Stefano Menti

I was CPO at Blend before their $4 billion IPO. We rejected the “forward deployed engineer” Palantir model that everyone is copying now. Here’s why: Blend could have followed the forward-deployed engineer approach. Both our CEO and CTO were early Palantir. They'd seen it work. Instead, we built a SaaS product with core components. Move fast, iterate, use data to learn. Not custom code for every client. That Palantir model is having a resurgence. Now Sierra, Decagon, and others are hiring forward-deployed engineers. But it’s not the right approach for every company. It’s fundamentally the wrong answer for Assembled’s customers. Here's what we understand about support teams and why we’re not going the forward deployed route: Customer Support teams are used to getting no resources and no support. On the surface, you'd think they'd be excited about engineers building custom solutions. "Finally, someone's offering us developers!" The reality is support teams are terrified of getting help they can't afford to keep. Support teams are wary because they know the math. They don't have budget for expensive, scarce resources. At some point the shoe drops. The engineers disappear when priorities shift. And custom solutions break. Support is left holding the bag with something they can't maintain. They're resource-starved by design. The idea of a dependency on resources they can't control is terrifying. So instead, the goal should be to give support teams the ability to manage, change, and solve their own problems. Not dependencies on engineers they can't keep. Smart, competent people who teach them to do it themselves. Palantir wasn't even a product company for years. They were a custom development shop with reusable components. Show up, build something bespoke, maybe extract learnings. That's not scalable for any but the largest support teams who need sustainability. Forward-deployed engineers are the new shiny object. I wasn’t a fan of it at Blend. And I’m not a fan today either. What I see in the customer support space is that teams need empowerment, not dependency. I want the teams we work with to be able to say: "I love that you’re partnering with me. And I love that I can do this myself too." That's the difference between building a product and building a consulting practice.

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LLMs aren’t good enough yet for the general public to use it for shopping… but they’re getting dangerously close. When AI cracks commerce, traffic behavior will change faster than most businesses are ready for.👇

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Love it or hate it, being in the SF Bay Area is still one of the biggest hacks for First Builders to discover huge opportunities before anyone else. But what can you do if you’re not here? Cecilia for example moved here in 2001 and instantly joined Yahoo after feeling the palpable energy around early internet. And it’s been nonstop ever since as she later joined Amazon, Cruise, Replit, and her own breakout company GC AI. She credits a lot to the emerging tech density and optimism in the Bay Area. But she also mentions additional ways people can identify these opportunities early on: - Follow intuition around big waves - Follow your envy - Never lose curiosity - Watch the teen hackers - Talk to peers early and often (builds long term networks/insights)

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I wrote about how product managers should deliberately decide which features should be built to an A grade and which ones should be built to a B or even only a C grade. Making the resource tradeoffs to optimize business outcomes is a core job of product management, and building every capability to an A grade is a poor use of scarce resources. https://lnkd.in/gTXBfNYE

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When the stakes are high, the right tech partner makes all the difference. For LSKD—a global movement rooted in performance, community, and grit—that moment came when they uncovered over $100K in fraudulent affiliate commissions. They didn’t just need data. They needed action. That’s where Moby Agents from Triple Whale stepped in: ✅ $100K+ in fraud detected and prevented ✅ 40% lift in blended ROAS ✅ 3+ hours saved per week on reporting and analysis ✅ $8M AUD in revenue growth—with no increase in ad spend But the real story is how it happened: Agent-powered anomaly detection spotted affiliate abuse during a major sale—something even seasoned analysts could’ve missed. AI-driven reporting replaced siloed, inflated platform data—becoming LSKD’s new source of truth. Smarter budget reallocation fueled efficient growth, driven by real impact—not last-click noise. This is what it looks like when brands use AI strategically, not reactively. And here’s the key: Moby Agents didn’t replace the LSKD team—they enhanced it. By handling heavy, repetitive analysis, agents freed the team to focus on what really moves the needle: strategy, creativity, and innovation. This isn’t about less human input. It’s about higher-value human output. Full story here: https://lnkd.in/eNiVtyrC #eCommerce #ArtificialIntelligence #PerformanceMarketing #RetailTech #DTC #GrowthMarketing #triplewhale #moby

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We'll learn a lot more about today's outages over the next couple of days as we start to get post mortems from the different platforms that were impacted. But it was interesting to see that traffic on our PNIs to GCP were not impacted uniformly with big traffic drops in LAX, ORD, SEA, SJC and DFW, but increases in traffic in FRA and AMS as customer services failed over. This highlights the important of proper DR planning with a minimum of multi-region and ideally multi-cloud.

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Excited to share my conversation with Marc Baselga and Ben Erez on the Supra Insider podcast. We went deep on one of the biggest shifts happening in product leadership right now: why many experienced PMs and product execs are returning to individual contributor (IC) roles in the AI era, and why that’s not a step back, but a strategic leap forward. Other topics we discussed: -- Why the leverage equation has changed—and how managing AI agents now rivals managing people -- The critical importance of hands-on credibility in AI-native companies -- Why great PMs in the future will blend product, design, analytics, and frontend execution -- What it takes to deliver real customer outcomes (not just launch features) -- How to evaluate breakout companies—and what founders often get wrong when hiring product leaders I also shared my thoughts on: -- Joining the right company: mission, trajectory, talent density, and founder courage -- Crushing it as a “super IC”: the power of speed, ownership, and infectious energy -- Transitioning from product leader to GM or founder with the right mindset If you’re navigating your next chapter in product (or just trying to stay sharp in the AI-native world), I hope you’ll find this useful. Episode links: Spotify: https://lnkd.in/gK48PFWK Apple: https://lnkd.in/gmT265BY YouTube: https://lnkd.in/gtr4RAKd Substack: https://lnkd.in/gM38FSWc

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When we were planning the Productboard Spark public beta, our initial instinct was to release it quietly. Save the bigger push for when we hit GA. But we kept coming back to the same question: why are we holding back? So we made a different call. We decided to go big, to talk openly about what we were building, and to bring people along for the ride as we were doing it, not after. That's what building in public means to me. It's not a campaign tactic. It's a mindset shift that touches everyone at the company. Product, engineering, marketing, people, finance—everyone sharing how they're using Spark, what they're learning, what's surprising them. The whole journey, not just the highlight reel. Here's what that actually looks like in practice: Make it everyone's job, not just marketing's. Every person at Productboard regardless of team, was asked to share how they use Spark in their day to day. Not polished campaigns. Just real, honest usage. Use it as a feedback engine. We're refining our messaging week over week based on what's actually landing — not waiting on a quarterly research cycle or lengthy user testing. The community tells you fast. Let it replace the blank page. One of my favorite examples: I used Spark to synthesize everything our CEO has written about AI into a cohesive narrative. What would have taken weeks of back and forth took a fraction of the time. When we put it in front of him, nothing felt out of place. Start small if it feels uncomfortable. Building in public isn't natural for everyone. It doesn't have to be an elaborate post or a big thought leadership piece. You build the muscle over time. From a product marketing perspective, this has been genuinely energizing. We're getting real feedback in real time. We're building relationships with the product management community, people who deeply understand the value of iterating in the open. I've done more traditional launches before. The kind where you hold everything close and wait for the perfect moment. There's a logic to that approach. But in a space moving as fast as AI is right now, I think there's more to be gained from showing your work than from waiting until it's polished. We're still learning. And honestly, that's kind of the point.

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The Rant - From Black Friday to AI Friday. Happy Thanksgiving. While you digest the turkey, the internet is shifting beneath our feet. We are witnessing the death of the "Search" era and the birth of Agentic Commerce. On black fridays, for decades, we lined up at physical stores, then the digital economy shifted us to laptops. But whether you were shivering in a parking lot or refreshing a browser to catch that once-a-year deal, you still did the work. You searched, you filtered, you compared tabs. Not this year. This is the first holiday where we stop searching for deals and start assigning tasks to agents. The friction of decision-making is collapsing to zero. 𝗪𝗲 𝗮𝗿𝗲𝗻'𝘁 𝗯𝘂𝘆𝗶𝗻𝗴 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀; 𝘄𝗲 𝗮𝗿𝗲 𝗼𝘂𝘁𝘀𝗼𝘂𝗿𝗰𝗶𝗻𝗴 𝘁𝗵𝗲 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻. Here is your toolkit for the first true AI holiday. 𝟭. 𝗧𝗵𝗲 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿: 𝗣𝗲𝗿𝗽𝗹𝗲𝘅𝗶𝘁𝘆 The "Do It For Me" button is finally here. Perplexity, in partnership with PayPal, launched Instant Buy. You don’t browse; you prompt: "Buy the best noise-canceling headphones under $200." It handles the login, the shipping, and the payment. One click. Done. (Note: Keep an eye out for their 1-year free subscription offer—it’s a no-brainer). 𝟮. 𝗧𝗵𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗦𝗵𝗼𝗽𝗽𝗶𝗻𝗴 No more reading "Top 10" lists or watching 20-minute YouTube videos comparing minor spec differences. OpenAI’s new Shopping Research feature is your personal analyst. It doesn't just list links; it synthesizes the entire web into a comparison table of Pros, Cons, and Price History. 𝟯. 𝗧𝗵𝗲 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲: 𝗔𝗺𝗮𝘇𝗼𝗻 𝗥𝘂𝗳𝘂𝘀 Don't read 500 reviews to see if a coffee maker leaks. Ask Rufus: "Does this machine actually last longer than a year?" It reads the reviews for you and gives you the semantic truth, not the marketing copy. 🧠 𝗧𝗵𝗲 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 We are shifting from an economy of Cheap Goods to Cheap Services. The value of Black Friday used to be the $200 discount on a laptop. Now, the value is the 3 hours of research time you get back. The AI isn't just finding the deal; it's performing the service of curation at zero marginal cost. 🤔 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗧𝗮𝘀𝘁𝗲? Today, the agent buys the product. Tomorrow, when you realize you hate the color, will the Agent be the one to initiate the return, print the label, and schedule the pickup? If AI removes the friction of buying, it must eventually remove the friction of regret. Welcome to the age of automated taste !! #AgenticCommerce #AIFriday #AITrends #AIShopping

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Highly recommend watching this episode from Lenny Rachitsky's podcast with the one and only Jeanne DeWitt Grosser (COO of Vercel) on the future of GTM in an AI world. She provides a deep dive into how Vercel is using AI to benefit their sales org today. Must watch.

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VictoriaMetrics is great at handling serious scale. But storage is only half the story. As ingestion grows, the real pain usually shows up upstream with config drift, over-collection, manual rollouts, and inconsistent processing between clusters. That’s why we added native destinations for VictoriaMetrics, VictoriaTraces, and VictoriaLogs in Bindplane. The idea is simple: VictoriaMetrics handles the scale and Bindplane controls what actually gets sent there. You can sample traces intentionally, normalize and enrich only once, and roll changes out safely across fleets. When you’re pushing real volume, being intentional about what gets ingested matters just as much as where it lands.

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⚠️ “The most dangerous phrase in product? We’ve always done it this way.” In 2025, the winning orgs are treating AI + PM as a hybrid craft. Humans set intent; AI accelerates discovery, analysis, and iteration. 📈 Trend to test: AI-assisted product discovery & delivery (human judgment × AI leverage). 👩‍💻 Test-and-learn plan (6 weeks): 1. Scope one riskiest assumption for a live bet (who/what changes if we’re wrong). 2. AI-accelerate discovery: generate JTBD hypotheses + cluster qual notes; human review gates. Mind the Product 3. Rapid briefs → micro-PRDs: 1-pager with success and counter-metrics. 4. Prototype 2 variants (human-crafted + AI-assisted); ship to 10–15% traffic. 5. Guardrails: eval set, hallucination checks, rollback plan. 6. Decide from deltas: promote only if primary ↑ and guardrails stay green; log decision. Prompt to steal: “List 5 user struggles for [persona/job]. Map to hypotheses with evidence score. Flag missing data.” (You still review.) #innovation #change #mindtheproduct #productmanagement #AIproduct #experimentation #discovery #JTBD #saas #usa #productleadership

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In any organization, time and resources are finite—but distractions are not. When teams align around clear priorities and focus on what truly matters, execution accelerates, quality improves, and impact compounds. Strategic focus is not just about doing less—it’s about doing what drives the highest return. The result? A high-yield dividend in productivity, morale, and long-term value. Revenue growth is a nice side effect. https://lnkd.in/d3m82ipd

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I rarely make predictions, but... No company founded after 2026 will organically grow to more than 10k FTEs. The leverage of rapid iterative decision loops by teams using best-in-class AI has reached escape velocity over traditional economies of scale.

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I took my second Waymo across Los Angeles last night, and the experience was largely excellent. From the smooth pickup to navigating the freeway (which is now supported here in LA!), the technology is undeniably impressive. However, the "last mile" wasn't quite perfect. I was dropped off on the left side of a two lane, one way street, directly in traffic rather than on the shoulder. Also, my destination was on the right. As I've written previously (https://lnkd.in/gHw7drrc), there is great promise in using technologies like AI in countless ways, from autonomous driving to talent measurement. However, my recent Waymo experience highlights two critical lessons: 1. Humans Must Own the Outcome: Whether you are a passenger in an autonomous vehicle or managing talent, you cannot delegate accountability to the machine. Technology is a powerful tool, but humans must own the final result. In hiring, as in transport, we must remain in the driver's seat to ensure the "drop off" (i.e. the final decision) is the right one. 2. The Feedback Loop is Non Negotiable: Without human feedback to these systems, technology will never improve. I made sure to provide feedback on my ride, and we must do the same in our talent systems. If we don’t provide constant, iterative feedback, we lose the opportunity to refine them into something better. Progress happens when human intuition meets technological scale. We provide the feedback; the tech provides the lift.

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AI has fundamentally changed how marketplaces offer help. In the traditional model, the handoff from a platform to a human can lack context about where they are in their workflow and what they are trying to accomplish. With AI, that happens instantly, and with intent. That means there is no room for a trust gap because everything has to go right in a split second. Perfecting this handoff requires more than just good code and a smart person. It requires a unified ecosystem under one roof. There are only a handful of companies equipped to deliver this world class experience, and I enjoyed listening to my colleague Alex Balazs discuss the topic with Aprio's Jordan Fladell. Give it a listen and let me know your thoughts > https://lnkd.in/ggdrxdzK

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Here’s the thing nobody tells you about long-term product adoption: It’s driven by people, not feature updates. Community is the multiplier. It creates superfans. It builds a moat. It deepens product usage. It gives you real signal. It expands your brand through stories you didn’t have to write. When you put all of that together, you stop thinking of community as a program. You realize it’s part of the product. And it's probably the most important part.

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