Felix Watson Jr.

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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Stop asking for “2-3yrs product experience” for APM roles. APM roles help grads become product managers. Or give them a path to becoming one. But now I keep seeing “2-3yrs product experience needed”. Companies need to stop asking for this. And start calling the role the right title: Product Manager.

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One of the least acknowledged parts of product work is how much emotional labor it requires. We like to talk about product as if it’s mostly analytical. Frameworks. Roadmaps. Metrics. Decisions. But a huge portion of the job lives somewhere else. Product work is sitting in ambiguity longer than most roles. It’s absorbing uncertainty without spreading panic. It’s managing expectations that were never written down. It’s translating between groups who are using the same words but meaning different things. A lot of the work looks like this: Holding tension when priorities conflict. Saying “not yet” without eroding trust. Letting people feel heard without promising what can’t happen. Taking responsibility for outcomes you don’t fully control. Staying calm when everyone else wants certainty. None of that shows up on a roadmap. But it determines whether the roadmap works. Here’s the lens I use to manage this without burning out: • Name the tension explicitly instead of carrying it silently • Separate understanding from agreement so people feel heard without false promises • Write down decisions and tradeoffs to reduce emotional rework • Slow the conversation before speeding the execution • Treat clarity as a form of care, not overhead When emotional labor is handled well, it’s invisible. Things just feel calmer. Decisions feel clearer. Progress feels steadier. When it’s missing, everything feels harder than it should. Product work isn’t just about building things. It’s about holding complexity so others don’t have to. #productmanagement #workculture #leadership #emotionalintelligence #productthinking #decisionmaking

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63% of companies are now using AI for most coding. But many engineering leaders are still stuck stitching together dashboards just to understand what’s happening. That gap is what we’re tackling at Jellyfish. Today, we're introducing a major update to Jellyfish Assistant – an AI-powered conversational tool that turns engineering data into instant, actionable insights. Instead of digging through reports, you can ask: • Where are we slipping on delivery? • How is effort aligning to our priorities? • Which teams are at risk? …and get answers with context and recommendations. You can learn more about what a smarter Jellyfish Assistant means for you here: https://lnkd.in/eDd_AGzs

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"Because engineers had been through brainstorming and story mapping, they were able to identify the most promising direction and find a way to implement one key piece of it pretty quickly." Want to get your engineers more involved in continuous discovery? Learn how Ellen Juhlin, Senior Director of Product Management at Orion Labs, successfully brought engineers into the discovery process. Key takeaways from Ellen's experience: 🎯 Start by inviting engineers to customer interviews as listeners and note-takers �� Include engineers in brainstorming and solution mapping sessions 🗺️ Use assumption mapping to help engineers focus on validating ideas 🔄 Don't worry about following a linear process - iteration is key 🎯 Begin with just one or two engineers rather than the whole team 🤝 Keep engineers connected to business outcomes Check out the comments for a link to the article. ❓ What's one small step you've taken to include engineers in discovery work? Share your thoughts in the comments below.

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The best part of the new approach is the time given back to employees. Using technology to reduce the bureaucratic aspects of the process and accelerate the relevant signals.

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rqlite -- and the way I develop it -- is fundamentally changing. LLMs are in the loop -- from ideation to Pull Request. https://lnkd.in/ejwXGddR

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If you are a TPM getting lost in all chaos of the role, you can fall back on this simple goal (which has helped me more than once re-focus and be a high-impact TPM): Your goal is to drive towards business outcomes. That's it. Now it gets more interesting when you ask "HOW DO I GET THERE??" Here's a vehicle you can use to get there: CAPE. CAPE is the trusty vehicle I've relied on many times to model my approach. Here's what it means... Clarity. (With no clarity, it is impossible to get cross-functional alignment) Alignment. (With no alignment, good luck making any meaningful progress) Progress. (With no progress, you'll never be able to improve efficiency) Efficiency. (With no efficiency, you'll never improve or reach the outcomes in a timely manner) All this leads to outcomes! So if you've got a struggling program, swim upstream until you find which one of these is missing. Then as you fix it, results will flow downstream again. Drive towards outcomes. That's your job!

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Energized by "AI Meets Formal – Redefining the Verification Frontier". Just wrapped up an incredible panel discussion. I felt energized hearing from our FV friends on how they are seeing and seizing this opportunity to lower the barrier and increase the formal footprint. Huge shoutout to VC Formal and Sandeep Jana for creating this amazing platform where our formal verification community can come together to share insights and learn from each other. Key Takeaways : - Embrace, don't fear GenAI – It's time to face this technology head-on and make it our ally - Fundamentals still matter – Strong formal verification foundations remain our competitive edge - Think systematically – We need methodical, scalable approaches to GenAI integration for long-term sustainability - The barrier is lowering – GenAI is set to democratize formal verification, making it more accessible than ever #Intel #FVCTO #FormalVerification #EDA #GenAI #HardwareVerification

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🚀Hi all - I’m launching a newsletter (Reflections by TK) for builders and leaders in tech. I've spent over a decade operating at the intersection of product, engineering, and business. This newsletter is my attempt to slow down, reflect out loud, and synthesize my learnings on building, scaling, and leading in complex environments—with the hope it helps others in navigating their own path with more clarity and confidence. It is also an attempt to finally shape my writing dream. ✍️ The first post is about a tension many of us wrestle with: How do you scale good judgment without replacing it with undue process? In large organizations, we rely on frameworks and processes to scale good judgment and reduce risk of poor decision-making. But somewhere along the way, it’s easy to forget that these are just tools, not truths. We start optimizing for metrics instead of solving real problems, and following checklists instead of thinking from first principles. The best leaders know when to follow the lines, and when to redraw them. In this piece, I share: 🔹 Why process is necessary 🔹 How it starts to break 🔹 When to override it 🔹 How to build a culture of first principles thinking 📖 Would love for you to give it a read and let me know what resonates. How have you balanced the need for process with critical thinking in your teams? #productleadership #techstrategy #firstprinciples #decisionmaking #leadership #reflectionsbytk

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Every week I get pings from colleagues asking to pilot or bring in a new AI tool. The curiosity is high. The desire to move fast is strong. Recently, I spent time with several Bay Area CIOs and a common question came up: How do you move from experiments to adoption to measurable outcomes? At LinkedIn, we enabled teams to pilot AI tools quickly. Unlike traditional SaaS or multi year roadmaps we were used to, this required getting comfortable with constant change. We partnered closely with the business, finance, legal, and security to de risk and fast track experimentation where it made sense. We did not get everything right. Some pilots created real impact, while others faced adoption challenges or did not scale effectively. In a few cases, certain AI tools should have been filtered out before testing. The AI tools that are succeeding are those built into clear golden paths and tightly integrated with existing workflows. If you are not clear on the problem you are solving, the workflow you are changing, and the outcome you expect, AI will not create impact. It will create motion. And likely a new line item in your budget and a dusty dashboard no one logs into. AI enablement is not measured by the number of tools. It is measured by the value delivered. Do not mistake motion for progress. #EnterpriseAI #AIEnablement #AITransformation

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Vibe prototyping**, enabled by LLMs, is not solving meaningful design or product problems. It is degrading work systems, lowering real productivity, and crowding out good design practices. ** 𝘐 𝘥𝘰 𝘴𝘦𝘦 𝘵𝘩𝘦 𝘷𝘢𝘭𝘶𝘦 𝘪𝘯 𝘲𝘶𝘪𝘤𝘬 & 𝘥𝘪𝘳𝘵𝘺 𝘱𝘳𝘰𝘵𝘰𝘵𝘺𝘱𝘪𝘯𝘨 𝘵𝘰 𝘤𝘰𝘯𝘷𝘦𝘺 𝘢 𝘯𝘦𝘸 𝘤𝘰𝘯𝘤𝘦𝘱𝘵 𝘰𝘳 𝘪𝘥𝘦𝘢 𝘵𝘰 𝘪𝘯𝘵𝘦𝘳𝘯𝘢𝘭 𝘴𝘵𝘢𝘬𝘦𝘩𝘰𝘭𝘥𝘦𝘳𝘴 𝘰𝘳 𝘵𝘰 𝘦𝘹𝘱𝘦𝘳𝘪𝘮𝘦𝘯𝘵 𝘸𝘪𝘵𝘩 𝘯𝘦𝘸 𝘤𝘰𝘯𝘤𝘦𝘱𝘵𝘴; 𝘵𝘩𝘢𝘵 𝘣𝘦𝘪𝘯𝘨 𝘴𝘢𝘪𝘥, 𝘵𝘩𝘦 𝘳𝘦𝘴𝘵 𝘰𝘧 𝘵𝘩𝘪𝘴 𝘱𝘰𝘴𝘵 𝘱𝘦𝘳𝘵𝘢𝘪𝘯𝘴 𝘵𝘰 𝘨𝘰𝘪𝘯𝘨 𝘥𝘪𝘳𝘦𝘤𝘵𝘭𝘺 𝘧𝘳𝘰𝘮 𝘷𝘪𝘣𝘦 𝘱𝘳𝘰𝘵𝘰𝘵𝘺𝘱𝘪𝘯𝘨 𝘵𝘰 𝘴𝘩𝘪𝘱𝘱𝘪𝘯𝘨 𝘱𝘳𝘰𝘥𝘶𝘤𝘵/𝘧𝘦𝘢𝘵𝘶𝘳𝘦𝘴. 𝐖𝐡𝐚𝐭 𝐠𝐨𝐨𝐝 𝐝𝐞𝐬𝐢𝐠𝐧 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐧𝐞𝐞𝐝𝐬 Good design requires an environment that supports maintenance, thoughtful iteration, psychological safety, and play. Design work is not linear or purely execution-driven. Designers already do maintenance work, but organizations tend to devalue it in favor of speed and visible output. 𝐖𝐡𝐚𝐭 𝐛𝐫𝐞𝐚𝐤𝐬 𝐰𝐢𝐭𝐡 𝐀𝐈 𝐝𝐫𝐢𝐯𝐞𝐧 𝐯𝐢𝐛𝐞 𝐩𝐫𝐨𝐭𝐨𝐭𝐲𝐩𝐢𝐧𝐠 LLM-powered prototyping creates a culture that rewards velocity over thinking. Executives adopt these tools believing they can bypass expertise and ship ideas directly. This turns experts into “workslop sifters,” forced to clean up low-quality AI output instead of doing real work. Because AI is treated as inevitable, teams cannot opt out or push back. 𝐓𝐡𝐞 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 𝐢𝐥𝐥𝐮𝐬𝐢𝐨𝐧 AI-driven prototyping creates phantom velocity. Output appears to increase, but actual productivity declines. As pressure mounts, teams default to shipping prototypes rather than questioning assumptions, framing problems, or exploring alternatives. Assumptions harden into unexamined truths. 𝐖𝐡𝐲 𝐭𝐞𝐚𝐦𝐬 𝐟𝐚𝐥𝐥 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐭𝐫𝐚𝐩 Vibe prototypes are fast, flashy, and emotionally convincing. They feel like progress even as rigor, focus, and shared understanding collapse. 𝐇𝐨𝐰 𝐭𝐨 𝐫𝐞𝐬𝐢𝐬𝐭 Teams should refocus on intent rather than artifacts. Ask what question a prototype is meant to answer or what decision it supports. Use LLMs diagnostically to reveal where people are overextended or under-resourced, not as a shortcut to skip thinking. Eliminate work that was never important instead of accelerating it. ~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~-~- At the end of the day, AI did not just add a new tool. It reshaped organizational incentives in ways that actively undermine good design. Repairing the system requires re-centering problem framing, resisting fake velocity, and rebuilding conditions where thoughtful design is possible. I've had the pleasure of working with some amazing designers throughout my professional career, so I'm curious to hear what some of my designer (and product manager) colleagues think about this? #ProductManagement #ProductDesign #UXDesign #ResponsibleAI #ProductStrategy

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I just finished watching Satya Nadella’s interview on the All-In Podcast, and two leadership takeaways on the future of work and organizational velocity really stood out: 1️⃣ The Shift to "Full Stack Builders" Satya discussed how LinkedIn is collapsing the traditional silos between Product Managers, Designers, and Engineers into a single role: the Full Stack Builder. In the AI era, the goal is to eliminate "handoff friction." By using AI to "macro-delegate" tasks while "micro-steering" the output, one person can now drive the entire lifecycle of an idea at a velocity that was previously impossible. 2️⃣ Treating "Today" and "Future" as First-Class Citizens It is easy to get lost in the "shiny" future of AI, but Satya emphasized that leaders cannot simply live in the future. He treats current operational excellence—like hot-patching Windows—as a "first-class" priority, just like building the next generation of AI evaluations. Success requires being ambidextrous: you must deliver quality today while simultaneously building the infrastructure for tomorrow.

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Navigating Integration Decisions: The Power of Disagree and Commit Many of you may know that I spent significant time at Amazon before embarking on my journey with Gotara. A principle that deeply resonated with me during my time there is "disagree and commit." While this concept has roots beyond Amazon, its effectiveness in fostering robust decision-making is undeniable. Recently, I had the honor of sharing insights on a panel alongside my esteemed colleagues, Lisa Clarke and Dr. Pamela Mattsson, PhD. We delved into how "disagree and commit" serves as a powerful framework for making integration decisions, allowing diverse perspectives to flourish.   Here is a snippet of our conversation: I mentioned: "At first glance, disagreeing and then committing seems straightforward, but in practice, it can be quite challenging. As leaders, we must exemplify this principle. We must engage at every level, bringing forth facts and data to explore the implications of our choices fully." Pamela Mattsson, PhD, commented: "Too often, we focus heavily on the 'commit' aspect, but a lack of genuine commitment can signal that not all voices have been heard. We must critically evaluate how effectively we foster the 'disagree' phase, involving not just presenting facts and data, but also addressing emotions and underlying sentiments." Lisa Clarke expressed: "It's essential to identify individuals who have valuable insights to gain diverse perspectives. Don't just rely on the official tour guides or the executive team. Seek out the woman who has spent 30 years in the quality department and truly understands what's happening." Lastly, I said: "From my own experiences, I've seen that when individuals feel genuinely heard, they are much more likely to embrace and commit to the final decision." So, let's take the pledge during post-M&A integration: to disagree constructively, to listen actively, to cover the whole human experience by allowing facts, figures, and feelings to come into play, and then to commit wholeheartedly. #DisagreeAndCommit #LeadershipPrinciples #MergersAndAcquisitions #IntegrationStrategy #DecisionMaking __ Hey, I'm Sangeeta! If this resonated, follow along as I share real stories and lessons on how companies unlock results—or DM me for a free consulting call. Link to my website in the comments. 

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Stop designing your interview process alone. Your senior engineer knows how to assess system design better than you do. Your product leader knows how to spot strategic thinking. Your future teammate knows what collaboration looks like on the ground. That's what I am covering in Week 3 of the Process Design Series. If you want to cuts interview time while still assessing everything you need, build interviewer buy-in and ensure nothing falls through the cracks, involve your panel. Leverage their expertise. Link below 👇

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Shipping LLM-powered assistants at scale is like building a plane while flying it. Outputs are probabilistic, behavior emerges from the system, and success depends on aligning humans, processes, and leadership around uncertainty. This post shares lessons from the frontline: what changes when probabilistic systems meet deterministic organizations and how teams can learn safely while moving fast.

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LLMs are getting better fast, but switching isn’t easy. As they remember more about you, that memory becomes a valuable asset. Personalization creates lock-in and that’s a real moat.

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Execution capacity used to scale with headcount. Throw more people at the problem, get more done. That math has changed. A small team with strong AI orchestration skills will outpace a large team without them. Every time. The new constraint is finding the right people. That's the bottleneck now.

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