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.

*Save this post for the next time you interview*

These are the 30 questions Eugene Segal has seen most in his mid-level PM leadership job search (Director/GPM/Head Of).

AND - Here’s the proven way to answer these questions and succeed in your interviews: 𝗧𝗛𝗘 𝟰 𝗕𝗜𝗚 𝗟𝗘𝗩𝗘𝗥𝗦 𝗬𝗢𝗨 𝗡𝗘𝗘𝗗 𝗧𝗢 𝗦𝗨𝗖𝗖𝗘𝗘𝗗

1. Structure

↳ Use a framework like STAR (Situation, Task, Action, Result) to keep your responses concise, impactful, and resonant.
↳ Also, using structure isn’t “cringe”; it’s a foolproof way to communicate your thoughts with clarity and confidence.

You're likely to hear this question in most interviews.

"Why this company?"

Most people pass by with an 'okay' answer.

Here's a Framework to take your answer from that okay 6/10 to an awesome 10/10: 1. Deep Dive into Company Research

Go beyond the company's website. Dive into recent product launches, financial reports, and industry news,

Create a "company insights" document. For every piece of information you find, add a note about why it matters to you.

The secret sauce to landing high-paying jobs: cold emails.

It’s how I broke into Epic Games—and how I’ve helped others secure roles at some of the world’s top companies.

If you’re aiming for your dream job, this is for you: There’s no doubt about it—cold email works.

A high schooler emailed Snapchat’s CEO and landed an internship.

So if you’re not getting responses, it’s not because it is broken.

You’re probably approaching it the wrong way.

Let’s start by fixing the most common mistakes:

If you’re building a company or trying to figure out how to grow it fast, this is for you.

Today, we’re breaking down 7 GTM motions (with 28 guides) from top companies that are scaling at breakneck speed.

Here’s your roadmap to mastering Go-to-Market like a pro: 𝗔 𝗣𝗿𝗶𝗺𝗲𝗿 𝗼𝗻 𝗚𝗼-𝘁𝗼-𝗠𝗮𝗿𝗸𝗲𝘁 𝗠𝗼𝘁𝗶𝗼𝗻𝘀

When it comes to Go-to-Market (GTM), it’s all about the right combination of motions.

From driving virality to closing enterprise deals, each motion is a tool that you can leverage.

It may seem like a mystery...

You can become an AI PM with no experience.

Competition for those roles paying $300-900K is sky high.

But here's how to differentiate: Dr. Nancy Li and I have gone in-depth on the path, with our lessons after having guided students to AI PM roles at:

- OpenAI (TC: $850K)
- Amazon (TC: $485K)
- Microsoft (TC: $373K)

How do you create a well-structured product strategy?

Here are the 7 steps you should follow: 𝟭: 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲

Without a definition of success, a strategy is meaningless.

Strategies are always the response to a challenge. They answer the question: “what is the best way to do X?”.

A great format is: Objective = mission + measure

Two founders turned a $150K loan into a $10B+ startup while coding in their underwear.

Here’s the wild journey of Notion from 0 to 30M active users: Countless productivity tools like Miro, Canva, and Coda have launched in recent years.

But Notion cut through the noise to become the go-to platform for seamless productivity.

Today, it’s valued at $10B, with 30M users and generating $576M in revenue.

Let's break it down.

One of the secrets to make better product decisions:

"If it isn’t a clear yes, then it’s a clear no."

Prioritization is one of the most important skills in product management. There are always more ideas than resources, more problems than solutions, more features than benefits.

How do you decide what to work on next?

There are many frameworks and tools for prioritization, such as RICE, MoSCoW, Kano model, etc. But they all boil down to one simple principle: if it isn't a clear yes, then it's a clear no. What does this mean?

It means that you should only work on things that have a strong positive impact on your product goals and metrics, and that are aligned with your product vision and strategy. Anything else is a distraction, a waste of time and energy, or worse, a source of confusion and frustration for your users and stakeholders.

I make more as a creator than I did as a VP of Product at a unicorn.

But how did I turn 60-minute writing sessions into a full-time job?

Here’s how it all unfolded: 𝗖𝗵𝗮𝗽𝘁𝗲𝗿 𝟭: 𝗘𝗽𝗶𝗰 𝗚𝗮𝗺𝗲𝘀 𝗮𝗻𝗱 𝗖𝗼𝘃𝗶𝗱

In 2020, I was deep in my role at Epic Games, working long hours like most people in the industry.

Then the pandemic hit, and suddenly, I was working from home with extra time on my hands.

The retention hierarchy of needs is my go-to concept to solving retention problems.

Here's how it works: The idea is retention is a pyramid.

You have to satisfy the lower layers first before tackling the one’s above them:

"Don't Make Me Think" is one of the most underrated & ignored growth levers.

It's not "just" a UX concept.

It's an investment program - that's about minimizing cognitive load for users and prioritizing UX from a first-principles. But how do you implement it?

I broke it down with Director of Product Growth at @Houzz Kunal Thadani.

There are 5 key principles:

Revealed: How to Succeed in Meta Product Manager Interviews.

Here's the exact doc Meta shares with candidates for its PM Interviews:

Steal this template for your homepage: 1. Above the Fold

This is the most important part of the homepage. Too many people waste the space:

A. Convey the category of software your product is in, and how your solution differs
B. Add in wow logos from industries in your ICP (ideal customer profile)

There’s a paradox of seniority: people get more messages, yet they are even more responsive.

This is especially true for important emails.

Here’s why: THE IC LIFE

As an Individual Contributor, your work (outside of certain roles) mostly is internal to your company.

You can spend the vast majority of your day in Slack for responsive tasks.

Email responsiveness matters, but it’s not the most important thing.

For a company founded in '93, Nvidia's ascent to $2.7T market cap has been FAST. But what really is Nvidia's moat?

Let's break it down.

PART 1 — SOFTWARE

The story starts all the way back in the early 2000s. That's when Jensen Huang, Nvidia CEO, and his team were out meeting researchers using their products.

Most researchers were hacking graphics packages to run complex parallel compute tasks. It was not ideal. To say the least.

So, when the Nvidia team met Ian Buck, who had the vision of running general purpose programming languages on GPUs, they funded his Ph.D. After graduation, Ian came to Nvidia to commercialize the tech.

Two years later, in 2006, Nvidia released CUDA.

C ompute
U nified
D evice
A rchitecture

CUDA made all those parallelization hacks the researchers were doing available to everyone. Over time, CUDA became the default choice for researchers.

CUDA allowed accessible customization of the low-level hardware. So developers loved it.

Nowadays, when startups like MosaicML evaluate the available technology vs CUDA, they inevitably choose CUDA.

The ecosystem around CUDA has grown so robust that its lead is virtually unbeatable. This software layer is at the core of Nvidia's moat.

PART 2 — HARDWARE

The other side of Nvidia's moat is hardware. But it's not graphics cards for crypto and gaming. The hardware that matters is AI supercomputers.

The story of these supercomputers begins in the late 2000s. As Nvidia was developing CUDA, Jensen asked the team to build a supercomputer to help him build better chips.

The result was a massive supercomputer that weighed 100 pounds and strung together many GPUs with world-class networking for ultra-fast computing.

In the early 2010s, Jensen gave a talk at a conference about this AI supercomputer. Elon Musk got wind of it and said, "I want one."

So, in 2016, Jensen actually donated one to Elon Musk's relatively unknown nonprofit, OpenAI. He hand delivered it, and there's photographic proof.

OpenAI quickly learned the supercomputer worked really well. Especially for training large neural networks. That 2016 Pascal architecture delivered an impressive 19 TFLOPS of FP16 operations.

That's 19 trillion floating point operations per second. It's a massive amount. But that was just the beginning.

Since then, Jensen and the Nvidia team have been lapping the industry in delivering more TFLOPS, growing them at an exponential rate.

The latest Blackwell architecture delivers a massive 5000 TFLOPS. That's >260x AI computer in 8 years. And sells for more than $75K. But buyers like Meta, OpenAI, Google, and Amazon just can't get enough, as their internal ASICs are nowhere near Nvidia's level.

As a result, Nvidia's profits and market cap continue to soar, cementing its position as a leader in the AI hardware and software space. Jensen is one of the most impressive entrepreneurs alive.

He spotted the AI revolution before any other semiconductor CEO and bet the company on it.

That's a rare trait.