Best Of Product Roundup - February 28, 2026

The best expert-curated product content from the best product thinkers from around the Internet delivered twice a month.

Every two weeks, we dive into the vast ocean of product content online, both free and paid. We sift through it all to bring you the crème de la crème. Our round-up includes carefully curated links and concise excerpts from top content.

This week’s highlights feature:

• Top 3 Long-Form Articles/Podcasts

• Top 3 Tweets

• Top 3 LinkedIn Posts or Short Videos

Our expert product team has selected these resources to help you save thousands of dollars on unnecessary subscriptions, avoid wasting time reading instead of taking action, and save years following the wrong advice. Our mission is to introduce you to brilliant product thinkers, whether they’re renowned experts or emerging voices.

In case you missed them, here are the recent posts from Emergent:

1. What Jony Ive and OpenAI Are Really Building

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Top Long-Form Content

1. A framework for filtering AI noise

   1. What we liked: A brutally practical antidote to AI FOMO. Diego lays out a clear 2-path framework for deciding when to integrate AI into your roadmap vs. when to ignore it—backed by real examples of failed pilots and “vibe-based QA.” Required reading for PMs being pressured to ship GenAI features without guardrails.

   2. Choice quote: “95% of GenAI pilots deliver zero P&L impact.”

2. Founders, your software company is not going to IPO

   1. What we liked: A cold splash of water for late-stage founders. Instead of chasing the IPO fantasy, this forces product leaders to design for durability, efficiency, and alternative outcomes. It challenges growth-at-all-costs thinking at exactly the right moment.

   2. Choice quote: “853 US companies are valued at $1B+. According to SVB, fewer than 5% meet the revenue + efficiency bar required to go public.”

3. Will vibe coding end like the maker movement?

   1. What we liked: A thoughtful critique of vibe coding as performance vs. durable value creation. The “no scenius phase” insight explains why so many AI prototypes feel manic but shallow. Strong perspective on where value will actually accrue in the stack.

   2. Choice quote: “Vibe coding skipped that phase entirely.”

Top Tweets

1. Cursor’s $7,225 invoice broke the internet. So I mapped pricing across 50 AI startups to figure out what’s actually working.

   1. What we liked: This takes what could’ve been surface-level outrage bait and turns it into operating insight. Instead of debating whether one invoice was “crazy,” it zooms out and analyzes patterns across 50 AI startups. The result isn’t opinion — it’s taxonomy. Six pricing models. Clear tradeoffs. A decision tree founders can actually use. It reframes pricing from vibes to design.

   2. Choice quote: “Six models emerged, four case studies, and one decision tree for picking yours.”

2. Things I wish someone told me before I almost gave up on OpenClaw

   1. What we liked: This reads like someone who bled for the knowledge. It dismantles the “just let it run overnight” fantasy and replaces it with structure: model tiering, explicit skills folders, cron isolation, verification loops. The meta-lesson is bigger than OpenClaw — agents don’t get smarter with freedom, they get better with constraints. It’s a manual for becoming an orchestrator, not just a user.

   2. Choice quote: “The agents that actually work? They’re not smarter. They’re more constrained.”

3. Most PMs write documentation backwards.

   1. What we liked: A clean mental model for product work maturity. The 1-pager → 3-pager → 5-pager progression forces validation before overinvestment. It normalizes killing ideas early and writing specs only for work that survives scrutiny. The sharpest line reframes documentation entirely: it shouldn’t scale with your anxiety — it should scale with your confidence.

   2. Choice quote: “Documentation should match your confidence level, not your anxiety level.”

Top LinkedIn Posts & Short Videos

1. The old stuff still works

   1. What we liked: A provocative take on the disutility in too much vibe coding.

   2. Choice quote: “Turns out all the engineering discipline I picked up over 20 years didn’t become obsolete, it got more important.”

2. “How much money will this feature make?”

   1. What we liked: Sharp reframing of a question every PM hears. It separates what a team can directly influence from what it merely contributes to.

   2. Choice quote: “We need to separate what our solutions can influence directly from what they only contribute to.”

3. When it comes to Strategy, what separates the Company, Product, and Feature levels?

   1. What we liked: The Russian matryoshka analogy lands because it exposes the failure mode: feature thinking masquerading as strategy. Company sets context. Product defines how to win. Features should reinforce — not rewrite — that choice. Clean mental model. Hard to argue with.

   2. Choice quote: “Features should prove the strategy, not define it.”

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In case you missed them — Adam’s recent LinkedIn Content

1. The ‘AI boom’ looks big until you put it next to past economic shifts.

2. I’ve watched city after city approve the same failed playbook: a chatbot here, a pilot there, a “proof of concept” that proves nothing except the vendor got paid and the “AI” box gets checked.

3. Japan’s 60-year-old bullet train, the Shinkansen line, refunded hundreds of passengers for a 35-second delay.

4. Olympic airtime for all of my favourite sports combined: 11 minutes.

5. We’ve been scared of the wrong thing when it comes to AI.

6. 9 in 10 managers regularly spend time on admin tasks. On average, those tasks consume 2 full days every week.

7. I’ve worked with hundreds of startup CEOs, and I can safely say that there is one trait that is remarkably common among the failure cases – the CEO does not have self awareness of their weaknesses.