Best Of Product Roundup - May 23, 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.

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Top Long-Form Content

1. The PM of the Future Is More Hedge Fund Manager Than Builder

   1. What we liked: The hedge-fund manager analogy reframes the job in a way that’s actually motivating rather than threatening, and the piece comes loaded with usable frames — “flare wide, focus hard, ship less,” the 85-90% agent reliability problem, the Vitamix blender test for shipping with AI, and a back-and-forth PRD workflow that beats one-shot prompting. The owner-versus-worker mentality closer is the kind of line PMs will quote in Slack.

   2. Choice quote: “If you’re a worker, every new AI model release is a threat. It’s getting closer to you, doing more of what you do, and that’s terrifying. If you’re an owner, every new release is a capability upgrade.”

2. The Tool Selection Problem: Why AI Agents Call The Wrong Tool And How To Fix It

   1. What we liked: Four named failure modes (ambiguous overlap, missing negative constraints, misleading parameter names, indiscriminate calling) each paired with a specific description-level fix, grounded in a reproducible minimal-agent experiment. PMs and founders shipping agents should make this required reading for the team this week.

   2. Choice quote: “Wrong tool calls are a description problem, not a reasoning problem. The model is following the signals you gave it, and those signals are ambiguous.”

3. Welcome to the Age of Artificial Stupidity

   1. What we liked: It’s worth reading an occasional pessimistic view of AI.

   2. Choice quote: “We had better be quite sure that the purpose put into the machine is the purpose which we really desire and not merely a colorful imitation of it.”

Top Tweets

1. Don’t Outsource the Learning.

   1. What we liked: A serious look at what happens to engineering teams when AI handles the closing-of-tasks but nobody asks whether anyone is getting sharper in the process. The piece pulls together converging research — a 2026 Anthropic randomized trial where AI users scored 50% on comprehension vs. 67% for the manual group.

   2. Choice quote: “I’d rather ship 80% of what I could have and learn 100% of what I needed to, than the reverse. Over years, those two strategies produce very different engineers.”

2. The Last Three Things AI Can’t Copy.

   1. What we liked: A thoughtful, well-written piece on what’s actually scarce in an AI-native world. Less prescriptive than the others, but the framing helps PMs and founders think about where they should be spending their attention.

   2. Choice quote: “AI doesn’t change that mechanism. AI changes the volume. The choosing muscle that used to be a small part of the job is becoming most of the job.”

3. The First Step in Building My AI Native Team: Shared Brain First, Boundaries Second.

   1. What we liked: Easily the best framework-plus-implementation piece of the batch. The “boundaries matter more than connections” point is something most teams learn the hard way after their shared brain turns into a trash bin.

   2. Choice quote: “Lose a capability, the team gets dumber. Lose oxygen, the team stops breathing. I don’t want the memory system to be the single point of death for the whole team.”

Top LinkedIn Posts & Short Videos

1. More (Artificial) Intelligence Does Not Mean More Understanding.

   1. What we liked: “Cognitive debt” is a genuinely new lens — and one that maps cleanly onto how PMs should think about AI-assisted product decisions. Most “AI risk” posts cover the same five technical failure modes; this one names a different failure that’s already happening inside teams shipping AI tools.

   2. Choice quote: “An organization flooded with generated insights may actually become less strategically aware if nobody meaningfully interrogates the assumptions underlying the outputs.”

2. Physical AI: When Artificial Intelligence Meets the Real World.

   1. What we liked: A clean explain on the Physical AI frontier—robotics, autonomics systems, factory floors. The brain/body framing is the cleanest articulation we’ve seen of why the two stacks are converging.

   2. Choice quote: “The workflows and knowledge bases being built today may soon be the intelligence layer powering physical systems tomorrow.”

3. Is Artificial General Intelligence Really Five Years Away or Is That Just the Hype Talking?

   1. What we liked: Useful counterweight for founders fielding investor questions about AGI timelines, or PMs being asked to plan against them. The “deadline without a roadmap is not a forecast — it’s a story with good marketing” line is the kind of thing worth keeping in your back pocket.

   2. Choice quote: “The leaders who win will be the ones building clearly with what AI can actually do today, not waiting on a countdown that most researchers do not believe.”

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

1. Everyone across the public and private sector is suddenly becoming very interested in two pieces of contract language because of AI.

2. On long Australian highways, road authorities install trivia signs in fatigue zones.

3. 43% of government employees are now using AI at least a few times a year (source: Gallup).

4. The bad version of an AI product strategy conversation goes like this.

5. Most PMs using AI for customer research are doing the wrong things faster.

6. We were in a meeting recently, going back and forth on a question that had been sitting with the team for a while.