I write Lenny's Newsletter (the #1 business newsletter on Substack), and host Lenny's Podcast (top ten tech podcast globally). Previously, I spent seven years at Airbnb leading supply growth, conversion, trip quality, and community, and before Airbnb, I was an engineer and a founder (exited to Airbnb).

Lenny Rachitsky

Here are the full transcripts from all 320 of my podcast episodes.

It's been super fun for me to play with AI to extract insights from this data. Now you can too.

My only ask is that if you do something cool with it, just let me know.

I'll keep this folder updated as each new episode comes out.

Have fun: https://lnkd.in/gSFrFEHV

[update: Claire Vo created a handy GitHub repo:  https://lnkd.in/gGikBYxw]

Deeply researched no-nonsense product, growth, and career advice

My biggest takeaways from Elena Verna (Head of Growth at Lovable):

1. In AI, you now need to find product-market fit every three months. Product-market fit used to mean: build something people want, then scale it for years. In AI, the underlying technology changes so fast—and customer expectations with it—that you’re constantly re-earning that fit. Even at $200M ARR.

2. The growth playbook has fundamentally changed for AI companies. Elena has led growth at Miro, Dropbox, and Amplitude and advised dozens more companies on growth. At Lovable, she says only 30% to 40% of what she learned in 20 years still applies.

3. At Lovable, growth is driven mostly through new features, not optimizing funnels. At the fastest-growing company in history, optimization drives about 5% of their growth. The other 95% comes from launching new features and products. Small tweaks don’t move the needle when everything is changing.

4. Ship constantly, and talk about it. Lovable’s main growth and retention strategy: ship features fast enough that customers feel the product is always alive. Engineers announce their own updates. The founder tweets progress daily. This keeps users curious—and keeps competitors scrambling.

5. Give your product away like candy. AI products are expensive to run, so most companies gate them behind paywalls. Lovable does the opposite: they fund hackathons, sponsor events, and hand out free credits. They treat this spending as marketing, not cost—and it compounds through word of mouth.

6. Influencer marketing outperforms paid ads by 10x. Lovable found that short videos showing what the product can do spread faster and convert better than traditional paid advertising. Showing beats telling.

7. “Minimum viable product” is dead. Elena describes the new minimum bar as “minimum lovable product.” If the experience doesn’t delight people, they won’t tell anyone. And word of mouth is your primary engine.

8. Community isn’t a nice-to-have. It’s a key lever for growth. Lovable’s Discord has hundreds of thousands of members helping each other. This amplifies word of mouth, drives retention, and makes customers feel like insiders. Building the product alone isn’t enough anymore—you’re building a world.

9. Hire people who create clarity from chaos. Fast-moving AI companies don’t have neat job descriptions or stable roadmaps. Elena looks for high-agency people who thrive in mess, including new graduates who are AI-native and former founders who know how to operate without instructions.

10. You can work at one of the fastest-growing companies in history and still see your kids. Elena wakes at 6 a.m. Stockholm time, protects her gym and family hours, and refuses to treat burnout as a badge of honor. Her point: if you set boundaries, the work will fill the available time—not all the time.

Deeply researched product, growth, and career advice

My biggest takeaways from Molly Graham:

1. No company needs more than three goals. Facebook ran on just three goals for five years while growing 10x: growth (MAUs), engagement, and revenue. One goal must also “win in a fight” to create true clarity (at Facebook, engagement trumped all). Goals should be simple enough that “an intern who started Monday should understand them,” and they should hurt—if your prioritization process isn’t painful, you’re not making real tradeoffs.

2. In rapidly scaling organizations, you need to over-communicate by a factor of 10x. Molly observed at Facebook that Zuck would repeat the same message in multiple formats (all-hands, email, 1:1s, team meetings), because people need to hear something seven to 10 times before it actually sinks in. Leaders often assume everyone heard and understood them the first time, but in chaotic, fast-growing environments, repetition is key.

3. “Give away your Legos.” Hand off the things you built and love doing—your “Legos”—to others as the company grows. Even when it feels painful. Clinging to work/projects/teams stunts your career growth.

4. "Snorkel before you scuba." 80% of team issues stem from structural company problems (unclear goals, roles, expectations) or team dynamics, not individual performance. So before assuming the person is flawed or unfit, start at the surface level by asking, “Does everyone know what their job is and what success looks like?” Only dive deeper into interpersonal or individual issues if structural fixes aren’t the issue.

5. The best careers look like J-curves, not stairs. The most valuable career moves feel like jumping off cliffs. You’ll fall for six to nine months, feeling incompetent and asking “dumb” questions. But when you climb out, you’ll reach heights the “stairs” approach could never access. Molly’s advice: “Different kinds of fear tell you different things. Financial fear might be worth listening to, but the fear of ‘I can’t do this’ is a flashing green light to go for it.”

6. Escalation is a tool, not a failure. When two people with equal power disagree, they often waste weeks debating. Instead, go together to someone with more context or authority as soon as you’re stuck. This isn’t tattling—it’s using management for its intended purpose: unblocking teams.

7. The biggest mistake leaders make during hypergrowth is trying to preserve how things used to work. Molly saw this repeatedly at Google, Facebook, and Quip: The leaders who clung to old structures, old ways of working, or old team cultures became obstacles. The companies that scaled successfully were the ones where leaders embraced that their job was to constantly reinvent how the org operated.

8. Six rules for effective goal-setting: goals must be specific, measurable, time-bound, public, few in number, and actually used to make decisions. Most companies fail because they set too many goals, make them too vague, or never reference them after the kickoff meeting.

The Lovable story will be one for the history books:
- Valued at $6.6B just 2 years after founding
- Zero to $200M ARR in under 1 year 
- Only ~100 employees (!!)
- Based in Stockholm, Sweden

Elena Verna joined Lovable 8 months ago to lead growth, and in our in-depth conversation (her fourth visit to the pod, a record!), we discuss:
🔸 Why she's had to throw out 70% of her growth playbook at Lovable
🔸 The specific growth tactics driving Lovable’s unprecedented growth
🔸 Why giving away product is a key growth strategy in AI
🔸 Her take on Lovable's retention numbers
🔸 Why in AI you have to re-find PMF every 3 months
🔸 Whether you should join an AI startup (her honest tradeoffs)
🔸 Much more

Listen now 👇
• YouTube: https://lnkd.in/gmd3xHWX
• Spotify: https://lnkd.in/gtepsBrt
• Apple: https://lnkd.in/gtyRHdtk

Thank you to our wonderful sponsors for supporting the podcast: 
🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: https://workos.com/lenny
🏆 Vercel— Your collaborative AI assistant to design, iterate, and scale full-stack applications for the web: https://lnkd.in/gpSJTjmm
🏆 Persona — A global leader in digital identity verification: https://lnkd.in/gn5Y6ict

The future of product management looks a lot like Zevi Arnovitz.

Zevi is an IC PM at Meta, has no technical background—is scared to even look at code—but has taught himself how to use Cursor and Claude Code to build significant and real product. 

He's developed his own very powerful Cursor workflow that allows him to quickly add his ideas to Linear, develop a plan using Claude Code, build it within Cursor, and then have different LLMs review the code.

I haven’t stopped thinking about this conversation since we had it, and everyone needs to pay close attention to what AI us unlocking for non-technical people.

In our conversation, we discuss:
🔸 The complete AI workflow that lets non-technical people build in Cursor
🔸 How to use multiple AI models for different tasks (Claude for planning, Gemini for UI)
🔸Specific slash commands to automate key prompts
🔸 Zevi’s “peer review” technique, which uses different LLM models to review each other’s code
🔸 Why this might be the best time to be a junior in tech, despite the challenging job market
🔸 How Zevi used AI to prepare for his Meta PM interviews

Listen now 👇
- YouTube: https://lnkd.in/gwdHkj2X
- Spotify: https://lnkd.in/gQC4Av_c
- Apple: https://lnkd.in/gvXvCwP8

Thank you to our wonderful sponsors for supporting the podcast: 
🏆 10Web.io — Vibe coding platform as an API https://10web.io/lenny
🏆 DX — The developer intelligence platform designed by leading researchers https://getdx.com/lenny
🏆 Framer — Build better websites faster https://framer.com/lenny

The biggest opportunities for AI startups today

Noam Segal and I surveyed my readers about how they're using AI today, and more importantly, how they *want* to be using AI.

For PMs, the biggest opportunity is research. User research shows the largest demand gap of any task. Only 4.7% say it’s their primary AI use case today, but nearly a third want it to be. PMs have figured out how to use AI for output tasks like writing PRDs and drafting communications, but they’re hungry to apply it upstream, to the messy work of understanding what to build.

Prototyping is a breakout category across functions, both today and in the future. For PMs, “creating mockups/prototypes” jumps from 19.8% (currently using) to 44.4% (want to use next), a +24.6pp swing that makes it the single most-wanted future use case. 

For designers, prototyping and interaction design show similar momentum (+27.8pp). This tracks with the rise of tools like Lovable, v0, Replit, and Figma Make.

Engineers are shifting their use of AI to handle work after writing the code. Writing code is by far their most popular use case (51% current), but it has a demand gap of only +5.6pp. However, documentation (+25.8pp), code review (+24.5pp), and writing tests (+23.5pp) all show massive opportunities for growth in engineering AI tooling.

Founders are doubling down on AI as a thinking partner. Product ideation shows massive demand, jumping from 19.6% (currently using) to 48.6% (want to use next), a +29.0pp gap. Growth strategy and GTM planning (+24.7pp) and market analysis (+24.0pp) follow close behind.

Founders already use AI heavily for personal productivity (32.9% currently), but they want to move upstream. They’re looking for pressure-test ideas, explore markets, and think through go-to-market. AI as a co-founder, not just an assistant.

Full report: https://lnkd.in/gR5G88yA

Results from our large-scale AI productivity survey

You can build a ChatGPT app in 30 minutes that reaches 800 million users.

This is the kind of distribution opportunity that comes around once or twice a decade—like the App Store in 2008 or SEO in the early 2000s. Early movers lock in new habits.

Today's post by Colin Matthews breaks it all down:
→ How users discover ChatGPT apps (hint: they don't—ChatGPT suggests apps based on intent)
→ How it works under the hood (tools, MCP, the message flow)
→ Two paths to build your first app: Replit or Chippy.build 
→ Prompts you can copy and paste + technical deep-dive

800M users. Low barrier. The opportunity is open.

Here's your guide:

Open article: ChatGPT apps are about to be the next big distribution channel: Here’s how to build one by lennysnewsletter.com

ChatGPT apps are about to be the next big distribution channel: Here’s how to build one

My biggest takeaways from Jason Cohen:

1. “Too expensive” is never the real reason customers cancel. They already saw your pricing and decided to buy, so something else changed. When customers cite price, dig deeper—the actual reason might be changing needs, integration issues, or feature gaps.

2. Ask “What made you cancel?” instead of “Why did you cancel?” Jason tested both phrasings and saw response quality double with the “what made you” version. The first version directs attention to the product or situation and invites one-word deflections like “budget.”

3. Most companies undercharge because they just guessed at pricing and never revisited it. One founder selling to enterprise charged $300 per year, and Jason advised switching to $300 per month. Signups stayed exactly the same. When you 12x your price and conversion doesn’t budge, you’re not even close to finding the right number yet.

4. Pricing selects your market more than it signals value. When your product costs too little, larger companies assume it can’t be serious: not mature enough, no governance policies, inadequate support. Raise prices and you don’t necessarily lose customers; you enter a different market segment where your price signals credibility. The founder who went from $300 per year to $300 per month and saw no change in signups—he just shifted who was buying.

5. Your churn rate sets a ceiling on your business that most founders underestimate. The math is simple: divide your monthly new customers by your monthly cancellation rate, and that’s the maximum number of customers you will ever have. This is why logo churn is the first metric to examine when growth stalls.

6. Onboarding is the highest-leverage lever to reduce churn. Small improvements in the first 30 days compound into retention gains over the customer lifetime. When you don’t know where to start on retention, start with onboarding.

7. Positioning can allow you to charge an order of magnitude more without changing your product. The same exact product can command higher prices depending on how you frame it. “Cut your ad costs in half” caps what customers will pay—they’ll only give you a fraction of the savings you drive them. While “double your leads” aligns with what executives actually want and opens a much higher budget. CEOs reward growth; they merely acknowledge cost savings.

8. Sometimes the right answer is accepting that not growing is OK. If you’ve optimized churn, pricing, NRR, and channels, maybe growth has natural limits. Bootstrap founders reach a point where healthy annual dividends make further scaling optional. The question “Do you need to grow?” deserves honest examination—not because stasis is fine but because growth at all costs can destroy what made the company good.

Coolest transcripts project so far, by Lazar Jovanovic

https://lnkd.in/g-KaUNNg

Replit CEO Amjad Masad on why product managers are the best vibe coders

Exactly right: https://lnkd.in/ei5-Vi-w

"80% of the culture of a company is defined by the personality of the founder. 

Facebook is Mark. Google is Larry and Sergey. 

Our job as leaders around founders is to help articulate the culture that they're creating, and to help extend it.

Your job is to build a company that would make a decision the way the founder would when they're not in the room. That is the work of building a company around a founder. 

Your job is not to shape culture. I've seen and watched friends like try to shape a set of values at a company, and it just doesn't match who the founder is. 

You say move fast and break things, and your founder loves ambiguity and is perfectly happy with not making decisions—all that leads to is cultural dissonance." — Molly Graham

My conversation with Marc Andreessen, co-founder of Andreessen Horowitz

Marc is a many-time founder, investor, and co-founder of Netscape. In this very special conversation, we dig into why we’re living through one of the most unique times in history, and what comes next.

We discuss:
🔸 Why AI is arriving at the perfect moment to counter demographic collapse and declining productivity
🔸 How Marc is raising his 10yo kid to thrive in the AI future
🔸 The 3-way standoff that’s happening between PMs, designers, and engineers
🔸 What’s actually going to happen with AI and jobs (spoiler: he thinks the panic is “totally off base”)
🔸Why AI is "the philosopher’s stone"
🔸 Why you should still learn to code
🔸 His media diet: X and old books, nothing in between
🔸 So much more

Don't miss this one.

Listen now 👇
• YouTube: https://lnkd.in/gpDskgpG
• Spotify: https://lnkd.in/gBp8ep_R
• Apple: https://lnkd.in/gZ2k2Nqg

Thank you to our wonderful sponsors for supporting the podcast: 
🏆 DX — The developer intelligence platform designed by leading researchers: https://getdx.com/lenny
🏆 Brex — The banking solution for startups: https://lnkd.in/gakmrKrh
🏆 Datadog — Now home to Eppo, the leading experimentation and feature flagging platform: https://lnkd.in/gjexiSqH

"This course was a revelation." - EM, Cisco

"Wish I had taken it sooner." - Principal PM, Microsoft

"This course completely changed how I think about evals." - Sr. Director of AI Engineering, Microsoft 

"One of the best investments I've made recently" - Sr. Director of Product, Square

"This class is mandatory knowledge for anyone building AI systems." — Sr. PM, Google

Just a few testimonials from listeners who watched my chat with Hamel Husain and Shreya Shankar and took their course.

Here's the episode, which includes step-by-step walkthrough showing you how to building evals for your product: https://lnkd.in/gTnNjqAQ

Here's their course (#1 most popular course on Maven, nbd). The next cohort starts in a few weeks: https://lnkd.in/gBGSfnDY

Pain is the new moat

"Anybody who wants to build something these days, the information is at your fingertips more than it's ever been. You can learn anything overnight. So going through the pain of learning and understanding what works and what doesn't work, and going through this pain of developing multiple approaches, and then solving the problem—I feel that is going to be the real moat as an individual going forward." — Kiriti Badam, Codex eng lead at OpenAI

Full conversation: https://lnkd.in/gHTecv23

Marc Andreessen on why the AI job loss panic is wrong

Fintech | Embedded Finance | E-commerce

I am a fintech enthusiast and product leader passionate about crafting simple solutions for complex problems. I have successfully launched several products in the payments and fintech space, consistently driving growth for early-stage startups and enhancing customer satisfaction for established ventures. Additionally, I run the Fintech Wrap Up newsletter, sharing the latest industry news and insights, and serve as a pro bono peer mentor in product management, demonstrating my commitment to giving back to the community.

Sam Boboev

AI in financial services is no longer a “future bet.” It’s a budget line item with proven ROI.

NVIDIA’s latest State of AI in Financial Services survey (800+ professionals) shows the industry has crossed the adoption threshold and entered the scaling phase.

Key signals:

- 65% of firms are actively using AI (up from 45% last year). This is not experimentation. This is operational deployment. 
- Nearly 100% say AI budgets will increase or stay the same. Once AI is in production, it becomes infrastructure, not a project. 
- 61% are using or assessing GenAI, up 52% YoY. GenAI is shifting from “chatbots” to workflow automation. 
- 42% are using or assessing agentic AI, and 21% already deployed AI agents. The agent layer is becoming real. 
- Open source has become strategic: 84% say open-source models/software are important to their AI strategy, because financial institutions want customization and control. 
____

The most important part is not the buzzwords. It’s the economic logic.

This report explicitly frames AI as a driver of measurable return:

- 89% say AI helps increase revenue and decrease costs
- 64% report revenue uplift >5%
- 61% report cost reduction >5% 

That means the “AI debate” is over. The real competition now is about:

- Who turns proprietary data into proprietary models
- Who builds AI-native workflows instead of AI wrappers
- Who avoids vendor lock-in while still shipping fast

AI in financial services has crossed a threshold. The question is no longer whether to adopt. It is whether the architecture underneath can sustain autonomy at scale

Full report is in the comments section.

Founder & CEO at Fintech Wrap Up | Payments | Wallets | AI

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