#product-management

Moats are more important than ever in the AI era. Anyone can create a product; they can do it faster than ever before, and everyone is building the same things.

perhaps because it seems harder to measure, people often skip one of the most important criteria: Does it have a moat?

A company can check all your boxes and look like a rocket ship. If it doesn’t have a moat, though, it’s going to stall out. It won’t be able to keep growing once competition gets fierce

Moats are more important than ever in the AI era. Anyone can create a product; they can do it faster than ever before, and everyone is building the same things. But only some of these products will win.

The framework has three steps:

1. Identify a compounding advantage

2. Gauge how strong that advantage could be

3. Measure if it’s working

In reality, only a small subset of what people call moats actually drive the vast majority of long-term value.

They’re what I’ll call compounding advantages: not just a temporary edge, but a self-reinforcing flywheel that gets stronger and stronger over time, ultimately making a company very hard to compete with.

How to identify the compounding advantage

Your job is to identify which kind of compounding advantage a startup has the potential to build. It won’t be in full force yet (it takes a long time to develop), but it should be pretty clear which path they are on. That’s because it’s not something you can “bolt on”; it’s not some clever competitive tactic or a tweak you can make to your business model.

A compounding advantage is the core loop at the center of the business through which you add more value for customers. Defensibility emerges as a byproduct of turning the flywheel over and over.

So as you evaluate a company, don’t focus on what they say they are or what their “strategy” is. Focus on what they’re actually doing. What is the team working on every day? What’s on their roadmap? Which teams are large and influential inside the company?

Mike Vernal recently wrote something of a manifesto for anyone building this kind of business today:

If you are building a venture-backed software company, I think the answer is once again “do it all.” Just build the whole thing—your moat is the totality of what you’ve built and completely owning the buying center.

The bigger cost is what I’ll call the “workflow tax”: all of the time the team spends learning a product and building it into how they work, and how disruptive it is to do that over again.

At the core of these companies is a machine to both deepen and broaden customer engagement. The roadmap is rich with efforts to better serve existing use cases or create ways to serve new ones. They’ll often be focused on integrations and working with other parts of their customers’ stack. The team should be heavily tilted toward product and engineering, with an emphasis on shipping rapidly.

Network-effect products are common in industries like social (LinkedIn), gaming (Roblox), and marketplaces (Amazon).

The emphasis of a network business is less on building out the surface area or feature set of the product, and more on growing the network and making it easier and easier for that network to interact.

3. Scale

Businesses with scale as their compounding advantage are all about making it possible to offer the same product at a lower cost or a higher-quality product at the same cost.

4. Data effects

These are businesses with data at the core. It’s the input that makes the product higher-quality, more efficient, or work for more use cases. And by using the product, customers generate even more data, which further compounds this effect.

In these companies, you’ll often see a large effort to collect and sanitize data, and to build algorithms and products on top of it. ML and data science are usually large and influential teams.

1. PMF Metric

How would you feel if you could no longer use Simulator by GoPractice!? 2. Recommendation

Have you recommended Simulator by GoPractice! to anyone? 3. Referrer

How did you discover Simulator by GoPractice!? 4. Alternatives

What would you likely use as an alternative if Simulator by GoPractice! were no longer available? 5. Benefits What is the primary benefit that you have received from Simulator by GoPractice!?

NB: please note that these are 5 random query results. You can see the rest of the results further below. A lot of practice a lot of practice Engaging way to get a data-driven mindset Working with analytics tool Working with Amplitude 6. Audience What type of person do you think would benefit most from Simulator by GoPractice!?

NB: please note that these are 5 random query results. You can see the rest of the results further below. A beginner PM a beginner PM Anyone in analytics department or product dep. Junior and middle product managers Anyone in the field 7. Improvement How can we improve Simulator by GoPractice! to better meet your needs?

NB: please note that these are 5 random query results. You can see the rest of the results further below. No idea no idea Make it shorter Use more gamification

The product-market fit definitions I had found were vivid and compelling, but they were lagging indicators — by the time investment bankers are staking out your house, you already have product-market fit. Instead, Ellis had found a leading indicator: just ask users “how would you feel if you could no longer use the product?” and measure the percent who answer “very disappointed.”

After benchmarking nearly a hundred startups with his customer development survey, Ellis found that the magic number was 40%. Companies that struggled to find growth almost always had less than 40% of users respond “very disappointed,” whereas companies with strong traction almost always exceeded that threshold.

A helpful example comes from Hiten Shah, who posed Ellis’ question to 731 Slack users in a 2015 open research project. 51% of these users responded that they would be very disappointed without Slack, revealing that the product had indeed reached product-market fit when it had around half a million paying users. Today, this isn’t too surprising, given Slack’s legendary success story. Truly, this example shows just how hard it is to beat the 40% benchmark.

With only 22% opting for the “very disappointed” answer, it was clear that Superhuman had not reached product-market fit. And while this result may seem disheartening, I was instead energized. I had a tool to explain our situation to the team and — most excitingly — a plan to boost our product-market fit

As an early-stage team, you could just narrow the market with preconceived notions of who you think the product is for, but that won’t teach you anything new. If you instead use the "very disappointed" group of survey respondents as a lens to narrow the market, the data can speak for itself — and you may even uncover different markets where your product resonates very strongly.

To start, we grouped the survey responses by their answer to the first question (“How would you feel if you could no longer use Superhuman?”):

We then assigned a persona to each person who filled out a survey.

Next, we looked at the personas that appeared in the very disappointed group — the 22% that were our biggest supporters — and used those to narrow the market. In this simplified example, you can see we focused on founders, managers, executives and business development  — temporarily ignoring all other personas.

With this more segmented view of our data, the numbers shifted. By segmenting down to the very disappointed group that loved our product most, our product-market fit score jumped by 10%. We weren’t quite at that coveted 40% yet, but we were a lot closer with minimal effort.

Our data shows that more than 80% of customers surveyed would pay once (as a pay-what-you-want and/or one-time purchase).

Yes, despite what many people assume, and while big tech frequently gravitates toward SaaS or subscription models as the route to sustainability, most customers and developers of OSS would strongly prefer the one-time payment approach. Why does this disconnect exist?

We believe it partially stems from subscription fatigue in an increasingly subscription-heavy digital economy. Insights from our survey comments suggest customers find managing recurring payments painful and exhausting.

revealed an average willingness to pay of approximately $18 USD (a conservative estimate, using the bottom of the selection band). Interestingly, digging deeper showed that those who self-identified as 'frequent' donors to open source projects leaned higher, averaging around $27 USD. It's worth noting this group represented about 10% of our respondents, so consider that context when looking at this higher figure–your mileage may vary.

At the same time, our survey reveals what truly motivates customers to pay for open source projects. A clear roadmap leads the way (68.3%), with usability (63.5%) and funding transparency (59.8%) following closely behind.

“You've got to start with the customer experience and work backwards to the technology. You can't start with the technology and try to figure out where you're going to try to sell it”

• Steve Jobs, 1997

I still believe this is a big part of it. There is something handy about a chat experience, but it can be the only one:

It might, but it’s at least equally likely that they’re stuck on the blank screen problem, or that the chatbot itself just isn’t the right product and experience for their use-cases no matter how good the model is.

Interesting. Shows my bubble. As a geek I just love Anthropics offering. 😅

In the meantime, when you have an undifferentiated product, early leads in adoption tend not to be durable, and competition tends to shift to brand and distribution. We can see this today in the rapid market share gains for Gemini and Meta AI: the products look much the same to the typical user (though people in tech wrote off Llama 4 as a fiasco, Meta’s numbers seem to be good), and Google and Meta have distribution to leverage. Conversely, Anthropic’s Claude models are regularly at the top of the benchmarks but it has no consumer strategy or product (Claude Cowork asks you to install Git!) and close to zero consumer awareness.

!!!

For a lot of last year, it felt like OpenAI's answer was “everything, all at once, yesterday”. An app platform! No, another app platform! A browser! A social video app! Jony Ive! Medical research! Advertising! More stuff I've forgotten! And, of course, trillions of dollars of capex announcements, or at least capex aspirations.

That is indeed how Windows or iOS worked. The trouble is, I really don't think that's the right analogy. I don't think OpenAI has any of this. It doesn’t have the kind of platform and ecosystem dynamics that Microsoft or Apple had, and that flywheel diagram doesn’t actually show a flywheel.

So, when Sam Altman says he’s raised $100bn or $200bn, and when he says he’d like OpenAI to be building a gigawatt of compute every week (implying something in the order of a trillion dollars of annual capex), it would be easy to laugh at this as ‘braggawatts’, and apparently people at TSMC once dismissed him as ‘podcast bro’, but he’s trying to create a self-fulfilling prophecy. He’s trying to get OpenAI, a company with no revenue three years ago, a seat at a table where you’ll probably need to spend couple of hundred billion dollars a year on infrastructure, through force of will. His force of will has turned out to be pretty powerful so far.

Foundation models are certainly multipliers: massive amounts of new stuff will be built with them. But do you have a reason why everyone has to use your thing, even though your competitors have built the same thing? And are there reasons why your thing will always be better than the competition no matter how much money and effort they throw at it? That's how the entire consumer tech industry has worked for all of our lives. If not, then the only thing you have is execution, every single day. Executing better than everyone else is certainly an aspiration, and some companies have managed it over extended periods and even persuaded themselves that they’ve institutionalised this, but it’s not a strategy.

I have long defined minimum viable product as the smallest possible product that has three critical characteristics: people choose to use it or buy it; people can figure out how to use it; and we can deliver it when we need it with the resources available – also known as valuable, usable and feasible.

I love the concept popularized by Eric Ries of the smallest possible experiment to test a specific hypothesis, but I refer to that that as an “MVP Test” so that people don’t confuse an experiment with a product.