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).

Gamma is wild:
- Just 50 people
- Generating $100M ARR
- Valued at over $2B
- Profitable for 15+ consecutive months
- With more cash in the bank than they raised
- In a category that most VCs dismissed

In fact, one investor told Grant Lee his idea was “the dumbest idea he had ever heard.”

Today, Gamma is one of the hottest AI startups in the world, and unlike most fast-growing AI startups, it's a model for how to build a sustainable, profitable, and durable AI product and brand.

In my conversation with CEO Grant Lee, we discuss:
🔸 How Gamma discovered product-market fit
🔸 Their process for building a “word-of-mouth growth machine”
🔸 How they leveraged more than 1,000 micro-influencers (instead of big names)
🔸 Why focusing on the “first 30 seconds” transformed their business
🔸 Their pricing strategy that led to profitability within months
🔸 How Grant thinks about building a durable “GPT wrapper” business

Listen now 👇
• YouTube: https://lnkd.in/g8j6KTJJ
• Spotify: https://lnkd.in/gPb34cyn
• Apple: https://lnkd.in/ghvQSEYq

Thank you to our wonderful sponsors for supporting the podcast: 
🏆 Vanta—Automate compliance. Simplify security: https://vanta.com/lenny
🏆 Justworks—The all-in-one HR solution for managing your small business with confidence: https://www.justworks.com/
🏆 Miro—The AI Innovation Workspace where teams discover, plan, and ship breakthrough products: https://miro.com/lenny

Lenny Rachitsky

Deeply researched product, growth, and career advice

My biggest takeaways from Stewart Butterfield:

1. Product design is about creating understanding, not removing friction. Teams obsess over reducing friction and removing steps, but 70% to 80% of product design challenges are actually about helping people *understand* what your product does and what to do next. Users arrive barely interested and confused about what you offer. If they can’t quickly grasp what they’re looking at, they’ll leave. Making it easier to do confusing things just gets users to the exit quicker. The mantra should be “Don’t make me think,” not “reduce friction.”

2. You’re not selling features—you’re selling outcomes. Nobody wants a saddle; they want to go horseback riding. Nobody wants a hammer; they want something built. People understand cars and beer without explanation, but new software needs an explanation of both what it is and why they should want it. Slack wasn’t selling messaging features—it was selling better team coordination and reduced email chaos. If you can’t articulate the transformation your product creates in people’s lives, you’re just listing features.

3. Organizations naturally fill with fake work that looks exactly like real work, what Stewart calls “hyper-realistic work-like activities.” Meetings to preview deck slides, analysis of tiny feature differences, elaborate processes around insignificant decisions. People aren’t lazy; they’re responding to having more time than valuable work to do. Leaders must continuously ensure there’s enough valuable work and explicitly say no to projects that can’t possibly generate meaningful impact.

4. Small conveniences create emotional connections that drive word-of-mouth growth. No one switches products because of a good time-zone picker or smooth password recovery, but these details make users love or hate your product. Slack grew largely because people who used it at one company would join a new company and advocate strongly for adopting it. That advocacy came from accumulated small delights, not major features.

5. The “owner’s delusion” explains why bad experiences persist everywhere. Restaurant owners create terrible websites even though they’ve experienced the frustration of visiting other terrible restaurant websites. Business owners assume visitors care deeply about their product, when in reality people arrive distracted, in a hurry, just above the threshold of caring at all. The solution is to regularly step back, be a normal person with limited time and patience, and honestly evaluate if your product makes sense.

6. Only pivot after exhausting all reasonable ideas. The right time to pivot isn’t when things get hard—it’s when you’ve genuinely tried every non-ridiculous approach and can coldly, rationally assess that the expected value has dropped below alternatives. Pivoting is humiliating because you’ve convinced investors, employees, and users of a vision you’re now abandoning. That emotional cost means most people pivot too quickly or too late.

Top takeaways from my chat with Grant Lee (CEO of Gamma):

1. The first 30 seconds of using your product should be so good it earns the next 30 seconds. When Gamma wasn’t growing, they stopped everything and spent three months perfecting just the first 30 seconds of using their product. They made it so compelling that new users would immediately tell their friends. This single change transformed their growth trajectory.

2. Focus on one simple promise, not many features. Think of it like throwing eggs to someone: they can catch one, but if you throw five at once, they’ll drop them all. Your users are selfish, vain, and lazy—you have 30 seconds to show value before they leave. Gamma focused on “create a slide in seconds” rather than listing 10 features.

3. Don’t spend on ads until over half your growth comes from word of mouth. If you try to buy growth before your product spreads organically, you’re wasting money filling a leaky bucket.

4. Work with hundreds of small creators instead of a few big influencers. Rather than blowing your budget on five or six well-known influencers who treat it like just another ad read, find thousands of micro-influencers whose audiences genuinely care about tools like yours. Teachers sharing with teachers, consultants with consultants—these tight communities create authentic word of mouth that spreads fast.

5. Spend time personally onboarding each early creator like they’re joining your team. Don’t just send influencers a script. Jump on calls, walk them through the product, help them understand what makes it special, and let them tell your story in their own voice. This investment turns them into genuine advocates who post about you repeatedly instead of treating it like any other sponsorship.

6. Hire painfully slowly and only exceptional people. Gamma serves 50 million users with just 50 people and makes a profit. All 10 original employees are still there five years later. They never set headcount goals because that makes you hire to hit a number instead of hiring only when you find someone exceptional. When someone is exceptional, give them more responsibility, not less—top performers want harder challenges.

7. Test prototypes with 20 people on UserTesting before investing in a big project. Use platforms like Voicepanel or UserTesting to watch real people try your prototype. They’ll show you problems you never see because you’re too close to your product. Gamma goes from idea to results in a single day—morning idea, afternoon testing, evening results. This saves months of building things nobody wants.

8. Choose problems you’ll care about for 10 years. Before worrying about technology or tactics, ask if this problem matters enough to you personally that you’d dedicate a decade to solving it. Founders who are missionaries rather than mercenaries build better products because their authentic commitment shows through to customers and attracts people who want to build alongside you for the long term.

Women dominating my podcast leaderboard 💪

AI makes it so much easier to build. But here's the catch: everyone else has that same speed advantage.

So you turn to distribution. But here’s the second catch: the channels you’ve long relied on for growth (virality, sales, SEO, ads) are increasingly becoming less effective—for that same reason.

So how do you get your product noticed?

The answer is *ecosystem*. Instead of going directly to your prospects, go through intermediaries who already have access and trust with your audience.

Tom Orbach, director of growth marketing at Wiz, said it best: “Why start at zero when you can start at 10,000?”

You’re likely familiar with the individual tactics within an ecosystem strategy: influencer and creator relationships, channel partnerships, developer relations, communities, product integrations, and customer marketing. But the growth unlock doesn’t come from any one of these activities on their own.

Instead, a flywheel emerges when you properly implement this overarching strategy: your ecosystem partners create and distribute content, you amplify and repurpose their efforts, together you drive greater reach and credibility, new customers and partners come on board, and the cycle strengthens with each turn. Now your ecosystem is a powerful extension of your GTM team.

In today's powerful guest post, Emily Kramer shares how you can implement this strategy yourself, including dozens of real-world examples of how top AI companies like Lovable, Gamma, Clay, Vercel, Supabase, Vanta, Baseten, HubSpot, and others are executing this strategy.

Don't miss this one: https://lnkd.in/gPNUKDzi

Stewart Butterfield rarely does interviews. After 2 years of trying, I finally convinced him to come on. 

In this special conversation, Stewart shares the frameworks and mental models that most helped him build two of the most important products in tech history (Flickr, and Slack—which he sold for $28B, and which powers how basically every company collaborates these days).

We discuss:
🔸 "Utility curves" — his framework for prioritizing ideas
🔸 "The owner's delusion" — why restaurant websites suck
🔸 "Tilting your umbrella" — a hilarious Slack core value
🔸 "Hyper-realistic work-like activities" — my new  favorite concept
🔸 "Don't make me think" — Stewart's foundational design philosophy
🔸 The story behind "We don't sell saddles here"

Listen now 👇
• YouTube: https://lnkd.in/gA5pEgPg
• Spotify: https://lnkd.in/gGSMvJ8m
• Apple: https://lnkd.in/gKEmDYhR

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
🏆 Metronome — Monetization infrastructure for modern software companies: https://metronome.com/
🏆 Lovable — Build apps by simply chatting with AI: https://lovable.dev

My top takeaways from Tomer Cohen (LinkedIn CPO):

1. Don’t wait for permission or perfect conditions to make change. Start using AI tools immediately, build examples of success, and demonstrate what’s possible. The incentives align: organizations need people who can adapt quickly, and you need to stay relevant in your career.

2. Building products isn’t complex—the process has made it complex. Product development itself is simple: research a problem, design it, code it, launch it, and iterate. But over time, companies turned each step into dozens of sub-steps requiring multiple teams, reviews, and functions.

3. According to LinkedIn's data, by 2030, 70% of the skills needed to do your current job will change. Whether or not you’re planning to change jobs, your job is changing, and the only question is whether you can keep up.

4. LinkedIn replaced its Associate Product Manager program with an Associate "Product Builder" program, which teaches employees coding, design, and product management all together. Starting in January 2025, new hires learn all three disciplines from day one rather than specializing in one function. This represents a fundamental shift in how companies develop talent.

5. Feeding AI agents curated “gold examples” works far better than giving them access to everything. When LinkedIn tried letting AI tools access all their internal documents and data, it failed miserably—the AI couldn’t determine what was important and produced unreliable results. The key is carefully selecting the best examples and highest-quality information. Quality matters more than quantity.

6. Top performers adopt AI tools more than struggling employees do, making great people even better. Contrary to expectations that AI would help lower performers catch up, the best talent at LinkedIn uses these tools most actively. AI appears to amplify existing capabilities rather than level the playing field, with top performers having an innate drive to stay at the cutting edge of their craft.

6. The formula for measuring AI’s value is: experimentation volume × quality ÷ time to launch. This framework helps assess whether AI implementation is actually working. Success means teams can run more experiments, produce higher-quality work, and reduce the time from idea to launch—all three factors must improve together.

7. Getting AI adoption is 20% technology, 80% change management. Only about 5% of employees will naturally adopt cutting-edge tools on their own. The vast majority need active support through cultural change: updating performance reviews, celebrating wins publicly, dedicated training, and having leaders personally demonstrate tools rather than delegate. You need to give people time to invest before expecting productivity gains.

My biggest takeaways from Fei-Fei Li:

1. Just nine years ago, calling yourself an AI company was considered bad for business. Nobody believed the technology would work back in 2016. By 2017, companies started embracing the term. Today, virtually every company calls itself an AI company.

2. The modern AI revolution started with a simple but overlooked insight from Fei Fei: AI models needed large amounts of labeled data. While researchers focused on sophisticated mathematical models and algorithms, she realized the missing ingredient was data. Her team spent three years working with tens of thousands of people across more than 100 countries to label 15 million images, creating ImageNet. This dataset became the foundation for today’s AI systems.

3. The human brain’s efficiency vastly exceeds current AI systems. Humans operate on about 20 watts of power—less than any lightbulb—yet accomplish tasks that require AI systems to use massive computing resources. Current AI still can’t do things elementary school children find easy.

4. Simply scaling current approaches won’t be enough. While adding more data, computing power, and bigger models will continue advancing AI, fundamental innovations are still needed. Throughout AI history, simpler approaches combined with enormous datasets consistently outperformed sophisticated algorithms with limited data.

5. Breakthrough technologies often start as toys or fun experiments before changing the world. ChatGPT was tweeted by Sam Altman as “Here’s a cool thing we’re playing with” and became the fastest-growing product in history. What seems like play today might transform civilization tomorrow.

6. Spatial intelligence is as crucial as language for real-world applications. In emergency situations like fires or natural disasters, first responders organize rescue efforts through spatial awareness, movement coordination, and understanding physical environments—not primarily through language. This is why world models that understand three-dimensional space represent the next frontier beyond text-based chatbots.

7. Physical robots face much harder challenges than self-driving cars, which took 20 years from prototype to street deployment and still aren’t finished. Self-driving cars are metal boxes moving on flat surfaces, trying not to touch anything. Robots are three-dimensional objects moving in three-dimensional spaces, specifically trying to touch and manipulate things. This makes robotics far harder than creating chatbots.

8. Everyone has a role in AI’s future, regardless of profession. Whether you’re an artist using AI tools to tell unique stories, a farmer participating in community decisions about AI deployment, or a nurse who could benefit from AI assistance in an overworked health-care system, you can and should engage with this technology. AI should augment human dignity and agency, not replace it—which means both using AI as a tool and having a voice in how it’s governed.

AI Researcher & Professor, Stanford University; Co-Director, Stanford Human-Centered AI Institute; Scientific Partner, Radical Ventures; Author ("The Worlds I See")

George Yury Revutsky

My buddy Tara (ex-Stripe, Mercury, Plaid, Gusto) just launched The Right Turn—a program for high-achieving women who are done waiting to feel “ready” and want to build their own consulting/solopreneur business. 

Check it out 👇

Helping Women Build Freedom-Based Consulting Businesses | Ex-Stripe, Plaid, Gusto, Mercury | Founder, Kinara Solutions

A $1M+ business didn’t fix my self-doubt.
It taught me how to face it.

We love to talk about revenue, momentum, and all the wins…
but not the nerves, the identity shifts, the moments you quietly wonder:

“Am I actually ready for this next step?”

Two years ago, I wanted more: more ownership, more freedom, more alignment.

But I also secretly wanted someone to tap me on the shoulder and say:
You’re ready.
You’re capable.
You don’t need to wait for permission.

I never got that tap. So I built it myself.

Today, I’m launching The Right Turn - a program for high-achieving women who are done waiting to feel “ready,” and are here to build their consulting/ solopreneur business.

Inside, I show you exactly:
→ how to monetize the skills you already have from your 9-5
→ how to price and sell confidently (even when you don’t feel it)
→ how to land clients without chasing
→ how I replaced my salary and then passed $1M (and how to scale your way there)
→ how to build a business that feels like you

Not theory.
Not hype.
What actually worked.

If you want to build a life where you’re not waiting to feel ready, but learning how to believe in yourself through doing…

The Right Turn is now live.

Comment or DM me 'RIGHT TURN' and I’ll send you everything directly. 🤍

Confidence wasn’t what got me here.
It’s what I earned by choosing myself first.

P.S. Even if this isn’t for you, a like or comment will help us reach the women who need this most. 🫶

#WomenInBusiness #CareerChange #FounderStory #FromGrindToGrowth

LinkedIn just scrapped its APM program and replaced it with a radical new approach to product development that fully embraces what AI makes possible. It's called the "Full-Stack Builder" program.

The FSB program teaches employees how to build, design, and ship products from idea to production, with the help of their custom-built AI.

They even introduced a “Full Stack Builder” title for employees who take this path, with a full career ladder, and I strongly believe this will be a model for how established companies transform themselves to be AI-native.

In my chat with Tomer Cohen, CPO, we discuss:
🔸 Why the traditional product development model is broken: organizational bloat slows features to a six-month cycle
🔸 The Full Stack Builder model
🔸 Three pillars of making FSB work: platform, agents, and culture (culture matters most)
🔸 What it takes to build these specialized agents
🔸 Why off-the-shelf AI tools never work on enterprise code without customization
🔸 Why top performers adopt AI tools fastest
🔸 Change management tactics: celebrating wins, making tools exclusive, updating performance reviews

Listen now 👇
• YouTube: https://lnkd.in/gA6Y6arA
• Spotify: https://lnkd.in/gziTs-XJ
• Apple: https://lnkd.in/g9hQhbWg

Thank you to our wonderful sponsors for supporting the podcast: 
🏆 Vanta — Automate compliance. Simplify security: https://vanta.com/lenny
🏆 Figma Make — A prompt-to-code tool for making ideas real: https://lnkd.in/ga7CZx9P
🏆 Miro — The AI Innovation Workspace where teams discover, plan, and ship breakthrough products: https://miro.com/lenny

Fei-Fei Li is known as the “godmother of AI.” 

For the past two decades, she’s been at the center of AI’s most significant breakthroughs, including:
- Spearheading ImageNet, the dataset that sparked the AI explosion we’re living through right now
- Serving as Chief Scientist of AI/ML at Google Cloud 
- Stanford Artificial Intelligence Laboratory (SAIL)
- Co-founding Stanford’s Institute for Human-Centered AI
- Being named as Time's AI 100 most influential people 
- Serving on the United Nations AI Scientific Advisory Board
- Just this week launching the worlds first Large World Model with her startup World Labs  

In this conversation, Fei-Fei shares the rarely told history of how we got to today—and what comes next.

We discuss:
🔸 The backstory on ImageNet
🔸 Why robotics faces unique challenges compared with language models and what’s needed to overcome them
🔸 Why Fei-Fei believes AI won’t replace humans but will require us to take responsibility for ourselves
🔸 Why world models and spatial intelligence represent the next frontier in AI, beyond large language models
🔸 The surprising applications of Marble, from movie production to psychological research
🔸 How to participate in AI regardless of your role
🔸 Much more

Listen now 👇
• YouTube: https://lnkd.in/gyvgvrcJ
• Spotify: https://lnkd.in/g3c8MxGb
• Apple: https://lnkd.in/gtTCj9mz

Thank you to our wonderful sponsors for supporting the podcast: 
🏆 Figma Make — A prompt-to-code tool for making ideas real: https://lnkd.in/ga7CZx9P
🏆 Justworks — The all-in-one HR solution for managing your small business with confidence: https://www.justworks.com/
🏆 Sinch — Build messaging, email, and calling into your product: https://sinch.com/lenny

My top takeaways from executive coach Rachel Lockett:

1. The biggest skill gap in new leaders is knowing when to coach vs. when to tell people what to do. When you constantly provide answers, you train your team to bring you every problem instead of building their own problem-solving skills. The people you hire are experts in their domain—ask curious questions to help them reach their own solutions, which makes them more motivated and capable. Save direct advice for urgent situations or when someone genuinely lacks the necessary skills.

2. Use these four questions to coach someone to figure out the answer or themselves: When someone brings you a problem, use GROW: Goal, Reality, Options, and Way forward. Ask about their desired goal (what does success look like?), their current reality (where are you stuck?), possible options for a path forward (what could you do next?), and a concrete way forward (what will you actually do next?). These questions help people discover solutions they already have the context to find. You don’t need to follow this exact order; just use whichever type fits the moment.

3. Use this four-step framework for difficult conversations: Observations, Feelings, Needs, Requests. Start with factual observations anyone could verify (not interpretations). Share your feelings without blame (I felt anxious, confused, disconnected—not “I feel like you. . .”). Name your underlying human needs (clarity, collaboration, connection). Make a small, achievable request the other person can actually fulfill. Stay on your side of the net—talk about your experience, not what you assume about them. This lets you be bold without triggering defensiveness.

4. In conflict, aim for mutual understanding, not proving you’re right. When you enter a difficult conversation trying to convince someone they’re wrong, they become defensive and armor up. Instead, focus on helping the other person understand your experience so they can empathize and see clearly what’s happening. This shift from convincing to connecting creates space for genuine dialogue where both people can be heard and find solutions together.

5. Burnout happens when you spend too much time outside your natural strengths, not just from working too hard. For two weeks, write down the five things each day that energized you most and the five that drained you most. Look for patterns. People burn out not just from working hard but from spending too much time doing things that deplete them—even if they’re good at those things.

6. Co-founder relationships need scheduled maintenance time, like marriages. Sixty-five percent of startups fail because of co-founder conflict, not business problems. Set up regular check-ins—weekly touch-bases, monthly lunches, quarterly in-person reviews—to ask: How is this working for you? Are we aligned on vision and strategy? What am I doing that frustrates you? What’s gone unsaid?

You now get a year free of PostHog ($16,500 value) with your Lenny's Newsletter annual subscription 🙌🙌🙌

This is the first time PostHog has ever offered a deal like this, and you won’t find it anywhere else.

This brings the total value of the Product Pass to over $25,000 (!!!). Your subscription now comes with a year free of 19 premium products: Lovable, Replit, Gamma, Devin AI, bolt.new, Linear, n8n, Warp, Wispr Flow, Granola, Descript, Superhuman Mail, Magic Patterns, Perplexity, Raycast, ChatPRD, Mobbin, and Stripe Atlas. 

Simply absurd.

If you're not familiar with PostHog, they help you build successful products by giving you literally every piece of SaaS that you need: analytics, experimentation, feature flags, session replay, surveys, error tracking, data warehouse, LLM analytics, and more—all in one platform. 

In the words of the founders, “We keep adding new capabilities at a pace that’s borderline unreasonable.”

The all-in-one platform strategy that PostHog is executing is really interesting to me. I rarely see it done this well, because it’s hard to do. But if you pull it off—like they are—you can build a generational business. Being able to follow an issue from a session recording, to its impact in analytics, to shipping a fix as a feature flag, to testing a variant, to collecting feedback with surveys—that’s the holy grail.

Learn more about the deal: https://lnkd.in/gknPtaGZ

Redeem your free year of PostHog here: https://lnkd.in/gxNKSj_Y

Deal terms:
1. Lenny’s Newsletter subscribers get one full year’s worth of PostHog Scale and 2x the free-tier credits, a $16,500 value. 
2. You must be a new customer of PostHog to take advantage of the deal. If you haven’t paid for PostHog at some point in the past, you’re eligible for the deal.
3. You must get an Annual or Insider subscription to Lenny’s Newsletter to be eligible for this offer. Monthly subscribers aren’t eligible.
4. Both existing and new subscribers of Lenny’s Newsletter are eligible for this deal. 
5. We may run out of codes for a product at any time. Your code is only secured once you claim it. You need to claim each code individually, so hop on the products you are most excited about.

Redeem the deal here: https://lnkd.in/gxNKSj_Y

My biggest takeaways from Jeanne DeWitt Grosser (ex-Chief Business Officer at Stripe, currently Vercel COO):

1. What failed seven years ago now works with AI. In 2017, Jeanne tried to build a system at Stripe that would automatically personalize outbound emails based on company data. Despite working with world-class data scientists, it failed due to too many errors. Today, that exact same approach works. This shows how AI has made previously impossible ideas suddenly viable.

2. A single GTM engineer at Vercel reduced a 10-person sales team to 1 (in just 6 weeks). Jeanne’s team at Vercel had an engineer build an AI agent that handles inbound lead qualification, outbound prospecting, and deal loss evaluation. The agent costs $1,000 per year to run versus over $1 million in salaries for the sales team. The nine displaced team members moved to higher-value work rather than being laid off, and the remaining salesperson is 10 times more efficient.

3. Their AI deal-loss agent has become better at understanding what went wrong than humans. When Jeanne analyzed her biggest loss of the quarter, the salesperson blamed pricing. But an AI agent reviewed every email, call transcript, and Slack message and discovered the real reason: they never spoke to the person who controls the budget, and when ROI came up, the customer clearly didn’t believe the value claims. They are now using AI to analyze sales calls in real time and send alerts like “You’re halfway through the sales process and haven’t talked to a budget decision-maker yet.”

4. Wait until $1 million in revenue before hiring your first salesperson. Founders should continue selling themselves until they reach around $1 million in annual revenue with a repeatable process. The key is having a defined ideal customer profile—customers who look alike.

5. Segment customers on what drives their buying decisions, not just company size. OpenAI has roughly 3,000 employees, which would typically put them in the “mid-market” category. But they’re a top-25 website globally by traffic, so Vercel treats them as enterprise customers requiring complex sales. Effective segmentation combines company size with growth rate, web traffic, workload type, and industry—because selling to e-commerce companies requires completely different language than selling to crypto companies.

6. Most customers buy to avoid risk, not to gain opportunity. About 80% of customers purchase to reduce pain or avoid problems, while only 20% buy to increase upside. This means you should focus your sales messaging on what could go wrong without your product—like falling behind competitors or damaging their reputation—rather than just talking about exciting features. This is especially true when selling to larger companies, where individual careers are on the line.

Full conversation: https://lnkd.in/gj47Efek

Open video: What world-class GTM looks like in 2026 | Jeanne DeWitt Grosser (Vercel, Stripe, Google) on youtube.com

What world-class GTM looks like in 2026 | Jeanne DeWitt Grosser (Vercel, Stripe, Google)

🎧 Watch me do a live coaching session with Rachel Lockett, a sought-after executive coach and former leader at Stripe and Pinterest.

We discuss:
🔸 When to coach vs. when to just tell people what to do
🔸 The GROW technique for helping people figure things out for themselves
🔸 Tips for making difficult conversations less difficult
🔸 Building and sustaining a healthy co-founder relationship
🔸 Avoiding burnout and designing a more energizing career
🔸 Why you should ask, “Would I enthusiastically rehire this person?” to clarify talent decisions

Listen now 👇
• YouTube: https://lnkd.in/gtYqcSF2
• Spotify: https://lnkd.in/g5sk_ZSE
• Apple: https://lnkd.in/gv4NBAzv

Thank you to our wonderful sponsors for supporting the podcast: 
🏆 Stripe — Helping companies of all sizes grow revenue: https://stripe.com/
🏆 Vanta — Automate compliance. Simplify security: https://vanta.com/lenny
🏆 Persona— A global leader in digital identity verification: https://lnkd.in/gn5Y6ict

Top takeaways from Edwin Chen (founder of Surge AI):

1. We’ll soon see $100-million-per-employee companies as AI makes everything more efficient. Surge hit $1B in revenue with fewer than 100 people, completely bootstrapped, in just 4 years. This will break the traditional Silicon Valley VC playbook.

2. AGI is likely a decade or more away. Moving from 80% of a task to 90% is very different from reaching 99.9%—each jump takes substantially longer than the one before. Objective test questions are easier to optimize for than real-world problems.

3. The industry is optimizing AI for “dopamine instead of truth” and thus delaying AGI. Edwin worries we’re building AI that chases engagement rather than advancing humanity. Popular leaderboards like LMArena reward flashy responses with emojis and formatting over accuracy, forcing labs to optimize for superficial metrics that make their models worse at actual tasks.

4. Creating high-quality data to train AI on requires taste. Anyone can verify that a poem has eight lines and mentions the moon. The hard part is identifying poetry that surprises you, stirs emotion, and teaches you something new about language. This subjective, nuanced definition of quality is what separates average AI from exceptional AI.

5. Training AI is more like raising a child than labeling photos. You’re not just feeding information; you’re teaching values, creativity, and countless subtle things about what makes something beautiful or true. The right question isn’t “What test do we want AI to pass?” but “What kind of entity do we want to raise?”

6. The next frontier in AI training is RL environments, where models learn by doing. These are detailed simulations of real-world scenarios—like a startup with Gmail, Slack, code repositories, and databases where AWS suddenly goes down. Models learn by attempting tasks in these environments and receiving rewards based on their full approach, not just final answers. This mirrors how humans actually learn through trial and error in realistic situations.

7. Building a successful company doesn’t require changing who you are. Edwin never thought he’d start a company, because he assumed he’d have to become “a businessperson looking at financials all day.” Instead, he built Surge like a research lab, staying hands-on with data and analysis. His advice: “You don’t need to become someone you’re not. You can build a successful company by simply building something so good that it cuts through all the noise.”

8. The Silicon Valley playbook—pivot constantly, blitzscale, chase hype—isn’t the only path to success. The alternative: find one big idea you believe in, say no to everything else, and keep building even when it’s hard. Chasing trends (crypto, then NFTs, then AI) creates companies with no consistency or mission. Build something that wouldn’t exist without your unique combination of experiences, interests, and expertise.

What world-class GTM looks like in 2026 with Jeanne DeWitt Grosser

Jeanne built Stripe's legendary sales team from the ground up, and is currently COO at Vercel, where she oversees marketing, sales, customer Success, and rev ops.

In our conversation, we discuss:
🔸 Why GTM is becoming an even bigger deal in the AI era
🔸 How AI is changing sales
🔸 The rise of the "GTM engineer" role
🔸 How Vercel made their SDRs 10x more efficient
🔸 A primer on segmentation
🔸 How to build a sales org that product teams love
🔸 Much more

Listen now 👇
• YouTube: https://lnkd.in/ghvZhBjH
• Spotify: https://lnkd.in/gYa_zfZi
• Apple: https://lnkd.in/gQkb6Awn

Thank you to our wonderful sponsors for supporting the podcast: 
🏆 Datadog — Now home to Eppo, the leading experimentation and feature flagging platform: https://lnkd.in/gjexiSqH
🏆 Lovable — Build apps by simply chatting with AI: https://lovable.dev/
🏆 Stripe — Helping companies of all sizes grow revenue: https://stripe.com/

The fastest company in history to $1B did it with no VC money and fewer than 100 people.

Surge AI has become the secret weapon behind Anthropic and Google's best models. Founder Edwin Chen built it without playing the Silicon Valley game—no viral posts, no fundraising treadmill.

In my powerful conversation with Edwin, we discuss:
🔸 How Claude got so good at coding and writing
🔸 Why the AI industry is optimizing for "dopamine instead of truth"—and delaying AGI
🔸 The problems with AI benchmarks and leaderboards
🔸 Why RL environments are the next frontier in AI training
🔸 Why taste and human judgment determine which AI models win
🔸 Why Edwin believes we’re still a decade away from AGI

Listen now 👇
• YouTube: https://lnkd.in/gegNQBGw
• Spotify: https://lnkd.in/gxh8EkCm
• Apple: https://lnkd.in/gEcJSVD3

Thank you to our wonderful sponsors for supporting the podcast: 
🏆 Vanta — Automate compliance. Simplify security: https://vanta.com/lenny
🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: https://workos.com/lenny
🏆 Coda — The all-in-one collaborative workspace: https://coda.io/lenny

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