If I had to learn AI PM again, I would start here:

($2,000 course totally free) 1️⃣ AI PM Foundations

@marilynika: How to become an AI PM - youtube.com/watch?v=GQd54g…

Sam Kawsarani: 15+ Years PM in Era of AI - tinyurl.com/2txs9mab

@_amankhan: Prototyping → Observability → Evals - youtube.com/watch?v=Ej4pBD…

You want to code with AI.

But you don't know what tool to use.

Here's your guide on when to use which AI coding tools:

(+ free resources to learn each) 1: Cursor

They surpassed $500M ARR for a reason. Your developer probably uses it. It's great for pros - but it's also great for non-technical folks now, as its agent can build things just via natural language.

Free guide:

There's a new PM interview in town.

But most PMs don't know it yet.

Last week, a PM messaged me in panic:

"I just got destroyed in a Google interview. They wanted me to prototype something with AI tools. I'd never even heard of 'vibe coding' before."

Here's what happening: This isn't isolated. I'm coaching more PMs who are blindsided by this new interview format every week.

Here's what's happening behind the scenes:

The nature of the PM role is undergoing its biggest shift in years.

We're now moving to the era of AI PMs.

>50% of PM interviews now ask metrics questions.

But not all metrics questions are the same.

5 categories + 16 sub-categories to know: 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 (𝟮𝟭% 𝗼𝗳 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀)

Three major flavors here:

Product launch success: Measuring launching GPT-5.

Feature success: Measuring a mobile payment feature.

Business model success: What would you look at if we launched a marketplace?

Context engineering is the new prompt engineering.

Here's everything you need to know: 1. Etymology

It's unclear who coined the term, but two folks have been particularly important in its rise:

Andrej Karpathy and Dex Horthy.

Which is it: use LLMs to improve the prompt, or is that over-engineering?

By now, we've all seen a 1000 conflicting prompt guides.

So, I wanted to understand:

• What do actual studies say?
• What do experts at OpenAI, Anthropic, & Google say?

Here are the answers: I spent the past month in Google Scholar, figuring it out.

I firmed up the learnings with Miqdad Jaffer at OpenAI.

Some of my favorite takeaways from the research:

If you're preparing for PM interviews in 2025, there's one question type you cannot afford to mess up: Metrics.

Here's the history of how it over took product hiring and why it's the silent killer of PM dreams: PMs have been getting hit with questions like these...

And while they’re not as sexy as product sense or design...

They’ve quietly become non-negotiable in most interviews.

Jira Product Discovery (JPD) just launched their biggest update yet.

It will take them from 18K → 36K customers.

Here’s why I think so: 1. From Teams → Teams of Teams

I covered Jira Product Discovery (JPD) earlier this year.

They were really good for a product team.

Now, with this month’s launch of Premium, they are really good for multiple product teams.

This is a huge unlock…

AI prototyping has changed what it means to be a PM, designer, and engineer in forward-thinking organizations.

Here's how: The Old Way

Here’s what most product development lifecycles look like:

1. Ideation

Most teams barely prototype at the idea stage.

A rare few exceptional designers and PMs do (~5%)

OpenAI released that there will be 5 levels of AGI.

If you want to build the future of AI, you should deeply understand it.

We are just crossing step 2 of 5 to AGI.

Yet, somehow, teams are still building like we are in levels 1 or 3.

Let me explain: LEVEL 1: CONVERSATIONAL AI

Remember those awkward chatbots from 2019?

They sounded human...until they didn’t.

You’d ask for help…
They’d return gibberish.
That’s Level 1.

Might be useful but it can't be your strategic moat.

I've seen my fair share of product development processes.

JPD's approach stands out as particularly principled and well thought out.

Here are the five most important things about how they build product: 𝗙𝗮𝗰𝗲𝘁 𝗢𝗻𝗲 - 𝗧𝗵𝗲 𝗟𝗶𝗴𝗵𝘁𝗵𝗼𝘂𝘀𝗲 𝗣𝗵𝗶𝗹𝗼𝘀𝗼𝗽𝗵𝘆

As Catalin Bridinel, Head of Design, explains:

"The product is a ship, and the user is a lighthouse that gives you direction."

This is more than a cute metaphor - it's a fundamental operating principle.

Most people are still prompting wrong.

I've found this framework, which was even shared by OpenAI President Greg Brockman.

Here’s how it works: 𝗧𝗵𝗲 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴

4 words in lowercase doesn’t work.

If you want to write a PRD, or a product strategy, or something for work, you have to help the LLM get your context.

This is done in 4 parts.

We’ve been sold a million use cases for PMs to use AI.

For my money, here are the top 5: AI won’t replace PMs.

But PMs who use AI will replace PMs who don’t.

You don’t even need to learn coding to join the AI revolution.

There’s one archetype taking over in organizations right now: the AI-powered PM.

Here are the top 5 ways AI-powered PMs are changing the game:

Used right, OKRs can be the most powerful product process.

But most orgs completely mess them up.

From driving empowerment to becoming tools for control…

Here’s what you need to know about when to add or remove OKRs: 𝗧𝗵𝗲 𝗗𝗲𝗯𝗮𝘁𝗲 𝗔𝗿𝗼𝘂𝗻𝗱 𝗢𝗞𝗥𝘀: 𝗔 𝗗𝗲𝗲𝗽𝗲𝗿 𝗧𝗿𝘂𝘁𝗵 𝗔𝗯𝗼𝘂𝘁 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁

The conversation around OKRs reveals a fundamental truth:

Alignment mechanisms aren’t one-size-fits-all.

Take Ramp, for example.

They built a $10B company without OKRs.

If you want to move from a PM to a product leader, there’s one skill you absolutely need to master:

Product processes.

Here are the 12 most essential ones to drive impact, speed, and quality: 𝗢𝗡𝗘 - 𝗢𝗞𝗥𝘀

→ When to Add: When your team is growing fast, shifting priorities, or you need a system to track progress across teams.

→ When to Drop: If OKRs become a never-ending admin task and feel disconnected from day-to-day work, it’s time to simplify.

One thing that will remain the same in the 2025 job landscape:

Hiring managers will still ask, "Why this company?"

Nail it, and you’re halfway to landing your dream role.

Here’s how to master this: Over the years, I’ve worked with countless aspiring and senior PMs.

99% of them give a 4/10 answer.

Here’s where they go wrong:

1. Generic fluff → They rattle off buzzwords like “innovative culture” or “market leader.” It’s uninspired.

How do the best product teams ship better and faster without things breaking?

They embed quality into every step of the development process.

Here’s how you can do the same: 𝗙𝗶𝘃𝗲 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗖𝗵𝗲𝗰𝗸𝗽𝗼𝗶𝗻𝘁𝘀

𝗢𝗡𝗘 - 𝗕𝗲𝗳𝗼𝗿𝗲 𝗙𝗶𝗻𝗮𝗹𝗶𝘇𝗶𝗻𝗴 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀

→ When: Right after you’ve done your research and before locking down specs.

PMs, you can add at least $50k to your salary by becoming an AI PM.

However, many people miss the mark, making the same mistakes over and over again.

Here are the 7 mistakes to avoid if you want to secure your dream AI role: 𝗠𝗶𝘀𝘁𝗮𝗸𝗲 #𝟭: 𝗧𝗵𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗼𝗿’𝘀 𝗖𝘂𝗿𝘀𝗲

→ Collecting certificates may feel productive, but it’s often just an illusion of progress.

→ Instead of paper chasing, identify a problem, build a prototype, and document your process.

Looking to land a PM role at Google, Amazon, or Meta?

Here’s the truth: It’s not enough to just secure the offer.

You need to nail the team matching process.

Here’s how to land the team (and role) you want: 𝗪𝗛𝗔𝗧 𝗜𝗦 𝗧𝗘𝗔𝗠 𝗠𝗔𝗧𝗖𝗛𝗜𝗡𝗚 𝗣𝗥𝗢𝗖𝗘𝗦𝗦?

At top tech companies, landing an offer is only the beginning.

After that, it’s time for team matching: the stage where PMs find their “home base” within the company.

Why this process is crucial for both parties?

The biggest bottleneck in scaling your product with enterprise clients?

Not getting your product ready.

Here’s what you need to know to transform for enterprise: 𝗧𝗛𝗘 𝗣𝗜𝗧𝗙𝗔𝗟𝗟𝗦 𝗜𝗡 𝗖𝗢𝗡𝗩𝗘𝗡𝗧𝗜𝗢𝗡𝗔𝗟 𝗪𝗜𝗦𝗗𝗢𝗠

Many believe an “enterprise-ready” product only needs the basics: RBAC, SSO, SLAs, and audit logs.

These are important but just the price of admission...

This founder dropped out of college and built a $20B company.

Here's the crazy story of how Figma went from nothing to one of the iconic startups: CHAPTER 1 - SOLVING THE FILE PROBLEM

In 2012, Figma's co-founders @zoink and @evanwallace set out to solve a problem that every designer struggled with:

The file problem.