Lenny’s Podcast107 min
Varun Mohan · Apr 20, 2025
Key learnings- Expect your first big bet to be wrong; stay irrationally optimistic about the vision while ruthlessly testing and discarding the…
- Be willing to pivot fully and quickly when hypotheses break, as Varun's team did when model architectures converged on…
- Build the interface where AI-generated code gets reviewed, not just where it gets typed; Windsurf's tripled acceptance rate after…
@ttorres · Teresa Torres on X♥ 9
💭 "I wish I could click on this card and be like, 'Clean this up.'" When a customer pointed out a flat, unstructured branch in her AI-generated opportunity solution tree, it sparked a three-week journey to fix the problem at its source. Here's what you'll learn from this article: How one customer…
Lenny’s Newsletter♥ 421
Feb 17, 2026 · 26 min
Subscriber post — summary onlyCaitlin Sullivan, a user-research practitioner, explains four recurring ways AI-generated customer analysis goes wrong: invented or blended quotes, generic insights, signal that doesn't guide decisions, and missed…
Product Talk · Teresa TorresOct 22, 2025
Oct 22, 2025 · 12 min
“Every product team is wrestling with the rapid change brought on by generative AI. It's impacting our roadmaps and how we do our jobs. But not everything is changing. Many of the fundamentals stay exactly the same. Last week, I delivered this talk as part of The Future of”
@lennysan · Lenny Rachitsky on X♥ 469
My biggest takeaways from @thsottiaux: 1. Most actions on the internet will soon be taken by agents, and Tibo doesn’t think most people have priced this in yet. When Notion launched its MCP server, it saw a flood of traffic, which strained its systems and forced it to rethink its economics. Every…
Lenny’s Newsletter♥ 294
Apr 9, 2024 · 14 min
Subscriber post — summary onlyLenny Rachitsky argues that AI will hit the high-level strategic parts of product management, such as strategy, vision, and goal-setting, harder than the people-centric work. He contends that soft skills like…
@hnshah · Hiten Shah on X♥ 30
We asked Claude what changed among Linear's competitors. No skills: an old source treated as recent, and a guess stated as fact. Same model, with our competitive skills installed: every claim dated, the ranking marked as its own view, and a line about what it could not see.
Lenny’s Podcast71 min
Aparna Chennapragada · May 18, 2025
Key learnings- Prototype and build to see what you want before committing; prompt sets and prototypes can replace traditional PRDs as the…
- Time to first demo is shrinking while time to full deployment grows, so raise the bar for what earns scale and avoid chasing…
- Treat conversational interfaces as designed products: prompts, editable plans, progress display, and follow-up suggestions are…
Lenny’s Podcast115 min
Nikhyl Singhal · Apr 19, 2026
Key learnings- Expect the information-moving part of product work to be automated; judgment about what to build and whether changes are good…
- Build with AI tools to solve your own daily problems; you don't need to be an engineer, you need to know what good looks like.
- Aim to obsolete yourself: automate the parts of your job you dislike, as the best engineers do.
Lenny’s Podcast115 min
Max Schoenig
Key learnings- Prototyping in code forces you to understand the medium, so designers and PMs gain better judgment about what the real product…
- Agency, the willingness to treat your company and role as malleable, matters more than any single skill as AI tools make…
- Start by making things; tinkering builds the belief that the world around you can be changed, which Max sees as the root of…
Lenny’s Newsletter · Free post♥ 240
Feb 7, 2023 · 20 min
Lenny Rachitsky and Dan Shipper walk through how a Lenny-specific chatbot was built on GPT-3 using his newsletter archive. The piece explains the difference between GPT-3 and ChatGPT, why raw language models tend to…
Product Talk · Teresa TorresSep 17, 2025
Sep 17, 2025 · 19 min
Teresa Torres argues that people feel overwhelmed by AI but can build practical skills by starting with simple, everyday uses of large language models. She presents a graded set of use cases, from basic search…
The Looking Glass · Julie Zhuo♥ 159
Jun 24, 2025
Julie Zhuo argues that as AI systems get better at producing polished work, the traditional signals of skill and craft are eroding, pushing people to ask what human contribution still matters. The essay frames this…
The Beautiful Mess · John Cutler♥ 63
Sep 24, 2026
John Cutler argues that AI tools remove not only wasteful friction but also 'positive friction': the productive struggle, debate, and slow sense-making that builds shared understanding and sound judgment in product…
Lenny’s Podcast121 min
Howie Liu · Aug 31, 2025
Key learnings- Leaders should stay close to the product details, since in AI-era products the interaction design and underlying behavior are the…
- Use AI products constantly, even multiple times an hour, and build small weekend projects to understand what models can do and…
- Ask whether you would build the same mission from scratch as an AI-native company; if your existing assets don't give a real…
Lenny’s Podcast110 min
Aishwarya Naresh Reganti + Kiriti Badam · Jan 11, 2026
Key learnings- AI products differ from traditional software mainly because user inputs and model outputs are both non-deterministic, so behavior…
- Each time you grant an AI system more decision-making autonomy, you give up some human control; autonomy should be earned through…
- Start with low-agency, high-human-control versions (e.g., suggestions to human agents) and log human corrections to build a…
Lenny’s Podcast68 min
Amjad Masad · Nov 21, 2024
Key learnings- Replit aims to remove the setup friction of building software by bundling the editor, runtime, packages, database, and deployment…
- Once AI removes the build bottleneck, the limiting factor shifts to how fast you can generate and articulate good ideas, so…
- Product managers can use an AI agent to build a v1 prototype, test it with real users, and then hand a validated concept to…
@ttorres · Teresa Torres on X♥ 292
AI evals have been the "it" skill for product teams for over a year. I've even called evals a new discovery habit. But I still meet product teams who only have a vague idea of what evals are. And it's not their fault. Most of the writing on this topic is intended for engineers or just isn't…
Product Talk · Teresa TorresOct 28, 2025
Oct 28, 2025
This podcast episode argues that AI tools deliver useful product work only when they are given the right context, much as a new teammate needs onboarding. Teresa Torres and Petra Wille explain why most AI outputs…
Product Talk · Teresa TorresJan 15, 2026
Jan 15, 2026
This episode of Just Now Possible features Tendos AI's leaders discussing how they automate the tendering workflow for construction manufacturers, where teams manually parse huge bid-request PDFs, match products, price…
Lenny’s Podcast153 min
Peter Deng · Jun 22, 2025
Key learnings- Before scaling past product-market fit, invest in systems and architecture that let the team move sustainably faster, since…
- Build a growth team early; a growth leader forces instrumentation and rigorous questioning of what is actually happening in the…
- Balance growth-focused and craft-focused people deliberately, so the tension between metrics and product quality produces…
Lenny’s Newsletter♥ 177
Jul 2, 2024 · 15 min
Subscriber post — summary onlyLenny Rachitsky and Kyle Poyar compiled lessons from more than 20 AI product builders on what surprised them about shipping AI features. The post argues that AI products need fresh thinking: prototype to discover what…
Lenny’s Podcast103 min
Gustav Söderström · May 21, 2023
Key learnings- Expect the internet's next shift from recommendation to generation to require rethinking products and business models, much as…
- Design UIs to match your model's real accuracy: if predictions are right only one in four or five times, show several options at…
- Treat generative AI as a distinct paradigm rather than more of the same machine learning, and ask what experience couldn't exist…
Product Talk · Teresa TorresSep 2, 2026
Sep 2, 2026 · 26 min
Teresa Torres argues that AI evals should be a core discovery habit for product teams, not just an engineering concern. Because LLM outputs are probabilistic and semantic, traditional pass/fail unit tests don't work…