Feeling learning fatigue as a PM? You're not falling behind. You're burning out. Pick 1-2 areas that matter for your current role. Ignore the rest. FOMO is not a learning strategy. Focus is. #ProductManagement

Software development is no longer primarily about writing code, but about orchestrating long-running, multi-agent systems that do the coding while humans focus on architecture, problem selection, and oversight. resources.anthropic.... #AI

Swaying in the breeze!

How to go from PM to AI PM? Stop learning AI theory. Build something with AI. Ship it to users. Learn from what breaks. Hands-on beats courses. Every time. The best AI PMs are former skeptics who got their hands dirty. #ProductManagement #AI

Letting architecture dictate product direction? You're building for engineers, not users. Technical constraints matter. But they shouldn't lead discovery. User problems first. Technical feasibility second. Always. #ProductManagement

The core belief shift: “We’re a software company that occasionally uses AI” To “AI is the product, and traditional software is just the interface” www.nytimes.com/2026... #AI

In traditional SaaS: If the button works, it’s a success. In Generative AI: The button always "works," but the output is different every time. The biggest challenge isn't the ship, it's defining what "good" looks like at scale. #ProductManagement #AI

The time it takes us to study a new technology now exceeds that technology’s relevance window. #AI www.deloitte.com/us/...#AI #AI

Old PM workflow: Describe feature → Wait for eng → Review → Iterate New PM workflow: Build prototype yourself → Show stakeholders → Refine requirements → Hand off cleaner specs AI didn't replace engineers. It gave PMs better input for engineers. #ProductManagement #AI

Sometimes the fastest path to alignment is just... building the thing. #ProductManagement #AI

PMs winning with AI are solving problems that weren't economically viable to solve before.

Before building features, define your hypothesis. "We believe [this change] will [cause this outcome] for [these users]." No hypothesis = feature factory. Clear hypothesis = intentional product development. #ProductManagement

The real AI-native product doesn't ask users to follow steps. It asks them what outcome they want and figures out the rest. We're still designing for workflows when we should design for results. #AI

Stop putting dates on your roadmap. Use Now-Next-Later instead. Now: Committed work Next: Planned work Later: Ideas being explored Flexible enough to adapt. Clear enough to align. #ProductManagement

Copying your competitor's features is product management on easy mode. Understanding the problem they solved? That's the actual job. Feature parity keeps you in the race. Problem insight helps you win it. #ProductManagement

PMs are building prototypes with AI in hours that used to take two sprints. The skill isn't coding. It's knowing which prototype is worth building in the first place. #AI

"AI-native" doesn't mean slapping a chatbot on your product. It means rethinking what's now economically possible that wasn't before. That's a fundamentally different design question.

The alignment meeting should be a rubber stamp. Do 1:1s with key stakeholders before. Capture concerns. Address them privately. By the time everyone's in the room, you're confirming what's already agreed. No surprises = no chaos. #ProductManagement

Vibe coding is great for one thing: proving you can discard an idea quickly. Build a prototype in hours. Test it. Kill it if it fails. But production? That still needs real engineering. AI accelerates validation, not deployment. #ProductManagement #AI

All AI-generated apps look the same because prompts default to the same patterns. Fix: Be explicit about design elements. Color schemes, corner radii, font weights. Or feed it screenshots of designs you actually want. Generic prompts = generic output. #AI

Microservices trade-off nobody warns you about: Monolith: transactions are easy, deployments are scary Microservices: deployments are easy, transactions are heavy Know what you're signing up for. Distributed systems have distributed problems. #ProductManagement

Analytics is everyone's priority until quarterly planning. Then it's "we'll add tracking later." Later never comes. Build analytics into your definition of done. Not as a nice-to-have #ProductManagement

PMs don't need to write code. You need to understand enough to: • Spot technically impractical ideas before proposing them • Have meaningful discussions about timelines • Ask good questions in architecture reviews #ProductManagement

Stop trying to "learn AI" in the abstract. Pick one real problem in your workflow. Use one AI tool to solve it. Do this for 30 days straight. You'll learn more than any course could teach you. #ProductManagement #AI

Wild how vibe coding reversed expectations. Building the app: easy Maintaining it: nightmare AI generates functional code but lacks architectural judgment. Every fix feels like starting from scratch. Fast to ship ≠ easy to sustain. #ProductManagement #AI

"We get to sprinkle AI on top!" No. Connecting to OpenAI's APIs won't cut it in 2026. AI-native means flipping the stack: AI Data Layer becomes foundation, not frosting. That's not enhancement. That's a different architecture.

People’s IQs seem to double as soon as you give them responsibility and indicate that you trust them

As AI handles more execution-focused tasks, the demand for PMs will pivot toward strategic thinking and deep problem definition. Your value lies in validating why something should be built, not just managing its delivery. #FutureOfWork #ProductStrategy

⚠️ AI is great at reinforcing your pre-existing assumptions. To build truly user-centric products, you must validate AI-generated insights with human testing to catch what the algorithm overlooks. #ProductStrategy #UserTesting

AI tools can help you prototype faster. They can't help you discover problems worth solving. Don't mistake velocity for progress. #AI

A project can be flawlessly executed and still fail You delivered on time, on budget, on spec. But the market moved. Or the problem wasn't real enough. Execution excellence ≠ product success Question: Should this continue? Not just: Are we doing this well? #ProductManagement

Most AI pilots fail because we underestimate what "working" actually requires The AI has to do three things perfectly: ✓ Understand the intent ✓ Know the answer ✓ Do the work Miss either one, and you're paying for a chatbot that routes work back to humans #AI

Alignment isn't just a "task", it's the job. Expect to spend more time building consensus across stakeholders than on actual execution. The "alignment tax" only grows as your company does. #ProductManagement

Before you "fix" a broken process, understand why it exists. Chesterton's Fence: Don't tear down what you don't understand. That sprint chaos might be protecting the team from something worse upstream. Learn, then lead change. #ProductManagement

"When one Waymo learns, they all learn." We spend decades training individual humans. Humans can’t download experience, but machines can sync models. This is why AI out-scales us. #AI

Most people sprinkle AI on top of a product like salt. Better way: Break your JTBD down into granular skills. If an LLM or agent can perform a specific skill better/faster than a human, that’s where you build. Solve for the skill, not the buzzword. #AI

Stop making your LLMs start from scratch every time The most efficient design pattern emerging right now is Recursive Tool Building: having an LLM write deterministic code that it (or future sessions) can call to solve repetitive tasks How to build it 👇 1/4

Most PRDs fail because they’re either a novel or a sticky note. To ship high-quality features without the friction, you only need three things: 1️⃣ The Problem (Why are we doing this?) 2️⃣ The Outcome (What does success look like?) 3️⃣ The Prototype (How does it feel?)

Malleable software: tools that fit you like a good leather shoe, not the other way around. We've spent decades bending to our software's constraints. Now AI lets us evolve our tools as we use them. We're moving from "learn the tool" to "the tool learns you." #AI

Informal weekly stakeholder updates > Formal monthly reports Build trust through frequent, casual touchpoints. By the time big decisions come, you've already earned the buy-in. "Dig the well before you are thirsty" #ProductManagement

Traditional acceptance criteria break with AI. Nondeterministic outputs need continuous evals, not one-time sign-offs. Your QA process is now an ongoing process, not a gate. #ProductManagement #AI

PMs: If it takes longer to do manually than to describe to an AI, automate it. Use AI to handle the small stuff so you can focus on moving the needle. Read more on the hidden cost of not automating: gobiraj.substack.com... #AI

Visual‑language‑action stack = Full AI pipeline that lets a humanoid robot see, understand instructions, and then move its body to do a task bostondynamics.com/b... #AI

Drowning in meetings: a simple formula for effective meetings No agenda = No meeting No clear outcome = No meeting No one leaves with action items = Wasted meeting That's it. That's the formula. #ProductManagement

AI is splitting into two roles in our workflow. MIT's latest report (iceberg.mit.edu/repo...) breaks down work as Occupation → Tasks → Skills, showing each skill can be either automated or augmented. 1/3

Happy New Year!

The era of shipping static code is ending. 🛑 Soon, applications will extend themselves via in-product AI, building new features from natural language in real-time. Codebases are becoming living systems that co-evolve with us. #AI

We don’t talk enough about the "mental tax" of rapid AI advancement. To stay effective, you have to reset your assumptions about what models can do every 6–8 weeks. If you’re still prompting like it’s 2025, you’re leaving 50% of the model's potential on the table. #AI

There are moments in tech that felt like magic. 🪄 1️⃣ First time using a GUI. 2️⃣ First time using a smartphone. 3️⃣ Watching Claude Code translate a natural language prompt into a functional PR. We’re finally speaking to computers in our language. #AI

Common MVP mistake: "What can we cut to ship faster?" ❌ MVP mindset: "What is the fastest way to test our hypothesis?" ✅ Focus on the Return on Learning, not just the time to market