Paul Jeon

SWEs have LeetCode. Quants have QuantGuide. PMs? They now have the PMGrind.com, offering all-in-one PM prep for 24/7 practice. Two months ago, I made a post about the pains of PM recruiting that went viral. It got~1M hits, sparked 4.5K+ comments, 1K+ reactions, and filled my inbox with hundreds of DMs (and counting!). The message clearly struck a nerve: PM recruiting is broken. 🛠️ Aspiring PMs are stuck: ❌ Wasting 2 hours for every 1 hour of mock practice due to mandatory role-switching with peers. ❌ Receiving inconsistent feedback from other inexperienced candidates. ❌ Struggling to practice on their own schedule. So Aneesh Patnaik and I founded the PMGrind.com: ✅ AI-powered mock interviewing agent — simulates real PM interviews with dynamic back-and-forth (no clunky “press enter to continue”). ✅ Scoring engine trained on top company rubrics like Google, Amazon, and Meta — custom-tailored to your question type. ✅ Free — no waitlists, no awkward favors. 🚀 To celebrate the launch, I’m doing free resume reviews! Comment what company you want to see next on the PMGrind.com, and you’ll be entered to win a free review from me! 📄🔍 #productmanagement #pm #recruiting #leetcode #ai #agent #mock #interview

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Search interest in "prompt engineering" jumped 3,500% in the last 18 months. Vector databases saw 99x growth in the same period. Two years ago, neither of these skills existed in job descriptions. Today, they're blocking product launches. I know three founders who are hiring for the skills: someone who can architect AI features that actually work. The problem is finding people who understand retrieval-augmented generation, can tune embeddings, and know when Pinecone makes more sense than Postgres with pgvector. The AI stack professionalized faster than any infrastructure layer I've seen. Faster than containers, faster than cloud, faster than mobile. This creates a narrow window. The founders who invest in learning this stack now, who understand semantic search and context windows and token optimization, will ship products their competitors can't match. The ones who wait for it to "get easier" will find themselves explaining to customers why their AI features feel generic. The tooling won't simplify before the opportunity closes.

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I just vibe coded my portfolio from scratch. Not a resume site. A product portfolio — case studies with real decisions, real tradeoffs, and real outcomes. I'm a fintech product operator (3+ years, 0→1 startups +banking) moving fully into PM roles. Consumer fintech, payments, consumer internet. I want blunt feedback. Not "looks great!" — I want: → What made you stop reading? → What felt unclear or unbelievable? → What's missing? If you've hired PMs or reviewed PM portfolios — your take would mean the most. Link 👇 https://lnkd.in/grRbPU6Y #ProductManagement #ProductManager #Fintech #ConsumerInternet #ProductPortfolio #PMCommunity #IndiaStartups #FintechIndia #Banking #Payment

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A Staff PM with 10+ years in big tech (sf, Bay Area, etc) just told me I need to up my PRD writing game. Said he could write one in 15 mins. Is that supposed to be a flex? In 15 mins, I've already finished an entire vibe coded MVP and I'm halfway through demoing it with the dev team. Like Madhu Gurumurthy said (Head of Product at Google) A PRD was from a bygone era when it took forever just getting approval to build anything, since building took precious engineering resources that couldn't be spared. Nowadays, with AI? When a PRD takes just as long as building a whole vibe coded app. What are you doing wasting your time writing When you can be BUILDING instead? I strongly believe in a building first culture. Big tech has fostered a culture of "wait and see" from its non-technical people for far too long. If a company creates a culture where only the engineers are the ones building, I know its days are numbered. This includes 4 out of 5 big tech companies. Nobody is too important to build.

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I wasn’t burned out from Product Management. I was burned out from writing the same things… over and over again. Every PRD. Every user interview template. Every feedback summary. Started feeling like déjà vu. Until I made a tiny shift: Let AI handle the heavy lifting. I didn’t outsource thinking. I removed the formatting. Now? I use AI like my fastest teammate— Because judgment is mine, but time is limited. Here are 9 High-ROI ChatGPT Use Cases I use every week: 1/ Write crisp PRDs 2/ Prep interview questions 3/ Summarize user feedback 4/ Prioritize features (RICE) 5/ Run competitive research 6/ Build personas 7/ Write launch emails 8/ Run what-if analysis 9/ Brainstorm new features AI doesn’t make you obsolete. It makes space—for better thinking, better strategy, better product calls. P.S. Save this post if you want to work smarter, not harder. Repost it if you’re ready to use AI like a pro. #careercoach #sachinsharma #productmanagers #chatgpt #ai2025

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I saw a tweet the other day calling out YC for “breeding the AI bubble.” The argument? That they’re funding strange startups, some chasing niches so narrow you wonder how a founder, let alone a college dropout, could ever reach product-market fit. And I’ll admit it… I resonated with that take. There have been some odd startups coming out of recent batches. But here’s the thing: YC is still one of the most valuable institutions the startup world has ever had. It’s not just an accelerator. It’s a school of thought. A filter. A network. Yes, some bets are weird. Some won’t pan out. But if you zoom out, so much of the tech we rely on today started out looking weird, small, and maybe even “pointless.” The lesson for founders isn’t to dunk on YC or its startups. It’s to remember: Weird ideas sometimes fail… and sometimes change the world. Product-market fit is rare, but YC maximizes the surface area of people trying. Having the right ecosystem matters, credibility, mentors, and capital are leverage. So, while the AI hype cycle is real, I think YC’s role is less about fueling a bubble and more about giving the next great founder their shot. After all, the weirdest bets often become the best ones.

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When a VC fund mentions tinkering with openclaw in their analyst.md file and has a track record of supporting great builders in the ai space, it's a strong signal they're not living in the past. I wish there were more opportunities like that for junior analysts learning the craft early in their careers

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Many people have asked me how I broke into the PM role without an MBA, or even a US-based degree, and the reality is that I was lucky: - I was exposed to the right opportunity: a Microsoft PM internship. - At the right time: early in my career, with lots curiosity and energy to learn new things. These opportunities are rare, especially early in your career, which is why I was very excited to see my org at Stripe offering a new grad accelerator program. Feel free to reach out for advice/tips!

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In the race to the bottom, Delve apparently dived in feet first. When Y Combinator said "move fast and break things," I'm fairly sure they didn't mean this. A $32 million YC-backed compliance platform has been accused of generating pre-filled SOC 2 reports, fabricating board meeting minutes, and routing clients through audit firms that signed whatever was put in front of them. 494 draft reports leaked. The investigation claims 493 contained identical boilerplate, right down to the same grammatical errors. Auditor conclusions allegedly written before any auditor reviewed evidence. The person behind the investigation is plainly aggrieved, and they brought receipts. Hundreds of pages of them. Everyone on LinkedIn is shocked. Which is odd, because none of this should surprise anyone who's been paying attention. Less than 48 hours ago I wrote about compliance platforms inverting the entire point of certification. Generate the policies, map them to frameworks, collect the evidence, deliver a ready-made audit package. You can be certified without ever having had a functioning security programme. The auditors aren't pushing back, because the platforms feed them a steady pipeline of pre-packaged clients and nobody has much incentive to rock a boat that's paying everyone's invoices. Forty-eight hours. I didn't think I'd get a case study that fast. But this isn't just a compliance platform problem. I've been banging on about the same pattern across the whole industry for months. AI security vendors selling controls they haven't implemented on their own systems. Vendors certifying their security then disclaiming all responsibility for what their models actually do. Auditors who check whether policies exist but never ask whether anyone follows them. The appearance of security has been decoupling from actual security for years. Delve got caught out. The incentive structure that created them hasn't. Buyers want SOC 2 to close deals, not to be secure. Auditors are selected and paid by the company being assessed, which is a bit like letting students mark their own exams and then being surprised when everyone gets an A. The AICPA self-polices with no independent enforcement. When a SOC 2 report turns out to be rubbish, there's no regulator to pull it. Build a system with those incentives and you get exactly this outcome. The investigation describes pricing dropping from $15,000 to $6,000 with ISO 27001 and a pen test thrown in. This won't be the last one, I'm afraid. The investigation itself flagged a competitor at an even lower price point with a similar reputation. The architecture that created Delve is still intact. The question everyone's asking is "how could this happen?" The better one is why we built an industry where it was always going to. #SOC2 #GRC #CyberSecurity #infosec #CISO #Compliance

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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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