2025-12-23 21:45:19
👋 Hey there, I’m Lenny. Each week, I tackle reader questions about building product, driving growth, and accelerating your career. For more: Lennybot | Lenny’s Podcast | How I AI | Lenny’s Reads | Fav AI/PM courses | Fav public speaking course
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I’m excited to share my (record) fourth collaboration with the great Noam Segal, AI Insights Manager at Figma and former UXR leader at Zapier, Airbnb, Meta, Twitter, Intercom, and Wealthfront. Let’s get to it.
Author’s note: Names have been changed to preserve participant anonymity.
There’s no shortage of debate about AI’s impact on work. Is it delivering real productivity gains? Where’s the ROI? Hot takes abound, but data have been scarce.
We took it upon ourselves to find out what’s actually happening on the ground by running one of the largest independent, in-depth surveys on how AI is affecting productivity for tech workers (1,750 respondents). We surveyed product managers, engineers, designers, founders, and others about how they’re using AI at work.
tl;dr: AI is overdelivering.
55% of respondents say AI has exceeded their expectations, and almost 70% say it’s improved the quality of their work.
More than half of respondents said AI is saving them at least half a day per week on their most important tasks. We’ve never seen a tool deliver a productivity boost like this before.
Founders are getting the most out of AI. Half (49%) report that AI saves them over 6 hours per week, dramatically higher than for any other role. Close to half (45%) also feel that the quality of their work is “much better” thanks to AI.
Designers are seeing the fewest benefits. Only 45% report a positive ROI (compared with 78% of founders), and 31% report that AI has fallen below expectations, triple the rate among founders.
Engineers have accepted AI as a coding partner and now want it to handle the more boring (but necessary) work of building products: documentation, code review, and writing tests.
n8n is currently dominating the agent landscape, though actual adoption of agentic platforms in 2025 has been slow.
A whopping 92.4% of respondents report at least one significant downsides to using AI tools. There’s definitely room for improvement.
AI is far from the novelty it was a year or two ago. It has clearly cemented a place as work and productivity infrastructure, and AI tools are improving at a breathtaking pace. If AI is already giving most people back at least half a day per week in late 2025, what does 2026 look like? What about 2027? We’re watching the early innings of a compounding productivity revolution.
As Kevin Weil (VP at OpenAI) noted, “The AI model that you’re using today is the worst AI model you will ever use for the rest of your life.”
PMs are seeing the most value from AI tools to (1) write PRDs (21.5%), (2) create mockups/prototypes (19.8%), and (3) improve their communication across emails and presentations (18.5%).
Prototyping, at #2, signals one of many role-boundary shifts happening now. With tools like Lovable, v0, and others, PMs are increasingly going from idea to prototype without waiting on design.
But look farther down the list and a pattern emerges: AI is helping PMs produce, but it lags in helping them think. The top jobs are all production tasks (docs, prototypes, comms), while strategic and discovery work sits near the bottom (user research at 4.7%, roadmap ideas at 1.1%). PMs have cracked how to use AI for the “last mile” of getting ideas out of their head, but they still have a big opportunity to embrace AI for the upstream work of figuring out what to build.
Designers are finding AI most helpful with user research synthesis (22.3%), content and copy (17.4%), and design concepts ideation (16.5%). Visual design ranks #8, at just 3.3%.
AI is helping designers with everything around design (research synthesis, copy, ideation), but pushing pixels remains stubbornly human. Meanwhile, compare prototyping: PMs have it at #2 (19.8%), while designers have it at #4 (13.2%). AI is unlocking skills for PMs outside of their core work (at least in the case of prototyping), whereas designers aren’t seeing the marginal improvement benefits from AI doing their core work.
Founders lean heavily toward productivity and decision support (32.9%), product ideation (19.6%), and vision/strategy (19.1%).
Unlike others, founders are using AI to think, not just to produce. The top three jobs are all strategic: decision support, ideation, and vision/strategy. That’s a stark contrast to PMs (whose top jobs are documents and prototypes) and designers (research synthesis and copy). And look at that #1 category: “productivity/decision support,” at 32.9%, is unlike anything else in the survey. No other role has a single use case this dominant. Founders are treating AI as a thought partner and sounding board, not just a tool for specific deliverables. (This tracks with Tal’s excellent post on building AI copilots as long-term thinking partners and Amir’s recent post on building your second brain using ChatGPT.)
The surprise misses: Financial modeling sits at just 1.8%, despite founders living in spreadsheets during fundraising. Same with recruiting, at 1.3%, even though hiring consumes enormous founder time. These feel like opportunities waiting for better tools.
This pattern may explain why founders report the highest satisfaction throughout the survey—they’ve figured out how to use AI for higher-leverage strategic work, not just production tasks.
Engineers are the outlier. For them, AI is doing just one big job: writing code, the core engineering task. Whereas for the PMs and designers, AI is helping them with supporting work.
Farther down the list are jobs like documentation (7.7%), testing (6.2%), and code review (4.3%). These are the “boring but necessary” tasks engineers typically dislike. As you’ll see in the opportunities data below, that’s about to change. Engineers have accepted AI as a coding partner; now they want it to handle the tedious work that comes after the code has been written.
One more pattern worth noting: engineers report the most mixed results on quality later in the survey (51% better but 21% worse, the highest “worse” of any role).
ChatGPT is the #1 most popular AI tool for most roles: 57.7% of PMs, 49.6% of designers, and 72.1% (!!!) of founders use ChatGPT over any other AI tool, with Claude coming in second for those three roles.
But engineers have a very different behavior. GitHub Copilot was first to market, has Microsoft and GitHub’s distribution muscle, and is baked into the world’s most popular code repository. Yet it sits behind three tools that launched after it. Engineers are choosing newer (better) alternatives over the incumbent.
For engineers, the top three are in a dead heat: Cursor (33.2%), ChatGPT (30.8%), and Claude Code (29.0%) are all within 4 percentage points. This market hasn’t consolidated, and switching costs are low. Also notable: Claude Code (29.0%) outpaces Claude’s chat interface (20.7%). Purpose-built tools are winning, but Claude is also helpful with several core coding-related tasks (e.g. code migration and more) that put it at fourth.
Gemini sits at a distant 10.6%, but a caveat: this space shifts fast. A few strong model releases or product updates could reshape these rankings quickly. What’s true today may look very different in six months.
ChatGPT is a far-and-away winner for PMs.
Perplexity is also surprisingly highly ranked, probably due to its strong research capabilities.
However, farther down the list, Lovable (8.7%) and Cursor (7.7%) are cracking the top seven for PMs. This reinforces the pattern we saw earlier: PMs are increasingly building things themselves, encroaching on what’s traditionally design and engineering work. The PM toolkit is expanding beyond documents and decks.
One note: Copilot (8.4%) edges out Cursor (7.7%) among PMs, though the reverse is true for engineers. This may reflect Microsoft ecosystem lock-in at larger companies, or simply that PMs discovered Copilot first and haven’t yet explored alternatives.
63% of PMs and 83% of founders report that AI saves 4+ hours per week. Even the most skeptical group, designers, still shows 47.5% reporting 4+ hours saved. Only 1% to 5% of respondents across roles say AI is “no faster than manual work.”
On quality, though, the story is more nuanced. PMs and founders are bullish (over 70% report quality improvements), but engineers are more mixed. 51% of engineers tell us that AI makes the quality of their work better, but 21% say it’s worse. Designers fall in between, at 60% better, 13% worse. The quality ratings among engineers may reflect the higher bar for correctness in code: a “somewhat better” first draft of a PRD is useful; a “somewhat better” but buggy function is not. Also, bad code is easier to spot than a bad PRD.
The gap between where people are using AI today and where they want to use it next reveals a lot about where the opportunities are for founders and startups to jump in and deliver new tools and functions.
For PMs, the biggest opportunity story is research. User research shows the largest demand gap of any task (+27.2pp). Only 4.7% say it’s their primary AI use case today, but nearly a third want it to be. The pattern is clear: 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. 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: people have seen what’s possible and want more.
Engineers are shifting their use of AI to handle work after writing the code. Writing code was 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 a strategic collaborator to pressure-test ideas, explore markets, and think through go-to-market—AI as a co-founder, not just an assistant.
Based on these reported gaps, the next wave of AI adoption will require not just better models but better workflows for human-AI collaboration on fuzzy problems. Writing a PRD has a clear output; competitive research does not. Writing code can be tested; “product ideation” cannot.
We asked: “Which AI tool(s) would you be very disappointed to lose access to?” The classic Sean Ellis PMF question. 83.6% named at least one tool, which is itself a remarkable signal of how embedded AI has become in daily workflows. But the relationship between the number of people who regularly use a tool and would miss that tool if it went away tells the story of the products that have truly found product-market fit.
ChatGPT dominates, perhaps only for now. Half of respondents (50.2%) would be very disappointed to lose ChatGPT, but that’s notably lower than the 60% to 75% of respondents across most roles who say they regularly use the tool. This, in part, explains why OpenAI recently declared a “Code Red” as it watches Gemini and Claude begin to erode market share. Switching costs in AI are still very low.
ChatGPT, Claude, and Gemini top the list for PMs—they’re such multi-purpose tools well-suited to the PM job. It’s most interesting to see Cursor right behind Gemini (we wouldn’t expect an engineering tool like Cursor to be so popular among PMs), followed by Lovable (which currently seems to be winning in the prototyping market).
Designers (23.3%) and founders (20.6%) index highest on Claude. The Claude ecosystem (Claude and Claude Code combined) reaches 27.5% overall. This feels like a big win for Anthropic.
Specialized engineering tools have found loyal users and a clear product-market fit among engineers. For engineers, the PMF leaderboard looks completely different from everyone else: ChatGPT (25.3%), Cursor (20.7%), Claude Code (17.1%), and Claude (13.4%). Three of the top four products they’d miss are coding-specific tools. Engineers have found—and want to hold onto—specialized tools that fit their needs, rather than relying on general-purpose chat interfaces. Cursor’s 20.7% PMF among engineers (vs. 7% to 9% for other roles) shows how deeply it has embedded into coding workflows.
In fact, a handful of role-specific tools are winning their niches.
2025-12-23 00:02:34
Here’s a weekly recap of new podcast episodes across Lenny’s Podcast Network:
Every Monday, host Claire Vo shares a 30- to 45-minute episode with a new guest demoing a practical, impactful way they’ve learned to use AI in their work or life. No pontificating—just specific and actionable advice.
Brought to you by:
Google Gemini—Your everyday AI assistant
Lovable—Build apps by simply chatting with AI
Brian Greenbaum is a product designer at Pendo who accidentally became the company’s AI champion, starting with a single Slack message he sent while on paternity leave. What followed: hands-on workshops, simple demos that changed leadership’s minds, a clear “golden path” for tools and policies, and real adoption across PMs, designers, and engineers. In this episode, Brian breaks down a practical, repeatable playbook for getting your team excited about AI—and actually using it.
Biggest takeaways:
Becoming your company’s AI champion is a career accelerator. Brian notes, “This initiative has opened so many doors internally. . . I’m having influence way beyond my scope.” As a senior IC designer, he’s now influencing company strategy, appearing on podcasts, and being sought out by colleagues across departments. The first person to raise their hand gets this opportunity.
Rapid tool experimentation beats careful selection. Rather than spending months evaluating one tool, Brian’s approach was to create a fast-track approval process for trying many tools. This allowed the team to discover what actually worked through hands-on experience rather than theoretical evaluation.
AI can reignite designers’ imagination muscle. Years of “minimum viable product” thinking has trained designers to self-censor their creative ideas. AI’s ability to quickly prototype ambitious concepts (like animated characters waving in an intro screen) lets designers think beyond constraints again—leading to more delightful products.
▶️ Listen now on YouTube | Spotify | Apple Podcasts
More shows coming soon. . . 👀
If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.
Catch you next week,
Lenny
P.S. Want every new episode delivered the moment it drops? Hit “Follow” on your favorite podcast app.
2025-12-22 21:03:27
Brian Greenbaum is a Senior Staff Product Designer at Pendo who led a company-wide AI transformation after a personal epiphany while on paternity leave. After experiencing the power of AI coding tools firsthand, he created a structured approach to help his entire product organization adopt AI. In this episode, Brian shares his complete playbook for driving AI adoption across teams, measuring success, and navigating the organizational challenges that come with new technology adoption.
Listen or watch on YouTube, Spotify, or Apple Podcasts
The exact Slack message Brian sent while on paternity leave that kickstarted his company’s AI transformation
How to structure both synchronous and asynchronous AI learning opportunities for maximum adoption
The two-pronged approach that dramatically increased AI tool usage across teams
Why becoming your company’s AI champion is one of the best career moves you can make right now
How to measure AI adoption success with sentiment surveys and clear metrics
The critical role of creating a “golden path” for AI tool usage with legal, security, and finance teams
Google Gemini—Your everyday AI assistant
Lovable—Build apps by simply chatting with AI
(00:00) Introduction to Brian Greenbaum
(01:38) Brian’s paternity leave epiphany that sparked an AI initiative
(05:00) Sending the message that launched a transformation
(12:25) The two-pronged approach: synchronous and asynchronous learning
(17:29) Encouraging experimentation and creative exploration
(18:41) How AI enables designers to move beyond MVP thinking
(22:00) Quick summary of the two-pronged approach
(24:43) Measuring AI adoption
(33:48) Creating a centralized AI knowledge center
(35:58) Building an MCP server to demonstrate AI’s potential
(44:08) Why technical understanding is crucial for non-technical roles
(46:01) Final thoughts
• Cursor: https://cursor.com/
• Bolt.new: https://bolt.new/
• Claude: https://claude.ai/
• ChatGPT: https://chat.openai.com/
• Midjourney: https://www.midjourney.com/
• Gemini: https://gemini.google.com/
• Pendo: https://www.pendo.io/
• Confluence: https://www.atlassian.com/software/confluence
• Slack: https://slack.com/
LinkedIn: https://www.linkedin.com/in/briangreenbaum/
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
2025-12-21 21:31:22
Sander Schulhoff is an AI researcher specializing in AI security, prompt injection, and red teaming. He wrote the first comprehensive guide on prompt engineering and ran the first-ever prompt injection competition, working with top AI labs and companies. His dataset is now used by Fortune 500 companies to benchmark their AI systems security, he’s spent more time than anyone alive studying how attackers break AI systems, and what he’s found isn’t reassuring: the guardrails companies are buying don’t actually work, and we’ve been lucky we haven’t seen more harm so far, only because AI agents aren’t capable enough yet to do real damage.
Listen on YouTube, Spotify and Apple Podcasts
The difference between jailbreaking and prompt injection attacks on AI systems
Why AI guardrails don’t work
Why we haven’t seen major AI security incidents yet (but soon will)
Why AI browser agents are vulnerable to hidden attacks embedded in webpages
The practical steps organizations should take instead of buying ineffective security tools
Why solving this requires merging classical cybersecurity expertise with AI knowledge
Datadog—Now home to Eppo, the leading experimentation and feature flagging platform
Metronome—Monetization infrastructure for modern software companies
GoFundMe Giving Funds—Make year-end giving easy
• X: https://x.com/sanderschulhoff
• LinkedIn: https://www.linkedin.com/in/sander-schulhoff
• Website: https://sanderschulhoff.com
• AI Red Teaming and AI Security Masterclass on Maven: https://bit.ly/44lLSbC
• AI prompt engineering in 2025: What works and what doesn’t | Sander Schulhoff (Learn Prompting, HackAPrompt): https://www.lennysnewsletter.com/p/ai-prompt-engineering-in-2025-sander-schulhoff
• The AI Security Industry is Bullshit: https://sanderschulhoff.substack.com/p/the-ai-security-industry-is-bullshit
• The Prompt Report: Insights from the Most Comprehensive Study of Prompting Ever Done: https://learnprompting.org/blog/the_prompt_report?srsltid=AfmBOoo7CRNNCtavzhyLbCMxc0LDmkSUakJ4P8XBaITbE6GXL1i2SvA0
• OpenAI: https://openai.com
• Scale: https://scale.com
• Hugging Face: https://huggingface.co
• Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition: https://www.semanticscholar.org/paper/Ignore-This-Title-and-HackAPrompt%3A-Exposing-of-LLMs-Schulhoff-Pinto/f3de6ea08e2464190673c0ec8f78e5ec1cd08642
• Simon Willison’s Weblog: https://simonwillison.net
• ServiceNow: https://www.servicenow.com
• ServiceNow AI Agents Can Be Tricked Into Acting Against Each Other via Second-Order Prompts: https://thehackernews.com/2025/11/servicenow-ai-agents-can-be-tricked.html
• Alex Komoroske on X: https://x.com/komorama
• Twitter pranksters derail GPT-3 bot with newly discovered “prompt injection” hack: https://arstechnica.com/information-technology/2022/09/twitter-pranksters-derail-gpt-3-bot-with-newly-discovered-prompt-injection-hack
• MathGPT: https://math-gpt.org
• 2025 Las Vegas Cybertruck explosion: https://en.wikipedia.org/wiki/2025_Las_Vegas_Cybertruck_explosion
• Disrupting the first reported AI-orchestrated cyber espionage campaign: https://www.anthropic.com/news/disrupting-AI-espionage
• Thinking like a gardener not a builder, organizing teams like slime mold, the adjacent possible, and other unconventional product advice | Alex Komoroske (Stripe, Google): https://www.lennysnewsletter.com/p/unconventional-product-advice-alex-komoroske
• Prompt Optimization and Evaluation for LLM Automated Red Teaming: https://arxiv.org/abs/2507.22133
• MATS Research: https://substack.com/@matsresearch
• CBRN: https://en.wikipedia.org/wiki/CBRN_defense
• CaMeL offers a promising new direction for mitigating prompt injection attacks: https://simonwillison.net/2025/Apr/11/camel
• Trustible: https://trustible.ai
• Repello: https://repello.ai
• Do not write that jailbreak paper: https://javirando.com/blog/2024/jailbreaks
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
Lenny may be an investor in the companies discussed.
2025-12-21 01:00:41
👋 Hello and welcome to this week’s edition of ✨ Community Wisdom ✨ a subscriber-only email, delivered every Saturday, highlighting the most helpful conversations in our members-only Slack community.
2025-12-18 21:31:55
Elena Verna is the head of growth at Lovable, the leading AI-powered app builder that hit $200 million in annual recurring revenue in under a year with just 100 employees. In this record fourth appearance on the podcast, Elena shares how the traditional growth playbook has been completely rewritten for AI companies. She explains why Lovable focuses on innovation over optimization, how they’ve shifted from activation to building new features, and why giving away their product for free has become their most powerful growth strategy.
Listen on YouTube, Spotify and Apple Podcasts
Why 60% to 70% of traditional growth tactics no longer apply in AI
Why you have to re-find product-market fit every 3 months
The specific growth tactics driving Lovable’s unprecedented growth
Why giving away product is a growth strategy that beats paid ads
“Minimum lovable product” as the new standard (not minimum viable product)
Why activation now belongs to product teams, not growth teams
Whether you should join an AI startup (honest tradeoffs)
WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs
Vercel—Your collaborative AI assistant to design, iterate, and scale full-stack applications for the web
Persona—A global leader in digital identity verification
• LinkedIn: https://www.linkedin.com/in/elenaverna
• Newsletter: https://www.elenaverna.com
• Elena Verna on how B2B growth is changing, product-led growth, product-led sales, why you should go freemium not trial, what features to make free, and much more: https://www.lennysnewsletter.com/p/elena-verna-on-why-every-company
• The ultimate guide to product-led sales | Elena Verna: https://www.lennysnewsletter.com/p/the-ultimate-guide-to-product-led
• 10 growth tactics that never work | Elena Verna (Amplitude, Miro, Dropbox, SurveyMonkey): https://www.lennysnewsletter.com/p/10-growth-tactics-that-never-work-elena-verna
• Lovable: https://lovable.dev
• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (co-founder and CEO): https://www.lennysnewsletter.com/p/building-lovable-anton-osika
• Stripe: https://stripe.com
• What differentiates the highest-performing product teams | John Cutler (Amplitude, The Beautiful Mess): https://www.lennysnewsletter.com/p/what-differentiates-the-highest-performing
• How to win in the AI era: Ship a feature every week, embrace technical debt, ruthlessly cut scope, and create magic your competitors can’t copy | Gaurav Misra (CEO and co-founder of Captions): https://www.lennysnewsletter.com/p/how-to-win-in-the-ai-era-gaurav-misra
• “Dumbest idea I’ve heard” to $100M ARR: Inside the rise of Gamma | Grant Lee (CEO): https://www.lennysnewsletter.com/p/how-50-people-built-a-profitable-ai-unicorn
• Eric Ries on LinkedIn: https://www.linkedin.com/in/eries
• Elena’s post on LinkedIn about Lovable Missions: https://www.linkedin.com/posts/elenaverna_everythingispossible-lovableway-activity-7401627519646474242-hn6e
• SheBuilds: https://shebuilds.lovable.app
• Shopify + Lovable: https://lovable.dev/shopify
• The Product-Market Fit Treadmill: Why every AI company is sprinting just to stay in place: https://www.elenaverna.com/p/the-product-market-fit-treadmill
• Cursor: https://cursor.com
• The rise of Cursor: The $300M ARR AI tool that engineers can’t stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell
• Unorthodox frameworks for growing your product, career, and impact | Bangaly Kaba (YouTube, Instagram, Facebook, Instacart): https://www.lennysnewsletter.com/p/frameworks-for-growing-your-career-bangaly-kaba
• The adjacent user: https://brianbalfour.com/quick-takes/the-adjacent-user
• Granola: https://www.granola.ai
• Wispr Flow: https://wisprflow.ai
• I’m worried about women in tech: https://www.elenaverna.com/p/im-worried-about-women-in-tech
• Slack founder: Mental models for building products people love ft. Stewart Butterfield: https://www.lennysnewsletter.com/p/slack-founder-stewart-butterfield
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
Lenny may be an investor in the companies discussed.