Building digital products is changing.
If you were able to take anything away from my somewhat meandering and self-aggrandizing Becoming a Product Builder essay, it was probably this.
It’s easier than ever to build good software.
After spending the past 90 days doing it, I can confirm this is true. But that really wasn’t the point I was trying to make, nor is it really insightful.
And while I’d be one step closer to becoming a proper influencer if all I did was pound my chest, I do actually like writing things that at least attempt to have a nugget of insight within them.
So, here is Becoming a Product Builder, Part 2: The Actual Insight.
AI Builds the Average
Strauss Zelnick, the CEO of Take-Two Interactive, has a really thoughtful take on this in his interview on David Senra’s podcast.
He points out that AI is inherently backward-looking. It is trained on vast amounts of existing information and then creates new information by remixing it.
I think he’s right. AI is exceptionally good at giving you a derivative product, a product that is like another product but slightly different. It produces roughly the average of everything it’s seen.
When building with AI, it’s easy to think that its initial response to a prompt is good enough.
I can state confidently, it is not. AI nearly always produces “good” output; it rarely produces “great” output.
This concept is why so many of the real influencers are harping on the importance of “taste”. In a world that is going to become increasingly saturated by the average as more and more people leverage AI as their primary means of creating output, the experiences and businesses that stand out are going to be the ones where the people leveraging AI have taste.
The people who can compile all of the average output of AI and combine it into something greater than the sum of its parts are the ones who will be able to find success. When building with AI, never settle; always strive for something that is uniquely delightful.
The cost of delight has never been cheaper
The Old Way
A delightful product has always been valuable. Books have been written on how to structure your backlog so that delight is included (if you haven’t read Product Delight by Dr. Nesrine Changuel, pick it up; it’s a good one).
But historically, building something uniquely delightful has been exceptionally difficult, and not because it’s actually hard to build.
In the old way of product building, nearly everything was a trade-off.
As the PM, I’d have a backlog of things I could ask my very expensive engineers to work on, and the things that would trickle to the top of the backlog almost always had some kind of return on investment.
You could argue that a product manager’s primary role is to maximize the return on the company’s investment in engineering team members.
In that environment, it is difficult to justify investment in weird stuff. It’s the premise of The Innovator’s Dilemma; it’s why the phrase “No one ever got fired for buying IBM” exists. It’s the antithesis of classical OKR’s.
In the face of uncertain ROI, it’s easiest to make rational, defensible positions. Invest in what you can measure.
AI changes the equation.
The New Way
Here’s a simplified example.
One of the many products I’ve built recently is a custom web app that shows on the projector as you enter the suite. It welcomes you to the suite, tells you when your reservation starts, etc. Pretty simple. Nothing fancy.
It’s June 15th. I see July 4th around the corner. I think “Man, it’d be fun to make the web app explode with fireworks before it unlocks.”
In the old world, building this would have looked something like this:
- Meet with the designer, tell them what I’d like it to look like.
- Get the designs.
- Prioritize the work over another feature, probably one that was enabling a measurable, ROI-driving metric.
- Get the engineers to focus on the fireworks and build and deploy it.
Not impossible, but at the end of the quarter when I share with my leadership team how my team missed our metrics, saying “But I added fireworks to the lock screen” probably wouldn’t win me any awards.
In the new world, it took about 30 minutes of back-and-forth with AI, and it was deployed. The cost? Essentially nothing.
The Point I Missed
The point I failed to make in “Becoming a Product Builder” is that becoming a good product builder isn’t about replacing software engineering. It isn’t about how much you can output in 90 days.
The point is, as the number of average products exponentially increases due to AI simplifying software development, the importance of learning how to move beyond the average to differentiate your products has never been higher.
The point is, AI allows you to build delightful products in a way that sidesteps the classic ROI paradox most product teams face when trying to invest in disruptive or off-the-wall ideas. It lowers the barrier of entry to building good software, but it also lowers the barrier to entry of building great software if you just make a concerted effort to do so.
This is the paradox of AI.
Yeah, it will probably make the average internet a vastly less interesting place filled with slop and weird videos of cats in parliament.
But if you use it right, it gives smart people an opportunity to stand out amongst a sea of slop.
So go create delight in your products. Don’t settle for good software; settle for great software.
⁃ Brian