ProductLab 2025 Conference Redux
I attended the ProductLab conference here in Berlin in September and thoroughly enjoyed it. While I posted on LinkedIn, they were abbreviated due to the character limit; here’s a longer summary.
Again, thanks and kudos to Daniele and the ProductLab team for putting together a fantastic day—it’s not an easy feat! Note also that there were three parallel tracks: the sessions I attended and have summarized here don’t represent the whole conference.
High-Level Thoughts
AI and vibe-coding are allowing more people to build higher-fidelity prototypes. On a high-trust, psychologically safe team this can democratize idea-generation and spark great conversations.
AI seems to be the catalyst for a lot of teams reevaluating their processes in ways that aren’t specific to AI. Processes have calcified and become overly “waterfall” and pedantic; this hits PMs especially hard because great product-development requires ambiguity and flexibility, which is in conflict with predictable process. For example: I’m still a believer in understanding the customer problem separately from testing solutions, which is a bit of process/framework…but those don’t have to happen in series and sometimes it’s fine for them to bleed into each other.
We’re starting to get real about AI. I see this in the news and on LinkedIn, too. That’s great: all the “AI will take all our jobs and solve all our problems and destroy the world” talk is exhausting and makes us feel gaslit. Whereas, “Hey, AI can do some cool stuff and we’re figuring it out,” is energizing.
Vibe-coding is not production coding — I’ve always felt that way and it was good to hear it from multiple speakers.
Humans are great at stuff that AIs aren’t, and some of that stuff is needed in product management. Find things that are “un-LLM-able” and do them.
Stefan Ostwald: The Intelligence Age
Stefan talked about his vision for AI and agents in particular. He laid out some useful principles when thinking about AI product design:
Design for reference, not description. I’ve talked about this as “direct manipulation” — basically people will want to manipulate artifacts or parts of artifacts with AI. Not, “Change the blue one second from the right,” but, “Here, change this one.” This is also a critical component of the project I’m doing with.
Design for review, not perfection. AI makes mistakes, so build products that expect it to.
Design for behavior validation, not configuration. This is in part about evals and automatic prompt optimization.
Fabian Strunden: AI from Discovery to Delivery
In this roundtable, Fabian laid out the first compelling case I’ve heard for PMs vibe-coding prototypes. His team uses them as a communication tool, acknowledging that they’re not “real” designs and simply serve to communicate an idea — it’s expected that designers and others will push back and it’s really just about the conversation. They use the hashtag #badidea to indicate these moments.
This makes a lot more sense to me than what I see on LinkedIn — “PMs can stop doing everything else and just vibe-code prototypes!” — since it doesn’t imply that such prototype are sufficient…just useful. A couple things occurred to me as a result of this roundtable:
We see a lot more talk about AI accelerating solution discovery than problem discovery…but in fact it’s useful for both, e.g., automating literature reviews, coding user research transcripts. (I’ve been doing a bit of this in my productivity-app research.)
For prototypes to work as conversation, you need a high degree of trust and psychological safety within a team.
Pranav Pathak: Scale Experiments with AI
Pranav got into specific frameworks and examples for how to prioritize and evaluate AI product experiments. He explained the PROVE-IT prioritization framework — Pain, Reach, Own data, Verifiable, Execution loop, Integration time, Total cost. He then took us through two real-world examples from Booking explaining how the framework applied, and how each of the projects turned out.
In a world of vague hype it was great to see such concrete examples and analysis.
I also liked what he had to say about metrics: track the right metrics. A good metric is clear, reflects the actual product goal, and is iterable. Business metrics are problematic because they’re also sensitive to outside factors like the global economy; engagement metrics are too easy to game.
Lucie McLean: Navigating Organizational Change
Lucie led this roundtable discussion of organizational change — a topic that’s always on people’s minds and especially these days with layoffs and the spectre of AI. As is so often the case, I was impressed and humbled by people’s willingness to ask for and offer advice with thoughtfulness and vulnerability.
Elena Leonova: The Brutal Truth About AI Product Success
A lot of AI strategy is hype. Key point here: Your moat can’t just be the AI model because everybody has the model. What meaningful differentiator are you delivering beyond a prompt? Potential moats include your data, your behavioral understanding, or your workflow (i.e., does it match what your users need and expect?)
Asya Kuznetsova: Escaping the Paid Growth Trap
Enjoyed this not only because it wasn’t about AI, but because Growth isn’t my core skillset. Asys says beware paid growth: its techniques keep changing, and it can be expensive and unsustainable. She then went into detail about techniques and perspectives that work:
Word-of-mouth is powerful and is something you can drive.
Organic recommendations are powerful: encourage customers to notify other people about your product as part of the actual functionality. Ex: splitting the bill in Revolut, requesting money from non-user contacts in Wise. (I recall emails saying, “You’ve got cash!” in the early days of PayPal, too.)
Ask yourself: How can customers benefit from having others use your product?
Incentivized recommendations (e.g., referrals) work well, too. Things like rewards or discounts for referring friends & colleagues. The right incentives vary not only by product but also culturally.
Counterintuitively, you want to nudge customers to refer early in the journey: after the first food order, etc. This is when delight is highest.
In a noisy world, people still trust people—a sentiment echoed later in Francesca Cortesi’s closing keynote.
Simone Basso: The Community Bet
Simone talked about a new product he and his team built at WeRoad. They started with an idea and, with a team of 5–6, went from zero to MVP in a couple months. Two interesting tidbits here:
Yes, they vibe-coded a prototype in a couple days…and then redid it from scratch to create the actual MVP.
What they learned from the MVP? They hadn’t fully understood the customer problem. I asked Simone about this after the talk: if he did it over, would he pause and do a little research? He said no: this was the fastest way to learn. Reminder that discovery and implementation are different activities using some of the same methods.
Francesca Cortesi: AI is here — Now What?!
There’s so much uncertainty, ambiguity, and rapid change around AI. What’s happening? What’s going to happen next? Cortesi doesn’t know. Most of us don’t know. And that’s OK. Instead of reacting with fear, let’s react with curiosity.
AI is machines: when you play the speed game with machines, you lose. But humans are good at things that AI is not — things that are “un-LLM-able.” Find the un-LLM-able and focus on that instead. Curiosity, learning and unlearning. Thinking creatively.
LLMs offer the illusion of thinking but can’t think outside the box. Trust: LLMs hallucinate and that makes trust an un-LLM-able skill. “Trust is your most important currency.”
Don’t react: strategize. That’s un-LLM-able too. You can go fast…or you can go intentionally in the right direction. (I’m reminded of the old saying, “Don’t mistake motion for progress,” which has always been an issue.)
Here are Francesca’s slides, shared with her permission.
is a versatile product leader with 25 years of experience spanning Product, Design, and Engineering. A two-time founder with exits to Google, he’s held leadership roles at Google, Facebook, Heap, and Yahoo. Now based in Berlin, Dave advises the ProductLab community on industry trends and maintains strong connections to the US product scene. You can follow him on LinkedIn.
 | A guest post by
|