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. AI Made Coding Faster. So Why Is Everything Breaking? (Video)

Top Long-Form Content

1. Will AI Create More Product Managers?

   1. What we liked: A genuinely contrarian economic argument with a usable three-tier taxonomy. Rather than the usual “AI changes everything” handwaving, it names which specific PM functions cheapen, which don’t, and what that means for where the value pools.

   2. Choice quote: “Two of three PM functions got dramatically cheaper. One stayed the same.”

2. Where Your AI Time Actually Goes

   1. What we liked: A clean diagnostic for the “I used AI all day and made nothing” feeling, with an allocation target you can measure yourself against. Reframes tool-switching and endless prompt tuning as modern forms of work avoidance.

   2. Choice quote: “The fourth rewrite of your instruction file, undertaken because the third one didn’t feel quite right, is almost always avoidance. Ship something, and let the output tell you what needs fixing.”

3. The Best AI Users in Any Company Aren’t the Engineers. They’re the Product Managers. Here’s Why!

   1. What we liked: A sharp, contrarian reframe that turns the “PMs are at risk from AI” narrative on its head. The “if you work with 6 to 10 engineers, you already manage 6 to 10 context windows” line makes an abstract argument concrete enough to act on tomorrow.

   2. Choice quote: “Start treating your AI conversations the way you treat your engineering partnerships. Write a brief before you prompt. Review the output like you’d review a PR. Give specific feedback, not vague reactions. Run multiple sessions in parallel on different workstreams.”

Top Tweets

1. Shipping Faster Is Making Your Product Worse

   1. What we liked: A genuinely counterintuitive framing for AI-era PMs — that more features, faster, is quietly killing adoption — from one of the most recognized voices in product. The “speed compounds in one direction, understanding compounds in another” line is the kind of reframe that changes how a roadmap conversation goes. Bonus points for the “don’t call it AI” tactical insight.

   2. Choice quote: “A lot of SaaS products are about to struggle because they were built on an assumption that is breaking: Users won’t keep coming to your UI to ask questions. They expect answers to come to them.”

2. AI made you efficient but are you compounding

   1. What we liked: A blunt reframe of what “AI-pilled” should even mean, aimed directly at founders. The efficiency-vs-compounding distinction gives PMs and founders a sharper standard for judging whether their AI investments are actually working. Coming from a prominent early-stage investor, it doubles as a signal of what the best AI-native companies are actually doing.

   2. Choice quote: “This is why the best AI-native teams don’t just feel faster. They’re not operating on a tighter loop. They’re operating on a different curve. They are compounding.”

3. Put the bot where the work happens

   1. What we liked: The calculator-mode-vs-team-member-mode framing is the kind of distinction that’s obvious once you see it but reframes how teams should actually deploy AI. For PMs and founders thinking about org-wide AI rollout, this is a clean argument for why the deployment surface matters more than model selection — with a concrete example of the “what did we decide on pricing again?” moment most teams lose hours to every week.

   2. Choice quote: “Execution breaks less often than coordination. People ship good work in parallel, then discover they were building different versions of the future.”

Top LinkedIn Posts & Short Videos

1. AI and the Death of Critical Thinking

   1. What we liked: Names a failure mode most PMs are blind to — your team getting faster while getting worse at the thing that matters. The frameworks are genuinely new and directly portable to how we evaluate AI-assisted work.

   2. Choice quote: “The most dangerous worker in the AI age is not the unskilled one; it is the smoothly misled one.”

2. Product Management in the AI era is no longer about prioritization.

   1. What we liked: Dense and PM-native. The “90% correct fails in Fintech, wins in Creative Writing” line alone is worth the read. Every PM can slot their current product into those three axes tomorrow morning.

   2. Choice quote: “Tech is now the commodity. Product judgment is the moat.”

3. We Call It Artificial Intelligence. We Should Call It Alien Intelligence. Here Is Why.

   1. What we liked: The rare philosophical piece grounded in actual deployment context (Data Fabric, Collections, Finance, HR platforms). The “alien intelligence” reframe is useful shorthand for why foundation models behave in ways our traditional product intuition misses.

   2. Choice quote: “The ‘designer’ of human emotion is not a mind. It is a process. A very slow, very powerful, very blind optimisation process running on biological hardware.”

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This article is brought to you by Productboard

Trusted by the world’s best product teams, Productboard turns customer needs into high-impact strategy in one end-to-end platform. With Productboard Spark, you can go from raw idea to polished product brief in seconds using AI that already understands your business.

See how Productboard Spark can help your product teams ship faster →

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

1. I traveled to a Palantir alumni meetup in the middle of our busiest onboarding month so far.

2. Before 9/11, the Twin Towers were proof that the US government could build something the entire world looked up at.

3. We just rewrote about 70% of an AI course we launched 10 months ago.

4. Japan is in the top 5 for most earthquakes of any country on earth, but its death toll per earthquake is among the lowest in the world.

5. Singapore pays its entry-level ministers $1.1M/year. The US pays its Fed Chair, the person steering a $30 trillion economy, less than a mid-level PM at Google.

6. Every six months, I cancel all of our customer meetings for a week to make room for the one thing most teams never give themselves enough time to do: be creative.

7. In 2025, Zuckerberg told Joe Rogan, "all junior and mid-level developer jobs will be gone by the end of the year."

8. Two years ago, a design mock that looked rough would kill a deal.

9. Sweden built a road system in 1997 based on a premise most countries still reject: humans will always make mistakes behind the wheel.
   

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 4 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. (Video) Your AI Eval is Overcomplicated

2. Building Revolutionary Technology for Seniors

Top Long-Form Content

1. Avoiding The Eye of Sauron

   1. What we liked: A sharp, original framework for assessing startup risk in the age of foundation models. The “workflow vs. chassis” distinction and the counterintuitive insight that “boring” enterprise customers with low technical capability are now the most attractive ICP is the kind of contrarian thinking founders and PMs need right now.

   2. Choice quote: “The labs aren’t just building better models. They’re arming your customers.”

2. They Tried to Kill Product Managers. Now Everyone Needs to Be One.

   1. What we liked: A punchy, well-structured argument that flips the “PM is dead” narrative on its head with real examples from the author’s own AI building spree. The framework diagram showing where AI compresses the value chain (build) vs. where it doesn’t (discovery, GTM) is worth saving.

   2. Choice quote: “AI isn’t killing the need for product management. It’s making product management a skill everyone needs.”

3. AI Is Helping PMs Do the Wrong Things Faster

   1. What we liked: A sharp, experience-backed takedown of how most teams are misusing AI for discovery, with a clear “downstream vs. upstream” framework and concrete alternative prompts you can use today. The 10% moves vs. 10x moves distinction is one every PM should internalize.

   2. Choice quote: “It feels productive. But it’s automating confirmation bias at scale.”

4. Should PMs Care About AI Agents Going Rogue?

   1. What we liked: A thoughtful review of the story of an agent going rogue and writing a hit piece against a human, and what it teaches us about building in the AI-era.

   2. Choice quote: “The same root cause every time: agents with too much autonomy, too few guardrails, and no product-level decision about where the boundaries should be.”

Top Tweets

1. From Hierarchy to Intelligence

   1. What we liked: This is the most ambitious and original post in the batch — a genuine first-principles rethinking of how companies should be organized in the age of AI, from a founder actively implementing it at scale. The historical grounding gives the argument real intellectual weight, and the concrete four-layer architecture (capabilities → world model → intelligence layer → interfaces) is a framework product leaders can steal tomorrow. This is the kind of piece that redefines a conversation.

   2. Choice quote: “When the intelligence layer tries to compose a solution and can’t because the capability doesn’t exist, that failure signal is the future roadmap.”

2. Commoditizing AI Inference with Instruments

   1. What we liked: A genuinely contrarian and well-reasoned take that most product leaders haven’t internalized yet. The commodity market analogy is sharp and the framework is immediately useful for any PM or engineering leader making inference procurement decisions. The observation that every “handle” people use to pick models (parameter count, reasoning vs. non-reasoning) describes how inference is made, not what it delivers, is a powerful reframe.

   2. Choice quote: “Can you define what ‘good enough’ looks like, and let the market figure out who fills it cheapest?”

3. Stop AI Rework: 4 Operating Rules That Keep Speed and Accuracy

   1. What we liked: Short, sharp, and immediately actionable. Every rule maps to a specific failure mode that anyone working with AI in production will recognize. The reframe of “iteration” as “preventable churn” is a useful diagnostic for teams. The advice to ask Claude to build a skill whenever you finish a repeatable job is the kind of simple behavioral change that compounds.

   2. Choice quote: “AI leverage is not just better prompting. It’s better control of the seams: understanding → generation → review → action.”

Top LinkedIn Posts & Short Videos

1. In a World of Agents, the Product Role Splits in Two

   1. What we liked: A provocative and specific vision from a top-tier thinker on what PM rituals look like when agents do the execution. The “instead of standups / instead of OKRs / instead of PRDs” structure makes it immediately applicable to anyone rethinking their team’s operating model.

   2. Choice quote: “The daily ritual becomes reviewing deltas, scanning failures, and deciding which ones matter. Less reporting, more triage.”

2. Product Improv: Is Your Product Team Drifting to Failure?

   1. What we liked: A contrarian take that cuts against the “just add AI tools” narrative. The core argument — that AI without PM training creates strategic drift at higher velocity — is one of the most important warnings in the current landscape.

   2. Choice quote: “You’re not just making mediocre decisions faster — you’re scaling them.”

3. Claude Code Isn’t Just a Coding Assistant — It’s 512,000 Lines of Infrastructure

   1. What we liked: A masterclass in what “building around the model” actually looks like at production scale. Any PM building AI products should study this post — the LLM is one piece, the system around it is the product.

   2. Choice quote: “The model generates the output. The system makes sure nothing breaks on the way there.”

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This article is brought to you by Productboard

Trusted by the world’s best product teams, Productboard turns customer needs into high-impact strategy in one end-to-end platform. With Productboard Spark, you can go from raw idea to polished product brief in seconds using AI that already understands your business.

See how Productboard Spark can help your product teams ship faster →

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

1. 2007: The head of design at Apple, Jony Ive, helped put a computer in your pocket and train a generation to check a glowing rectangle 96 times a day.

2. Yesterday, I was scrolling through Google’s Gemini page when I had to stop and screen record what I was seeing.

3. Everyone’s talking about how great OpenClaw is.

4. Most AI gadgets we’ve seen so far have failed in the real world.

5. I was asked recently what product would solve a problem that I feel the most, but hasn’t been made yet.

6. Your first few onboarding sessions are very delicate.

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 2 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. Building Revolutionary Technology for Seniors (podcast)

Top Long-Form Content

1. Why Product Sense Is the Only Product Skill That Will Matter in the AI Age

   1. What we liked: One of the most respected product sense thinkers uses a multi-turn dialogue with Claude, to try to disprove the title and yet still proves it true.

   2. Choice quote: “The only real long-term career moat for product people is how you can improve on the already-brilliant, already-comprehensive inputs and outputs that AI will provide for you.”

2. Claude Code for Product Managers

   1. What we liked: This doesn’t just describe what’s possible — it walks through the actual skill architecture, context strategy taxonomy (local files, CLI tools, MCPs, APIs, browser agent), and output-shaping techniques that make the difference between useful automation and expensive slop. The honest section on limitations earns the piece real credibility.

   2. Choice quote: “Taking the output, deciding if it’s good enough, and iterating on the workflow until it meets your quality bar — that’s the essential skill of the AI-powered product manager that still can’t be replaced by AI.”

3. AI Is Helping PMs Do the Wrong Things Faster

   1. What we liked: This piece names something most teams are quietly doing and calling discovery. The case study — where asking about goals instead of features uncovered an entirely unserved segment and drove 10x usage growth in year one — makes the argument concrete. The 10% vs. 10x move distinction is a clean, memorable frame for where senior PM attention should actually go.

   2. Choice quote: “When every company has access to the same AI-generated market data, the same AI-summarized support tickets, the same AI-clustered usage patterns, the competitive advantage shifts to whoever has proprietary insight that AI can’t generate.”

Top Tweets

1. Junior PM: Our DAU is terrible. Users log in once a month.

   1. What we liked: A rare post that names the most common PM failure pattern directly: building for the user you wish you had instead of the user you actually have. The dialogue format makes dense strategic thinking remarkably accessible. The GitHub example lands hard — daily usage is earned through indispensability, not manufactured through streaks and push notifications.

   2. Choice quote: “Every failing product needs those [notifications, streaks, points]. Make them unnecessary first.”

2. I fired 24 people in 24 hours.

   1. What we liked: Founders and PMs building zero-to-one products in AI will feel this one. The anti-”scale fast” thesis is contrarian in a VC-dominated discourse, and the personal price tag makes it credible rather than theoretical. Relevant for any PM thinking about growth strategy or organizational health under pressure.

   2. Choice quote: “Scaling fast is overrated. Slow, sustainable, cash-flow growth buys peace.”

3. The Cost of Overthinking: Why Smart People Get Stuck

   1. What we liked: Moderately relevant for PMs prone to over-planning roadmaps or deferring decisions waiting for perfect information. The replies surfaced a useful framing: “analyze → decide → execute → adjust.” However, this post leans more motivational than analytically rigorous, and the content is largely platform-agnostic rather than PM- or AI-specific. Ranked last among the five for that reason.

   2. Choice quote: “Overthinking isn’t the problem. Doing nothing while you overthink is.”

Top LinkedIn Posts & Short Videos

1. Many companies are so addicted to revenue they’re growing themselves broke.

   1. What we liked: Cuts to a real structural dysfunction holding most companies back from genuine AI-era competitiveness. The framing of “revenue addiction” as an organizational pathology rather than a strategy problem is novel and resonant for product leaders trying to justify foundational AI investments.

   2. Choice quote: “The real edge was never ‘hitting this quarter’s number.’ It’s always been building the capability that makes next quarter’s number inevitable.”

2. Most product teams have one launch checklist. They use it for everything. And that’s why customers tune out.

   1. What we liked: A practical framework any PM or PMM team can implement immediately. The point about re-launches is genuinely underrated — most teams treat launch as a one-time event and leave significant adoption on the table.

   2. Choice quote: “When every release is ‘game-changing,’ your audience learns to ignore all of them. Save the big words for when they earn it.”

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This article is brought to you by Productboard

Trusted by the world’s best product teams, Productboard turns customer needs into high-impact strategy in one end-to-end platform. With Productboard Spark, you can go from raw idea to polished product brief in seconds using AI that already understands your business.

See how Productboard Spark can help your product teams ship faster →

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

1. Hot product take: There is no single “right” way to build.

2. Volvo invented the 3-point seatbelt in 1959.

3. We had our first PR crisis recently, ironically because of someone else’s AI.

4. “Projects that used to require big teams can now be accomplished by a single very talented person.”

5. The Director of US civilian cyber defense is facing heat for pasting sensitive documents into public ChatGPT.

6. NEW EPISODE 🎙: How Chapter Is Building the Trust Layer for Seniors

7. 2,000 years ago, the Romans built gravity-fed aqueducts to move water tens of miles across mountains and valleys into their cities.

8. 1 in 2 people on the planet have used the products built by Meta.

9. Most residents today still prefer to call city hall rather than visit the city website.

10. Hong Kong has been flushing its toilets with seawater since the late 1950s.

We dive into how Chapter has built the best provider directory and prescription pricing engine in the country to match seniors with the right plan across 25,000+ Medicare permutations—without the conflicts of interest that plague the rest of the industry. Cobi shares why he chose to keep the team lean, how Chapter’s B2B2C model unlocked enterprise health system partnerships, what it took to testify before Congress and catalyze regulatory reform, and how he thinks about building a full retirement platform beyond Medicare.

If you’ve ever wondered how Medicare actually works, why the brokerage industry is under DOJ investigation, or what it takes to build a product-plus-service company with real defensibility, this episode is for you.

Subscribe now

Key Takeaways

• Medicare covers roughly 70 million Americans, with about 25,000 plan permutations across the country—seniors typically choose from 100 to 200 options in their zip code with no HR department to guide them

• Chapter takes a radical approach to being a Medicare brokerage that decouples plan recommendations from economics—advisors don’t know what plans pay the company, and Chapter enrolls people in plans on which it earns nothing every day

• The company built the best provider directory in the country for Medicare using data integrations, ML models, and AI-powered automations—it’s the only company that can tell you the exact benefits in each plan and what a prescription will cost at a given pharmacy on a given plan.

• Counterintuitively, Seniors use phones more than people in their 30s and 40s, but for communication, not transactions—this shapes Chapter’s product strategy toward phone and text-based engagement over web apps.

• Voice AI has massive potential for seniors but is only effective today for narrow, well-scoped tasks like checking a specific benefit—open-ended Medicare guidance still requires human advisors.

• How Chapter tested paid ads for direct to consumer but ultimately landed on a B2B2C where health systems refer patients

• Why Cobi has no hiring targets and how he still finds amazing people and remains quite lean for a company with Chapter’s level of traction

• How Chapter earned the right to testify before Congress without lobbyists or PR firms

• Chapter’s broader vision for seniors beyond Medicare involves reverse engineering the use-money vs. earn-money life phase to find optimal solutions.

—

Brought to you by:

Productboard

This episode is brought to you by Productboard, the intelligent product management platform. For over 10 years, Productboard has helped leading organizations like Zoom, Salesforce, and Autodesk build the right products faster. Its end-to-end platform is purpose-built for helping product teams uncover customer needs, define their strategy, and align everyone around the roadmap. And now, it’s equipping product organizations to excel in the AI era with Productboard Spark, its new specialized agent for product management. Explore new product ideas or generate a product brief in seconds with AI that already understands your product and your business. See how Productboard can help your product teams ship faster and deliver high-impact products. Head to productboard.com/judelson.

PropelAuth

Startups and corporate innovation teams routinely waste months building core user authentication and authorization flows into their products. PropelAuth has automated all the most important pieces of the user management lifecycle. Instantly set up waitlists, magic links, social login, user impersonation, user management, Role-Based-Access-Control, SAML integrations for enterprise, team management and much more. With PropelAuth, each of these challenges takes just a few lines of code instead months of development and integration time. And with thoughtful control over design and skinning, it’s easy to keep your product fully on-brand. Get a generous free starter pack that scales through 1,000 Monthly Active Users or mention EMERGENT25 to receive 25% off for the first 6 months for more mature products. Get back to building your actual product and let PropelAuth handle your user management needs. Visit www.propelauth.com to learn more.

FlyKitt

This episode is brought to you by FlyKitt “F-L-Y-K-I-T-T”, the first full solution to jet lag. Based on research with Navy SEALs and fighter pilots, the FlyKitt team discovered that long flights cause inflammation in addition to disrupting your circadian rhythm, and they figured out how to solve both these challenges using a customized program for when to eat, sleep, and use their patented supplements and blue light blocking glasses. It’s simple to follow and you don’t have to change anything before your travel day. With FlyKitt, more than 90% of people can sleep well in Europe, Asia, or anywhere else in the world on night 1. Visit flykitt.com, and use coupon code FLYWITHEMERGENT to get 15% off FlyKitt.

—

In this episode, we cover:

• Introduction (00:00:42)

• What Medicare actually is and the different plan types (00:03:52)

• What happens when you turn 65 and have to figure out Medicare (00:05:19)

• What Chapter builds: the data engine behind plan matching (00:07:37)

• The perverse incentive structure of Medicare brokerages (00:11:33)

• Chapter’s unbiased model and why they enroll people in plans they earn nothing for (00:14:22)

• Why there are 25,000 Medicare plan permutations across the country (00:15:46)

• Building tech for seniors: misconceptions and design principles (00:17:28)

• Voice AI for seniors: where it works and where it doesn’t (00:21:46)

• From Palantir to consumer: learning the B2B2C model (00:24:01)

• How Chapter’s enterprise partnerships actually work (00:26:50)

• Keeping the team lean: hiring philosophy and ruthless prioritization (00:30:16)

• Testifying before Congress without lobbyists or PR firms (00:36:02)

• The service-plus-product hybrid: advisors as an asset, not a seasonal hiring problem (00:41:40)

• Beyond Medicare: building the retirement platform of the future (00:47:53)

• What was obvious at the start and what the company got wrong initially (00:51:41)

• Where to find Cobi and Chapter (00:55:26)

—

You can connect with Cobi Blumenfeld-Gantz through the following platforms:

• LinkedIn: https://www.linkedin.com/in/cobibgantz/

• Website: https://askchapter.org/

—

You can connect with Adam through the following platforms:

• LinkedIn: https://www.linkedin.com/in/adam-judelson/

• Substack: https://www.emergentproduct.com

• X: https://twitter.com/Ajuggles

• Website: www.firstprinciples.la

—

Where to find Emergent Newsletter and Podcast:

• Youtube: https://www.youtube.com/@emergentproduct

• Spotify: https://open.spotify.com/show/5TMMwU5zoSlX5Ex1A4vl9D

• Apple Podcast: https://podcasts.apple.com/us/podcast/emergent-building-the-future/id1746963428

• Instagram: https://www.instagram.com/adamjudelsonemergent

• TikTok: https://www.tiktok.com/@adamjudelson

• Website: https://www.emergentproduct.com

—

For inquiries about sponsoring the podcast or becoming a guest, email adam@firstprinciples.la.

—

Get full access of the Emergent’s Newsletter at www.emergentproduct.com/subscribe.

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

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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This article is brought to you by Productboard

Trusted by the world’s best product teams, Productboard turns customer needs into high-impact strategy in one end-to-end platform. With Productboard Spark, you can go from raw idea to polished product brief in seconds using AI that already understands your business.

See how Productboard Spark can help your product teams ship faster →

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.

Original image from OpenAI, AI overlay from Emergent.

Jony Ive’s iPhone trained a generation to check a glowing rectangle 96 times a day. It supercharged the attention economy. And now, after a $6.5 billion acquisition in May of 2025 that brought Apple’s former hardware design leadership into OpenAI, Ive and team are building something designed to ask for less of your attention, not more. It is supposed to be a device that doesn’t sit in your pocket waiting to be pulled out, but perceives your environment and decides when to help. It engages you, not the other way around.

Nobody outside OpenAI has seen it yet. The first device is expected in the second half of 2026. But between official statements, court filings, and supply chain leaks, we know enough to understand what they’re attempting: a new category of computing that sits alongside your laptop and phone, what Sam Altman described to employees as a third core device, according to the Wall Street Journal.

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Here’s what we know, what it could be, and why it matters for anyone building products.

The Vision: Calm Over Chaos

Altman keeps returning to one metaphor. Using current devices, he told an audience at Emerson Collective’s Demo Day last November, feels like walking through Times Square with flashing lights and noise. What they’re building should feel like “sitting in the most beautiful cabin by a lake and in the mountains and sort of just enjoying the peace and calm.” The OpenAI device is designed for attention liberation instead of attention capture. Altman has described a device that can filter information and understand when something is important enough to surface, and when to stay quiet, using sensors and spatial awareness to build what he called “incredible contextual awareness of your whole life.” At the same event, Altman recalled that Ive told the team early on: “We are going to make people smile. We are going to make people feel joy. Whatever the product does, it has to do that.“ At DevDay in October, Ive put it in his own terms: he hopes the devices will “make us happy and fulfilled, and more peaceful and less anxious, and less disconnected.” It’s lofty rhetoric, so let’s explore what it might be.

What It Could Be

Based on researching all the clues available in the public domain, here is what they could be building:

• Sweetpea (more concrete leaks): A behind-the-ear wearable, closer to hearing aids in form factor than traditional earphones. The battery and processor sit behind the ear, enabling longer always-on use. It features a custom chip and ultrasonic transmitters that suggest environmental sensing and contextual awareness rather than just passive audio. According to leaked details first reported by TechRadar, the estimated release is near September 2026, with 40 to 50 million units expected to ship at a price point close to that of a smartphone.

• Gumdrop (more speculative): A pen-shaped device that could record writing and voice notes, with ChatGPT integration to make sense of memos and discussions. Reported by TechRadar as one of three ideas under consideration, with manufacturing partners already in discussions.

• Something else entirely: Every description from Altman and Ive emphasizes a new category. The device won’t be a phone. Altman told employees it isn’t glasses, adding that Ive had been skeptical about building something worn on the body. The form factor that ships may not match any current leak.

There are consistent threads across all possibilities: screen-less, proactive rather than waiting to be summoned, and designed to complement phones rather than replace them.

As Altman told Bloomberg, “In the same way that the smartphone didn’t make the laptop go away, I don’t think our first thing is going to make the smartphone go away.” Whether it’s earbuds, a pen, or a necklace, the bet is on ambient and proactive over another glowing rectangle.

No matter what it is, Altman told employees he expects to ship 100 million AI “companions,” predicting OpenAI would reach that number “faster than any company has ever shipped 100 million of something new before.“ That ambition tells us something about what they think they have, or possibly just their level of confidence!

Why This Attempt Might Be Different

AI hardware has mostly failed to win over the mass consumer market (so far). Humane’s Pin was discontinued and its assets sold to HP. Rabbit’s R1 couldn’t justify why your phone wasn’t better. The Friend pendant sparked enough public backlash that New Yorkers defaced its subway ads and organized a protest in Washington Square Park. Meta’s Ray-Ban glasses are the closest thing to a success, but even 2 million pairs sold is a long way from the 100 million promised.

The OpenAI attempt has one thing previous efforts lacked: Jony Ive, and the team he built. Ive led Apple’s industrial design team for over two decades, playing a central role in the iMac, iPod, iPhone, iPad, MacBook, and Apple Watch. Steve Jobs called him his “spiritual partner at Apple.” Tim Cook said his role in Apple’s revival “cannot be overstated.” He didn’t come to OpenAI alone. The 55-person team that joined through the acquisition includes senior Apple veterans who were responsible for shipping iPhones and Apple Watches at scale, not just designing prototypes but getting them manufactured and into hundreds of millions of hands. His team is responsible for billions of consumer device sales. That’s what $6.5 billion bought.

How We Got Here

It started in early 2023, with private dinners between Altman and Ive arranged by Airbnb CEO Brian Chesky. By September, news leaked that they were discussing AI hardware, and the Financial Times dubbed the project “the iPhone of artificial intelligence.”

In 2024, Ive assembled the team, co-founded io with Tang Tan, Evans Hankey, and Scott Cannon. He confirmed the partnership publicly in a September New York Times profile in which The Times described the project’s goal as creating “a computing experience that is less socially disruptive than the iPhone.”

In 2025, it got real. After OpenAI’s acquisition, the company almost immediately got tangled in a trademark dispute with Google-backed iyO, which was building AI-powered audio devices. The resulting court filings gave us our first clear window into the hardware work: the io team had purchased at least 30 headphone sets from various manufacturers to explore the market, and Tang Tan stated the first device would not be an in-ear or wearable device. By November, at an Emerson Collective event, Ive and Altman confirmed working prototypes exist. “I can’t believe how jaw-dropping good the work is,“ Altman said.

Then January 2026 broke the story wide open. In just three weeks, details of Sweetpea emerged from a leaker on X and were reported by TechRadar, rumors surfaced about a pen-shaped device codenamed Gumdrop via the same leaker, and OpenAI’s Chris Lehane confirmed at Davos that the first device is “on track“ for a second-half 2026 unveiling.

What This Means for Product Builders

If they succeed amid the hype, here’s what it will signal:

1. Contextual awareness is the next frontier. What OpenAI is building requires integrating structured data (calendars, contacts, preferences) with unstructured data (conversations, environmental perception, behavioral patterns). Large businesses have spent decades building unified views of structured data through CRMs, ERPs, and data warehouses. What OpenAI is describing adds real-world context on top: not just what’s in your database, but what’s happening in the user’s immediate environment. For product builders: what do you not know about your users’ environments and situations that would change everything if you did?
   

2. Rethink what you optimize for. The OpenAI device isn’t about less engagement. It’s a different kind of engagement: hands-off, ambient, proactive rather than reactive. Instead of maximizing time-in-app, you’d optimize for tasks completed without friction, moments where the device helped without being asked, cognitive load reduced. What would you build differently if “user didn’t have to think about it” was a success metric?
   

3. The disappearing interface. This vision has a name in design circles: calm technology. The idea that the best interface is one you forget you’re using. Current devices demand your attention constantly. The OpenAI device is designed to disappear: to handle things on your behalf without requiring you to look at a screen, tap a button, or even speak a command. If they pull it off, it’s not just a new product. It’s a new relationship with technology entirely.

Altman is betting $6.5 billion on a category that has failed repeatedly. That looks crazy until it works. The skeptics have history on their side. The believers have Jony Ive.

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References

TechCrunch. Kyle Wiggers and Maxwell Zeff. “Jony Ive to Lead OpenAI’s Design Work Following $6.5B Acquisition of His Company.” May 21, 2025.

Axios. Ina Fried and Dave Lawler. “OpenAI Aims to Debut First Device in 2026, Exec Tells Axios.” January 19, 2026.

Wall Street Journal. Berber Jin. “What Sam Altman Told OpenAI About the Secret Device He’s Making With Jony Ive.” May 21, 2025.

CNBC. Kif Leswing. “Execs Say OpenAI Has First Hardware Prototypes, Plan to Reveal Device in 2 Years or Less.” November 24, 2025.

VentureBeat. Michael Nunez. “Here’s What Jony Ive and Sam Altman Revealed About Their Secretive AI Hardware Project at OpenAI’s Dev Day.” October 2025.

TechCrunch. Maxwell Zeff. “Court Filings Reveal OpenAI and io’s Early Work on an AI Device.” June 23, 2025.

TechRadar. Alex Blake. “Big OpenAI Leak Claims the ChatGPT Maker Is Developing an Earbud-Style Wearable with a Surprising Twist.” January 13, 2026.

TechRadar. David Nield. “OpenAI’s Mysterious ChatGPT Gadget Could Take the Form of an AI-Powered Pen.” December 31, 2025.

9to5Mac. Zac Hall. “Jony Ive and Sam Altman Have First Prototype of Mysterious AI Hardware.” November 24, 2025.

Bloomberg. Mark Gurman and Shirin Ghaffary. “OpenAI to Buy AI Device Startup From Apple Veteran Jony Ive in $6.5 Billion Deal.” May 21, 2025.

CNN. Clare Duffy. “How This Tiny Device Became a Symbol for the Backlash Against AI.” November 16, 2025.

The Verge. Sean Hollister. “Meta’s Ray-Bans Have Sold 2 Million Pairs.” February 14, 2025.

TechCrunch. Maxwell Zeff. “Humane’s AI Pin Is Dead, as HP Buys Startup’s Assets for $116M.” February 18, 2025.

Wikipedia. “Jony Ive.”

Apple Newsroom. “Jony Ive to Form Independent Design Company with Apple as Client.” June 27, 2019.

CNBC Make It. Tom Huddleston Jr. “Steve Jobs Once Called Designer Jony Ive His ‘Spiritual Partner’ at Apple.” May 24, 2025.

TechCrunch. Maxwell Zeff. “Meta’s Former Hardware Lead for Orion Is Joining OpenAI.” November 4, 2024.

New York Times. Tripp Mickle. “After Apple, Jony Ive Is Building an Empire of His Own.” September 21, 2024.

Reuters. “OpenAI, Jony Ive in Talks to Raise $1 Billion from SoftBank for AI Device Venture.” September 28, 2023.

Emerson Collective. “In Conversation: OpenAI’s Sam Altman and LoveFrom’s Jony Ive with Laurene Powell Jobs.” EC Demo Day, November 2025.

OpenAI. “A Conversation with Sam and Jony.” DevDay 2025, October 2025.

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 4 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. AI Isn’t Replacing Junior Devs - It’s Exposing Their True Value

Top Long-Form Content

1. Every Maker Is Now a Manager (of AI)

   1. What we liked: A deeply researched, contrarian take from someone actively building in this space. Instead of the typical “AI makes you more productive!” narrative, this names the psychological toll that most practitioners feel but can’t articulate — and proposes a design-level fix.

   2. Choice quote: “We spend our attention evaluating results instead of supervising the process. As we would with a capable colleague!”

2. Digital Twins for Product Feedback

   1. What we liked: This is the kind of specific, replicable PM workflow that readers can implement Monday morning. The honest limitations section (you need a year of built-up context, human judgment is non-negotiable, the twin will sometimes be wrong) adds credibility. A pure signal piece for working PMs.

   2. Choice quote: “I used to approach documents hoping they’d survive first contact. Now I send them knowing they already have.”

3. A Personal OS in Claude Code

   1. What we liked: The most immediately copy-paste-able article of the week. Where most “personal OS” content stays at the concept level, this one gives you the folder structure, the CLAUDE.md file, the workflow specs, and the example commands. The templates vs. workflows distinction alone is worth the read.

   2. Choice quote: “Less files = less LLM decision-making about what to prioritize. Claude can pick up what it needs from one consolidated doc.”

Top Tweets

1. Software development is undergoing a renaissance in front of our eyes.

   1. What we liked: This is the post of the week. A co-founder of OpenAI sharing the actual internal memo for how they’re restructuring their own teams around coding agents. The specificity is remarkable — skills directories, AGENTS.md files, “say no to slop” as a code quality principle. Every product leader should read this as a preview of what their own org will need to do in the next 6 months.

   2. Choice quote: “For any technical task, the tool of first resort for humans is interacting with an agent rather than using an editor or terminal.”

2. The combination is rare because most orgs actively select against it.

   1. What we liked: A contrarian, honest take on why the “high agency” mantra falls flat in practice. The insight that PMs who survive 10+ years optimize for just enough agreeability to stay employed is uncomfortable and true. Directly relevant to AI-first PMs who need to push orgs toward uncomfortable transformations.

   2. Choice quote: “Real high agency means burning political capital regularly. You need enough wins to replenish faster than you spend.”

3. 9 prioritization lessons I learned the hard way.

   1. What we liked: The “ignorance tax” framing is memorable and immediately applicable. The contrarian point that you won’t need frameworks if your strategy is focused enough challenges the PM community’s obsession with scoring models.

   2. Choice quote: “Most of the time you won’t need any frameworks if you have an adequately focused strategy.”

Top LinkedIn Posts & Short Videos

1. “Have you ever noticed that AI gets worse the longer you talk to it?”

   1. What we liked: Takes a phenomenon every PM has experienced but few can explain and turns it into a systematic playbook. The parallels drawn to human cognition (recency bias, working memory, overload) make the concept immediately intuitive. 365+ reactions and 42 comments show massive resonance. Highly practical for any PM using AI tools daily.

   2. Choice quote: “Long conversations dilute attention, not because the model is ‘forgetting,’ but because it’s deciding what matters now.”

2. AI coding tools are turning PMs into “builders” for the wrong features.

   1. What we liked: A timely contrarian take that challenges the vibe-coding hype. The core insight — that engineering scarcity used to force better thinking, and AI abundance has removed that constraint — is sharp and important. The three pre-build questions are immediately applicable.

   2. Choice quote: “AI doesn’t fix unclear thinking. It just executes it faster.”

3. Everyone’s building AI agents. Very few understand what’s actually under the hood.

   1. What we liked: A solid architectural mental model for PMs who need to understand agent infrastructure without getting lost in engineering details. The body-as-metaphor framework (brain, hands, nervous system) makes the stack intuitive and memorable. Useful for any PM scoping or evaluating AI agent products.

   2. Choice quote: “A model without tools is just a chatbot.”

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

1. In 2024, I joined South Park Commons, founded by Ruchi Sanghvi and Aditya Agarwal, to spend time figuring out what was actually worth building.

2. Nearly 80% of organizations say they’re using AI, yet only ~1% have it fully embedded across teams.

3. A Florida commuter wired a signal jammer into his car to block mobile phone use on his daily drive to increase his safety from distracted drivers. Instead of praise, he was hit with a $48,000 fine.

4. India built a public payments system that now accounts for roughly 49% of all real-time payment transactions in the world.

5. People keep framing the AI race like a sprint.

6. Steel from WW2 pulled from old shipwrecks is used to build some of the most precise scientific instruments on Earth.

I'm excited to introduce Artur Grigio as the guest author of this article. Artur is a Principal Engineer at Common Sense, where he's building AI tooling to help local governments and municipalities make an impact. Artur and I work together daily navigating the realities of AI-first development—managing coding agents, vibe coding, and figuring out what actually works. With nearly a decade of experience across AWS, Amazon, and high-growth startups, Artur has built everything from fraud detection systems to horizontally scalable cloud infrastructure. He's seen firsthand how AI changes the developer workflow—and why human judgment still matters. Connect with Artur on LinkedIn to follow his work at the intersection of AI and public sector impact.

I recently joined Adam at Common Sense, a VC-backed AI company with an AI-first mentality. In other words, I am a manager of AI coding agents, and I vibe code a lot.

Every day, I go back and forth between having my mind blown by AI to wondering how it can be so thoughtless. Particularly as we hire and scale, I’ve been wondering if Zuck was right when he told Rogan “all junior and mid-level developer jobs will be gone by the end of 2025.”

It’s already 2026 and yet the anxiety behind that statement hasn’t disappeared. Models write code faster than humans, reason across your entire repository, and never get tired. Are the CEOs right?

What I’ve seen so far is not the end of junior developers—but a misunderstanding of their value. As public company CEOs increasingly point to productivity gains from GenAI as the justifications for large layoffs, we’d recommend considering these three points before you fire or stop hiring all your junior developers.

1. Cost: Humans Are Still Cheaper Than You Think

A single serious instruction to a frontier model can easily consume 30K–60K tokens. AI is incentivized to be verbose. Larger contexts, richer reasoning, more retries, more agents—token usage explodes fast.

Check out Claude’s initial set of instructions. If you think you’re saving tokens by not saying “thank you” or “please”… those are drops in the bucket.

There are two popular schools of thought about where this goes next:

• Optimistic view: Token costs will approach $0 as compute becomes abundant and we move from training-heavy to inference-heavy usage.

• Monetization view: AI companies will simply allow larger and larger contexts so they can charge for richer inputs—codebases, documents, images, video, everything.

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But the reality is much simpler.

OpenAI, Anthropic, and Google are locked in a race. Models are released in near lockstep. Each generation requires enormous capital expenditure—compute, energy, talent, and training time. And yet the lifecycle of each model is incredibly short. Many never come close to recouping their costs. Meanwhile, they are each pouring billions into costs that consumers don’t really see, so while it’s hard to know for sure, their pricing could be a competitive mirage.

It wouldn’t be surprising to learn that today’s frontier models are 90–95% subsidized for end users. Further, a smart pricing strategy would be to wait years for lock-in and then dramatically raise prices because the switching costs will be too high to do anything other than pay.

Eventually, that bill will come due.

When it does, token pricing won’t be $0.15 per million tokens for the best models. It could be $15+ per million tokens—or more—if you want state-of-the-art performance, reliability, and privacy guarantees. Let’s be honest, how many of us still use GPT-4.0? It’s not even a dropdown in Cursor!.

At that point, a junior developer who can reason, refactor, and maintain systems looks less like a cost—and more like a bargain. Counterintuitively, the junior developer might become the more efficient spend, or at least not the “no brainer” to outsource to AI as it is portrayed today.

2. Someone Has to Wrangle the Codebase

AI generates code aggressively.

That’s not a bug—it’s the business model. More tokens means more value captured for foundational model companies. But code generation at scale creates a new problem: entropy.

AI doesn’t feel architectural pain. It doesn’t sense when abstractions are leaking, when naming conventions drift, or when a codebase becomes conceptually incoherent. It will happily generate five different patterns to solve the same problem across five files.

Left unchecked, you don’t get a clean system. ifs are nested in for loops, which are nested in while loops… AI doesn’t care that your brain can only go 4-6 levels deep.

Junior developers are uniquely valuable here. They:

• Are lazy! They want to reuse code so much that they eventually stumble onto a good, succinct, simple solution.

• Are afraid of big PRs and won’t submit Sonnet’s rewrites of the entire component, just because you asked it to useState.

• Normalize patterns, simplify logic, and reduce duplication

• Turn AI output into maintainable human systems

In other words, they act as entropy reducers. The AI lacks even basic mental awareness of why your business exists and what might come next. It knows how to write a PaymentProcessor function, but it doesn’t know that the company is planning to pivot to subscriptions next month. A human junior developer sits in the meetings, hears the “why” and builds for a future that isn’t in the current prompt context.

AI can write a thousand lines in a minute. But someone still has to review it and make the codebase understandable again. That work does not disappear—it increases.

3. When the Product Breaks, Humans Fix It

More generated code means more surface area.

More surface area means more edge cases.

More edge cases means your product will break—in new, creative, and highly inconvenient ways.

AI is excellent at proposing fixes in isolation. It is far worse at:

• Understanding real user behavior

• Debugging production-only failures

• Reasoning about partial outages, degraded states, or cascading failures

• Making judgment calls under uncertainty

• And, it won’t press “Acknowledge” on that 2AM emergency page, which will escalate to you

This is where junior developers grow into indispensable engineers.

They triage bugs. They reproduce issues. They follow logs, metrics, and traces. They learn how the system actually behaves, not how it was designed to behave. Over time, they become the people who know where the bodies are buried. Without them, who will write your Root Cause Analysis (correctly) instead of regurgitating the same exact failure states time after time?

A system maintained only by AI becomes fragile.

Of course, we assume humans remain the primary consumers of source code for some time to come. If the next generation of LLMs can navigate ‘messy’ logic as easily as clean abstractions, the premium on code elegance will vanish, giving way to ‘dark’ codebases—systems that are mathematically functional and perfectly optimized for machine execution, but fundamentally illegible to any human mind. However, we’ve seen very limited evidence that we’ve reached this reality. AI fixes a lot of small bugs, but has poor performance on medium or high complexity issues across the code base.

Our Recommendation

Hire SOME amazing, thoughtful junior developers that use their brains AND use AI.

What is changing is that AI is replacing the illusion that software development was ever just about typing syntactically correct code.

Junior developers are how you:

• Control costs in an increasingly expensive AI ecosystem

• Keep codebases coherent instead of chaotic, and protected from serious bugs

• Build the next generation of senior engineers who actually understand the system
  

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If you stop hiring juniors, you don’t eliminate the work, you just defer the pain — and make it much more expensive later.

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 4 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. English Is the Silent Default in AI Models (Video)

Top Long-Form Content

1. Management as AI Superpower

   1. What we liked: Reframes “management” from a soft skill to the core competency of the AI era. The “Equation of Agentic Work” framework for deciding when to delegate to AI is immediately actionable for PMs and builders alike.

   2. Choice quote: “The skills that are so often dismissed as ‘soft’ turned out to be the hard ones.”

2. How to Write a Good Spec for AI Agents

   1. What we liked: Solves the real pain point every PM and developer faces: how to communicate intent to AI without overwhelming it. The “six core areas” checklist and three-tier boundary framework (Always/Ask first/Never) are immediately deployable.

   2. Choice quote: “The key is to write smart specs: documents that guide the agent clearly, stay within practical context sizes, and evolve with the project.”

3. The 80% Problem in Agentic Coding

   1. What we liked: A brutally honest examination of the emerging “comprehension debt” crisis as developers ship code they don’t fully understand. Critical for product and startup leaders to be aware of.

   2. Choice quote: “If your ability to ‘read’ doesn’t scale at the same rate as the agent’s ability to ‘output,’ you aren’t engineering anymore. You’re rubber stamping.”

Top Tweets

1. The most underrated line in @karpathy’s post...

   1. What we liked: Captures an important nuance most miss—the difference between making something work quickly versus building something scalable and monetizable. Highly relevant for PMs navigating the build-vs-buy decision in the AI era.

   2. Choice quote: “There’s vibe coding (make it work now) and there’s vibe engineering (build the right thing in a way you can scale and monetize).”

2. Most product strategy is optimistic guessing wearing a suit.

   1. What we liked: Offers a practical mental model that flips conventional strategy thinking on its head. The framework—current state, projected mess, ideal future, then ideal current state—is immediately actionable.

   2. Choice quote: “If you can’t articulate the failure path, you don’t understand your position. You’re hoping.”

3. Every PM who wants to survive the AI era should watch this video

   1. What we liked: Dense with actionable wisdom from someone who built core products at Google, Facebook, Square, and DoorDash. The insight that PMs should define outcomes as customer state changes rather than features shipped is a mindset shift worth internalizing.

   2. Choice quote: “Your value is editorial capability—looking at 100 features and identifying the 2 things that will actually drive customer outcomes.”

Top LinkedIn Posts & Short Videos

1. Hot take: AI isn’t going to replace Product Managers. It’s going to expose who was never a PM to begin with.

   1. What we liked: A bold, contrarian take with a concrete two-stack framework that distinguishes between enduring PM skills and emerging AI competencies. The specific technical skills listed in Stack 2 provide a practical learning roadmap for PMs wanting to stay relevant.

   2. Choice quote: “It’s replacing artifact PMs - the ones whose entire job is ‘formatting’ thinking, not doing thinking THEMSELVES.”

2. Stop asking AI to do your job. Ask it to make you better at it.

   1. What we liked: An actionable framework that flips conventional AI usage on its head. The specific prompt examples are immediately usable, and the insight that AI should stress-test thinking rather than bypass it is genuinely novel.

   2. Choice quote: “If you use AI to bypass the thinking process, you’re automating away your own value. But if you use AI to stress-test your thinking, you become 10x more effective.”

3. Most people want AI to sound like them. But no one does this first (and it takes 47 mins).

   1. What we liked: A deeply practical system for solving the generic AI output problem. The emphasis on documenting rejections rather than just preferences is a clever insight, and the portability across AI tools makes this a foundational productivity technique.

   2. Choice quote: “80% of your file should be what you’d never say.”

4. Everyone tells you what you should do. Yet, no one knows your context.

   1. What we liked: A refreshing counter to the framework-obsessed PM culture. The practical action items are immediately executable, and the honest self-reflection about blaming company rigidity resonates with many PMs.

   2. Choice quote: “Product folks like playing a game, finding excuse not to do the job. I played that for too long, blamed the company rigidness. Once I stopped playing that stupid game, growth became natural.”

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

1. Wikipedia hasn’t had a real competitor in 30 years.

2. Tesla’s trillion-dollar 2025 shareholder-approved pay package matters for one simple reason: it only rewards results that materially change the landscape of the American economy.

3. 10 shocking facts everyone should know about our government today:

4. I finally found a great use case for OpenAI’s Operator agent in my personal life.

5. Around 30% of US job openings have disappeared since ChatGPT launched in 2022.

6. Your parking ticket was issued by AI, and now your parking laws might be written by it, too.

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.

Top Long-Form Content

1. AI evals are changing the PM craft

   1. What we liked: Tackles the most critical evolution in AI-first PM craft with concrete frameworks for integrating evals throughout product development, from customer research to PRD writing to testing, complete with real examples from Amplitude, Harvey, and AirOps.

   2. Choice quote: “Instead of measuring ‘# users who completed the 5-step email creation flow,’ PMs need to ask, ‘Did the email meet our quality bar? How many edits were needed before the user sent the email we drafted?’”

2. Claude Code and What Comes Next

   1. What we liked: An analytical approach to unpack why this moment in AI tools represents a genuine capability leap. The hands-on experiment (Claude working independently for 74 minutes to build a functional startup) makes the abstract concrete.

   2. Choice quote: “I’ve never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between.”

3. Data Is Your Only Moat

   1. A strategic 2x2 framework analyzing AI applications by adoption difficulty and problem complexity. Explains why coding agents improved so fast (easy adoption → data flywheel), and predicts the “hard-hard” quadrant will see the next wave of winners.

   2. What we liked: Finally, someone articulates why different AI categories are succeeding at different rates. The insight that enterprise integration difficulty creates its own moat is counterintuitive but compelling for founders and PMs positioning their products.

   3. Choice quote: “Easy to adopt also means easy to displace. Hard to adopt products have their own data moat: Once you’re embedded in an enterprise, you learn about how that company works in a way that makes your product incredibly hard to replace.”

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4. 10 Ideas for 2026

   1. What we liked: Shreyas cuts through the noise with pithy observations that lodge in your brain. The piece on being “perpetual pawns of problems that shouldn’t have arisen” is a perfect diagnosis of chronic organizational fire-fighting.

   2. Choice quote: “The reason you are saying we don’t have time to think right now and we must do something right away is that you said the same thing last time and before that and the time before that, and so we are now perpetual pawns of problems that shouldn’t have arisen in the first place.”

Top Tweets

1. I just saved hours per week with ONE hour and ONE button press inside Claude.

   1. What we liked: A highly actionable tutorial from one of AI’s most prominent voices that shows exactly how to leverage Claude’s underutilized Skills feature for productivity gains. This is the kind of practical workflow optimization AI-first PMs should be implementing immediately.

   2. Choice quote: “Now, for the rest of eternity, I just have to type into a conversation ‘write me a newsletter subject line’ it will apply all of my rules and preferences and style and structure and output them in the exact way and order that I want.”

2. We built Openwork!

   1. What we liked: A practical, open-source tool announcement that addresses real cost concerns with Claude Cowork while demonstrating concrete automation workflows. The ability to chain local and cloud actions is particularly valuable for PMs managing complex project portfolios.

   2. Choice quote: “In the demo we use it to scan through my laptop and find all my unfinished projects and put them in a notion database with completion status.”

3. I know exactly what would fix our product org.

   1. What we liked: Cuts through typical PM platitudes to address the structural dysfunction most PMs face daily. The tactical advice to document rework patterns and reframe pitches around cost rather than process is immediately applicable. The honest acknowledgment that some systems simply can’t be fixed without executive intervention will resonate with frustrated PMs.

   2. Choice quote: “Problems move people. Solutions don’t. Your manager doesn’t care about product operating models. They care about the $2M you’re burning on coordination overhead.”

Top LinkedIn Posts & Short Videos

1. The real reason you get pushback on Discovery is because you communicate it as a phase and “Plan this for Q1.”

   1. What we liked: Reframes Discovery from ceremony to continuous uncertainty reduction, making it more palatable to stakeholders and more effective for teams. The practical Freemium launch example demonstrates how to identify and test critical assumptions without ritual.

   2. Choice quote: “Discovery isn’t about running a prescribed set of activities—interviews, prototypes, opportunity solution trees—because you’re supposed to. It’s about identifying your riskiest assumptions and testing them as cheaply as possible before you commit engineering capacity and organizational credibility.”

2. Do you know how to correctly set user personas for your product?

   1. What we liked: Uses a memorable, humorous example to drive home a critical point about behavior-based segmentation. Clarifies the distinction between demographics (useful for product building) and personas (representing usage patterns).

   2. Choice quote: “User personas are about identifying patterns of behavior, not about any specific user, fictional or otherwise.”

3. GTM ≠ product launches.

   1. What we liked: Challenges the common conflation of GTM and launches with a memorable analogy and actionable framework. The visual comparison chart makes the distinction immediately usable for product teams and founders.

   2. Choice quote: “A launch can’t fix a weak GTM strategy... A launch is an accelerant. It amplifies whatever you give it. If your product is an absolute ball of crap, that’s what the launch will amplify.”

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

1. Every city leader I meet wants faster service delivery with AI. And increasingly, the teams running those services want to deliver it.

2. City and county leaders: stop hiring incumbents who treat your contract like a guaranteed paycheck.

3. A city once invited us in for an in-person working session.

4. I just finished one of the most talked-about books in government right now, “Breakneck: China’s Quest to Engineer the Future,” and it made me rethink something we see every day inside cities.

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.

Top Long-Form Content

1. Beyond “AI Product Managers” - How Proper PMs Can Start Thinking About AI Product Strategy

   1. What we liked: A refreshingly contrarian take that challenges the “AI Product Manager” title craze with a simple, actionable framework for deciding which AI initiatives actually deserve investment versus which are just checkbox exercises for boards.

   2. Choice quote: “If everyone’s super for wrapping an LLM into their product, then no one is.”

2. 13 Product-building Lessons from Palantir

   1. What we liked: Real war stories from the trenches with specific examples of what went wrong and why, from building calculator widgets that users rejected to managing custom deployments that nearly sank into technical debt - lessons you can’t get from frameworks.

   2. Choice quote: “Secondhand understanding is almost always incomplete. When engineers only read tickets or summaries, they miss the context that shapes a user’s real-world decisions.”

Read more

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 4 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.

Top Long-Form Content

1. The 3 Forces Killing AI Startups (And How the 10% Survive)

   1. What we liked: A contrarian take on AI startup economics showing how successful AI companies can access 80-133x larger TAMs by targeting HR budgets ($96k-$160k/year per agent) versus traditional SaaS IT budgets ($1.2k/year per seat).

   2. Choice quote: “You’re not competing with other AI tools. You’re competing with hiring decisions.”

2. Why AI Cannot Simulate Your Customers’ Behaviour

   1. What we liked: A reality check for product managers showing why talking to real customers remains irreplaceable, with a clear 4-step framework for validating actual customer needs versus AI-generated hypotheses.

   2. Choice quote: “Founders would literally rather boil the ocean than talk to customers.”

Read more

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 4 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.

Top Long-Form Content

1. The AI Application Spending Report

   1. What we liked: This data-driven report reveals which AI tools are gaining real traction among startups, showing that horizontal applications (60%) are dominating vertical ones (40%), and that creative tools and “vibe coding” have become truly cross-functional capabilities.

   2. Choice quote: “AI is changing what skills people have, what tasks people do, and what teams look like... not only is AI augmenting employees in specific roles, it’s turning specific roles into broadly deployed skills across a company (everyone can be a creative now!).”

2. The Emerging AI-Native GTM Playbook: 6 Patterns from companies racing to $100M

   1. What we liked: Identifies six counterintuitive patterns that separate explosive AI-native growth from incremental progress, backed by real data from companies reaching $100M ARR with remarkably lean teams.

   2. Choice quote: “The playbook for building billion-dollar AI companies looks radically different from the SaaS era. While traditional enterprise software companies spent years building sales teams and marketing engines, a new generation of AI-native startups is reaching massive scale with lean teams, minimal funding, and unconventional strategies.”

Read more

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 4 Long-Form Articles/Podcasts

• Top 3 Tweets

• Top 4 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.

Top Long-Form Content

1. The Execution Trap for Product Managers

   a. What we liked: A highly actionable 5-step framework for inspiring teams while orchestrating complex product launches. A bit touchy-feely, but on point.

   b. Choice quote: “When everything looks like a high-priority, dependency-free ‘win,’ teams tune out. Orchestration without inspiration turns into background noise.”

2. 15 Hot Takes That Cut Through the AI Hype

   a. What we liked: The author’s contrarian takes challenge conventional wisdom with brutal honesty—like “AI agents often aren’t the right solution” and his decision tree for when to actually build an AI agent. The practical guidance on building AI-native teams and avoiding “vibe coding” provides immediate value for product leaders.

   b. Choice quote: “The term ‘AI PM’ has become a meaningless branding exercise. Everyone’s an ‘AI PM’ on LinkedIn these days. What matters more is showing proof of work—what did you actually ship with AI recently?”

Read more

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 4 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.

Top Long-Form Content

1. The AI Productivity Paradox: High Adoption, Low Transformation

   1. What we liked: Strong framing of the “shadow AI” economy and the need for adaptive systems and process redesign.

   2. Choice quote: “Most organizations aren’t failing because they lack AI tools. They’re failing because AI sits in the shadows, disconnected from workflows, never compounding into real productivity.”

2. 15 AI Distribution Plays That Build Real Moats

   1. What we liked: It gives PMs permission to make distribution requirements first-class PRD elements alongside UX and technical specs.

   2. Choice quote: “In AI, the half-life of differentiation is measured in weeks…This means distribution is not a GTM exercise. It’s a product design principle.”

Read more

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 4 Long-Form Articles/Podcasts

• Top 4 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 posts from Emergent from the past three weeks:

1. Best Of Product Roundup - August 16, 2025

2. (Video) AI Is Here—So Why Are We Still Hiring Designers and Engineers? (free)

3. (Video) Why the Future of Product Management Is Deeply Technical (free)

Top Long-Form Content

1. OpenAI’s Product Lead Reveals the 4-Part Framework for AI Product Strategy

   1. What we liked: A sharp warning for product leaders that defensibility in AI comes from moats like data, trust, and distribution — not just adding AI features.

   2. Choice quote: “AI itself isn’t the moat. Everyone can access GPT-4o, Claude, Llama, Mistral. The barrier to entry is zero. If your strategy is ‘use OpenAI’s API and wrap a UI around it,’ you don’t have a company, you have an expensive demo that can be cloned overnight.”

2. You think you are AI-first, but you probably aren't

   1. What we liked: This challenges superficial AI adoption and urges product professionals to rethink their systems from the ground up for meaningful transformation.

   2. Choice quote: "You can’t really be ‘AI-first’ or ‘AI-native’ unless you reimagine your business around the architectural properties of AI."

Read more