Templates by Aakash Gupta

Designers have adopted the portfolio for decades. Now, PMs are joining them.

I surveyed 325 PMs and chatted with 15 hiring managers (5 in each geo).

Here's what I learned:

Globally, the numbers are pedestrian:

• 16% of PMs have a portfolio

• 61% know of them, but don't have one

• 23% have never even heard of one!

But - the percentage using them is growing steadily.

The ascent has been accelerated with 3 bumps:

1. The dot com era brought many more people into tech, resulting in some competing with portfolios

2. The great financial crisis led to myriad layoffs, prompting struggling PMs to adopt them

3. The ZIRP era led to a huge expansion of the field, driving former designers and engineers to switch and bring along their portfolios

But this pick up hasn't been global.

It has been geographically specific:

• Adoption in the Americas is a pedestrian 10%

• In Europe and Australia, it's nearly double that

• In Asia and the Middle East, nearly 40% have one

In India, EG, many PM job postings even ask for one!

This got me thinking...

Could a great portfolio help distinguish candidates?

So, I recruited 5 interested mentees in my Slack community to work with me on a portfolio strategy.

I'll be honest: we had lots of fits and starts along the way.

• People not reading the portfolios

• People actually disliking the portfolios

• One mentee giving up on the strategy...

But, ultimately, we've been able to figure it out:

1. A CS major got an APM gig

2. A principal PM finally broke into FAANG

3. An aspiring PM finally secured their first PM role

4. A director of Product finally left a toxic manager

5. A PM laid off for 11 months got a job again

The key?

Building a differentiated portfolio that shows not just what you did, but how you did it.

Of course, the portfolio doesn't do all the work. It's one piece of the puzzle.

But in a world where so few use it, it can be a massive differentiator.

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Want to learn what makes a great portfolio? Check out the deep dive: https://lnkd.in/eQd32YUA

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 it 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.

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Ready to go further? Curious whether the stock is a hold?

You'll love the deep dive: https://lnkd.in/d6nVmtUP

I found Meta's 5 criteria for the product sense interview. They're surprising. Let's break them down:

Reminder... this is the interview where they ask questions like:

• Improve ChatGPT.

• How would you differentiate Reels from TikTok?

• What's your fave product? How would you improve it?

And why should you care about Meta related to this?

Meta originated product sense interviews back in '08. Their criteria have propagated to the rest of the industry. I saw similar one's when I worked at Affirm and Apollo.

—

Here's the details:

𝗖𝗿𝗶𝘁𝗲𝗿𝗶𝗮 𝟭 — 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗹𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲 𝗮𝗻𝗱 𝗺𝗼𝘁𝗶𝘃𝗮𝘁𝗶𝗼𝗻

A great product sense interview response actually goes through:

• What problem is are we solving?

• What business goal is this achieving?

• What are the competitive alternatives?

So, your interview framework should give you the opportunity to answer all 3 of these questions.

𝗖𝗿𝗶𝘁𝗲𝗿𝗶𝗮 𝟮 — 𝗗𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝗻𝗴 𝘁𝗵𝗲 𝗮𝘂𝗱𝗶𝗲𝗻𝗰𝗲 𝗼𝗿 𝗽𝗲𝗼𝗽𝗹𝗲 𝘂𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁

Product case interviews worship at the altar of the user.

It's important to not just identify who the user is, but who are the people who matter in the ecosystem?

EG, a common 'gotcha' in marketplace questions is when the interviewee only focuses on one side of the equation. (Think creators and consumers.)

𝗖𝗿𝗶𝘁𝗲𝗿𝗶𝗮 𝟯 — 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆𝗶𝗻𝗴 𝗮𝗻𝗱 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗶𝗻𝗴 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺

The biggest mistake people who haven't practiced much make is jumping straight into solutions.

The biggest mistake people who have practiced make is not exploring a 𝘷𝘢𝘳𝘪𝘦𝘵𝘺 of problems.

It's critical to brainstorm several problems, and then prioritize one. And provide a good reason why.

𝗖𝗿𝗶𝘁𝗲𝗿𝗶𝗮 𝟰 — 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗶𝗻𝗴 𝗰𝗿𝗲𝗮𝘁𝗶𝘃𝗲 𝗮𝗻𝗱 𝗶𝗺𝗽𝗮𝗰𝘁𝗳𝘂𝗹 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀

Once you've narrowed into one problem, you can't just get excited about one awesome solution.

You need to brainstorm several creative one's—that the interviewer hasn't heard before.

Going back to the user and business problem can help you think outside the box.

𝗖𝗿𝗶𝘁𝗲𝗿𝗶𝗮 𝟱 — 𝗠𝗮𝗸𝗶𝗻𝗴 𝗶𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻𝗮𝗹 𝗱𝗲𝘀𝗶𝗴𝗻 𝗰𝗵𝗼𝗶𝗰𝗲𝘀

It's counter-intuitive, because the product sense interview and product design interview are often different...

But you should actually get into some design choices in your product sense interview.

In fact, a key component of real-world product sense IS design. So don't cut short your framework or answer.

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I know several people who have aced all their interviews except product sense.

And they didn't get the offer.

Don't neglect this round.

I cover all the angles in my deep dive: https://lnkd.in/eTqEFtPM

There is no one-size-fits-all when it comes to GTM.

Maja Voje and I studied 12 leading B2B SaaS companies.

(including interviews with their teams)

Here’s what we learned:

1. PLG is eating the world

>80% of the companies in our study employ PLG in some fashion. Even enterprise companies like Snowflake and Salesforce are adding free trials & freemium. It’s the new normal.

Why is this working for them? In 2024, the best marketing is often your product. Users rarely want to lock in a $500K+ contract without trying the product first. But you do need to layer on a strong product-led sales motion to make enterprise work.

2. Dominate one at first, then layer on many

Every company we studied got one GTM motion massively right. And, in each case, they still use that GTM motion in some form today. But, they layer on other motions over time.

The ideal way to layer is symbiotically:

• ABM couples nicely with outbound

• Inbound supports outbound

• Partnerships amplify PLG

For instance: Dropbox grew at first massively on referrals. Now, other channels are much more important.

3. ABM and Outbound are pillars of enterprise

For 5- and 6-figure deals, it’s difficult to rely on inbound or PLG alone. The buyer is used to a different process. They want to be hand-held.

This is where motions like ABM and outbound shine. That’s why you still see the Snowflake’s and Salesforce’s of the world focusing on them. They’re the bread and butter of enterprise.

So… bringing it all together, here’s where to start based on your buyer.

If you’re selling to consumers or prosumers:

• Lean into PLG, community, and partnerships early on

• Layer in paid marketing as you find product-market fit and have budget to scale

If you're selling to SMBs:

• Blend inbound and outbound motions to build awareness and relationships

• Paid digital can accelerate pipeline generation as you dial in your ICP

If you're selling to enterprises:

• Focus on targeted ABM and partner ecosystems

• Inbound is great for air cover, but outbound is crucial for landing large accounts

If you have a complex or technical product:

• Make sure you have developer docs, free tooling, and community support from day one

• Don’t underrate channels like partnerships & paid digital; they can still be crucial support

And above all:

1. Remember what works at one stage may not work another

2. Remember the law of diminishing returns

3. Be willing to pivot when necessary

PM changed in 3 major ways in 2023:

1. The PM as GM

2. PMs managing higher ratios of engineers

3. A compression of PM 'middle management' for ICs

All three of these trends were actually trending in the other direction for the prior decade of the tech bull market:

• PMs were focusing on input metrics

• Ratios of PM:Eng were skewing to less Eng

• We saw lots of Directors of Product and Group PMs

Here's what happened.

1. 𝗧𝗵𝗲 𝗣𝗠 𝗮𝘀 𝗚𝗠

Input metrics are great for OKRs. Product teams can actually move them.

The problem is Goodhart's Law: as soon as something has a target, it ceases to be a good metric.

As a result, more and more product leaders had to take on output metrics, like a GM.

2. 𝗣𝗠𝘀 𝗺𝗮𝗻𝗮𝗴𝗶𝗻𝗴 𝗵𝗶𝗴𝗵𝗲𝗿 𝗿𝗮𝘁𝗶𝗼𝘀 𝗼𝗳 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀

Having a PM for just 2 engineers was a ZIRP phenomenon.

Now, PMs are being asked to lead the ship for even 10-15 engineers.

PMs in lesser ratios were viewed as "overly administrative."

When times got tight, they were laid off.

3. 𝗔 𝗰𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗼𝗳 𝗣𝗠 '𝗺𝗶𝗱𝗱𝗹𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁' 𝗳𝗼𝗿 𝗜𝗖𝘀

As companies cut costs, middle managers were the first targets to go.

This was equally, if not more true, for PMs.

Where there was a proliferation of PM leaders with 1-3 reports by 2021, 2023 saw a reversion to the mean.

𝗢𝗻 𝘁𝗵𝗲 𝗯𝗿𝗶𝗴𝗵𝘁 𝘀𝗶𝗱𝗲:

One area of PM did grow by leaps and bounds:

AI PMs.

With the breakthroughs this year, tons of big tech companies and startups hired AI PMs.

This looks like a new sub-species of PM that is hear to say - and be quite well-compensated.

𝗣𝗠 𝗵𝗮𝗱 𝗮 𝗿𝗼𝗰𝗸𝘆 𝘆𝗲𝗮𝗿 - 𝗯𝘂𝘁 𝗵𝗮𝘀 𝗮 𝗯𝗿𝗶𝗴𝗵𝘁 𝗳𝘂𝘁𝘂𝗿𝗲:

• PMs took on more output metrics like revenue

• PMs started leading even bigger engineering teams

• PM leaders got closer to the work with less layers in between

• A new sub-specialty of PM was borne and grew rapidly

Only 3 of the top 50 consumer software products use reverse trials. The other 47 are missing out.

(Those 3 companies? Masterclass, Skillshare, and Spotify.)

I was stunned when I ran the numbers on companies from Audible to Uber. Only 6%?

B2B has caught on faster. And 'caught on' is the right term. Because the data shows reverse trials work:

→ I took the midpoint of consumer cos who do share conversions (only a third, so small sample).

→ While the freemium, trial, and combined ended up ~10%, reverse trials came in at ~14.5%.

→ That matches up strikingly close to what OpenView in its B2B benchmarks.

So clearly—B2B or B2C—you can significantly increase paid conversion with this model.

Reverse trials leverage the psychological phenomena of loss aversion. People treat losses more seriously than gains. And the loss of premium hurts.

A single taste of premium functionality can leave you yearning for more.

So I expect more companies to adopt reverse trials. It's not just free trial or freemium in 2024.

5 years from now, that 6% will likely be 24%+.

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To catch the full study of all 50 companies, check out the deep dive.

I go much further on how to succeed with reverse trials there: https://lnkd.in/eB9rBJQH