Best Reads of the Week

• The Best AI Products Expect Errors - AI products fail not because the models are imperfect, but because we pretend they're not.

• The rise of the AI-native employee - This is what happens when you stop treating AI as a feature and start treating it as a way of thinking—35 people hitting $80M ARR because they build instead of meeting about building.

• Why it's so hard to "see" demand - Most startups fail because they're building products people "should want" instead of finding the actual problems people are desperately trying to solve.

• Why "AI Hate" is Your Next Billion-Dollar Opportunity [Markets] - The real money is building the stuff that addresses why people actually hate AI: transparency, control, and systems that don't treat humans like obstacles to optimize away.

• Stop Building AI Agents - Everyone's building complex AI agent systems when they actually need simple chaining and routing patterns

The Two Faces of Technical Angst

The Paralysis Camp

These are the product people who've essentially given up on AI features. They keep pushing AI initiatives to "next quarter" because they fundamentally don't understand what AI can or can't do for their users.

I know a PM at a customer service platform who's been avoiding AI features for eight months. Not because the engineering team can't build them. Not because customers don't want them. But because he doesn't grasp what AI actually is beyond the hype.

Every time the team discusses AI, he deflects to "more research needed" or "let's see what competitors do first." Meanwhile, her competitors are shipping AI features that users love, and his team is getting frustrated.

The paralysis isn't about lacking technical skills. It's about not understanding AI's basic capabilities and limitations. It’s about refusing to, yes, starting with a potential solution looking for a problem to solve.

The Overcompensation Camp

Then there are the product people who swing the other way. They're learning everything they can about transformer architectures, spending weekends reading papers about different embedding models, and using terms like "LSTM" in product requirements.

I watched a founder spend three weeks researching vector database options. He could explain the difference between FAISS and Pinecone in detail. But he'd never talked to a single customer about whether they actually wanted the AI feature he was planning to build.

These product people think technical depth equals product credibility. They're wrong.

Why This Happens

AI moves fast, and technical knowledge feels like the only "real" knowledge. When your engineering team talks about model performance metrics, it sounds objective and important. When you talk about user workflows, it feels soft and subjective.

This creates a toxic dynamic. Product people start believing their value comes from technical understanding rather than product insight. They either avoid AI entirely or try to become amateur engineers.

Both responses miss the point entirely.

The Real Cost

When Product People Freeze Up

• AI opportunities get missed while waiting to "understand enough"

• Engineering teams make product decisions by default

• User needs get lost in technical possibilities

• Competitors ship features you could have built six months ago

When Product People Overcompensate

• Time spent learning irrelevant technical details instead of understanding user problems

• Shallow knowledge of many concepts instead of deep understanding of user workflows

• Engineering teams frustrated by product managers trying to do their job

• Products that are technically impressive but solve no real problems

What You Actually Need to Know

Here's the truth: You don't need to understand how AI works. You need to understand what AI can do and what it can't do.

You don't need to know transformer architecture. You do need to know that current AI is great at pattern recognition but terrible at logical reasoning.

You don't need to understand embedding vectors. You do need to know that AI can find similar content but might miss important context.

You don't need to build evaluation frameworks. You do need to know what success looks like for your users.

The goal isn't technical literacy. It's product judgment informed by technical reality.

The Division of Labor That Works

The best AI product teams I've seen have clear boundaries:

Engineers ask: "How might we build this?"
Product asks: "What should we build and why?"
Together they ask: "What are the tradeoffs and what should we optimize for?"

Your engineering team can tell you if something is technically possible.
You need to tell them if it's worth building.

Your engineering team can measure model accuracy.
You need to measure user value.

Your engineering team can optimize algorithms.
You need to optimize user workflows.

Building Confidence in Your Actual Value

Stop trying to be a mediocre engineer.
Start being an exceptional product person.

Your competitive advantage isn't understanding neural networks.
It's understanding users better than anyone else.

Your value isn't technical depth.
It's translating user needs into product requirements that engineering can execute.

Your job isn't to optimize algorithms.
It's to optimize user workflows and business outcomes.

What Good Looks Like

The best AI product managers I know do these things:

They ask different questions in technical meetings:

• Instead of "How does this algorithm work?" they ask "What does this mean for our users?"

• Instead of "What's our model accuracy?" they ask "How does this impact user success rates?"

• Instead of "Should we use GPT-4 or Claude?" they ask "Which option better serves our use case?"

They own the user perspective completely:

• They know their users' workflows better than anyone

• They can articulate the business problem clearly

• They translate between user needs and technical constraints

They partner with engineering strategically:

• They defer to engineering on technical implementation

• They collaborate on defining success metrics

• They lead on prioritization and user impact

Moving Forward

If you recognize yourself in the paralysis camp, start here: Learn what AI can and can't do for your users. Not how it works, but what it enables. Spend time with GPT-4, Claude, or other AI tools. Use them for real tasks. Understand their strengths and limitations through experience, not theory.

If you're in the overcompensation camp, step back. Audit how you spend your time. How much goes to technical deep dives versus user research? Rebalance toward understanding problems, not solutions.

For everyone: The next time you're in a technical AI discussion, focus on implications, not implementations. Ask "What does this enable for our users?" instead of "How does this work?"

Your engineering team doesn't need you to understand transformers.
They need you to understand users.

Your users don't care about your technical knowledge.
They care about products that solve their problems.

Your job is to bridge that gap. Not by becoming a worse engineer, but by becoming a better product person.

The Reality Check

The companies winning with AI aren't necessarily the ones with the most sophisticated technical teams. They're the ones with the clearest understanding of user problems and the best product judgment about how AI can solve them.

That's your domain. Own it.

The future belongs to product people who understand users better, not transformers better. Stop trying to out-engineer the engineers. Start out-product-ing everyone else.

That's where you win.

More to explore:

• AI-native vs AI-enabled

• “Evals” Aren’t Just For Models, They’re for Products

• What It Really Takes to Build an AI Product

Share your thoughts:
How did you like today’s newsletter?
You can share your thoughts at [email protected] or share the newsletter using this link.