The Growth Podcast

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Today's Episode
"We no longer need to wait for engineers to build growth experiments."
That's what James McAulay told me. Halfway through our recording, he showed me what he meant, on my own site. He ran one Claude Code skill, and a few minutes later I had a list of SEO fixes I'd been ignoring while my search traffic slid.
So I made this episode for every PM and founder doing growth with no team behind them. James led Creator Growth at ElevenLabs, and now he runs his whole business from one folder in Claude Code. I asked him to open it up.
He walks through:
The goal cascade that stops Claude (and you) from building features nobody asked for
An SEO audit skill wired to Firecrawl, DataForSEO and Lighthouse, run live on my site
Multi-agent refine, the panel of critic agents he runs on every plan before anything gets built
He runs his actual business on every piece of it, and that's why it holds up.
If you own growth and never seem to have time for it, start here.
Key Takeaways
1. Run growth from one folder - James's growth OS is one folder with three layers: tools with real APIs, skills he uses every day, and weekly routines that improve the setup. If the agent can't read a tool's data, the tool doesn't make the cut. That rule alone keeps the whole stack usable by an agent.
2. Tie every session to your goals - A goal cascade runs from a yearly mission file to quarterly OKRs, monthly and weekly retros, and a daily standup. A session hook shows Claude the mission at the start of every session. When you drift, it pushes back.
3. The weekly retro is the one that matters - James sometimes skips the standup, but never the Friday retro. Connected to his to-do list and GitHub, it caught him refactoring for fun while outreach tasks went stale for weeks. Your gut says one thing, the data checks it.
4. Do the long onboarding - The full setup is an interview with Claude that can run two hours. It feels like a slog. But once your context lives in files, every answer after it gets better.
5. Close every experiment - Each test starts from a template with a hypothesis, baseline, primary metric, confounders and stop conditions. The weekly retro and daily standup flag finished and stale tests. Every one ends in a documented learning instead of a forgotten dashboard.
6. Pull baselines, don't type them - When you set up a test, numbers like baseline conversion and traffic should come straight from PostHog, Mixpanel or Amplitude. James says nobody should be answering those questions by hand. Connect the analytics tool before you design the test.
7. Rebuild your SEO audit with cheap APIs - James's SEO audit skill adds Firecrawl to read any site, DataForSEO for keyword research at a few cents a term, and the Lighthouse CLI for page speed. Hand the report to the agent in your repo and it fixes the issues. What used to take an agency and a backlog weeks now takes hours.
8. Put critics in front of every build - Multi-agent refine sends a plan to 3 to 10 subagents, each with a different lens, then ranks the findings P0 to P3. The "what's missing" critic finds the most. Use Sonnet or Opus subagents so you don't burn through your limits.
9. Define every analytics event - James had Claude write definitions for all 350 of his PostHog events in about two hours. A CI test now blocks any PR that ships without tracking. Clean event data lets any agent or contractor measure your funnel correctly.
10. Ask your agent to improve itself - Ask Claude how to improve its own Claude.md, prompt or skill, and act on the answer. Most people miss this because chat can't edit its own instructions. Claude Code and Cowork can.
Related content
1. Podcasts
How to build a Company Operating System with Hermes and OpenClaw
How to Build Effective Product Loops in Claude Code, with Tyler Folkman
This CPO Uses Claude Code to Run his Entire Work Life, with Dave Killeen
2. Newsletters
I spent 100s of hours building a PM OS for you
How to build product-led growth in 2026
AI Agents: The Ultimate Guide for PMs
Where to find James McAulay
LinkedIn: https://www.linkedin.com/in/jamesmcaulay
X: https://x.com/thejamesmcaulay
YouTube: https://www.youtube.com/@JamesMcaulay0
Website: https://www.jamesmcaulay.co.uk
Agent Accelerator: https://agentaccelerator.ai
Where to find Aakash
X: https://x.com/aakashgupta
LinkedIn: https://www.linkedin.com/in/aagupta/
Newsletter: https://www.news.aakashg.com
PS. Please subscribe on YouTube and follow on Apple & Spotify. It helps!
If you want to advertise, please email productgrowthppp[at]gmail[dot]com.
To hear more, visit news.aakashg.com

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Product Faculty - Get $150 off their #1 AI PM Certification via code AAKASH150
If you want access to my AI tool stack, grab Aakash's bundle.
Today's Episode
Shipping features has never been cheaper.
Which means shipping the wrong ones has never been more expensive.
Most teams have cranked output up 10x, even 100x. Very few can tell you if any of it moved a customer or the business.
So I brought in Tim Herbig. He was a PM for eight years before becoming an adviser, coach, and the author of Real Progress. He's worked with hundreds of PMs on making their product strategy actually make an impact.
I've been reading his Product Practice newsletter for years. It's one of the few I never skip.
In today's episode, Tim walks through:
The one test every strategy has to pass
The stupidity flip, to check if you've actually made a choice
Audience trees, worked through a full marketplace case study
Which differentiators still hold when everyone has the same AI models
The metrics that tell you whether your strategy worked
If you own strategy for your product area, this is the one to watch before your next planning cycle.
10 Key Takeaways
1. Cheap features make wrong bets expensive - AI made shipping fast, so picking the wrong feature now costs more, not less. You still have to get people to use everything you ship. The real question is whether any of that output moved customers and the business.
2. A strategy is only real if it helps you say no - Deck, one-pager or framework, the test is the same. If it doesn't help teams say no to opportunities, it isn't a strategy. A healthy one has most of your team's energy going to work connected to it.
3. Every strategy answers the same core questions - A narrative for why you should act now. A playing field for who you serve and who else competes for them. Winning moves for how you deliver value and differentiate. Canvases and templates are just windows into that messy work, so start with the questions.
4. AI is a resource, not a value proposition - Everyone has access to the same frontier models, so you can't compete on the technology alone. Use AI to amplify the reason customers already choose you. Brand and data are the moats that remain.
5. Run the stupidity flip - Borrowed from Roger Martin. Flip a strategic choice and check whether the opposite is obviously stupid. If it is, you haven't made a real choice. Flip individual components like audience or value prop, not the whole statement.
6. Strategy doesn't have to cover everything - Focusing on paid users doesn't mean turning off the product for free users. The segment choice just has to fit the narrative behind it. Every component has to reinforce the others.
7. Build an audience tree - Land somewhere between too broad and comically narrow. Slice by region, buyer type and value until you can say no. Tim's fictional marketplace lands on repeat buyers of cars worth more than 100K in Germany, and AI speeds up sizing whether that market is big enough.
8. Differentiate on what takes longest to build - Run your winning moves through Hamilton Helmer's 7 Powers. Brand and network effects hold up, while process power and AI features get copied. No one buys a car from you because your marketplace has AI search.
9. Measure behavior change, not tokens - First say in plain English what you'll do differently next quarter, then how you'll know it worked. Tokens used is a value exchange for the model provider, not your customers. Ask whose behavior has to change, and by how much.
10. Tie metrics to your specific choices - Generic KPIs like revenue are too noisy and too slow to tell you if a strategy worked. Pick metrics tied to the exact segment and value prop you chose. Then map every roadmap item back to those metrics.
🎁 Win a copy of Real Progress
As a lil treat, I'm giving away 5 copies of Tim's book, Real Progress, in just 3 steps:
1. Subscribe on YouTube and comment on the episode
2. Follow the show on Apple or Spotify
3. DM me a screenshot of both on LinkedIn
The first 5 people get a FREE copy!
Where to find Tim Herbig
LinkedIn: https://www.linkedin.com/in/herbigt/
Website: https://herbig.co/
Where to find Aakash
Twitter: https://www.x.com/aakashg0
LinkedIn: https://www.linkedin.com/in/aagupta/
Newsletter: https://www.news.aakashg.com
Check out the conversation on YouTube, Apple, and Spotify.
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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!
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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. That choice is what produced 10x growth in a year.
9. Deleting the metrics that would make money faster - No lead gen target and no per-seat pricing, because profitability means n8n can think in years instead of quarters. The company does not push free self-hosted users onto paid hosting either. The internal goal moved from a billion in ARR to a billion users so nobody confuses the mission with money.
10. Talent density over headcount - The target is a billion users with fewer than a thousand employees, which keeps the hiring bar high. Jan wants tinkerers who run home automation and understand scale, evals and reliability. Live problem-solving beats take-home tasks now that AI can do the take-home for you.
Go Deeper
Build your first n8n workflow with Pawel Huryn’s guide.
Then follow Mahesh Yadav’s learning path in How to Become a Builder PM. Prototype your next agent there, and promote it into n8n through the MCP server.
Write 20 test cases and run them as an eval.
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***
If you want to advertise, please email productgrowthppp[at]gmail[dot]com.


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

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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 swimming against the current.
Go Deeper
If you ask me, start by shipping your first pull request. Then learn loops to automate your repetitive tasks. Give your system a memory on Claude Code or Hermes or OpenClaw. Then version everything you build for PMs on GitHub. You can also bring the whole org on the same page with an all-in-one team OS. And once the system is running, build your first eval, as auditing is the whole point here.
I’ll see you in the next one :)
If you want to advertise, please email productgrowthppp[at]gmail[dot]com.
PS. Please subscribe on YouTube and follow on Apple and Spotify. It helps!


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