Summary
Copying in product development is framed as a form of moral arbitrage, sparking debate about its defensibility. The conversation emphasizes that copying is a common practice in the tech industry, where many successful products are built on the foundations of existing ideas. The discussion highlights the importance of distinguishing between inspiration and outright copying, with references to notable figures like Steve Jobs and Mark Zuckerberg, who have navigated these lines in their careers.
Mark Pincus, founder of Zynga, shares insights from his new book, "Life at the Speed of Play," focusing on his Proven Better New framework for product development. He explains that successful products often emerge from a combination of proven concepts, enhancements that resonate with users, and innovative ideas. Pincus stresses the need for entrepreneurs to trust their instincts while remaining critical of their ideas, advocating for a disciplined approach to product development that prioritizes user feedback and proven success.
The conversation also touches on the evolving landscape of consumer technology, particularly in the context of AI. Pincus notes that while AI tools are gaining traction, there remains a significant opportunity for new consumer products that leverage AI in innovative ways. He encourages entrepreneurs to explore existing successful models while identifying areas for improvement and new applications, underscoring the importance of adapting to market dynamics and consumer needs.
Show Notes
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Brought to you by Mobbin: The internet's best library of real design patterns Get a full year of Mobbin Pro, along with 8 other AI tools, at bundle.aakashg.com. Today's Episode I quote Mark Pincus more than almost anyone, and I still feel a little guilty every time I do. For years, the internet calls him a "FarmVillain," the founder who copies his way to the top. Today he owns it. Mark (founder of Zynga, author of Life at the Speed of Play) steps up to my whiteboard and draws Proven Better New, the framework I reach for every time I plan a product. He shows me how Zynga Poker copies the leading tables down to the chip sounds and still wins on one small new bet. Then he makes the case that most AI founders today are being too clever for the moment. If you're building anything consumer right now, this is the one episode to watch. 10 Key takeaways 1. Copying is what every product maker already does - Mark argues that all music, movies, stories and games are derivative work. The real skill is the magic trick: you learn from someone else's proven success and make it feel new. Steve Jobs did it better than Bill Gates, even though both copied Xerox PARC. 2. Trust your instinct, doubt your idea - Mark puts your instinct at about 95% right and your specific idea at maybe 25%. Proven Better New lets you act on both at once. 3. Proven means pixel-level, for this audience on this platform - You copy the experience click by click, legally. "Instagram" can't go in your proven column. Today's best version of the specific experience can. 4. Better is what 10 out of 10 existing users would choose - What you think is better is actually new. Better is usually small: no download, free, fewer clicks, a cleaner onboarding. 5. Isolate your new so you fail for the right reason - Zynga Poker copies the leading tables down to the chip sounds, drops the download and adds one new thing, your real friends from Facebook and MySpace. Mark says friends end up at the same table 25% of the time. 6. Onboarding belongs in proven - Zynga's PMs call social Civilization and SimCity Social dead on arrival because of their long tutorials. In casual games you need to reach the good stuff in three clicks, not three minutes. 7. All new fails - Zynga still repeats this mantra. Expect your new to fail so you kill it fast, and treat new that works in the market with reverence, because it just became proven. 8. Early in a platform shift, ship less new - Supercell's Hay Day launches with 0% new, just a FarmVille built for mobile. In the AI cycle, users sit in discovery mode, so an AI version of something they already do beats an idea that's too clever for the moment. 9. Copy everything by default, then cut on audience - FarmVille copies Farm Town's gifting and its two-way neighbor bar, friction included. It cuts only the stranger marketplace, because that doesn't fit its core audience of middle-aged women on Facebook. 10. Test the top of the funnel before you build - Mark's problem with the MVP is the word viable, because it fills you with hope. Test names and web ads first, and set a click-through bar before you write real code. Go Deeper How you need to think about product strategy in the age of AI (with Tim Herbig) The Ultimate Guide to Onboarding Ultimate Guide: Activation The Multiplayer Lever Ultimate Guide: Growth Loops The PM's Guide to Governance with Eric Ries Listen and watch on Apple, Spotify, and YouTube. If you want to advertise, email productgrowthppp[at]gmail[dot]com.
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This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe
This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.news.aakashg.com/subscribe
Brought to you by Mobbin - Discover real-world design inspiration Get a full year of Mobbin Pro, along with 8 other AI tools, at bundle.aakashg.com. Today’s episode LinkedIn shut down its Associate Product Manager program and replaced it with an Associate Product Builder program. If you’re a regular reader, you know I’ve been writing about this shift for a long time. What’s new is that the market just gave it a full-fledged title. And a title in a job post comes with 3 things: a JD, interviews, and a comp range. So I brought back the one guest I knew would bring the best-case data. Ankit Shukla, founder of HelloPM, ran 12,500 PM job descriptions through Claude and GPT to see what top companies hire for. His AI PM roadmap is still my most popular episode ever. His evals episode is #2. This one goes further than both. 10 Key takeaways 1. The product builder role is real, and the data says so - 12,500 PM job postings analyzed with Claude and GPT. More than 30% now ask for hands-on AI building. The title has not caught up everywhere, but the requirements already have. 2. Judgment is the skill being paid for, not the tools - Across those postings, RAG, agentic AI and prompt engineering were not the number one skill. Identifying the right use case for AI was. The engineering part is what AI can already do for you. 3. The premium is real and it is bigger in India - More than 20% over traditional PM pay globally, with a $195,000 median in the US and senior roles reaching $250,000 to $560,000. Indian AI PM roles carry an even steeper premium over their non-AI counterparts. 4. Start with possibilities, not problems - Traditional product management starts with the problem. AI inverts that, because some problems you never framed as problems at all. You were treating them as harsh realities. Map what AI can do before you map what hurts. 5. Never start with the tool - The biggest takeaway Ankit gives is that POW comes before E. Possibilities, opportunities and workflows first. The moment you open n8n before you have done those three, you are building AI slop with extra steps. 6. The five levels, and why most people stall at level one - Prompting, then reusable Gems and custom GPTs, then skills and connectors, then vibe coding, then production builds with MCPs. Most people live at level zero and one. Level two is where the ROI actually starts. 7. Claude Code versus Codex is a settled debate - Ankit benchmarked both plus Cursor and a DeepSeek harness across e-commerce, social, and gaming builds. All of them shipped functional sites. For 90% of use cases the choice does not matter, so stop optimizing it. 8. The build was 20 minutes of human time and 20 hours of agent time - A production email platform on Node, TypeScript and AWS, built by someone who deliberately avoided the languages he already knew. He pulled the AWS MCP so he never touched the console, then slept while Claude shipped slices. 9. Audit what the agent spends - Claude proposed a $420 a month stack. Ankit knew AWS well enough to ask why it provisioned a machine that large and what Fargate was for. The revised stack came back at $110. The judgment is in the review, not the build. 10. The last 20% is where the job moved - Claude takes you 80% of the way overnight. Bug fixing, edge cases and errors are what remain, and in most companies the PM hands that part to an engineer. The PM's new job is producing the prototype that makes the conversation real. Please subscribe on YouTube and follow on Apple and Spotify. It helps! If you want to advertise, please email productgrowthppp[at]gmail[dot]com.
Brought to you by Customer.io - Send smarter messages using your product data Kameleoon - Leading AI experimentation platform Bolt - Ship AI-powered products 10x faster Jira Product Discovery - Plan with purpose, ship with confidence Product Faculty - Get $150 off their #1 AI PM Certification via code AAKASH150 Today's Episode n8n was the most written-about AI tool in this newsletter last year. I covered it 25 times and ranked it A tier, above Zapier and Make. Then Claude Code and Cowork arrived, the Google Trends line bent downward, and a tweet went around declaring n8n irrelevant. So today I went to the person with the most to lose. Jan Oberhauser is the founder and CEO of n8n, last valued at $5.2 billion after SAP's strategic investment. He says n8n has been declared dead roughly a thousand times, including the week OpenAI shipped its own agent builder, which turned out to be one of his best growth weeks ever. His argument is not that Claude Code is bad. He uses it. His argument is that they are different products and you need both. Claude Code is where you prototype in ten minutes. n8n is where the thing goes once it has to run every night, survive a model outage, pass an audit, and be handed to someone who did not build it. So he opened the product and showed me. A live agent build, an eight-minute AI assistant run, an approval gate that stops an agent mid-action, and an execution log that shows every decision the agent made. Then we got into the growth story. 10x revenue in a year. No lead gen target. No per-seat pricing. A goal of a billion users with fewer than a thousand employees. 10 Key takeaways 1. Claude Code and n8n are different products, and you need both - Claude Code runs Anthropic models in your terminal as an agentic tool. n8n is the orchestration layer connecting your tools, models and data sources on a visual canvas. Jan's own users prototype in Claude Code and migrate to n8n once the workflow has to be reliable. 2. Reliability is fallback models, self-hosting and maintained integrations - Every provider goes down, so you set a fallback model inside the same workflow. You can self-host next to your own data. Each integration is code n8n writes and maintains, so an API change gets fixed once for everyone instead of separately by every builder. 3. Auditability is the thing generated code cannot give you - With code you see the input and the output and nothing in between. n8n replays every past execution step by step, with the data going in and out of each node, and lets you rerun half a workflow from a single data point. 4. AI plus deterministic logic plus human in the loop - An if statement is cheaper, faster and 100% reliable, so it belongs next to the model rather than being replaced by it. Destructive actions sit behind an approval gate, so the agent asks before it sends the email or books the meeting. 5. The AI assistant builds the workflow for you - Jan prompted it the way you would prompt Claude Code. It asked clarifying questions, thought for eight minutes, and returned a working multi-tool agent. Extending it with one-on-one scheduling and Google Contacts took another five. 6. Start small, and skip AI when you do not need it - Companies that try to transform everything at once spend weeks building the wrong thing. Jan's favorite example is a company that automated employee password resets and saved multiple full-time employees a year. The small wins are also the ones that get colleagues interested. 7. n8n wins wherever the work is business-critical - Security orchestration, compliance, employee onboarding and offboarding, DevOps, monitoring. The rule Jan gives is that the more reliability and security matter, the better n8n fits, which covers nearly everything that is not a personal use case. 8. Sprinkling AI on top gets 10 to 30%, being in the value chain gets 10x - Adding an AI button somewhere is not a strategy. n8n's bet was that when someone decides to build an agent, they build it in n8n. T
Brought to you by Arize - Ship AI agents and features faster with fewer regressions. Get a full year of Arize’s paid plan, along with 8 other AI tools, at bundle.aakashg.com. Today’s episode I wrote a PRD using ChatGPT, with a considerable number of iterations, and gave it to the CEO of Zapier to grade. In 2024, that PRD would have killed in any product review. But Wade Foster called it a little old-fashioned. In 2026, the work that made you look smart in 2024 is now the bare minimum. Shopify, Meta, and Microsoft have all tied AI to employee evaluations. Zapier took it a notch higher and published a whole rubric on AI fluency. Then reintroduced another one just 10 months later! Read the complete update here. Wade co-founded Zapier and took it to a $5B valuation on roughly $1.4M of venture money. His rubric is the one a lot of companies have modeled their own on. So I got him to share his grading structure live. If you want to know where you’d be when your company starts grading, I got you. Key takeaways 1. Basic AI use is the floor, not the bar - Summarizing, drafting, looking things up and generating a basic PRD are table stakes at Zapier in 2026. Everything above that line needs structured, reusable systems for specs, prototypes and research. Wade is looking for people operating at a meaningfully higher level than the tool's default. 2. Adaptive is where the curve should sit - Zapier wants most PMs at adaptive most of the time, with one transformative example to point to. Living at transformative full time means you are tuning your systems instead of shipping. Gaps in the capable band are normal, even for strong PMs. 3. Transformative means an n of one - Wade's bar is that you are the first person doing a new thing and figuring it out as you go. On a team of 30 to 40 PMs, that is roughly one rating a half. He grades his own fluency as adaptive in most places and admits he steals his transformative tactics from his team. 4. You should be ahead of your CEO inside your own discipline - Wade's rule of thumb is that he should be behind his PMs on PM-specific AI fluency. When he spots a technique that is obvious to him and new to the PM, he gets nervous. You breathe this every day and he does not. 5. Never stop at the PRD - A document alone reads old-fashioned in 2026. Fire up a coding agent and build the prototype, even though production code is not the PM's job. A picture is worth a thousand words and a prototype is worth ten of those. 6. Evidence got cheap, so there is no excuse - Sift Gong calls, Zendesk tickets, subreddits, LinkedIn and X before asking anyone for buy-in. AI makes a mountain of sources searchable in the time it used to take to read a handful. The buy-in conversation now happens after you have proof, not before you start. 7. Judgment separates a turbo brain from a slop cannon - AI plus judgment looks like watching a new species get invented. AI without judgment generates so much noise that it is harder to work with than a teammate who uses no AI at all. At least that person is quiet. 8. Label the effort level when you share AI-assisted work - Say whether you skimmed it once or stand behind every sentence. Handing over unreviewed output just moves the judgment onto the reader. Speed is a fine reason to share something rough, as long as you say it is rough. 9. The job shifts from doing tasks to auditing the factory - When agents handle spec, prototype, eval and first-draft code, your job is to ask why the machine produced a bad batch, not to fix widgets by hand. Where the human gates go is the design question nobody has solved. Wade sees teams still scratching their heads on the loop design. 10. The new no-code is code, and the user is now the agent - Wade's daily brief runs on generated code he has never read and does not care about. Zapier's bet is deterministic execution in the cloud, so workflows run reliably without burning tokens or keeping a laptop open. If your software cannot run headless, you are swim