Lenny's Podcast: Product | Career | Growth

Muse is the #1 app in the App Store and arguably the fastest-growing AI consumer product of all time. Mona Sarantakos has led product on it since day one. She’s VP of Product at Meta Superintelligence Labs and a 10-year Meta veteran with over 20 years of experience as a PM. This is her first podcast interview.

We discuss:
1. How a weekend with OpenClaw on a Raspberry Pi turned into Muse
2. Why Muse waited seven months to launch, despite getting started before most of its competitors
3. How Nat Friedman, Alex Wang, and Mark Zuckerberg each left their imprint on the product
4. How the team uses “agent swarms” to surface user insights at scale
5. The use case nobody on the team predicted
6. How Jolly, a placeholder avatar, became an icon
7. The two levers that actually move activation for an AI agent
8. The #1 thing Mona looks for in PMs today
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Brought to you by:
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
DX—Engineering intelligence for the AI era
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Episode transcript: https://www.lennysnewsletter.com/p/metas-muse-how-we-launched-the-most
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Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0
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Highlights: https://lennyspodcast.com/mostreplayedmoments
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Where to find Mona Sarantakos:
• LinkedIn: https://www.linkedin.com/in/mona-sarantakos-a85873
• Threads: https://www.threads.com/@monachaudhuri
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Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• X: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
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In this episode, we cover:
(00:00) Teaser
(01:24) Introduction and Mona’s background
(02:14) A weekend with an early agentic AI
(09:08) The seven-month launch timeline
(14:07) "The claw is the law"
(15:53) Nat, Alex Wang, Zuckerberg, and how leadership shaped Muse
(23:37) The Jolly origin story
(29:53) Priority use cases and the shopping surprise
(35:03) Agentic commerce and how shopping works
(38:03) Agent swarms and synthetic data
(42:48) Evals: structure, hierarchy, and who writes them
(46:37) Activation and word of mouth
(51:09) The design bets that defined Muse
(57:04) Prototyping culture and the two Muses that fought
(01:01:54) How the PM role has changed
(01:09:05) Muse's first prototype
(01:10:37) The board meeting prototype that almost didn’t work
(01:12:00) Team size, closeness, and culture
(01:20:55) What comes next: ambient agents and financial betterment
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Referenced:
• Muse: https://muse.ai
• Introducing Muse Spark: MSL’s First Model, Purpose-Built to Prioritize People: https://about.fb.com/news/2026/04/introducing-muse-spark-meta-superintelligence-labs
• Nat Friedman’s website: https://nat.org
• OpenClaw: https://openclaw.ai
• From skeptic to true believer: How OpenClaw changed my life | Claire Vo: https://www.lennysnewsletter.com/p/how-openclaw-changed-my-life-claire-vo
• Personal Superintelligence: https://www.meta.com/superintelligence
• The Future is for Everyone: https://www.meta.com/thefutureisforeveryone
• Grok Bot: https://x.ai/bot
• How we built Grok Bot in a month | Roman Ugarte (SpaceXAI): https://www.lennysnewsletter.com/p/how-we-built-grok-bot-in-a-month
• Claude Duet: https://claude.com/product/claude-duet
• Dots: https://openai.com/index/introducing-dots
• Alex Wang on X: https://x.com/alexandr_wang
• Alex Cornell’s website: https://www.alexcornell.com
• How I AI: How Meta designed Muse with Alex Cornell: https://www.lennysnewsletter.com/p/how-i-ai-how-meta-designed-muse-with
• Christine Awad on X: https://x.com/christinesed
• Linear’s secret to building beloved B2B products | Nan Yu: https://www.lennysnewsletter.com/p/linears-secret-to-building-beloved-b2b-products-nan-yu
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
—
Lenny may be an investor in the companies discussed.


To hear more, visit www.lennysnewsletter.com

Tulsee Doshi is head of product for Google’s frontier AI models, including Gemini and other multimodal models, such as Nano Banana, Genie, Veo, and Lyria.

We discuss:
1. Why model development is not just engineering, but also like Snape’s potions class
2. Google’s full-stack frontier AI strategy
3. What PMs do inside a frontier AI lab
4. Tulsee’s 60-20-20 framework for balancing what works now and what’s almost working
5. How to write your first eval
6. The rising value of great PMs
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Brought to you by:
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
DX—Engineering intelligence for the AI era
—
Episode transcript: https://www.lennysnewsletter.com/p/what-it-actually-takes-to-build-frontier
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Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0
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Highlights: https://lennyspodcast.com/mostreplayedmoments
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Where to find Tulsee Doshi:
• X: https://x.com/tulseedoshi
• LinkedIn: linkedin.com/in/tulsee-doshi
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Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• X: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
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In this episode, we cover:
(00:00) Teaser
(01:08) What people misunderstand about how models are built
(06:28) Google’s strategy in the AI race
(09:57) AI safety, monitoring, and strengthening cyberdefenses
(14:10) Balancing fast launches with great user experiences
(17:02) Why the model itself has a UX
(19:21) Why product managers are becoming more valuable
(21:24) The skills that matter for research PMs
(23:04) How to build your first eval
(24:37) Getting better at judging a model’s vibes
(26:15) Improving skin tone representation with better evals
(28:25) Evaluating the full experience, not just the model
(30:38) From crowdsourced flash teams to agent swarms
(33:13) The 60-20-20 rule for building ahead of model capabilities
(35:46) Keeping your product adaptable as models improve
(36:42) What an AI harness actually is
(37:30) Upskilling PMs through shared skills and workshops
(41:18) Building the habit of asking, “Can AI do this for me?”
(42:38) Google’s opportunity to build a personal AI assistant
(44:10) Burnout, meaningful work, and the pressure to do more
(47:44) Avoiding AI slop
(49:30) How AI is changing the PM role
(51:02) Lightning round and final thoughts
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References: https://www.lennysnewsletter.com/p/what-it-actually-takes-to-build-frontier
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
—
Lenny may be an investor in the companies discussed.


To hear more, visit www.lennysnewsletter.com

Hector Serrano runs one of the most innovative and successful paid growth programs in Silicon Valley. As Head of Content at Wispr Flow, a leading AI voice-to-text tool, he built a roster of 50 creators who drive 500 to 600 paid user-generated content (UGC) ads every month. The creators are paid a cut of ad spend, and the best of them earn over $13,000 a month. It’s a playbook the best DTC brands have used for years, now adapted for SaaS. Hector joined Wispr Flow at 19, before the product had PMF, and over the past year, Wispr Flow has grown revenue 30x and raised at a $2 billion valuation.

We discuss:
1. Why paid UGC is one of the biggest untapped growth levers right now
2. The step-by-step playbook for building a successful paid-UGC program
3. Why the best marketers are “allergic to AI”
4. What makes a great modern marketing hire
5. Why YouTube is the most underrated growth lever in SaaS
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Brought to you by:
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
DX—Engineering intelligence for the AI era
—
Where to find Hector Serrano:
• X: https://x.com/hectorserrranoo
• LinkedIn: linkedin.com/in/hector-serrano-martin
• YouTube: https://www.youtube.com/@hectorchats
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Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• X: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
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In this episode, we cover:
(00:00) Teaser
(01:17) What Hector does at Wispr Flow
(03:10) The number-one growth lever: product-market fit
(04:25) When to start investing in paid ads
(08:27) The paid-UGC strategy and how it developed
(13:44) How creator compensation works: percent of ad spend vs. GMV
(20:07) The profile of a great modern marketing hire
(25:03) The importance of young talent
(28:22) Hector’s journey from D1 soccer to Wispr Flow
(31:03) Step-by-step: How to build a paid-UGC group from zero
(43:03) Reaching out to creators
(45:37) Building culture inside your creator group
(48:31) The role of the media buyer alongside the creative lead
(50:05) Team size and structure
(53:44) Why UGC works even in technical and enterprise SaaS
(56:15) Advice for people embarking on the creator path
(59:50) Common mistakes to avoid
(01:02:21) The sports approach to hiring
(01:03:52) Why the best marketers are allergic to AI output
(01:08:07) Founder-led content
(01:16:01) Why YouTube packaging matters more than production quality
(01:20:06) Lightning round and final thoughts
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Referenced:
• Wispr Flow: https://wisprflow.ai
• Tanay Kothari on X: https://x.com/tankots
• Matt Swulinski on LinkedIn: https://www.linkedin.com/in/mattswulinski
• Viktor: https://viktor.com
• Tobin Tang on LinkedIn: https://www.linkedin.com/in/tobinwtang
• Polymarket: https://polymarket.us
• Trybe: https://jointrybe.com
• Lukas Pakter on LinkedIn: https://www.linkedin.com/in/lukas-pakter-402117204
• SquidHaus: https://gohaus.com
• Daniel McCallum on LinkedIn: https://www.linkedin.com/in/danielmccallum
• “Everyone wants a great head of content but you won’t find one” on X: https://x.com/hectorserrranoo/status/2109703739754037530
• Eric Zaworski on LinkedIn: https://www.linkedin.com/in/eric-zaworski
• Jimmy Farley on LinkedIn: https://www.linkedin.com/in/jimmy-farley-349215296
• Physician’s Choice: https://physicianschoice.com
• Hector’s paid-UGC playbook on X: https://x.com/hectorserrranoo/status/2108260769603486100
• “The most successful founders, investors, or creators I’ve met...” on X: https://x.com/hectorserrranoo/status/2113109670491783661
• “Startups should recruit more like sports teams” on X: https://x.com/hectorserrranoo/status/2113676414973259782
• Real Madrid La Liga: https://www.laliga.com/en-GB/clubs/real-madrid
• “The best marketers are allergic to AI” on X: https://x.com/hectorserrranoo/status/2110432634451169699
• Why Netflix is betting on systems thinkers—not specialists—in the AI era | Elizabeth Stone (CPTO): https://www.lennysnewsletter.com/p/netflix-cpto-on-ai-and-the-future
• Naval Ravikant on X: https://x.com/naval
• You Can See Everything: https://www.imdb.com/title/tt45368240
• Miso: https://miso.com
• Steve Jobs quote: https://www.brainyquote.com/quotes/steve_jobs_416875
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Recommended books:
• The Almanack of Naval Ravikant: A Guide to Wealth and Happiness: https://www.amazon.com/dp/1544514212
• Ego Is the Enemy: The Fight to Master Our Greatest Opponent: https://www.amazon.com/dp/1781257019
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
—
Lenny may be an investor in the companies discussed.


To hear more, visit www.lennysnewsletter.com

Rob Snyder is the author of The Power of Pull and writes the newsletter The Physics of Startups. He has advised more than 50 startups on growing from zero to $1 million in ARR, drawing on his own experience as a founder who spent two years pushing a product the market didn’t want before accidentally stumbling onto what actually works. In this episode, we walk step by step through his framework for finding product-market fit, from coming up with an idea to closing your first handful of deals.

We discuss:
1. The PULL framework: Project, Unavoidable, List of options, Limitations
2. Where the best startup ideas come from
3. Tactics for cold outreach and running your first sales call
4. Why Rob advises against design partners
5. Goblin mode: what to do when nothing is working
6. How to apply the PULL framework inside larger companies
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Brought to you by:
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
DX—Engineering intelligence for the AI era
—
Where to find Rob Snyder:
• LinkedIn: https://www.linkedin.com/in/rsnyder1
• Newsletter: https://thephysicsofstartups.substack.com
• Podcast: https://open.spotify.com/show/4Cic56E1UEbU25P3v14Vkq
• Website: https://robsnyder.org
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Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• X: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
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In this episode, we cover:
(00:00) Introduction to Rob Snyder
(01:13) The PULL framework: what makes customers buy
(07:30) The “PULL” acronym
(09:44) How Jump found pull with financial advisors
(13:53) Why pull is discovered, not created
(16:10) Selling to a person, not a company
(17:13) Finding startup ideas through firsthand experience
(22:34) Testing your pull hypothesis with two slides
(26:41) Recognizing real buying intent
(27:24) When solving your own problem works
(29:02) The Hail Mary question that can unlock a pivot
(31:01) Common traps to avoid
(32:50) Quick recap
(34:46) Landing your first customer conversations
(39:18) Outreach benchmarks and diagnosing what isn’t working
(41:27) How to run your first sales call
(46:56) Successful pitch tactics
(49:59) Does your product need to be 10 times better?
(54:08) Closing the deal
(01:00:41) Using the PULL framework to set pricing
(01:02:22) Revising your hypothesis and going into goblin mode
(01:06:56) Designing onboarding to make customers stay
(01:09:28) What churn can teach you about your ideal customer
(01:11:37) Building a repeatable case study factory
(01:16:15) Applying the framework inside established companies
(01:20:03) Lightning round and final thoughts
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Referenced:
• The PULL framework: https://thephysicsofstartups.substack.com/p/the-pull-framework
• The ultimate guide to JTBD | Bob Moesta (co-creator of the framework): https://www.lennysnewsletter.com/p/the-ultimate-guide-to-jtbd-bob-moesta
• How to find work you love | Bob Moesta (Jobs-to-be-Done co-creator, author of “Job Moves”): https://www.lennysnewsletter.com/p/how-to-find-work-you-love-bob-moesta
• Jump: https://jump.ai
• Linear’s secret to building beloved B2B products | Nan Yu (Head of Product): https://www.lennysnewsletter.com/p/linears-secret-to-building-beloved-b2b-products-nan-yu
• Josh Woodward on LinkedIn: https://www.linkedin.com/in/joshwoodward/
• Clearly AI: https://clearly-ai.com
• Emily Choi-Greene on LinkedIn: https://www.linkedin.com/in/emilycg
• Spanx: https://spanx.com
• Floodgate: https://www.floodgate.com
• Snoop Dogg on X: https://x.com/SnoopDogg
• Jira: https://www.atlassian.com/software/jira
• Linear: https://linear.app
• “Sell the alpha, not the feature”: The enterprise sales playbook for $1M to $10M ARR | Jen Abel: https://www.lennysnewsletter.com/p/the-enterprise-sales-playbook-1m-to-10m-arr
• How to close $100K+ enterprise deals, step by step | Jen Abel: https://www.lennysnewsletter.com/p/how-to-close-100k-1m-deals-step-by
• Teero: https://www.teero.com
• A guide for finding product-market fit in B2B: https://www.lennysnewsletter.com/p/finding-product-market-fit
• Retention is the hard part: https://thephysicsofstartups.substack.com/p/retention-is-the-hard-part
• Mark Roberge on LinkedIn: https://www.linkedin.com/in/markroberge/
• Ali Ghodsi on LinkedIn: https://www.linkedin.com/in/alighodsi/
• Pride and Prejudice: https://www.imdb.com/title/tt0414387
• Penguins of Madagascar: https://www.imdb.com/title/tt1911658
• Muse: https://muse.ai
• Lessons from 1,000+ YC startups: Resilience, tar pit ideas, pivoting, more | Dalton Caldwell (Y Combinator, Managing Director): https://www.lennysnewsletter.com/p/lessons-from-1000-yc-startups
• Lemlist: https://www.lemlist.com
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Recommended books:
• The Power of Pull: What You Need to Know About Customer Demand to Build a Successful Startup (and Why Most Founders Get It Wrong): https://www.amazon.com/Power-Pull-Customer-Successful-Founders/dp/1541705955
• The Nature of Order, Book One: The Phenomenon of Life: An Essay on the Art of Building and the Nature of the Universe: https://www.amazon.com/dp/B0BHLGZHHD
• The Nature of Order, Book Two: The Process of Creating Life: An Essay on the Art of Building and the Nature of the Universe: https://www.amazon.com/Nature-Order-Book-Two-Creating-ebook/dp/B0F2SMKQT8
• The Nature of Order, Book Three: A Vision of a Living World: An Essay on the Art of Building and the Nature of the Universe: https://www.amazon.com/dp/B0F2SS62LG
• The Nature of Order, Book Four: The Luminous Ground: https://www.amazon.com/Luminous-Ground-Building-Alexander-Christopher/dp/B00CB5JTI6
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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
—
Lenny may be an investor in the companies discussed.


To hear more, visit www.lennysnewsletter.com

Emmanuel Ameisen is an interpretability researcher at Anthropic, where he looks inside LLMs like Claude to understand how they think. Before joining Anthropic, he spent years in applied ML at Stripe and Insight, and is the author of Building Machine Learning Powered Applications. His research has produced some of the most striking findings in the field: that models form general representations of concepts, plan many tokens ahead without stating those plans explicitly, and may exhibit something structurally similar to the distinction between conscious and unconscious processing in humans.

We discuss:
1. Why the “next-token predictor” framing undersells how models actually plan and think
2. Why anthropomorphizing AI can actually be the right approach
3. Why models are “grown,” not built
4. What the “J space” reveals about model self-awareness
5. The smooth scaling curve behind what look like sudden capability jumps
6. How interpretability connects to AI alignment and safety
—
Brought to you by:
WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more
DX—Engineering intelligence for the AI era
—
Episode transcript: https://www.lennysnewsletter.com/p/a-mind-expanding-conversation-on
—
Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0
—
Highlights: https://lennyspodcast.com/mostreplayedmoments
—
Where to find Emmanuel Ameisen:
• X: https://x.com/mlpowered
• LinkedIn: https://www.linkedin.com/in/ameisen
• Website: https://www.mlpowered.com
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Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• X: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
—
In this episode, we cover:
(00:00) Introduction to Emmanuel Ameisen
(01:30) Why AI models are grown, not built
(05:57) Why next-token prediction is harder than it sounds
(11:49) How models represent concepts and understand the world
(17:06) How LLMs plan ahead
(23:10) Could AI be conscious?
(24:44) Scaling laws and the power of text
(28:29) Why smooth progress creates sudden breakthroughs
(32:59) Why watching AI isn’t enough to solve alignment
(38:21) The Golden Gate Claude experiment
(39:01) Why Anthropic takes AI safety seriously
(42:06) When anthropomorphizing AI is useful
(48:06) What AI can teach us about intelligence
(51:44) Reasons for optimism about AI alignment
(55:43) Are we living in a simulation?
(59:00) Jev, decision models, and AI’s gut instincts
(01:01:37) Two more surprising findings about how models think
(01:03:33) How to get into interpretability research
(01:04:55) Lightning round and final thoughts
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References: https://www.lennysnewsletter.com/p/a-mind-expanding-conversation-on
—
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
—
Lenny may be an investor in the companies discussed.


To hear more, visit www.lennysnewsletter.com