changed the most. He doesn’t invent a new framework for it. He goes back to Hamilton Helmer’s 7 Powers, the classic list of sources of lasting advantage.

For each power, he asks

• What does it do?

• Can we actually execute it?

• Does it help us differentiate from our competition?

Notice the pattern in what survives. The durable powers are the ones that take the longest to build. You can’t speed up a brand or a reputation. You can destroy one incredibly quickly.

Counter-positioning is worth a second look, because the classic example is so clean. In 2000, Blockbuster collected nearly $800 million in late fees, 16% of its revenue. Netflix offered subscriptions with no late fees. Blockbuster couldn’t follow without breaking its own business model.

For Fair Mile, Tim picks brand and network effects, with dealership scale as a maybe. Switching costs don’t fit a car marketplace. And he would skip a fancy AI search: “No one will buy a car with you because you have an AI search on the marketplace.”

So if there’s an order: audience first, then powers?

The truth is, you can start anywhere. Joe from marketing walks in and says, We now differentiate on price. Fine, follow the link. Is our audience price sensitive? Why do customers pick competitors today?

And if the answer is price, get out of that market. You can’t win on price. Tim’s only exceptions are companies like the big AI labs, which can subsidize price for a while. You don’t have that luxury.

4. How to know if your strategy worked

When teams hear metrics, they jump straight to the framework. Instead, answer 2 questions in plain English first:

1. What will you do differently?

2. How will you know it worked?

This applies to B2C, B2B, and internal teams alike. AI teams usually reach for metrics like tokens used or output. Tim’s line: tokens used is not a value exchange for your customers. It’s a value exchange for Anthropic.

So ask whose behavior should change, and by how much. If the goal is 20x more AI feature output next quarter, ask what changes for whom as a result. Do you want your team more AI-fluent? Or customers solving their problems faster? Make that the goal.

The same goes for the North Star metric. Teams think it is EBITDA, but it’s not. Tim credits Sean Ellis with the concept. Ellis defined it as the metric that represents the value customers experience from your product. When it rises, business metrics should follow. The business metric itself is something else.

If you’re burning tokens to ship faster but still need months to see customer behavior change, what exactly is the point of that?

You can also use Ravi’s product strategy stack. Think of each level as a funnel. For every choice from the level above, ask whether it already lets me say no.

Before setting your strategy, ask what the company is already incredibly good at, and how your area can build on it. Personio calls these evergreen benefits.

If the company has mastered a distribution channel or has integrations in place, don’t reinvent them at the feature level.

Only then connect the roadmap. Every roadmap item should map to one of your strategy’s metrics.

Get Transcript

Where to find Tim Herbig

• LinkedIn

• Website

• Product Practice Newsletter

Share

And that’s all for today. Talk to you soon :)

P.S. You can get a year of the paid plan’s of my favorite AI tools with an annual membership: bundle.aakashg.com.

I’ve built what I think is the single most important software for you right now:

Why This Matters

Let me ask you: how many questions did you get this week that were just “context?”

• When is the feature going to go out?

• Can you share me the results of launching this feature?

• Can you look at this before it goes out?

I know when I was a PM and VP of Product I got at least 10 pings like this a day. As the founder of a media company with 5 revenue lines, I now get even more.

But do the math: 10 pings at 10 minutes each is over 8 hours a week! That’s 20% of your productivity. Gone. To routing.

What if I said you could cut that time in half?

Introducing the Company OS

I learned something pretty extraordinary from two Chief Product Officers (CPOs) I recently had on the podcast:

• Jiaona Zhang built the harness so well CSMs are shipping to production!

• Meanwhile, Mikhail Shcheglov made the entire system accessible by Telegram, added self-improving skills, and a way to measure shared context amongst your teams.

So I’ve spent the past 11 days knee deep in building a system like that for you. What I ended up building in the end is a system + an assistant.

Because, look, your setup is going to be different than Mikhail and Jiaona’s. Maybe you’re in a bigger enterprise with more security controls, for instance.

So, I invested in building out the enterprise controls like roles and permissions, as well as a dashboard for the person pushing the company OS out:

Plus, a helpful assistant that will literally help you with the early adoption and use cases messages with your team:

Then I tested the thing with my own team, and hardened it after all the learnings.

Now I’m ready to share it with you.

Everything You Get

What Happened When My Team Used It

The guy who manages my brand partnerships got asked for a media kit. We don’t really have one (just a Passionfroot page). So I asked the bot: have any other sponsors ever asked for one?

It read twelve months of sponsor emails and answered in seconds: yes, three by name, and more who asked for the same numbers in other words.

So I typed one line in Slack: let’s build a media kit.

Three minutes later it had a PRD built from that email, a prototype plan, an engineering spec and a task list for the engineering agent (all powered by my top skills from my PM OS, content OS, and Team OS’s):

After human approval, we now have a media kit page live on the site.

You can have that too.

How to get it

1. Paid subscribers: Company OS is included. The download is right below the paywall.

2. Founding members: you get the whole stack: Company OS, Team OS and PM OS.

3. Not a subscriber? Become a paid subscriber or buy it on Gumroad for $49, one time.

Subscribe now

Could You Build This Yourself?

Sure. By my description of it, you could set out a Claude Code agent to build this.

But do you really want to spend the 11 days of testing?

Not get all the features I didn’t mention?

For instance, it has:

1. Shared skills. Anyone can add repeatedly used skills into the OS, from Github or Slack. Think of an analyst tell you how to correctly analyze a feature.

2. Shared context

3. A security layer

4. Access where people work

And that’s just some of the other stuff I’m teasing…

I have the time to build a full-featured company OS because I write this newsletter. You don’t really have that luxury.

Why You Should Put in the Time Here

Companies are now formally evaluating you on AI usage, AKA AI Fluency. I showed Wade Foster, the CEO at Zapier (and arguably the origin of this trend), three levels of PM usage of AI:

1. Basic ChatGPT: he gave that a “Capable” rating

2. A Claude OS like my PM OS: he gave that an “Adoptive” rating

3. A complete Company OS for building: he gave that a “Transformative” rating

The Deep Dive

🔒 For paid subscribers, everything you need to roll out a Company OS at your company:

1. The Basic Intro

2. How to Build the Context

3. How to Implement Shared Skills

4. The ‘CSM to Production’ Playbook

5. How to Measure Team Connection

6. How to Manage Enterprise Security

And of course the Company OS kit: the repo with the Slack bot, the intake sheet with a sample company filled in, the setup guide, the server setup, the rollout dashboard and review page, Hermes and OpenClaw configs, and the all-hands deck.

Read more

Check out the conversation on Apple, Spotify, and YouTube.

Brought to you by

Mobbin - The internet’s best library of real design patterns

Check Out Mobbin

Get a full year of Mobbin Pro, along with 8 other AI tools, at bundle.aakashg.com.

Today’s Episode

Srini Raghavan, the CPO of Freshworks, predicted that the title “Product Builder” will replace engineer, designer, and product manager in 5 years.

If that’s true, we all have a lot of catching up to do.

So today I break it all down with one of my most popular guests, Ankit Shukla (founder, HelloPM):

• Is the Product Builder role legit…

• How much are Product Builders paid and where do they work…

• And what you need to know to become a product builder

Want a free live class covering the AI you need to know to become a product builder?

Join me tomorrow morning at 9AM:

Join the Webinar

Newsletter Deep Dive

I am so glad you let me in your inbox :)

As a thank-you, I wrote this complete guide to becoming a product builder. Enjoy!

1. What even is a Product Builder, who’s hiring, and does it pay?

2. How to find AI problems that are actually worth solving

3. How to ship a production app with Claude Code

4. How to land a product builder role

Subscribe now

1. What even is a Product Builder, who’s hiring, and does it pay?

What Even is a Product Builder

The big question on the role “Product Builder” right now is:

Ankit’s take? It’s real.

In the episode, Ankit pulled 12,500 PM job descriptions from top companies and had Claude and GPT read them. More than 30% of PM roles now ask for AI skills.

The number one skill those postings asked for was judgment: knowing which problems deserve AI.

So that judgment is what people are paying for.

- Ankit

Indeed, LinkedIn shut down its Associate Product Manager program and replaced it with an Associate Product Builder program.

So how does this differ from other jobs out there?

The two commonly confused ones are forward deployed engineer and product builder. Here’s the difference between the three:

Put simply:

• AI PMs are the PMs building AI models or features

• Forward Deployed Engineers are the on-the-ground consultants helping build deployments of an AI product for specific customers

• Product Builders are, today, any PM building with AI. Some believe it becomes the one title for design, engineering, and product management.

How Much Does it Pay

Right now, product builders really hold the title AI PM.

Looking at those roles, AI-skilled PM roles pay 15 to 20% more than traditional ones in the US and Europe, and 30 to 50% more in India.

How this breaks down in the US and India is:

In the US, Ankit found:

• $195K median

• Around $120K base if you’re just starting out

• $340K to $350K in senior roles

• Up to $2.5M in total comp for senior roles at the top labs

In India:

• ₹12 to ₹16 LPA for a fresher with solid AI projects

• ₹22 LPA at 2 YoE

For context, our survey of 1,019 Indian PMs put the average APM at ₹17 LPA.

Who’s Hiring

These days PMs as product builders is the hottest topic. Here are just three product leaders I recently chatted with who are hiring this archetype:

• Vishnu Gopalan, VP of Product at Series B Opsera

• Jiaona Zhang, CPO at Series C Laurel

• Srini Raghavan, CPO at Freshworks (public)

Many others I talk to have emphasized the same. I’d say this is the biggest trend in PM right now. So when in doubt, assume the product builder skillset is what any AI-forward company you interview with is looking for.

2. How to find AI problems that are actually worth solving

The most important skill every product builder needs to have is great AI product sense. They need to be able to find AI problems that are actually worth solving.

Here’s the framework Ankit drew out for us, POWER:

Let’s go deep on each letter.

P - Possibilities

Start from what AI can do. Ankit’s shorthand is UTG:

• Understand: read a whole folder of meeting transcripts

• Transform: turn those transcripts into one summary

• Generate: write the content or the code

Then go see what other people did with it. Your competitors. The industry next door. And his favorite shortcut, the customer stories pages at Anthropic, OpenAI, DeepSeek and Moonshot. Each one is a list of things a real company already made AI do.

Write all of it down. Ankit calls it a possibilities database, and says every company that’s serious about AI should keep one.

O - Opportunities

In possibilities, you looked at the world. In opportunities, you look at your company.

Two rules here:

1. Never start with one use case. You’ll pick before you’ve seen the whole map, and you’ll usually pick wrong.

2. Pick by frequency. Build for a rare problem and it never pays back the time it took.

This is the judgment Ankit kept coming back to all episode:

“For you as a product manager, one skill that is very important is about judgment of what problem to pick in order to solve with AI.”

W - Workflows

Ankit says this is the step where PMs need to put the most attention.

“Improve discovery with AI” sounds like a plan. It’s too high-level to build anything from.

You have to go down to the actual steps. Take a roadmap decision. The inputs are:

• Support tickets

• Reviews on Google Play or G2

• Sprint reviews

• Leadership meetings about the product

Where does each one go today? Who reads it? What gets dropped? You only find out one way: interview the people who do the work.

Then sort what you find into two buckets:

• Optimization: the three-hour weekly task now takes one

• Innovation: a process that couldn’t exist before, so nobody ran it

Innovation is where it gets fun.

E - Engineering

Notice something? Three letters in, and not one tool named.

That’s the point.

Only now do you pick: n8n, Make, Zapier, a Gem, a custom GPT, Claude.

Start from a real use case and you get ROI. Start from a tool and you spend your time retrofitting a problem to fit it. Ankit says that’s how the AI slop gets everywhere.

And here’s the stat I keep thinking about. Across those 12,500 job descriptions, the number one skill wasn’t RAG. It wasn’t agents or prompt engineering either.

It was identifying the right use case for AI.

R - Reflection

Ankit wrote the R on the whiteboard but didn’t get to it on the show. HelloPM’s own notes on POWER fill it in: R is for Reflection.

“After the AI does its work, you have to look at what came back, find the gaps, and improve your approach for next time.”

That’s what makes AI work compound instead of starting from zero every time. It’s also where evals come in. My guide to your first eval is the place to start.

3. How to ship a production app with Claude Code

Say POWER worked. You found a problem worth solving. Now you’re at E.

And the internet has a dozen opinions. One thread says Claude Code. Another swears by Codex. Your engineering lead wants n8n. A creator you follow built the whole thing with a Gem.

So most PMs either overbuild or underbuild.

Ankit’s answer is a ladder. Six levels, from a plain prompt at L0 to a production app at L5. Each level fixes a pain in the one below it, and each one costs more to build.

Claude Code or Codex?

Everyone asks Ankit this. So his team tested it.

They built the same four sites (an ecommerce store, a social commerce site, a social network and a gaming platform) with Claude Code, Codex, Cursor and a DeepSeek setup.

Every one of them shipped a working site.

“For 90% of the use cases, it does not matter.”

Stop debating the model. Start building.

4. How to land a product builder role

So where do you start? With the same roadmap one of HelloPM’s students just used to land a senior PM role at Sarvam AI (India’s latest unicorn):

Step 0 - Align

You can’t land a job you don’t understand.

So pull 25 to 30 job listings. AI PM roles, or PM roles at AI-native companies. Banks, fintech and health tech count too. They’re all asking for AI now.

Read the qualification lines, not the titles. When I read 113 AI PM listings by hand in August:

• 7 in 10 wanted prior AI experience

• Only 1 in 8 wanted it in production

• Plain “Product Manager” listings asked for AI almost as often as “AI PM” ones

Then build your skills map. By hand. Ankit was firm on this one. Reading job descriptions closely is where humans still have the edge.

Last, name your edge. Ankit says nobody with two years of experience has zero:

• BA or product owner: stakeholders and the SDLC

• Marketing or sales: customers, distribution and usually a domain

• Engineer: solutions and technology

Your background is already a head start. Write it down.

Step 1 - Acquire

Take each skill on your map to ChatGPT or Claude for a brief. Watch the videos. Now you have information.

“Information actually gives you a false sense of confidence that is shattered the moment that you enter the interview.”

You need knowledge. Ankit says it comes from two places.

Build something. Run POWER on a real problem and climb the ladder. A live build you can defend (the model choice, the evals) clears most of the market. A build with real users beats it.

Talk to people in the role. Search LinkedIn for “AI product manager,” filter to People, and send:

Hi, this is who I am. I’m learning AI product management and I find your profile inspiring. Could I take 10 minutes of your time? I’m happy to compensate you for it.

Most will help. Most will turn the money down.

Ankit’s estimate for this step: one to four months.

Step 2 - Reach out

Five job boards, not one. In India, that’s Instahyre, Naukri, Indeed, Wellfound and LinkedIn.

And attach your portfolio to every application.

“People don’t owe you trust. You have to make them trust you.”

Two things I’d add:

1. Mirror the listing’s words. The ATS, LinkedIn’s Job Match and a ten-second recruiter skim all match on the literal string.

2. Put the build everywhere. Headline, About, Featured. Of 20 profiles I reviewed for my portfolio guide, 15 wasted Featured.

Step 3 - Build for them first

This is the step that actually gets replies.

1. Pick 25 to 30 midsize companies you admire

2. For each one, ask: if I were the PM here, what would I do in my first six months?

3. Build a deck or a prototype that answers it

4. Send it to at least two decision makers. Apollo and RocketReach both have free credits

5. Follow up three times in a week before you call it

Expect silence from the first four or five. Ankit’s blunt about why: your work isn’t good enough yet.

By the fifth, sixth, seventh, it is. That’s when replies start. And when you walk into the interview, you end up talking about your projects instead of hypotheticals.

Then Ace the Interview

There are at least 3 interview rounds you must be ready to master. Let’s decode each:

1. Fit tests risk

Can they trust you in their domain? Switching industries no longer takes years. A few months of projects in the new domain get you most of the way to credible.

2. Product sense tests judgment

The same judgment we talked about in section 1. Builder skills get you in the room. This round decides if you stay.

3. AI depth tests whether you built your projects

Nobody asks about the best-case scenario. They ask what can go off track, and only the person who built it knows. Built a RAG system? Ankit says expect: where does it break, what happens at 100 million documents, what if you swap the model, how did you evaluate it?

For the AI depth round, my evals episode with Aparna Dhinakaran is the best prep I know. My AI PM interview guide covers the rest.

When you rehearse, the best tool is my Job Search OS.

Just remember to practice out loud. Ankit was specific on that.

And that’s all for today. See you in the next episode :)

Get Transcript

Where to find Ankit Shukla

• LinkedIn

• X

• HelloPM

• HelloPM on YouTube

• HelloPM on Substack

PS. Please subscribe on YouTube and follow on Apple and Spotify. It helps!

PS2. I used AI’s assistance to write today’s piece. But I stand by every word.

Check out the conversation on Apple, Spotify, and YouTube.

Brought to you by:

• Customer.io - Send smarter messages using your product data

• Kameleoon - Leading AI experimentation platform

• Bolt - Ship AI-powered products 10x faster

• Jira Product Discovery - Plan with purpose, ship with confidence

• Product Faculty - Get $150 off their #1 AI PM Certification via code AAKASH150

Today’s Episode

On October 6, 2025, OpenAI launched its Agent Builder. By the next day, the posts were out: ChatGPT had killed n8n and Zapier.

It turned out to be one of n8n’s best weeks ever.

Now it’s Claude Code’s turn. Last month, this went viral:

So do you still need n8n?

That’s today’s episode with Jan Oberhauser, the founder and CEO of n8n. We talked about where n8n makes sense, where his own team uses Claude Code, and how to become an AI PM at n8n:

I’m giving a free webinar on the fundamentals of AI that anyone getting into PM must know. Tokenization, how LLM models work, all of it. Join me.

Newsletter Version

Here’s the newsletter version of our conversation:

1. What n8n does that Claude Code can’t

2. How to use n8n and Claude Code in tandem

3. Roadmap to becoming the AI PM n8n would hire

Subscribe now

1. What n8n does that Claude Code can’t

We have been called dead probably a thousand times. I don’t remember half of the things that killed us over the years. We’re still here.

—Jan

Over the course of 2025, Claude Code exploded. By the holidays, it surpassed n8n in search interest and never looked back:

This April, Anthropic also shipped Routines, which run Claude Code on a schedule in the cloud.

Is there really any space left for n8n?

I posed this question to Jan. From his point of view, things have never better.

In May, SAP invested at a $5.2B valuation, more than double the valuation n8n held the prior October. It also embedded n8n into Joule Studio, its agent builder. So every SAP customer (it has millions) gets n8n in the product with no setup required.

And today n8n has:

• 200K+ GitHub stars

• 1.5M active users

• 1,200 customers on the enterprise product

So how is n8n succeeding despite Claude Code? n8n evolved its use cases.

A year ago, people came to n8n for personal stuff, like an email summary. Now the growth is in business-critical workflows.

Claude Code is an agentic tool that runs Anthropic’s models in your terminal. n8n is an orchestration layer that connects your tools, models, and data on a visual canvas.

If it’s for when you cannot rely on something working 95% of the time. You want to be 100% sure that it really works. So, you choose n8n.

Jan gave us a course on how to build in it:

Step 1 - Describe it

Back in 2025, the viral n8n posts were all workflows. You don’t start on the canvas anymore.

Jan prompted his n8n AI Assistant to create an agent for a personal project. Like a typical Claude Code experience, it asked clarifying questions and came back with the full workflow.

Step 2 - Gate the risky tools

Your editing really starts on the canvas now.

The main thing you want to do is figure out your where to do approval.

For instance, Jan asked the agent to book a meeting with his VP of Sales for 3pm the next day. He’s added human approval for meetings.

So instead of book on its own, it showed him the event it was about to create and asked if it was allowed to. He clicked Add event. Then it booked.

Step 3 - Audit the run

Then it’s time to run and audit.

The delightful thing about n8n is every execution is logged node by node, with the exact input and output at each step.

So let’s say you add a deterministic node between the agent and the final output, the log will show which path it took.

And you can rerun the rest of the workflow from that exact point with the same data.

Step 4 - Hand-off

Then this is the special part of n8n: hand-offs.

How do you give something in Claude Code to someone on your team? You have to use Github, and it’s messy.

In n8n, you just invite them. They see version history and who changed what. Then they can publish versions with notes. You can also export a workflow and just email it.

So now you should understand the difference between Claude Code and n8n. Claude Code stops at step 1. n8n is the infrastructure to do steps 2-4.

2. How to Use Claude Code and n8n in tandem

One of the. most fascinating insights from Jan was that n8n are heavy users of Claude Code themselves!

So it’s worth figuring out where they use Claude Code and where they use n8n.

The rule of thumb is:

So, to know which workflows to move, I put together this questionnaire. If you get 2 or more yeses, move it to n8n.

n8n and Claude Code are very different products, and in the end you need both.

Claude Routines are built for one person running tasks against their own repos. Meanwhile, n8n is built for processes a whole team shares and signs off on.

You also don’t have to rebuild anything by hand when you move a workflow over!

Use this prompt:

3. Roadmap to becoming the AI PM n8n would hire

Having Jan on the podcast, I had to ask him about the AI PM job search.

Here’s what I learned about how they run product and what they hire for.

n8n’s product team runs in squads consisting of 3-5 engineers, 1-2 PMs, and a designer. They hire people for:

• Technical depth

  The more technical, the better, for every role. Even n8n’s designers are expected to use Claude Code.

• Builders

  Tinkerers who run home automation for fun, because they’re close to the users.

• Depth past the demo

  How an agent scales, how you evaluate it, and how you keep it reliable.

This gives you a really good path. Study technical interviews, build synthetic experience, and learn AI tools.

Here’s how I’d build a portfolio that passes that test:

1. Build your first n8n workflow with Pawel Huryn’s guide.

2. Then follow Mahesh Yadav’s learning path in How to Become a Builder PM. Prototype your next agent there, and promote it into n8n through the MCP server.

3. Write 20 test cases and run them as an eval.

As for the interview, n8n runs live sessions where you work through a problem together. Here’s my guide.

And that’s it for today!

Get Transcript

Where to find Jan Oberhauser

• LinkedIn

• X

• n8n

PS. Please subscribe on YouTube and follow on Apple and Spotify. It helps!

I’ve been waiting for a great guide to reveal itself, but I haven’t seen one. The best stuff out there is either too high-level or too engineering-level. So I’ve spent the past couple months learning everything about harness engineering.

After interviewing experts like Ryan Lopopolo , iterating on my own harnesses, and learning from PMs who have built great AI products, I’m ready to share the results.

Today’s Post

Here’s your complete guide to Harness Engineering, at the right level for PMs:

1. What the heck is a harness

2. The 4 types of harnesses PMs should care about

3. Learning harness engineering from a Personal OS

4. Case studies of harness engineering for product building

5. Who owns what, the Harness Spec, and an AI assistant to help

6. Why harness engineering has become more important over time

Subscribe now

Why This Matters

Before we get there… one quick thing.

Addy Osmani said it well, in a thread 857K people saw:

“A decent model with a great harness consistently beats a great model with a bad harness.”

A bad harness can have horrific consequences.

For instance, on August 26, this dev watched Fable nuke his computer:

Was it Fable that was at fault here? Partially.

But whatever the model did, the harness is what should have stopped it.

So we’ll learn how to build a good one today.

1. What the heck is a harness

Everyone has been hyping up agents since 2024. So you get it: agents matter.

Now here’s the thing to grok: why agents matter is also why harnesses matter.

It comes definitionally:

Agent = Model + Harness.

The harness is everything that isn’t the model. On its own, a model turns text into text. The harness is what lets it act, remember, and stop.

The Anatomy of a Harness

I’ve been studying all the best harnesses out there, and I’ve realized that they all boil down to 8 key components:

Let me walk you through this diagram quickly:

1. Instructions are what the agent reads every single time. In Claude Code that’s your CLAUDE.md. In Codex it’s AGENTS.md.

2. Context is what it knows about you, your product and your users.

3. Skills are step-by-step playbooks it only loads when a task needs them.

4. Memory is what it still knows tomorrow.

5. Permissions decide what it can do without asking you, and what it can never do.

6. Tools are what it can reach: your files, your terminal, and MCP connections.

7. Checks, including evals, catch its mistakes before you ever see them.

8. The loop is how it plans, acts, looks at the result, and decides it’s done. Its effort level sets how hard it thinks first.

Claude Code, Codex and Cowork are all harnesses. But most people build harnesses on top of them as well - often dubbed “operating systems.”

We’ll talk more about that in section 2.

2. The 4 Types of Harnesses PMs Should Care About

Once you know what a harness is, you start to see them everywhere.

As a PM, you’ll run into 4 types:

Your Personal OS is the harness you build for yourself. My PM OS is one of these, and 1000+ PMs have picked up the kit.

Then comes your Team OS. This is the harness your entire product team shares. I built you one.

After that comes your Company OS. Jiaona Zhang built one on Claude Code and Slack. Mikhail Shcheglov added Hermes and OpenClaw.

The final important category is your Feature Harness, for every AI feature you ship in your product.

As PMs, we should have a point of view on how all 4 are built.

Ashby’s Law

There’s an old law from cybernetics:

What this basically means is that your harness has to handle as many situations as your agent can get into.

Your Personal OS serves one user: you. So it’s the easiest, and it’s where we’ll start.

A team OS serves your team, so it gets more complex.

And a company OS serves your company, so that’s even more complex.

Finally, a feature harness serves every one of your customers. That’s the final stage of complexity, where you need to handle every strange request they’ll ever type.

Let’s work our way up to it.

3. Learning Harness Engineering Through Your Personal OS

Since your personal harness is the easiest one to start with, let’s learn the basics of harness engineering with it.

Just Chat

Where do we all start with AI? Just the model of course. Let’s give Claude a simple prompt of a feature results writeup for our fictional company Tandem:

It calls it a “solid launch.” That’s actually wrong, but it can’t know that, because it only knows what you typed.

Connect Product Analytics

The next step in our harness building journey is to connect our data. Let’s use MCP to connect to our product analytics tool and prompt simply:

Now we’re cooking. Segment by segment, it finds the real story on its own. We learn that there is critical nuance beyond the headline:

• SMB is working.

• But enterprise opt-outs are at 6.8%! That’s a bad sign, probably a reason to pause the enterprise expansion. But the simple chat missed it.

That’s why it’s so important to add tools to your harness.

But there are still more problems! The harness right now still doesn’t know your strategy, your customers, or what it’s allowed to do on its own.

That’s what the rest of the ladder adds.

The rest of this article is for paid subscribers only. They get access to:

• The Harness Spec 1-pager

• The full toolkit in Notion

• The rest of the ladder, from context to loops

• 5 case studies, OpenAI to Ramp

• The /harness-assistant skill

Read more

Check out the conversation on Apple, Spotify, and YouTube.

Brought to you by

Arize - Ship AI agents and features faster with fewer regressions.

Get a full year of Arize’s paid plan, along with 8 other AI tools, at bundle.aakashg.com.

Today’s episode

When Zapier published its AI fluency rubric, it went massively viral:

Tons of companies are following in their footsteps, explicitly adding AI fluency as one of the areas they evaluate their employees (and PMs) on.

So how do you get the transformative rating?

That’s today’s episode with Wade himself, the CEO and founder of Zapier. I present him 3 PM work products, and he grades them live:

Newsletter Version

Thank you for having me in your inbox! I also put together this newsletter guide to AI fluency for PMs:

1. The history of AI fluency

2. What capable, adoptive, and transformative look like

3. Why judgment still beats AI usage in the end

4. How to build your personal agent stack

5. Roadmap to a Transformative rating

Subscribe now

1. The history of AI fluency

AI fluency is only about three years old. Looking back at the history is helpful.

March 2023

On March 14, 2023 GPT-4 launched and the world changed. Remember, GPT-3 really first came out in May 2020. GPT-4 was a nearly 3-year wait. And it proved to be worth it.

GPT-4 was a huge leap that really proved that AI agents could be useful at work. Zapier themselves declared a company-wide code red and ran a week-long hackathon.

Weekly AI use went from about 10% of employees to about 50% in one week. By March 2026, Zapier put adoption at 100%.

April 2025

On April 7, 2025, Tobi Lütke posted Shopify's internal AI memo on X himself.

It had three main points:

1. The expectation is you reflexively reach for AI.

2. You have to show why AI couldn’t do the job before asking for headcount.

3. AI use will explicitly go into your performance reviews.

The memo became a genre within months.

At Microsoft, a senior engineering leader told her org that using AI is no longer optional, and that managers should factor it into evaluations.

Meta made AI-driven impact a core expectation in performance reviews starting in 2026.

Duolingo published a version of the same thing.

May 2025

Zapier had a different take. Instead of a memo, they released v1 of their AI Fluency rubric.

It went massively viral.

March 2026

But V1 had a problem, in Wade’s words:

“V1 only screened for usage. The floor is constantly moving with AI.”

So on March 31, 2026, Zapier published v2, and that’s what we’ll focus on:

V2 grades people on mindset, strategy, and building. It also added a new dimension: accountability. The more capable AI gets, the more damage low-accountability use can do.

As Wade said:

With AI, you can delegate the work, but not the accountability.

April 2026

Then, the memos began to crack.

Duolingo dropped AI usage as a formal review metric. Their CEO’s explanation:

I’m not going to force you to do that.

So by this summer, companies had tried two approaches. Mandate usage, or define what good looks like.

Zapier’s version is the one that seems to be lasting.

2. What capable, adoptive, and transformative look like

I did the work for this episode. I built 3 sets of artifacts and had Wade grade them live (no collusion).

They mapped to the three rating levels.

What Capable Looks Like

Capable for a PM is a ChatGPT assisted PRD in a pretty good format:

He pointed out it missed two things:

1. A prototype: Now that it’s so easy with AI, he would expect one.

2. Clear evidence: Where did customers ask for this? Gong calls, Zendesk tickets, Reddit threads, posts on X. AI makes sifting through that mountain of data fast, so it should be included.

Won’t you look at that! A CEO bringing up two of the key skills I’ve been banging the drum about by himself. The old PRD is dead.

Finally, as he said:

If there are things I know that you don’t know, that’s probably not a great signal.

So that’s a ‘Capable’ Rating. Now let’s look at ‘Adoptive.’

What Adoptive Looks Like

Here, I presented him a working prototype alongside a PRD that cited evidence. It was generated with my PM Operating System:

I managed to predict both his notes before the podcast!

What Transformative Looks Like

For the final rating, I showed Wade this:

It’s a full operating model for a product team where:

• Skills hold the team’s recurring work.

• Subagents review in parallel, including a skeptic that argues nothing should ship.

• The agent reads Zendesk, Gong, Amplitude and Linear directly.

• Everything the team learns lands in a git-versioned memory.

And on top of all that is the auto-build cycle:

It clusters raw customer signals, tags each as an observation, interpretation, or hypothesis, and flags anything contradicting a past bet. The PM decides what to promote. Then an auto-build chain produces a PRD, a review panel, a prototype, evals, and draft code, with a second human gate before merge.

Wade called it transformative:

This is not how product and software was built three years ago. Not even close.

3. Why judgment still beats AI usage

Dan Hockenmaier has this wonderful graphic, and we all should remember it:

We want to use AI with good judgment, so we seem like a Turbo Brain.

You don’t want to be labelled a Slop Cannon. That’s a good route to diminish your reputation with stakeholders and lose influence over time.

Say how much effort went in

The worst version is passing slop off as finished work. As Wade said:

“Now you’re making me do the task.”

I see this on my own team. Someone sends me a doc they didn’t filter, and what they’re really asking is “is this good?” That’s handing the judgment job up to me.

At Zapier, people say upfront how much effort went in. Wade will write something like “I’ve used AI to draft this and done a quick skim,” or the opposite, “I stand by every statement.”

Speed is fine. Just label it.

4. How to build your personal agent stack

Wade had a massively viral tweet that Zapier now has “more AI agents than employees.”

So I had to ask him about it. He showed off his “robot staff.” You can copy it. It’s the basic scaffolding you need to get capable or adoptive:

Agent 1 - The morning brief

At 6am, an automation pulls his calendar and to-do list and posts a brief to Slack. It’s the agent he tells everyone to build first, because connecting tools for the first time is easy here.

Agent 2 - The scribe

This is his favorite. At 5pm it loops over unanswered email, messages saved for later, and every meeting transcript from the day. It surfaces what he owes people and drafts the follow-ups.

Here’s a starter prompt for your own version.

Agent 3 - The agenda setter

Wade’s exec and board agendas are now mostly generated by an agent that reads his week of meetings, Slack, email, AI chat sessions and metrics. He approves or rejects each item. If a conversion rate dropped, the agent puts it on the agenda whether or not anyone wanted to raise it.

Agent 4 - The CEO CRM

Every Saturday night, an agent scans Zapier’s enterprise accounts for upcoming renewals and usage swings. By Sunday morning, 10 to 20 draft emails are sitting in his inbox, each with a reason to reach out this week.

How do you build these?

You can just point Cursor at this article with the Zapier MCP.

Wade doesn’t write the code himself. He describes the automations to Cursor and deploys the result to Zapier.

5. Roadmap to a Transformative rating

First, ask, do I really want a transformative rating?

You see it’s not that 20-30% of the team should have a transformative rating. Wade says just like 1 PM on a team of 40 PMs should get it:

That’s because the floor moved. What was transformative in 2024 is just adoptive in 2026.

On top of that, he said being Transformative all the time is a warning sign. It usually means you’re tweaking your systems instead of shipping. As Wade put it,

“If you are, you’re not actually getting work done.”

I asked Wade for a real example of a PM who got one.

Here’s what he shared:

Late last year, when Opus-4.6 landed, people at Zapier were building second brains: markdown files with their goals and projects, with a coding agent pointed at them.

One product leader decided a personal brain was too small, and built V1 of a company brain that every product person could use. In Wade’s words:

“Every product person at Zapier kind of got a jetpack put on their back.”

And no surprise, it’s something I’ve covered in the podcast :)

• Claude Code

• Hermes + OpenClaw

So really, being Transformative in many ways means implementing what you read here. And then making sure it means other people at your company work differently because of what you built:

1. Start by shipping your first pull request.

2. Then learn loops to automate your repetitive tasks.

3. Give your system a memory on Claude Code or Hermes or OpenClaw.

4. Then version everything you build for PMs on GitHub.

5. You can also bring the whole org on the same page with an all-in-one team OS.

6. Once the system is running, build a company OS.

Get Trasncript

Where to find Wade Online

• LinkedIn

• X

• Zapier

PS. Please subscribe on YouTube and follow on Apple and Spotify. It helps!

The company replied within the hour. “As you consider your options, we are also considering our other options. Please do not assume you still have an offer from us.”

The candidate asked for time - not money. And then 5.1M people saw them get their leash pulled. That’s why many people just decide:

I’m not going to ask.

That’s a huge mistake. Me and the teachers at the Land PM Job cohort have helped many PMs with negotiations this year. In today’s issue, I profile 5 who all saw an increase. And not a single offer was pulled. Two were even currently unemployed, laid off from their last jobs.

You’ll get the real numbers, real script, everything.

Why I Needed to Write This

Every guide on the internet I found assumes you have a competing offer.

• Haseeb Qureshi’s 10 rules for negotiation says it outright. “The strongest determinant of your final offer is the number and strength of the offers you receive.”

• Levels.fyi’s own guide recommends “I’m waiting to hear back from a few other companies,” then warns two paragraphs later that companies ask to see the letter.

The PM negotiating in 2026 usually has no alternative at all. This isn’t 2021 when having multiple offers was common. Usually, you have 1.

There’s no guide built for if you have no competing offer, and if you are unemployed right now due to a layoff or firing. That’s today’s article.

The other content also skips the situations my cohort actually landed in. I looked at the top 9 articles and 0 covered PE backed equity, contract roles, or the term and notice on a contract.

In today’s guide, I’m going to cover all these scenarios.

Today’s Post

Here’s the PM’s complete guide to negotiation in 2026:

1. What is actually negotiable in this job climate

2. How to interpret the components of an offer

3. Five negotiation case studies from real PMs

4. Your exact actions + scripts by stage

5. Negotiation psychology

6. The 10 Laws

7. Your AI coach

Subscribe now

1. What Is Actually Negotiable in this Job Climate

Let’s start here: what can you actually get?

Well, that depends on your level. Here’s real negotiation results I’ve helped people with over the last 12 months:

Both on a percentage and dollar basis, we saw larger gains the more senior you are. That’s because your leverage increases. Finding another ‘you’ is much harder for the company, and that translates into how much of a jump you can get.

I experienced something similar in my own journey of negotiation:

By the time I was interviewing at Apollo, I was one of the few PLG experts they could tap into. So I had more negotiation leverage. I was able to get $350K/year more in compensation (almost all equity).

In today’s post, we’ll help you achieve a similar result. And it starts with reading the offer right.

2. How to Interpret the Components of an Offer

When you get that e-mail:

Hey! We’re attaching the offer.

Your heart races. This is the moment. You’re here! Then all the self-doubt creeps in, “I don’t have other offers.” “What if they pull it?”

It’s normal. You have to accept it. But then, you need to get really smart on the terms of a standard PM offer.

There are 4 key money components you need to understand:

1. Base: Three quarters of Total Compensation (TC) as a PM, only 1/3rd director+

2. Stock per Year: The trickiest part to calculate, which becomes more and more of your comp as you rise

3. Bonus: Cash bonus that exists but isn’t usually huge for PM, unlike sales

4. Sign-On Bonus: The component most guides miss, but is critical and amongst the easiest to negotiate

Deep Dive Into Equity

Most of the 4 components are straightforward, with the exception of equity. Equity is a whole mess. Let me simplify it for you.

4 Types of Equity

There are 4 different variants you need to understand:

1. RSUs: Restricted Stock Units

   • Public company RSUs are shares on a given day’s price. You can sell it the day it vests if you like. They are taxed like income.

   • Private company RSUs are shares priced from a recent valuation. Most companies issue double-trigger RSUs which don’t actually settle, and get taxed, until a tender offer, IPO, or acquisition.

2. ISOs: Incentive Stock Options are the right to buy shares at a set strike price. You pay to exercise, which can trigger taxes then even before selling.

3. PPUs: Profit Participating Units are the right to a share of future profits rather than actual equity, priced off the last funding round.

4. PIs: Profits Interests are what many PE-owned companies issue. They’re a share of the company’s growth above its value on the day you’re granted them.

Now the question is how to value them. Let’s walk through each.

Valuing Public Company RSUs

This one is easy. Face value.

A $400K grant from Google is worth $400K. You can sell the shares the day they vest. That’s the gold standard everything else gets measured against.

Valuing Private Company RSUs and PPUs

Private company RSUs require some math. I advise you use this formula:

• r is your discount for time and illiquidity only. Use 15%. That’s the premium over public stock (~10%) for money you can’t touch. Don’t inflate r for company risk, that’s the next variable’s job.

• P(payout) is the probability the company ever delivers a payday at or above the quoted valuation. Late-stage with real revenue and a tender history, 70-90%. Growth stage, 40-60%. Series B or earlier, 20-40%.

For example, you get a $400K grant at a Series C, where you assess an IPO to be 5 years out at 40% odds. Then it’s worth $400K × 0.4 ÷ (1.15)^5 = ~$80K.

Now some late stage companies have tender offers to also factor in. Say it’s a late-stage company that runs a tender every year, capped at 15% of your vested stake. Split the grant in two. The sellable slice, 15% × $400K = $60K, valued near face because you can turn it into cash within a year. The locked remainder, $340K, runs through the formula with better inputs since this is a proven late-stage name. 80% odds, 4 years to full liquidity. $340K × 0.8 ÷ (1.15)^4 = ~$156K. The Total is then $60K + $156K = ~$216K.

The bottom-line:

• Even at the best late-stage company, that $400K grant is worth roughly half a $400K grant from Google.

• At a Series C with 5 year 40% odds, it’s worth one fifth.

One special case before we move on: OpenAI issues PPUs. These are a contractual claim on future profits rather than shares. Value them like RSUs with a haircut of ~20% for the terms. So Value = Quoted Equity ($1M) × Terms Discount (80%) × Payout Probability (90%) ÷ (1 + r (15%))^years to liquidity (4) = ~$412K.

Valuing ISOs

ISOs need their own formula.

Exit price is the terminal value per share × (0.85)^rounds until exit, because each round dilutes you about 15%. My default terminal value is 3x the last round with 2 rounds to go, so a $10 last round gives $10 × 3 × (0.85)² = ~$21.70.

P(payout) is 20-40% for the Series A-C startups that issue these, and years is usually 5-7. The 12% is roughly the S&P 500’s long-run return, so whatever comes out is what the grant is worth beyond just buying the index.

Say you get 50,000 options at a $7 strike. ($21.70 − $7) × 50,000 × 0.3 ÷ (1.12)^6 = ~$112K. That’s the number you negotiate on. The recruiter implied $500K.

Now the trap. When you leave, you usually get 90 days to exercise, which here means wiring $350K. Run the formula on the shares that cash buys, 4 years out, and you get ~$207K of value for $350K paid. That’s −$143K.

The option is worth $112K for as long as you can wait. The deadline forces you to bet before you know the answer. So if you’re leaving and your insider odds aren’t way above the base rate, walk away. And in the negotiation, ask for a longer post-termination exercise window. It costs the company almost nothing.

Sometimes it goes way beyond 3x. My ThredUp exit price per share was ~10x my strike, which is why I stayed in.

Valuing Profits Interests (PIs)

Profits interests use a close cousin of the ISO formula.

The target price is what the PE firm plans to sell each unit for, with no dilution since sponsors fund with debt. Years is what’s left of their hold, usually 4-7 from buy-in. If part of the grant only vests when the sponsor hits a return like 3x, give that part lower odds. (Getting PE options instead? Same formula, with your strike in place of grant-date value.)

Say you get 50,000 units at $10 today, and the sponsor targets $25 in 3 years. At 60% odds on the time-vested half and 30% on the performance half, it’s worth ~$240K.

You can’t see any of these inputs from the outside, so ask for the valuation, shares outstanding, how they got to the current value, 4-year projections, and your leaver terms. No data, weight it near zero. One of my students sent this exact list to his CEO mid-negotiation, and you’ll see in the case studies how that went.

The Timing Variable

On top of RSU vs ISO vs PPU vs PI, there’s the topic of vesting schedule. In the case of Amazon, where a 5/15/40/40 vesting schedule is common, here’s what your yearly comp actually looks like as a Director:

And that’s why “TC” might be the most misused term in tech. When it comes to negotiations, it’s less important to think about TC and more important to think about Effective Compensation (EC).

How to Calculate Effective Compensation

Effective Compensation is where you make a forecast about:

1. How long you will likely stay at the company

2. What the equity is actually worth, using the formula for your equity type above

Then you calculate it on the yearly level like this:

This is the real number you should be calculating. You don’t reveal it to the recruiter. But it’s where you figure out your leverage on what you want to negotiate.

Let’s give an example. You get a Series C offer of $200K base, $40K signing bonus, $30K target bonus, $400K RSU grant. The first thing is to determine how long you’re likely to stay. Let’s say it’s 2 years.

Then, your effective yearly compensation is just $270K/year:

That’s $100K a year less than the $370K the recruiter wants you to think the offer is worth. Their number is year 1 on paper: $200K base, the $40K sign-on, the $30K bonus, and a quarter of the grant at face value.

When you see it this way, you understand what to negotiate.

Your expected tenure tells you which component to negotiate hardest:

• Planning a shorter stay (2-3 years)? Push on sign-on and front-loaded vesting. The one-time money is worth more per year to you, and back-loaded equity you’ll never see is worth nothing.

• Planning a longer stay (4+ years)? Push on base and equity. Base and equity compound through every raise, bonus calculation, and equity refresher for years. Sign-on gets diluted into a rounding error.

Since all of this can be very difficult to calculate yourself, and there’s more nuance than I’ve just covered, I’ve built a skill that covers everything. Point your AI to this file and your offer:

Value Your Offer

Even better if you install it inside a Job Search OS.

The rest of this article is for paid subscribers only. They get access to:

• 5 real complete negotiation case studies from students of mine

• Scripts + the playbook for each and every stage of the negotiation

• How to handle the psychology of the negotiation

• The 10 Immutable Laws of Negotiation

Read more

Check out the conversation on Apple, Spotify, and YouTube.

Brought to you by

Arize - Ship AI agents and features faster with fewer regressions.

Get a full year of Arize’s paid plan, along with 8 other AI tools, at bundle.aakashg.com.

Today’s Episode

Some product teams are AI-pilled. Others take ‘AI-pilled’ to a whole new level.

Together AI is one of those teams. They have to be. They are valued at $8.3B, building a cloud for AI companies. Many of their customers already are agents.

So after experimenting at the absolute frontier of AI, what comes out is a product team stack that is 4 key components:

1. A shared context repo any harness can read (not just Claude Code)

2. Skills for feature research, PRD writing, and prototypes

3. An orchestrator for leaders like their CPO

4. Agent evals

I got their CPO, Charles Zedlewski, two PMs, Necoline Hubner and Pavneet Ahluwalia, as well as their developer experience tool lead, Hassan El Mghari, to break down how everything works and was built.

If you want to see the full stack of an AI native product team, this is for you.

Thanks for having me in your inbox. I also wrote up my key takeaways for you across each of their four tools.

Subscribe now

1. A Shared Context Repo

Charles, their CPO, perfectly captured the problem with what using AI turns into for almost anyone in any team these days:

The new party foul is flooding your coworker’s context window.

So the key is to engineer a set of AI tools that don’t make an individual more productive, they make the collective more productive.

Two Key Components

This all begins with a shared context repo. Necoline, the architect behind theirs, walked us through it:

If you’ve seen the team OS I built, based on the one demoed by Hannah Stulberg, then the shape will be familiar:

• The context library is your home for shared context. Together splits theirs by product area as well as by mission/milestone in their strategy. Every team has access to the same library. So before Necoline pitches anything to a PM in another area, for instance, she reads that PM’s up to date context files and understands exactly what they care about.

• The skills folder holds all the shared workflows. That’s where they keep everything from PRD writer to user research. Necoline’s topline-status skill, for instance, triggers automatically at the end each sprint. It reads Linear, Github, and the strategy to draft a weekly leadership update of what is shipped vs ongoing vs up next. Instead of spending 30 mins chasing engineers, she just edits the AI output.

Four Governing Rules

It’s mostly markdown files, some YAML. Nothing fancy. What makes it work is 4 governing rules:

1. Every skill lives next to the thing it acts on.

A skill that only works against one codebase (deploy steps, API conventions) goes in that engineering repo with the code, so it stays in sync with it.

Skills that aren’t tied to any codebase, like research or PRD writing, go in your personal repo if they’re just yours, or the shared repo if everyone does that work.

2. Skills earn their way to main.

Every skill starts out as a personal skill. Then, if a PM is using it often, they will push it to the shared repo. But if it’s niche, it stays on a branch forever.

They don’t gate what skills exist in the repo by committee. It’s by usage.

3. Ownership follows the area

Each folder in the context folder is owned by the PM who owns the area. So because Necoline is the PM on Together’s Sandbox product, she keeps the Sandboxes folder current.

Then, at the higher layer, strategy files get rewritten at planning sessions or when the CEO sends a direction letter.

Beyond these two mechanisms, if someone pulls context and finds it missing, they update it.

4. Not married to Claude

This is a team that hosts models for a living. So they’re acutely aware of the costs, and their point of view is Anthropic has gotten expensive.

Instead, they prefer picking the model per task, using GLM for coding and prototypes, Kimi for analysis, and even their own models for some tasks. So they’ve built the repo to run on OpenCode, Hermes, any harness.

2. Skills for feature research, PRD writing, and prototyping

Two of the most important skills sitting in that shared team repo are feature research and PRD writing. Together’s PM for Infrastructure, Pavneet, walked me through the process.

It starts with Research

He started with the idea that customers wanted to resize shared storage. Instead of jumping straight into a PRD skill, he first fired off their feature-research skill.

The skill examined their support tickets, engineering tickets, internal docs and came back with a complete report:

Here’s what Pavneet had to say about the quality of this report (someone who could actually judge it):

If I had to do this manually, this would have easily occupied half of my day, if not more. This is actually pretty good.

That’s real time savings at quality. Then he clicked on one insight to get directly into their customer insights tool, where he could see a customer who wanted a self-serve option for more memory.

He built a fairly complete picture of the customer/user insights side, which would have taken him half a day when he was at Amazon, in five minutes.

Then you move into writing

After the research, Pavneet started their prd-writer skill. It read the research, shared context, then interviewed him turn by turn.

They’ve specifically instructed it to challenge the PM: tradeoffs, one way doors, and the like.

This is their solution to launching slop at your teammates. Their skill doesn’t just output. It focuses on getting the right stuff out of you.

Then you build the prototype

The PRD used to be the primary PM artifact. It was an elaborate gating document. Companies used it to make sure all stakeholders were aligned before you build.

When Pavneet worked at Amazon, it was a 20 page document with detailed PRFAQ. The Together team has replaced that with one to two pages plus a prototype.

Those pages cover:

• The customer problem, defined well enough so everyone groks it

• Solution options, not necessarily fully worked out

• A sample user journey for the leading solution

The rest of a PM’s effort goes to using the ux-prototype skill, which creates a detailed prompt for Figma Make.

The prototype is where the real debate amongst stakeholders happens, because now everyone can click through the solution instead of imagining it.

While everything was human in the loop till now, after alignment on the prototype, it’s mostly automated. Build and ship is automated, kicked off with a single /goal that fans out sub agents and comes back with a PR. Then engineers review it.

3. An Orchestrator for Leaders Like Their CPO

PMs like Pavneet and Necoline have different use cases for AI than leaders like their CPO Charles. So he showed me where he spends his time:

What it does is it has every repo for every product in one place. Charles picks a repo, picks a harness (OpenCode, OpenCode Claude, or Cursor), picks a model, and asks a question.

He gave the live example of model shaping, their product for adapting open weight models. He asked which model was most recently enabled for supervised fine tuning. They have 30+ and the list changes every week.

Orchestrator spun up a sandbox, cloned just the slice of the repo it needed, and came back with the answer:

The Orchestrator inherits every skill and MCP server that lives locally in each team repo, server side. So Charles gets the latest tooling for any codebase without installing any of it.

As Charles said:

Most people don’t have a whole lot of motivation to want to understand all the depth and nuance of the context of somebody else’s area. They want to know just the amount they need to get one single question answered.

That’s where the Orchestrator comes in. Hassan, the developer behind it, said it took a few weeks to a month of engineering to build.

4. Agent Evals

Charles told me something that reframed the whole tool for me:

At this point, for a lot of our products, agents are already the majority user.

So when Together ships a feature, the test isn’t a user study. It’s whether Claude Code, handed their docs and an API key, can actually complete the task. Hassan built the tool that runs that test, and showed me:

Hassan sits with each PM and writes tasks that represent real usage. One from fine tuning is to run a job on this dataset, deploy the result as a dedicated endpoint, hit it with inference, evaluate the output, then delete the endpoint. The tool spins up a sandbox, launches Claude Code with that prompt and a key, watches it work, and grades it.

Then it runs the same task three ways. Docs only, with the MCP server, and with skills. So they can see which context layer is actually doing the work. It’s the eval discipline Daniel McKinnon showed me, pointed at your own product instead of a model.

The part that matters is the improvements tab:

On this run the agent couldn’t figure out which models were fine tunable. Hassan opened the transcript. The page existed. It just wasn’t linked from the fine tuning quick start, so the agent never got there. One docs PR fixed it. Dozens of docs fixes have come out of this exact loop.

Compare that to how you used to validate a design. Weeks of user tests, then conflicting signal. Now agents re-verify every few hours that the product is what the docs say it is. Charles calls that the new bar for UX.

If your users are humans on a GUI, you’d swap in a visual reasoning model that clicks through screens. Same architecture, different grader.

Bottomline

Where does the product manager end and the developer begin?

All in all, the essence of each job hasn’t changed. A PM brings unique and well-validated customer problems. An engineer brings designs that most efficiently meet their needs. Same as before AI.

What changed is reach. A PM can now finish small and medium tasks inside engineering. An engineer can open Customer Insights and run a query on what their customers need.

So how will you stand out as the edges are blurring?

I predict that PM roles will become product builder roles in the coming years. Then your differentiating factor will not be an individual AI setup, no matter how suave. Everybody will have one.

Your leverage is giving your whole org a bird’s-eye view. Start small today. Build a personal OS, expand to your team, then your company.

Try ‘em out and let me know how it goes! And I’ll see you in the next one :)

Get Transcript

Where to find the Together AI Team

• Together AI

• Charles Zedlewski

• Necoline Hubner

• Pavneet Singh Ahluwalia

• Hassan El Mghari

PS. Please subscribe on YouTube and follow on Apple and Spotify. It helps!

We’ve now crossed 240K subscribers. And it’s been over a year since I talked about what I’m doing here, or gave you a way to explore what I’ve done recently.

So that’s today’s post.

If you’re new here and mostly want the reading list, skip to section 5. That’s where the four roadmaps live.

Today’s Post

I wanted to give you a peek behind the curtain:

1. What I’ve been up to this year

2. What I plan to cover over the next year

3. Growth lessons in hitting 1M followers

4. How I hit $2M/year in revenue, and where I go from here

5. Four roadmaps to explore my content, with all the links to explore them

Subscribe now

1. What I’ve been up to this year

Part 1 - The Newsletter

People might be surprised by this. But I actually give 75% of my time to the newsletter deep dives. It’s my main focus, and everything else I run exists to feed it.

That focus produced 67 deep dives this year. I know it can be hard to navigate, so here’s a quick way to click through them all:

Explore this Year's Archive

In addition, I launched two new programs for paid subscribers who commit annually:

1. Aakash’s Bundle

2. The Founding Plan with quarterly digital product drops

The goal with both is to create a long-term relationship with you, rather than a subscription you reconsider every month.

I’m super proud of the tools we’ve partnered with to give you 1 year of their paid plans in the bundle. These are tools I personally use and like:

1. Linear for task management

2. Mobbin for real-world design inspiration

3. Dovetail for AI customer intelligence

4. Arize for AI evals and observability

5. Bolt.new for full-stack AI building

6. Descript for AI video editing

7. Magic Patterns for front-end AI prototyping

8. Speechify for dictation at 3x typing speed

I’m also quite happy with how the digital product drops in the founding plan went:

1. PM Prompt Library

2. PM OS

3. Job Search OS

4. Content OS

The PM OS was the runaway favorite, so I’m already cooking its follow-up. The plan is to keep dropping regular bundle partners and products so that this continues to be the most no-brainer yearly subscription in your life.

Part 2 - Everything Else

I also built two new lines to the business:

1. I created the AI by Aakash newsletter to help you stay up to date with AI

2. I launched the Land a PM Job cohort program to give you a higher touch outlet

Both of these launches were designed to make this newsletter better:

1. For AI by Aakash, I used it as an opportunity to move out news content from this newsletter. I found that you guys engaged with that type of stuff least.

2. For the Cohort program, I used it as an opportunity to get closer to how real job search candidates are experiencing the market, so I can turn around and coach you. Now my articles on job search have even more real world grounding.

Across everything new this year, I also hired very specific, very talented people that help me with each business line. There are now 8 of us. So the only time of mine pulled away from the newsletter is time that comes back to it.

2. What I plan to cover over the next year

My plan for the next year is to not add any more business lines, but to grow and optimize what I have.

I also want to slightly shift the aperture of focus. The past year, AI has been all the rage. But I’ve sensed the pendulum swing back, and so I’m going to go back to more classical growth, PM, and career content.

You saw that last week with my piece on Stakeholder Management. Here’s what else is already in motion:

1. The State of the PM Hiring Market

2. PM Salary Benchmarks (fill out my survey!)

3. Guide to Trust & Safety Product Management

4. PM’s Guide to Design Taste

AI won’t disappear. But it also won’t be everything.

The bar I’m holding myself to is simple. If you could get the same take by asking ChatGPT or Claude, I haven’t done my job. So expect more original research, more first-hand guests, and more of my time spent on the writing itself.

3. Growth lessons in hitting 1M followers

Growth across my portfolio has been strong this year:

• LinkedIn: 250K → 321K

• X: 116K → 331K

• YouTube: 17.2K → 45.8K

• Product Growth: 184K → 240K

• AI By Aakash: 0 → 140K

In total, I went from 567K followers to 1.08M followers. Here’s how I did it.

Lesson 1 - Flow with the algorithm

The most important principle for growing my followers has been to treat the follower count like a product metric.

I consistently look back at a dashboard of what’s working. This has been by far the most effective tactic available (‘Meta’ in gamer-speak). Because only your account data shows you the map of what works in your niche, right now.

Paying extra close attention to what topic, post content style, and length is working was how I managed to create this massive, 2,700 like LinkedIn post last week.

And it’s how I tripled my X with timely explainer quote tweets like this yesterday that received 1.3K likes.

Lesson 2 - Bring AI in (the right amount)

I did this all inside my Content Operating System built in Claude Code. It's been a big win for me this year.

Now here’s the caution. The output from the OS is good enough to publish, and that’s exactly the trap, because people don’t like to read “AI slop.”

So I take extra care to consistently:

1. Play a huge role in the original idea for any piece of content, usually via a “dictation ramble”

2. Always go in with my human touch to further edit

On social media, I use the Claude Code outputs a bit more readily. But here, in the newsletter, since this summer, I strive to be all human:

Lesson 3 - Don’t Overstretch

I was, at a time, trying hard to increase my following on Apple Podcasts, and Medium, and Instagram, and TikTok.

I since abandoned those pursuits.

I realized that I probably could grow my following on those platforms, but I’d need to spend a lot of time learning them.

In my experience, learning any particular platform requires a really long dedicated push to learn how to succeed on that platform. Typically a year per platform.

That’s why I have 5 platforms at the top of this section. I could do one a year. But it’s very hard to do more than that.

4. How I hit $2M/year in revenue, and where I go from here

It was a pretty good year. My cost basis nearly doubled moving from Durham to LA, and revenue managed to keep pace. How did I do it?

The Creator Revenue Equation

One thing I’ve found as a creator is that how hard you work + how well you cultivate your brand + how good a team you hire = your revenue.

This is quite different from a normal corporate job where, even if you vastly outperform your OKRs for a quarter, your income will only be increased a portion of that on a lag.

What this has meant is that I’ve been able to, in particular, work hard to increase my revenue to ~$2M, 89% above the $1.05M I was at last year.

What’s Next

My eventual dream goal 5 years from now is to be positively impacting 5x the people I am now.

And I have a specific vision for how it plays out as a lifestyle:

• I want to have an office

• I want it to be filled with smart, fun people

But to have an office itself costs a minimum of $200K in LA. Having a team of American staff costs about $1M. So I actually want my revenue to go up, but margins to go down.

My hope is, with that, the quality of content you get is even better.

5. Four roadmaps to read my content, with all the links to explore them

The most frustrating thing with a newsletter is it’s hard to search the content. So I wanted to end today’s newsletter with four roadmaps to help you get the most out of the content:

Roadmap 1 - The PM Course

Get all the PM Links Here

Roadmap 2 - The AI PM Course

Get all the AI PM Links Here

Roadmap 3 - Two Years of the Podcast

Get all the Podcast Links Here

Roadmap 4 - Five Years of the Newsletter

Get all the Newsletter Links Here

Final Words

Thanks so much for being part of this journey. It means the world that I can share this space in your inbox. Reply with any feedback you have, and tell me what you want covered next year. I read every one.

Talk to you tomorrow 👋

Aakash

Check out the conversation on Apple, Spotify and YouTube.

Brought to you by:

1. Customer.io - Send smarter messages using your product data

2. Ariso - Ship AI agents and features faster with fewer regressions

3. Viktor - AI employee connected to 3k+ tools. Every action approved by your team

4. Bolt - build a complete design system from your code

5. Product Faculty - Get $150 off their #1 AI PM Certification: code AAKASH150

Today’s episode

2023: Prompt Engineering
2024: Context Engineering
2025: Harness Engineering
2026: Loop Engineering

As the founder of OpenClaw said:

Loops are all the hype now. I’ve already written the web’s deepest guide to loops for PMs. Today, I bring you the practical screenshare.

My guest is , Chief AI and Product Officer at JobNimbus, which just raised Utah’s largest Series B ever.

And they are operating in the future. Design is building design loops that meet product loops that PMs build that meet engineering loops that engineers build.

Today, Tyler shows you everything:

Today’s deep dive

As a thank you for having me in your inbox, I wrote up my key takeaways and expanded upon some of Tyler’s key insights. Here’s your guide to loops for PMs:

1. What makes a loop a loop?

2. How to write your first loop in 10 minutes

3. 5 Loops every PM should build

Subscribe now

1. What makes a loop a loop?

The first thing to understand is why do we care about loops? Tyler put it very well:

Where prompting got hard is you always had to keep yourself in the loop, so you didn’t feel the gains from what you get from Agentic.

So that’s why we have loops. What is a loop?

Anyone can easily say ‘build a loop in claude code’ or run an agent forever and call it a loop. But are those legit? Probably not.

Tyler visualizes a legit loop like this:

1. Fetches its own inputs

2. Does the work

3. Has a gate

4. Writes a named artifact

And goes back to fetching its own input again.

That’s basically it. An agent or prompt become a loop when you feed the session back in and let it rewrite itself.

2. How to write your first loop in 10 minutes

Here’s some real alpha for you: write your first loop by hand, not with AI.

The advice genuinely surprised me, but Tyler was firm on it:

I’ve read some papers that suggest that skills authored by humans are often better than the ones authored by AI. You know more and can express more of what you want this thing to do.

And then he gave a second reason, too:

People are like, yeah, I got an ebike, but don’t worry, I still want to get exercise and we’ll pedal. Once you get on the ebike, you’re not pedaling.

Once you start using AI to write loops, it’s like an ebike. You only use the AI for pedaling (writing the loops).

So he opened an empty file and typed a skill by hand live. This is roughly it:

# Decide

Help me make good decisions.

I struggle with decision fatigue. Push me toward a decision once enough
thinking has been done.

Use contrarian thinking. Question me, use the Socratic method, and call
my BS. Bring in the latest research where it's relevant.

Be succinct.

That’s a skill. Then you save it, test it, and improve it. When that’s done, it’s one easy command to make it a loop:

Can you set this up as a loop and ask me what you need including a learning hook before closing out sessions that learns from the log.

It’s as simple as that. You set up a skill. You test it. You improve it. Then, you set it up as a loop.

You can also close the loop by hand, which is worth doing once so you see what it's actually doing:

Based on this conversation, how should we improve our skill to be better and more effective next time? 

It works in regular Claude Code. Tyler uses herdr, a terminal tool for running several Claude agents side by side, which further simplifies the process because it will set them off with the keyword ‘agent’ in your Claude Code prompts:

3. 5 Loops every PM should build

Tyler recommends PMs build at least these 5 loops:

Loop 1 - Customer outreach

This is the one Tyler says he gets the most value out of himself:

It’s a really good way to keep close to the customer without one of the biggest challenges, which was just the time to go find who to reach out to and send them a message.

Loop 2 - Project status

This is the boring one that saves the most hours. Pull the tickets, find what’s stale, find who’s blocked. Nothing clever about it.

Loop 3 - Thinking partner

This is the opposite of what everyone’s building. So I want to highlight it.

Tyler’s team writes skills that push back on the person instead of doing the person’s work:

It’s less about doing and more about pushing the thinking of the people on the team. Did you talk to customers? Did you look at the benchmarks in the industry? Where does the data come from?

The reason is that the treadmill is genuinely addictive:

You get all this stuff back and you’re like, I can’t possibly go and evaluate this, so I might as well just send it up the chain and see what happens. That’s not doing product work. That’s outsourcing your thinking.

That’s career damage for a PM in 2026. A year ago a huge deck made you look like a machine. Now people recognize it on sight and know you didn’t think.

Loop 4 - Divergent prototypes

While we were talking, his product skill was building prototypes in the background off a prompt this thin:

Create a crew schedule prototype for a contractor in a product like JobNimbus.
Build three variants: A minimal, B full-featured, C creative.

And this led to 3 divergent prototypes in just a few minutes. From Tyler’s point of view, you should always do this:

Never skip it, in my opinion. We used to talk about what your test coverage is, and now it should just be 100%. It’s basically free. Why not do it?

And then his product loops actually uses a synthetic customer to review the prototypes before any human sees them.

Loop 5 - Quality Watchdog

This is one to ask engineering for, not build yourself.

Here’s the math Tyler walked me through, and it’s the thing most teams are getting wrong right now:

If you ship twice as fast with AI and your quality rate maintains the same rate, your customer experiences twice as many bugs.

That’s his read on why uptime at big companies has been so bad lately. So build this loop to avoid low uptime!

And with these 5 loops, you’ll be ahead of most PMs out there.

Final words

A large part of the PM job used to be production for the majority of the last decade. That work was slow and deliberate and hence the most valued. It is now close to free.

So where does that leave you? Loops. My prediction is that they will be the fastest way to hand over the production half of your job to something that never gets tired of it.

And with the hours won back you can focus on calibration and discovery. So build the loops and then go spend the time in a room with a customer.

I will see you in the next issue.

Get Transcript

Where to find Tyler

• LinkedIn

• X

• YouTube

• Website

• AI Architect

Go Deeper

If you don’t know where to start on Claude loops, I got you covered with my ultimate guide on Loops for PMs. This includes my library of 12 top PM loops.

Then follow it up with The Complete PM Guide to /goal in Claude Code.

Finally, Tyler mentioned the evolving role of PMs and how it is shifting into a Product Builder role, so you can learn more in my podcast with Mahesh Yadav on How to Become a Builder PM.

Finally, please subscribe on YouTube and follow on Apple and Spotify. I have much more content like this coming!

Share Product Growth

Today we’re talking about one of the most classical skills in PM and product leadership.

I want to show you how to see this classical skill differently.

This is What You Don’t Want

Story Time

When I was VP of Product at Apollo.io (recently valued at $2.5B), one PM on the team was great at moving the metrics.

But there was a problem that started to bubble up through feedback channels about his work:

• A designer questioned whether he was bringing the right ideas. I took it as healthy disagreement.

• Then the CEO made a comment on his product area: “I don’t have visibility into what’s going on there.” It made me do a double take. Did he just make a dig at this PM?

It seemed like the designer and CEO respected this guy’s metrics moving. So I didn’t look too much into it.

But I kept getting small signals. Then I realized:

So, I spent some extra time with him on the area.

If I’m honest, things didn’t really turn around much for 2 quarters. But 3 quarters in, the compounding really started to kick off.

The CEO was mentioning his area’s success at a leadership offsite. The design team was praising his product area.

And that crystallized this learning for me:

So I want to share some thoughts on how to do this better.

The Best Stuff

I’ve spent the past week reading up on this topic in prep for this post, and what I found is that the PM literature isn’t the best place for thoughts here.

There’s some good stuff, surely, but the best insights I got were from 2 timeless books:

1. Never Split the Difference by Chris Voss

2. Influence Without Authority by Allan Cohen and David Bradford

So I’m bringing you some of the best of what those resources, and my own experience, have to offer to this critical PM skill.

Today’s Deep Dive

Here’s what we’ll cover today:

1. Stakeholder mapping

2. Move up the pyramid of trust

3. How the human brain works in relationships

4. How to create buy-in so your good ideas aren’t killed

Subscribe now

As Cohen and Bradford say:

Would anyone argue with these simple ideas? Of course not.

And yet, in the workplace with our colleagues, when it matters most for our professional success, so many of us fail to keep these ideas in mind or to use them as the foundation of our actions.

1. Stakeholder mapping

Remember that PM at Apollo.io? One of the first things we did was to do a stakeholder mapping exercise.

You see, the org chart doesn’t tell you everything. In reality, some random staff engineer and sales SME really mattered:

So, instead of relying on the org chart, what we did is to create a map of the actual situation. And that’s what you should do.

Just the exercise of building a map helps. Choose whatever format you like.

Ours looked something like this:

Starting from the inside:

1. In the innermost circle, we focused on his reporting line: me the VP of PM, plus our CPO.

2. Then we looked at the core product team including his designer and engineering manager.

3. Then we defined his third layer as 2 parts:

   • Leaders - Design Skip, Engineering Skip, CEO

   • Larger Product Team - Analyst, Legal, Compliance, Product Marketing, Sales Ops

4. Finally, we identified the influencers that matter:

   • PM Org - Other Directors of Product, Other PMs

   • Decision-Makers - SME in Sales who knows everything, CRO, CTO, CFO

Choose your own categorization that applies to your role (though it will likely be pretty similar to this).

Once you’ve mapped out everyone, you want to understand the current state of your trust equation. For that, we need to introduce a new framework…

2. The pyramid of trust

Level 1 - Not make mistakes

No matter how senior we get in our career, sometimes the work day can get the best of us. When we…

• Show up to meetings unprepared

• Ship AI slop in our communications

• Or don’t do the work expected of us on time

We start the game of respect and trust behind the starting line.

The first level to building trust back is to do your core job mistake-free. If you want to sway someone with your analytical findings, they should have trust in your track record of shipping that in the past.

In practice, what this often means is getting good at saying no to doing too much, so that what you do, you can do well.

It’s sometimes easy to forget that every meeting you attend, every email you receive, is basically an interview. Did you show up to the interview present? Did you respond to the communication on time?

Simply building out the systems to avoid making simple mistakes on these tasks gets you a long way on the trust pyramid. Many people aren’t hitting this basic bar.

Just the exercise of identifying mistakes together with my Apollo.io PM led to so many wins for us. This level really is the important first step of reflection.

Level 2 - Do good work

The next level up from not making mistakes is doing a good job at what each of those individuals on your stakeholder map considers is a good job for your job.

It’s worth thinking about that. What is the sum total of the exposure this person gets of my work?

• For a director of design in another part of the org, it might just be product reviews and all-hands.

• For your designer, it may be everything including product trio meetings.

There’s this funny lack of overlap between the actual work you do and the exposure people get to it:

Even your designer, who’s in the trenches with you day in and day out probably doesn’t see: your product team meetings, all your customer calls, the late night pings from execs, the extra meetings with product marketing and legal, plus a bunch more of the other things you do.

It’s not that you have to tell them, “oh look at all this work I’ve done.” But it’s worth thinking about: of the exposure they have to my work, have I given them a complete picture of what they want?

When we did this with my PM at Apollo.io, here’s some of what we found:

1. His designer expected him to be really close to the customer, and there wasn’t a place the designer learned about the PM’s customer interactions

2. The CEO mainly heard from PMs via OKR reviews and all-hands meetings, and the person hadn’t presented at all-hands

Then we systematically went about plugging those gaps:

1. We added a slack bot that posted his weekly discovery call takeaways to their trio channel when he submitted them on Dovetail

2. He presented his next big win at the all-hands meeting, going through the formal process with BizOps to get listed as a topic

That’s really the power of this exercise. You previously maybe didn’t have a reason to prioritize a Dovetail Slackbot or speaking at all hands.

But after going through this exercise, you do.

At this point, I should say: Doing level 1 + 2 is really enough for many good PMs. Levels 3 and 4 are not always necessary.

But, they represent the “advanced” levels of stakeholder management.

Level 3 - Treat stakeholders like customers

As PMs, we are trained in discovery.

So it’s worth running that same customer skillset on your stakeholders.

The basis of it all is to ask them really good questions and then listen.

As Chris Voss says:

The centerpiece of this book is called Tactical Empathy. This is listening as a martial art, balancing the subtle behaviors of emotional intelligence and the assertive skills of influence, to gain access to the mind of another person.

So to gain access to the mind of a stakeholder, ask questions like:

• To the Sales SME that holds the keys to the CEO’s heart about your big rock feature for next quarter: do you think this product solves the core problem we need to?

• To your design partner: what did you think of the PM artifacts you saw from me last quarter, and how can I improve them next quarter?

You want to show tactical empathy with the questions you ask, so you can really hear their answers and follow up with tangible work after. Here’s a bank of more:

Juicy Stakeholder Question bank

Each question in the bank is powered by one of two techniques from Voss:

1. The calibrated question. It starts with “how” or “what” and hands the other person the problem.

2. Labeling. You say what they said labelled, then you stop talking. People correct you or confirm you, so you can learn where they actually are.

These are the most powerful questions.

Now, don’t go too far with this. Treat your stakeholders like customers to the extent it makes sense. Understand their needs, take tangible steps to address them. Like when our customers make unreasonable demands, be able to have a backbone with stakeholders as well.

Level 4 - Create the failed simulation effect

Cal Newport has this very powerful framework called “The Failed Simulation Effect,” and your colleagues are running it on you.

Basically, everything they see from you they’re asking:

A failed simulation is when they couldn’t see themselves making something as good as you have. A failed simulation, then, makes you the right person (because you have the most expertise) to be doing that job.

This is especially tricky with PM in the AI era, where anyone can create a long document with lots of charts. The old PM tricks don’t work anymore. And it just has to be that when they see your roadmap, your PRDs, your feature results writeups, they should think:

Wow, this is better than I can do!

When you are able to create that failed simulation effect, then you’ll truly win more trust to do more.

And this is the top of the trust pyramid. You aren’t making mistakes, you’re doing good work, you’re meeting stakeholder needs like customers, and they can’t seem to figure out how you do it all so well.

Then you can really solve hazy stakeholder problems.

3. How the human brain works in relationships

Now that we’ve done the highly tactical stuff around stakeholder mapping, understanding gaps, and plugging them with a pyramid of trust, let’s get psychological.

As Chris Voss said:

In my short stay I realized that without a deep understanding of human psychology, without the acceptance that we are all crazy, irrational, impulsive, emotionally driven animals, all the raw intelligence and mathematical logic in the world is little help in the fraught, shifting interplay of two people negotiating.

There are things about how the human brain works in relationships that are worth advanced study for people hoping to improve their stakeholder management.

Read more

Check out the conversation on Apple, Spotify, and YouTube.

Brought to you by:

1. Bolt.new - Ship AI-powered products 10x faster

2. Product Faculty - Get $150 off their #1 AI PM Certification: code AAKASH150

3. Customer.io - Send smarter messages using your product data

4. Land PM Job - 12-week live course to master the PM job search

5. Viktor - Use $100 in starting credits to get 5x more done with this AI employee

Today’s Episode

I’ve showed you how to build a PM OS, team OS, and company OS in Claude Code. Today I show you how to build a Company OS on Hermes and OpenClaw.

Because Hermes and OpenClaw get you:

• Something that lives on Slack and Telegram

• Something that runs 24/7

• + Self-improvement

These are unique features that really take your Company OS to the next level. And if you’re the PM to lead the project, they give you tremendous impact.

Mikhail Shcheglov is the CPO at OLX Uzbekistan, and he’s had one in production for the last 5 months, so he’s showing you how to build one, mistakes he made along the way, and what it can do for you.

Enjoy:

Today’s Deep Dive

I’ve written up the key takeaways for newsletter subscribers as well:

1. Where to grab the open sourced version

2. How to create your own Company OS with this

3. What the future of product development and management look like in this world

1. Where to grab the open sourced version

Mikhail open-sourced the structural skeleton of the agent that runs his working life at OLX.

Go grab it for yourself:

Saul Goodman

The main caveat is: it’s still a skeleton. You’ll have to supply the vital organs, i.e., context, to watch it come to life.

Let’s show you how.

2. How to create your own Company OS

Now it’s time to really understand the system you’re building better. We’re using a blend of two frameworks:

• OpenClaw is the base runtime because it comes with everything out of the box: Slack and Telegram gateways, scheduler, persistent identity

• Hermes drives automated skill generation and self-improvement. It watches which tasks you request most often and builds them as skills

These come together in 4 system layers, with 3 layers of memory.

As the graphic shows, there are 5 steps to build it. Let’s cover each:

Step 1 - Measure First

Instead of jumping into the scaffolding, in classic PM fashion, let’s start with a metric. It’s called product context coverage, and here’s what it looks like when Mikhail maps it:

Pretty cool, huh? Here’s a prompt you can use to generate it. You can steal it:

The visualization is a separate two-liner afterward. Mikhail one-shotted his in Fable:

Once you have this dashboard, you now have something to goal on.

Mikhail’s screenshotted agent currently reads 54%. At 54%, the Company OS operates like a capable junior-to-mid PM that can make backlog decisions. Once you get to 70-90%, it can actually start doing strategy-level work.

Mikhail keeps that as a personal OKR.

The graph doubles as a management tool. You can drill into any team to see how much context they hold. Silos show up on sight.

For instance, his clusters revealed barely any overlap between the buyer and seller experience product teams:

That means those teams aren’t talking, and a leader can help create that connective tissue.

Step 2 - Scaffolding

The next step is to set up the scaffolding with Claude Code. Once you have forked the repo from Section 1, you’ll see the whole anatomy:

Now we need to fill it in. Hit it with this prompt:

It’ll quiz you as you asked, and then you’ll have everything built out:

Step 3 - Wire Memory

Now we’re ready for memory. Memory has 3 layers. Let’s build them bottom up:

Hit it with a prompt like this:

This will get your memory wired up.

Step 4 - Imperatives

Now let’s talk imperatives. Here’s the underlying problem, in Mikhail’s words:

LLMs are focused on the resemblance of good output rather than the actual results.

The fix is a long, tested list of “imperatives.” You can organize them like this:

So hit it with this prompt:

Step 5 - Put in Repo

Now you’ve done all the setup work, and it’s time to publish. Read my guide to GitHub if this is unfamiliar to you.

Otherwise, hit it with the prompt:

Since every change now lives in GitHub, you can tweak the agent from the Claude app on your phone, the way Mikhail runs his.

Then begin testing with your team!

3. What it means for your job

Here are some use cases you can test this on:

1. Stakeholder gatekeeper - ask stakeholders to ping Saul before a PM

2. Board skill - ask Hermes to abstract each board member’s mental model and give feedback on your next strategy

3. Chief of staff - ask it to give you a morning digest of your email and calendar

4. Recruiting - ask it to build a pipeline for outreach

What you’ll see once this is up and running is…

We’re slowly realizing with AI that the job of product manager is more important than ever.

You can build infinite tools like this, and they’re even more effective on the coding side, but you can’t replace true discovery, coordinating with stakeholders, true strategy, and the accountability to make sure things work as they were planned.

That’s where a PM lives, and that’s your wedge.

Use this to automate all the other work.

Get the Transcript

Get More of Mikhail

• X

• LinkedIn

• Corporate Waters

• Saul the AI Chief of Staff

Go deeper

Start with my guides to Hermes and OpenClaw. Then learn how to build Memory and a Company OS.

You might also enjoy my collaboration last year with Mikhail on What PM Interviewers Look For.

PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!

Meanwhile, there are 16,420 AI product manager job listings.

I’ve been pinging these searches for the last few years. And the results are astounding.

In February 2024, AI PM was 2% of open PM jobs. Today it’s 46%.

And what are hiring managers consistently looking for in these roles?

Experience having shipped AI products to production at scale.

But if you’re trying to break in, you face a catch-22:

That’s the subject of today’s post.

Introducing Nancy Li

Believe it or not, I resisted writing much about AI well into 2024. It was then that I published How to Become an AI PM with No Experience with Dr. Nancy Li. The post’s outperformance helped set off this newsletter’s heavier percentage focus on AI.

Now, Nancy is back. And today she’s sharing real case studies of how her students have nabbed AI PM roles without AI PM experience. You see, she runs the PM Accelerator and does this for a living (with free webinars and a paid cohort).

So she’s just the right person to help.

Today’s Roadmap

We’ve split the piece into the following 5 topics:

1. Why you need synthetic experience

2. Real case studies of synthetic experience

3. The AI PM synthetic experience pyramid

4. How to showcase synthetic PM experience

   • Resume

   • LinkedIn

   • Portfolio

5. The AI PM Synthetic Experience Pack

Subscribe now

1. Why you need synthetic experience

A few factors are conspiring to make synthetic AI PM experience so important. The first starts at a market, or economics, level.

The competition is tougher than ever

In the same span that AI PM went from 2% to 46% of open PM jobs, PM jobs themselves fell 18%.

That’s 7,887 PM roles gone since the August 2025 peak. And then if you look inside that “flat” year. AI PM listings more than doubled (+120%) while non AI-PM listings shrank.

I’ve been tracking this shift for a while. In The State of AI Product Management last year, I used different data, Live Data rather than LinkedIn, to come to similar conclusions.

What’s compounding the mess from a candidate point of view is the supply side. Layoffs keep feeding the supply:

• Per layoffs.fyi, 127,180 tech employees have been laid off this year - most recently, 200 at Apple.

• Mind the Product estimates roughly one in five product people out of a role by mid-2026 (survey + LinkedIn estimate, so treat it as directional).

And it has been running like this for two straight years:

So there’s fewer seats. And more people are chasing them.

Basically, the power of hiring managers is greater than job seekers’ right now. That’s not always the case. 2021 was the opposite.

And the seats that remain are increasingly AI seats:

Experience is a form of de-risking

Put yourself in the hiring manager’s shoes now. Why do they want prior AI experience so badly?

It’s about de-risking. As product leaders, we’re taught to de-risk product risk, de-risk business risk, and, in the case of an experienced PM, de-risk team risk.

If you can prove that you’ve had experience shipping AI features that work at scale, they assume you get it. You know how to work with AI engineers and researchers. You can write great evals. You won’t be surprised by working with product marketing to get the launch to go viral on X.

The 113-listing test

I worked with Claude to pull 113 AI PM listings off LinkedIn. Then I classified each of them by hand based on their stated requirements:

Check out the Database

Here’s what I learned:

You can call that seven in ten. Here’s what my process looked like to identify those:

I classified conservatively. But 40 of the rows were judgment calls. A softer reading might put the number at 55%. Either way, the majority of the market wants proof.

A few more things I learned from the analysis:

You don’t need AI in the title to demand AI PM experience. Generic “Product Manager” titled listings demanded AI experience almost as often as listings titled “AI Product Manager” (66% vs 70%).

“Production” is rarer than you fear. Only 14 of 113 listings (about one in eight) demanded AI experience specifically in production or at scale. The other listings that ask for AI experience leave the venue open.

The ask is for senior on paper, junior on AI. The median listing wants 5 years of product management experience. But when a listing separates PM years from AI years, the AI slice shrinks fast:

• 9fin wants 5+ years of PM with 2+ on ML or GenAI

• Deloitte wants 10+ years with 1+ year of GenAI work

• Goodera wants 2-4 years with 1-2 shipping AI.

What “synthetic experience” means

From here on out, we’ll refer to “synthetic experience” to mean AI product work you manufacture for yourself, ahead of any employer giving you the title.

You build something real, get it used, and document the product decisions along the way. Then it goes on your resume, portfolio, and LinkedIn as work.

Because it is work.

I’ve taught the same play for breaking into PM itself with no experience for years. This is that play, pointed at AI.

It is emphatically NOT fabricated experience.

Fabricated experience collapses at the first technical screen, or the first time someone asks you about your eval set. From here on out, we’re focused on producing artifacts a hiring manager can click, question you about, and verify.

Let’s have a look at some examples.

2. Real case studies of synthetic experience

Nancy and I have run the synthetic experience playbook with many candidates. Here, we show you the real results.

Case Study 1: Brian Luc

Brian spent roughly 15 years in hardware engineering at Apple. He started as a hardware engineer and then moved into a Senior Program Manager role.

He didn’t have a software product manager title, or a title with AI.

So he built Renosmarter.ai:

He used that to update his LinkedIn headline for his AI PM experience. He also posted about the project on LinkedIn. 5 months later, he was an AI PM at Cisco:

AI Product Manager @ Cisco | Building Applied AI Products That Save Time & Reduce Costs | Multimodal AI, RAG, LLM Orchestration | ex-Apple

He managed to use the synthetic experience to land the AI PM role at a ~$444B market cap company.

Case Study 2: Katherine Sao

Katherine is the case for everyone reading this post after a layoff.

🔒 The rest of this post is for paid subscribers only: 3 more case studies, the pyramid of what type of experience helps, and step by step how to show case it.

Plus, The AI PM Synthetic Experience Pack, with checklist and Claude skills to speed up your journey.

Read more

Check out the conversation on Apple, Spotify and YouTube.

Brought to you by

1. Customer.io - Send smarter messages using your product data

2. Ariso - The AI operating partner for every manager and team

3. Product Faculty - Get $150 off their #1 AI Builder Fellowship. Code AAKASH150

4. Land PM Job - Join me for Cohort 5 starting November 16th

5. Amplitude - Custom agents that monitor your funnel and file the ticket

Today’s Episode

Freshworks is a $3.4B giant of SaaS. They’ve been around since 2010. They have over 75,000 customers and 4,000 employees. Their 2026 revenue will be $960M.

They’re a colossus.

When Srini Raghavan joined them as Chief Product Officer, they had a 6 month release process. Under his tenure, they moved to a 2 week release cycle.

They embraced a new way of working powered by AI.

In today’s episode, he breaks down everything:

1. The AI PDLC they embraced

2. The AI Harness they created in Cursor, so it can run on any models

3. How the PM role shifted to Product Builder

I hope you enjoy it as much as I did:

I’ve written up the key takeaways for newsletter subscribers as well.

1. The AI PDLC they Embraced

How do you go from shipping every 6 months to every 2 weeks?

It’s not about becoming AI first! Most teams try that: they open Cursor or Figma Make and start prompting.

Srini made the point that it actually all begins with being Data First:

When you’re data first, you create the right foundation to actually be AI first. At Freshworks scale, with 300 million end users, you can’t afford a hallucination.

For Freshworks, that was three things:

1. A design system structured so agents can parse it

2. Coding standards written down explicitly

3. A single repo as the source of truth

They wrapped that all into a system they call Prism. It’s a knowledge hub that knows the product and its dependencies inside out, plus a context hub that passes feature context between phases, and a central library of what they call AI builder artifacts. These are the skills, rules, commands, and agents that describe how Freshworks specifically builds.

The AI PDLC sits atop all of that. And it looks like this:

It’s the same lifecycle every SaaS company runs from discovery through release, with two changes. First, there’s a governed AI agent working inside each. Second, there’s an evals phase at the end.

And that’s how their release cycle went from 6 months to 2 weeks.

But even in the episode, there were some cracks. Figma Make skipped a few design system components. Srini noted that those are the places where humans still have a role.

2. The AI Harness in Cursor they created

I began in engineering. After 14 years of not touching code, now I’m spending a lot of time in Cursor.

Srini is now spending lots of time in Cursor, and he showed us. Everything starts with a slash command, /fw-innit. Here’s what that kicks off:

The harness asks for the business unit, Epic ID, and feature team. Then it runs 12 phases, all the way from idea brief through to prototyping and QA.

Srini did the whole demo on Grok models! He explained:

I used to use Claude, but Grok seems to be working really well, and it’s really really fast. Nothing takes more than 10-15 seconds.

And indeed, that’s what we found. If you really need speed, consider Grok.

He live built a performance dashboard for their new EX Agent Studio. After he described the feature in plain English, the agent came back with questions a good PM would ask:

• How deep should the drill down go?

• Who is the primary persona?

• What does success look like in 6 months?

It then wrote its own SQL against Bel, their Databricks data lake with usage from all 75,000 customers. It assembled this into quantitative evidence in the PRD:

It looked across all 4,358 active ITSM accounts and showed its work.

At the end of the process is a step called CPO check which reviews the draft the way Srini would. His team named it after him.

From there, the PRD goes straight into Figma Make. Because of being data-first, they have a design system built into it. And after a few prompts we had a working prototype:

That’s the end-to-end process. No Claude Code or Codex. Just Cursor + any model.

3. How the PM role shifted to product builder

Which brings us back to where we started: what roles will execute work like this in the picture?

Srini manages 200+ people with titles like PM, UX researcher, and designer. His prediction is:

He didn’t say that to get clicks on Social Media. That’s how he’s running Freshworks. When he joined, Freshworks ran 1 PM and 1 designer for every 10 to 20 engineers.

Now, their new workflow is 1 PM to 1 engineer. Some teams have no dedicated designer at all.

Here’s why: those titles marked stations on an assembly line. The PM would write the PRD, hand it to design, and design would hand it to engineering. Now, like in section 2, one person can do the whole assembly line.

If you want to prepare for this future, I’ve put together the roadmap:

Pay close attention to step 8. Since one person can now run this assembly line, that person also owns whether the thing made any money.

Get more of Srini

Freshworks is hiring and you can find Srini on LinkedIn. He also generously shared his deck.

Since the episode was recorded, Srini actually left Freshworks. With his tenure as CPO at RingCentral, SVP at Five9, and Director at Cisco, I’d expect you see him at another high-powered role soon.

Go Deeper

• Hear from more CPOs on becoming AI native: Rachel Wolan, Jiaona Zhang

• Learn more about becoming a Builder PM: Mahesh Yadav

• Explore other Coding Harnesses: Codex, Claude Code

PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!

I’ve never shared how.

I’ve done it all inside of a Content Operating System built in Claude Code.

But What about AI Slop?

You might be asking yourself: but I don’t want to use AI in my content. And you’d be right to think that way.

LinkedIn is trying to remove AI slop content:

Substack is helping users detect AI slop as well. You can expect others to follow. That’s why…

This System is About Getting Jobs (and Customers)

This system is about automating all the work around content so you don’t generate slop.

In particular, it helps you with:

• Finding the right topics to post about

• Giving you inspiration from other creators

• Helping you create infographics that go viral

• Automatically collecting analytics on your performance

• Analyzing your funnel to help you drive job offers and customers

None of this involves drafting posts for you, or automating comments (ICK!).

It’s about automating the infrastructure around writing posts and comments so you have time to write yourself.

How it Generates Jobs

I got together with one of the fastest growing creators on LinkedIn in the PM niche, Basia Kubicka. And she shared her entire setup in this exclusive episode:

She uses a similar Claude Code operating system to receive 5 inbound opportunities per week 😮:

How it Generates Customers

I used this OS to publish this post a few days ago that drove 784 website visitors, 64 free subscribers, 21 LinkedIn followers, 3 paid subscribers to the newsletter, and 1 application to my cohort:

With an average cost per click on Meta ads between $0.40 and $1.20, most companies have to spend between $313.60 and $940.80 for those results.

I paid $0 for this (because it fit within my existing Claude Max plan).

The Content Operating System chose the topic of the infographic, assembled all the links in that post, and even made the infographic.

I just wrote the text.

How to Get It

Here are your options to get the Content Operating System:

1. Buy it on Gumroad for $49

2. Become a Founding Subscriber for $250 to get it + my other products

Not a founding subscriber and don’t want to buy? The video above is today’s alpha for subscribers. Believe me, it’s THE roadmap to get jobs from LinkedIn.

What’s Inside

Here’s a preview (not everything because people will copy it) of what you get:

And the content inside that context-library? Entirely populated by the system based on your profile link + those of creators you admire.

What’s especially powerful, and worth the money on its own, is the post lab, where you get to see the performance of other creators on your platforms:

Can You Build This on Your Own?

I used Claude Code to build this.

You could build this in Claude Code by yourself, too, if, like me, you iterate on the system for 8 months.

But if you buy the product, you get those 8 months of iterations in the price.

($49 on Gumroad or $250 as part of the Founder’s plan which also gets you my PM OS, Job Search OS, Prompt Library, and Bundle).

Today’s Deep Dive

Once you’ve got the thing, here’s how to use it well:

1. The 2-minute install

2. Then 10-minute setup

3. How to use the Content OS well

4. How I personally used it to grow

   • On X: from 117K to 320K followers

   • On LinkedIn: from 241K to 320K followers

5. Pro tips, mistakes, and FAQs

Subscribe now

1. How to Install the Content OS

It hardly takes 2 minutes and runs in your harness of choice:

Read more

So do PMs all need to learn graphs now?

I spent a few weeks testing them for you to find out. The answer is, perhaps unfortunately, yes.

But the good news is: they’re extremely easy to learn and prompt.

So let’s walk through it.

Today’s Post

1. What is a graph and how did we end up here

2. The graph lego blocks to build from

3. Graph construction in practice

4. The PM graph library

Subscribe now

1. What is a Graph, and how did we get here?

The five hype cycles we’ve seen in AI actually build upon each other.

Prompt → Context → Harness → Loop → Graph

That means everything we learned in the past still matter:

• Prompts define what a model should do

• Context defines what the model should know

• The harness is what the AI can access or change

• A loop is how one agent keeps improving

• Graphs are how the whole system coordinates

For simple tasks, a prompt with some context is enough. But for most high-leverage PM tasks, like writing a PRD, or a feature results writeup, you’re much better off placed with coordinating a whole graph.

(More proof on that in just a minute.)

2. The graph lego blocks to build from

Just like prompts spawned a sub-field of prompt engineering, context spawned context engineering, and harnesses spawned harness engineering, graphs have created the sub-field of graph engineering.

The best way I’ve found think of graph engineering is like putting together legos. If you learn the major pieces of legos, you can basically build anything. The same is true for graphs.

There are 5 most commonly used lego blocks:

• A sequence means one step feeds the next

• A router sorts each request to the right lane

• Parallel blocks do several things going at once

• An evaluator scores the output of another agent

• A review loop reviews an output till it passes a bar

You can combine in any order you’d like.

An extremely common pattern that people will build is an orchestrator + parallel + evaluator to create a diamond:

None of this is particularly new. Us software engineers were taught graphs back in college. Now, with prompting AI, they’re back.

3. Graph construction in practice

Now let’s see what this looks like in practice.

The easiest way to use Graphs is to build them with Claude Code workflows. It will even do the work of selecting the right pattern for you. Start with a prompt like this:

The keys to this prompt are:

1. Define the outcome you want

2. Explain the graph types you want it to use (combos of the types of above)

3. Identify the models you want to use for each task (you can see why I use Opus so much now!)

Then you can see it will go and build the prompts and do deep work, setting up parallel workflows where possible.

You can use /workflows to manage your graphs:

We basically defined a double diamond graph:

You can then monitor specific agents as they work. For instance, here’s the discover analytics agent:

Eventually, you get an output that is the result of many agents working for you, instead of a single agent.

Now onto the main course….

4. The PM graph library

So this brings up 3 really important questions :

What PM tasks deserve a complex graph?

The followup question to that is: What graph should they have?

And then: What’s the best way to prompt such a graph?

So, I used a bunch of graphs and spent 100M tokens on Fable 5 to test everything for you.

Step One - I identified 40 PM tasks are the best fits for AI

First I thought deeply: what tasks are best suited for AI out of the PM tool kit?

I found 40 tasks across 3 buckets:

Step Two - I built the strongest simple prompt possible for each

Then I created a great simple prompt (no graph) for each of those 40 tasks (using my PM Prompt Library and PM OS).

Because here’s the principle I ascribe to:

If a graph can’t beat a great prompt, the graph is just theater.

The idea was to get the single best one-shot prompt possible.

Step Three - I built a strong testing environment

Then I built an environment to test multiple graphs vs simple prompts.

I built a PM OS for a hypothetical PM at Instagram. It had everything: templates, files, even MCP connections.

But I didn’t make it “clean.” I planted issues:

• An action item that got canceled 20 minutes later in a meeting transcript

• A research claim marked “UNVERIFIED, do not cite”

• A sprint that overbooked an engineer on PTO…

Step Four - I ran the tests on the best graphs

Then I went about testing the simple prompts vs graphs. But I didn’t use any graphs.

I theorized about the best graph construction for each type, and tested up to FOUR structurally different graphs for each use case.

Three independent graders graded the simple + 4 graph outputs.

Graphs ultimately won 25 of 40 tasks. These 12 were the biggest winners:

That’s the essence of graph engineering. Find the tasks, write the graphs, test the results.

I also wanted to give you more than a newsletter today.

I’ve put all 25 graph winners, plus the winning 15 simple prompts, into the PM graph library.

Plus, I’ve created a skill that scans your entire repo for tasks and prompts, runs the graph engineering on them, and hands you updated prompts.

You have two options to get the package:

1. Buy the product on Gumroad for $49

2. Upgrade to a paid subscriber and you’ll be able to download it below:

Read more