Bo Peng

What Makes a Great Product Manager? Just watched an insightful talk that every aspiring and current product leader should check out: “The Art of Product Management with Sachin Rekhi” 🎥 a deep dive into what product management really is and how to excel in the role. In this talk (recorded at The Wharton School), Sachin breaks down the core pillars of product management from shaping vision and strategy to mastering design and execution and shares lessons from his years in Silicon Valley. 📌 Key takeaways to boost your PM mindset: • Product management isn’t just about features it’s about vision and customer value. • Strategy needs clarity, prioritization, and measurable outcomes. • Effective execution is rooted in cross-functional collaboration. • Learning the craft is a continual journey this talk gives timeless frameworks. 💡 Whether you’re aiming to step into your first PM role or looking to sharpen your approach, this talk is a must-watch resource in your product career toolkit. 👉 Watch here: https://lnkd.in/gUEDK5aw #ProductManagement #ProductLeadership #CareerGrowth #ProductStrategy #PMInsights

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We're really excited to share that Clay's $100M Series C at a $3.1B valuation led by Alphabet’s CapitalG! This momentum is a shift in the market. GTM has traditionally been around throwing bodies at a problem, sending massive volume of outreach for low conversion rates, and hours of manual research and data entry. But with better tools, AI used effectively, and a smarter strategy around it, growing your company doesn't have to be that way. The New York Times article touches on the understandable fear around AI replacing human jobs; Clay flips that on its head. You can't just point AI at a problem and expect it to be effective. It needs a human in the loop providing the right context while using their judgement to develop the guiderails and benchmark the quality of output. Clay puts the the person, the GTME operator, back in the driver seat by automating all of the manual, soul crushing work, while giving that person the space to do the complex and creative work that AI can't. As Everett Berry once said "the only effective GTM tactic is to always be changing tactics". We believe that you need to be constantly experimenting to stay ahead, and the best way to do that is with Clay.

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Every time you scale your team, you reset it. 🔄 Growth in Scaleups is often treated as a linear path, but in practice, I’ve seen scaling teams repeatedly reset their maturity. The classic playbook “hire into the team, split it in two, and repeat” keeps teams cycling through the “Forming”, “Storming”, and “Norming” phases, never reaching the “Performing” phase unless you expedite the team’s maturation. When new hires join an already-performing team, the team slows down for weeks. The team re-enters the Storming stage, where boundaries, roles, and expectations must be redefined under delivery pressure. The question isn't whether your team will reset. It's how fast you can get them back to Performing. Here is what you can do at each stage to accelerate team development and get to the “Performing” stage faster: 🌱 Forming (orientation). When the team is formed, and members don’t know each other yet, it’s important for the leader to lay the foundation for successful work: - Define the team’s goals - Clarify team roles - Set clear project management expectations Without structured leadership, this stage can last up to 6 weeks. With it for around 2 weeks. ⚡ Storming (transition). After getting to know each other, conflicts arise and performance declines. That is normal, but needs to be managed actively: - Mediating conflicts - Fostering open dialogue - Reiterating the shared goals Without facilitated alignment and explicit decision-making frameworks, it can last 4-10 weeks, while with them, it can be reduced to 2-4 weeks. 🔧 Norming (action). Team members begin to learn to work together in rhythm. Your job is to nourish it: - Encourage sharing ideas - Clarify ways of working - Eliminate ambiguity in responsibility This stage takes 3–8 weeks, but with structured actions, it can be shortened to 2-5 weeks. 🚀 Performing (production). Team achieves its peak effectiveness. Protect it: - Minimize changes - Scale the team gradually - Prioritize stability and predictable workflow At this stage, even small changes can trigger regressions. With structured leadership, it can be sustained long-term and recover quickly from disruptions. Companies that scale effectively are not those that avoid resets, but those that accelerate teams back to performance faster than others. Next up: how the famous “Five Dysfunctions of a Team” helps diagnose why teams get stuck in their development, and how to move them forward. #Leadership #TeamDevelopment #Scaling #EngineeringLeadership #ProductManagement #AgileTeams #PeopleManagement #StartupGrowth #TeamPerformance #ScaleUp

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At some point in my product journey, I realized that knowing many frameworks isn’t the real advantage. Knowing when not to use them is. Earlier on, it was easy for me to lean on frameworks for everything, RICE for every prioritization call, JTBD for every user interview, User Story Mapping for every feature idea. Sometimes that helps. Other times, it quietly slows things down. For example, I ran a RICE session thinking it will bring clarity, but the data isn’t solid. The numbers look neat on a spreadsheet, yet everyone still left the room unsure. The framework didn’t fail, the context did. That’s when the lesson becomes clear: frameworks don’t think for you, they support your thinking. Here are 3 product frameworks worth swearing by, and when to pause before using them: RICE Scoring – Great for prioritization It’s useful when you’re choosing between multiple competing features and need alignment. But if your inputs are assumptions, RICE turns prioritization into confident guesswork. User Story Mapping – Excellent for understanding user journeys. It shines when users move through multiple steps, decisions, and emotions. But if your product has one clear, linear path, mapping every step can feel like over-engineering. Jobs-To-Be-Done (JTBD) – Powerful for uncovering real motivations. It helps when users say one thing but behave another way. But if you only need quick validation, “Does this feature make sense?”, JTBD may have more depth than necessary. The real insight? Frameworks are tools, not commandments. They work best when paired with judgment, context, and experience. Which framework do you lean on most, and when has it been more helpful or more confusing? #ProductManagement #ProductStrategy #PMFrameworks #BetterDecisions

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Scaling doesn’t reveal how mature a product is. It usually reveals how much velocity was required to compensate for misalignment. There’s plenty of good advice about moving beyond feature delivery, being more intentional, clearer about outcomes, and more deliberate about how product work is framed. I see these as directionally right, but incomplete. In earlier product environments, progress often meant moving fast in the direction of whoever had the strongest pull. Work started quickly, and value was often obvious enough to justify after shipping. At larger scale, that stops working. Not because teams lose judgment, but because when many capable teams move in parallel, misplaced effort compounds fast, and the cost of being wrong becomes explicit. What changes most isn’t problem complexity, it’s that the value must be articulated before execution, not discovered through it. At a certain point, even expert intuition ceases to function as a coordination mechanism. Not because expertise disappears, but because intuition doesn’t scale across dozens of decision surfaces. That’s where maturity truly becomes evident. In more developed product environments, alignment isn’t seen as overhead: it’s regarded as essential product work, encompassing framing, shared language, explicit trade-offs, and clarity on what not to do. A somewhat unsettling conclusion I keep returning to: velocity looks like progress... until the product has enough blast radius for misalignment to matter. Photo by Ahmad Odeh on Unsplash.

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When you can talk your heart out on some hard hitting questions, those kind of conversations are far and few. I had such an amazing 1.5 hours of discussion on the podcast The Naked Product with Saurabh Saha, who is one of those extremely well read person who can give you a reference from any well known author and book at the drop of a hat. We went straight into the tectonic shifts happening right now: 💼 New job descriptions — why the PM role of 2025 looks nothing like 2015 🎨 Collaboration redefined — how PMs, design, engineering, and data science are building together differently 🧠 Skills that matter most — the capabilities that will make PMs indispensable in the next 3 years Then came the AI shockwave: ⚡ How generative AI is reshaping roadmaps, decision-making, and success metrics 🎭 The traps of “AI theater” (and how to avoid them) 📚 A practical AI upskilling playbook — from the 80/20 learning approach to building technical fluency without becoming a coder We also dove deep into leading AI-first teams: 🤝 Hiring for new capabilities in uncharted territory 🌪 Navigating uncertainty in a fast-changing tech landscape 🪢 Bridging research and business to ship impactful AI products ⚖️ Handling ethics, bias, and governance in AI Finally, we looked ahead to 2030’s PM: 🛠 The automation risks no one’s talking about 🏷 The fate of the “mini-CEO” label 🌍 The industries about to be completely reshaped by AI And yes to make it a bit entertaining — we ended with a rapid-fire round of tools, skills, and predictions that every PM should know before the AI tide rises even higher :) 🎥 If you’re a PM, an aspiring PM, or leading product teams, this isn’t just another podcast — it’s your field guide to thriving in the AI era. 👉 Watch the full conversation here: https://lnkd.in/g6wnmG-u Now tell me — what’s the one skill you believe PMs can’t afford to ignore in the AI era?

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🙌 Most recent Product Sense cohort had 10+ designers, a record. More than 50 designers total have joined to date (about half of them design managers).

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for the past year, I have seen too many startups falling into the trap of "shipping the AI PRD." let me explain: a PRD (product requirement document) is what a PM writes to communicate their vision to engineering and design teams. there are startups building AI that helps PMs chat and generate a PRD faster. here's the problem: that's a transient bottleneck. because tools like Lovable are skipping the PRD entirely. you don't need a document when you can show and communicate way better. when you're building with AI, you have to avoid building the "AI version of the old process." you have to ask: how do i circumvent this entire process and rip it apart at its seams? that's the long-lasting strong company. examples of AI PRDs to avoid: - AI that makes meetings more efficient (vs removing meetings) - AI that speeds up handoffs (vs removing handoffs) - AI that writes better docs (vs removing docs) the question isn't "how do we make the old way faster?" the question is "what becomes possible when we remove the constraint entirely?" that's how you find the real opportunity.

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Introducing Endorsed's new $159/user/mo Growth Plan for fraud detection at mid-sized businesses and growth stage startups. It's safe to say every team hiring remote engineers in 2026 is getting heavily affected by fraud. To make purchase decisions and internal alignment simpler, we've created a new standardized package of our best-in-breed fraud protection for teams. Features include: - 94%+ accuracy across 350+ signals - Unlimited fraud checks - Instant fraud checks, so no waiting for loading - "Cleansing Your ATS" by fraud-checking everything down-funnel upon connection - Fraud analytics - Full SOC 2, GDPR, and AI compliance - AI Applicant Review as an optional add-on Feel free to DM me with any questions. Link in description!

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Life got busy - personally and professionally - so I took a break from blogging. But I’m back! Depth builds intuition. Breadth builds perspective. A great PM learns to balance both. That’s the key takeaway from my conversation with Shreya Kale, Product Lead at Redis. We discussed how she discovered product management, navigated engineering-driven organizations, and continues to embrace learning every day. Read the full interview on my latest Substack post. (Link in comments)

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Finding product-market fit isn’t a neat checkpoint—it’s a complex, iterative journey full of uncertainty and tough calls. According to insights shared at TechCrunch Disrupt 2025, one key takeaway is that startups succeed by combining rigorous smart testing with real-time iteration while staying intensely tuned to user feedback without getting overwhelmed by noise. This approach moves founders beyond guesswork into actionable growth strategies. What really stands out is how product-market fit isn’t a static milestone but a continuous process of learning and adjusting. Rajat Bhageria from Chef Robotics, Ann Bordetsky from NEA, and Murali Joshi of ICONIQ break down how this messy phase can become a competitive advantage when founders embrace it as ongoing and dynamic, rather than a single achievement. Their collective experience spanning AI innovation, venture capital, and high-growth startups sheds light on practical ways to cut through the chaos. For anyone building a startup or scaling product lines, this reframing challenges us to move past the traditional product-market fit myth and instead focus on relentless user listening and nimble experimentation. How have you balanced the tension between user input and decisive product moves in your own ventures? #StartupGrowth #ProductMarketFit #TechCrunchDisrupt #FounderInsights

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"Is AI really on our side - or are we rushing headlong into hype? . . . . . . . Two eye-opening developments this week: Vibe coding is the buzz: top-tier firms like Visa and DoorDash are experimenting with AI-generated code in ideation phases - but leaders warn it still needs serious human oversight. David Sacks calls it a "Goldilocks moment" for AI - not dominated by one tech giant, but fuelled by healthy competition across models, platforms, and teams. In our work with startups and SMBs - proptechs, healthtechs, fintech - we see the magic and the mess. AI can accelerate MVP delivery and streamline workflows But without proper guardrails, it's too often brittle, unpredictable, or overpromised. Founders & CEOs - where do you land? Is AI your trusted teammate - or the wild card your strategy still needs to tame? Curious to hear your POV ↓" #startups #founders #MVPs #Funding #UnitedStates #Seed #Hiring #Stealthstartup #Fintech #Healthcare #RealEstate #Proptech #Prypco

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In every product leadership role I’ve taken, my key to success was spending as much time on recruiting as I did on product. Great teams build great products. I believe building teams deserves the same craft and intentionality as building the product itself. And with AI giving teams unprecedented leverage, teams that hire top talent faster will emerge as the winners. Given how much I value recruiting, it’s fitting that Paraform was the first investment I led at Felicis. Paraform is an AI-enabled, recruiter-first marketplace that helps companies hire 3x faster and helps recruiters earn 3x - 5x more than traditional recruiting roles. Their marketplace and tools give elite recruiters more speed, leverage, and time dedicated to helping companies hire their hardest-to-fill roles. The traction to date speaks for itself. Companies like Palantir, Ramp, Rippling, Cursor, Windsurf, and Decagon all rely on Paraform to help them hire the best people. Some recruiters have already earned $1M on their platform, and Paraform’s pace is accelerating. I’m so excited to partner with John Kim, Jeffrey Li, and the entire Paraform team on building the future of recruiting. Read more about why we invested here: https://lnkd.in/g6zb8ZrY

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Over time, I’ve realized that feature prioritization isn’t about picking the “right” framework — it’s about making thoughtful trade-offs with imperfect information. Whether it’s weighing user impact, effort, confidence, or urgency, the real challenge is aligning teams on why something should be built now versus later. Frameworks help structure thinking, but context, constraints, and clarity are what ultimately drive good product decisions. Still learning and refining how I approach these trade-offs. #ProductManagement

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MVP ≠ Incomplete: Rethinking What Launch-Ready Actually Means At Perisync Technologies, we help teams go from idea or half-built products to fully working MVPs in just 45 days. You get agile delivery, clean and scalable code, and full IP transfer. Fixed scope. Fixed cost. $9,999. But this is not just about delivery speed. You're not launching a product. You're launching a conversation. Most MVPs fail for the same reason. Not because they’re missing features. Because they don’t give teams meaningful insight. They produce noise instead of learning. Before any code is written, we focus on one thing: What is the smallest version of your product that delivers a complete, learnable loop? This isn’t a placeholder. It isn’t a "lite" version. It’s a testable solution that: ● Creates a clear outcome for users ● Produces real behavior data ● Unlocks the next stage of product growth One example: We helped a HealthTech team reduce onboarding time by 63%. We did it by building a single workflow that mattered, not the entire feature list. This is the core idea. Speed only works when you are clear on what you’re testing. If you’re trying to scope your MVP and unsure what to cut or keep, we can help. Our internal checklist has saved founders weeks of guesswork and rework. Let’s talk. #BuildInPublic #MVPStrategy #StartupExecution #LeanProductDevelopment #ProductLaunch #Perisync

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The real unlock in SaaS isn’t AI. It’s collaboration. AI-native startups like Lovable, Perplexity, and Runway are shipping faster than ever. But what makes them dangerous isn’t just the tech. It’s that they’re building products where people and AI work together. That’s what makes them sticky. That’s what makes them spread. And that’s what most enterprise products aren’t built for. It’s not enough to bolt on a chatbot. To stay competitive, your product needs to be: - Collaborative by default - Built for humans and AI agents - Designed to support fast, multi-persona workflows

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🚀 Tech Shoutout post When you’re building a PLG product, friction in onboarding is the silent killer. At DevAssure - AI Test Agent, we felt this first-hand. As we scaled, we noticed small but critical drop-offs in our user onboarding flow. Different teams had different hypothese. Was it the signup flow, the first test creation, or the way trial users explored features? We knew we had to act fast, but running multiple design experiments without a large PM team was slowing us down. That’s when Featurely AI stepped in. With their platform, we could: ✨ Simulate multiple user scenarios and see how different flows actually played out ✨ Identify hidden friction points in our funnel ✨ Run through design variations at speed without adding more PM overhead ✨ Get smart UX suggestions that directly improved funnel conversion. The results? A far clearer view of where users struggle and a faster path to building experiences that stick. Big shoutout to the Featurely team. You’re helping us (and countless other PLG-first companies) move fast, learn faster, and deliver experiences users love. 🙌 Great Job Krishna (Vasanth) Namasivayam Prajit Prakash

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Over the past week, I've spent 30+ hours live with Shreyas Doshi, and it’s already changing how I approach product work. A lot of courses hand you frameworks and textbook examples. World Class Product Sense is different. It's practical. You learn systematic ways to break down ambiguous problems and make structured, high-quality product decisions. A few topics that really clicked for me: - What drives users to choose a product (and what gets in the way) - Positioning + segmentation you can actually use - Building stronger “taste” and turning it into team-level action - Concrete creativity techniques for better product ideas If you’re trying to sharpen your product sense, I genuinely can’t think of a better investment.

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