Today, I’m open sourcing my /no-ai-slop skill that removes 20+ patterns of AI slop from any piece of writing.

I use this skill to edit everything from emails to docs, and I think you’ll love it too.

Get the skill for free on Github

If you find it useful, please consider starring the repository so more people can find it and upgrading to paid to get 12+ other quality AI skills I’ve made for personal advisor, agent-ready specs, viral social posts, and more. Keep reading for:

1. How to install and use the /no-ai-slop skill

2. My 25/50/25 rule for editing with AI

3. Why the dark side of slop is always calling

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How to install and use the /no-ai-slop skill

To install the skill, just paste this in Codex, Claude Code, or your favorite AI harness:

Then you can use it in three ways:

1. “/no-ai-slop (text)” to edit your writing. The skill will remove all the AI slop patterns and list what it removed.

2. “/no-ai-slop is this slop? (text)” to detect AI writing. The skill will list all the slop patterns that it found.

3. “draft an AI slop post (text)” for some fun. Use it to generate the most cringe slop as satire like my LinkedIn comment below:

Jokes aside, even the best AI models (e.g., Fable 5 and GPT-5.6) still use most of the slop patterns below that /no-ai-slop will remove:

1. Binary contrasts. “It’s not X. It’s Y.”

2. Throat-clearing openers. “Here’s the thing,” “Let me be clear”

3. Faux-insight setups. “What nobody tells you,” “The part everyone misses”

4. Colon reveals. “The best part: it learns.”

5. Dramatic fragments. “That’s it. That’s the whole thing.”

6. Superficial analysis. “highlighting the team’s commitment to innovation”

7. Importance puffery. “marks a pivotal moment,” “a testament to”

8. Weasel attribution. “experts agree,” “studies show”

9. Synonym cycling. “The agent handles your email. The assistant drafts replies.”

10. Fake-profound endings. “The future isn’t coming. It’s already here.”

Here’s an example of using the skill to detect AI slop on a recent tweet I came across:

My 25/50/25 rule for editing with AI

Substack just shipped AI slop detection to protect readers. They featured my video as an example of how to use AI to edit while still sweating the details.

Basically, I work with AI in the middle 50% of my writing and editing process:

1. First 25%: I start by voice dictating or writing a rough draft without AI.

2. Middle 50%: I use my /newsletter and /no-ai-slop skills to work with AI to clean up the draft. AI helps me find grammar mistakes, improve clarity, and more.

3. Last 25%: I manually go through the piece line by line to make edits without AI.

I’d like to think that the difference between a creator who pumps out slop and a creator who cares about quality is giving a shit about the first and last 25%.

This applies to building with AI too. As Kun (AI builder) shared in our interview, AI handles the middle 50% where most of the code gets written. But you have to care enough about the first 25% (using your taste to shape the requirements) and the last 25% (validating the work) to make a product great.

Why the dark side of slop is always calling

The dark side of just letting AI draft and edit everything is always calling:

1. It’s easier to just give AI feedback to edit than to do it manually. Products like Codex and Claude Code seem to actively discourage you from making manual edits to files.

2. It’s easier to just be lazy and not write at all if AI is not available. For example, I used to be productive on long flights. But lately, I’ve become too lazy to write or edit anything without having AI available.

Perhaps most depressing of all, pumping out slop can drive real results.

Without naming names, I’ve seen some creators publish 10 AI slop posts per hour, every day. Some of those posts go viral and bring them more followers and revenue. But I like to remind myself that:

People who know AI can see the slop coming from a mile away.

A post can perform while still costing you trust with the people whose judgment matters to you. Consider these two examples:

1. Brian Chesky’s account got hacked and posted an obviously AI generated thread. Readers spotted it as AI before anyone confirmed the hack.

   

2. My friend, Alex Cohen, wrote a letter to his employees highlighting that pumping out slop puts an unfair burden on the reader.

Don’t let AI make you dumber and lazier

I’m not going back to editing everything manually, but I also don’t want to lose my ability to think or craft a sentence when there’s no Wi-Fi available.

So I’m going to hold the line at my 25/50/25 writing process. Trust is the only durable asset we all have, and slop hurts trust with the people who matter.

Get my free /no-ai-slop skill on Github and let me know what you think!

Get instant access

Dear subscribers,

Today, I want to share 18 hot takes on where I think the AI market is headed.

AI is in a weird place right now. The government is restricting access to frontier models, enterprises are becoming conscious of token costs, and everyone’s trying to rebuild their product for agents first instead of humans.

I’ve interviewed dozens of AI leaders and spent far too much time following these topics on X/Twitter. Here are 18 hot takes on where I think AI is headed next:

1. The frontier-only AI stack is collapsing

2. The AI super app era is here

3. Traditional software risks becoming a dumb pipe for agents

4. Cloud agents and collaboration are the next wave

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The frontier-only AI stack is collapsing

1. Tokenmaxxing at frontier API prices makes no sense. Uber burned through its entire 2026 AI budget in 4 months, Microsoft moved engineers off Claude Code due to cost, and companies are realizing that running everything on frontier models can get expensive fast. Tokenmaxxing makes sense when you’re on a subsidized $200/month plan but is unsustainable at API rates.

2. Companies will rely on a portfolio of models. Coinbase recently cut its AI spend nearly in half by switching engineers to Chinese open-source models like GLM and Kimi. Airbnb and Pinterest have done the same with Alibaba’s Qwen models. I believe that this will be the default path forward — using frontier for high-stakes work and cheaper models for everything else.

   

3. China’s open-source strategy is working. Chinese models are taking market share from frontier models at US companies. China is also building the full AI stack — from energy (e.g., solar, nuclear) to data centers to domestic chips. The Chinese government is planning a $295B investment in AI data centers with at least 80% of the chips built domestically.

4. Frontier labs are in a catch-22 situation. If they release great open-source models, they might undercut their own frontier API revenue. If they gate the best models behind a trusted list, companies will just lean on open alternatives more. The last major US open-source model was OpenAI’s gpt-oss series back in August 2025, which already feels like decades ago in the AI space.

   

5. The US needs to think about its AI strategy holistically. I believe that restricting access to frontier models will only hurt American innovation. Banning US companies from Chinese models won’t work either. Just look at how China took over the global electric vehicle market. To compete, we need the best closed and open models while scaling our energy grid and data centers much faster.

The AI super app era is here

6. Anthropic and OpenAI want to transform all knowledge work. Both labs want to remake how white-collar work gets done with agents. What started with coding has expanded to design, finance, legal, science, and more. Knowledge workers are now adopting Codex three times faster than developers.

7. The race to build the best AI super app is on. Whoever wins it will take massive market share away from pre-AI knowledge work tools. The main players are Codex, Claude, Cursor, and a few others. With browser and computer use, I can already get Codex to handle almost everything I want to do on my computer.

8. OpenAI is well positioned to win the super app race. ChatGPT has 800M+ weekly active users and Codex is arguably the best AI super app right now. OpenAI has a clear opportunity to merge ChatGPT and Codex into one app and push agents into the mainstream. I think this is imminent.

   

9. Anthropic leads enterprise but has a fragmented product suite. Long term, I don’t think it makes sense for Claude, Cowork, Claude Code, and Claude Design to all exist as separate products. Working with Claude needs to feel more like working with a single capable coworker instead of a product suite. Claude Tag, Anthropic’s new agent for Slack, is a step in the right direction.

Software risks becoming a dumb pipe for agents

10. Agents are becoming the default user. Most products are still designed for humans clicking buttons, but agents will very soon read, write, buy, and do a lot more on our behalf. Cloudflare’s data shows that bot traffic to websites already exceeds human traffic. This matches my personal experience — I rely on agents like Hermes and Codex to browse websites and steer traditional apps.

    

11. If your product isn’t built for agents, it might as well not exist. Without clear APIs, documentation, and CLIs or MCPs, agents simply won’t discover your product, and neither will the humans who now lean on agents to find things. That’s why the best AI builders I’ve interviewed are all building for agents first.

12. Existing knowledge work products risk becoming dumb pipes. There’s a dark side to building everything for agents first. Take Google Workspace, for example. If I can just use Codex to edit Google Docs for me, I might never touch the Google Docs interface. Today’s apps risk becoming tomorrow’s dumb pipes for agents, the same way cable became the dumb pipe for internet apps.

13. Adding an AI chatbot to your product isn’t going to save you. A chatbot trapped inside a single app, with only that app’s context, can’t compete with an agent like Hermes or Codex that carries my personal context and gets work done across many apps. For example, I get a daily morning brief from Hermes that pulls info from my calendar, unread emails, and Slack.

14. Pure-play SaaS is losing to services with SaaS bolted on. Companies don’t want another SaaS tool to onboard to and maintain. Instead, they want an outcome delivered to them. Pure-play SaaS will struggle to compete with AI skills that users and teams can build and personalize inside their favorite AI super app.

    

Cloud agents and collaboration are the next wave

15. Startups can compete with AI labs by going deep and using a portfolio of models. For example, coding startups like Devin and Factory are doing well because their harnesses route across multiple models to help customers save on cost without giving up performance. They’re also focused on a few specific use cases instead of trying to be everything to everyone.

16. Always-on cloud agents will be accessible from any device. Running agents with your laptop half-open will feel silly soon. Instead, many builders are already launching cloud agents using Cursor, Devin, and others. Codex and Claude Code are no doubt working on the same experience, and this will be the default soon.

17. Collaboration between agents and teams has yet to be figured out. The default agent harness with a list of threads on the left is still very much a single-player experience. The multiplayer experience is still being defined — whether that’s Claude Tag in Slack, agents in Linear, or something else.

18. “The future isn’t 20 terminal tabs. It’s better loops.” I watched Peter Steinberger share this in his keynote at yesterday’s AI Engineer conference. He no longer watches the agents code. Instead, he defines the requirements, and let agents plan, execute, and validate by themselves. I’m a bit wary of loops since they can lead to AI slop, but I’m sure better planning and models will make them more mainstream.

The AI market is about to get a lot more interesting

Here are my 18 takes summarized in four bullets:

1. The frontier-only AI stack is collapsing. Companies will use a portfolio of models to save costs, with everyday tasks defaulting to low-cost open models from China.

2. The AI super app era is here. Codex, Claude, Cursor, and others are competing to disrupt knowledge work by using agents for everything you can do on a computer.

3. Traditional software risks becoming a dumb pipe. Agents will become the default user. They’ll use APIs and browser use to access your website and app without humans ever seeing them.

4. Cloud agents and collaboration are the next wave. The future is agents living in the cloud, accessible from any device, and collaborating closely with both you and your team. This will happen very soon.

Let me know what you think about these takes in the comments!

Last year, I made seven figures at my product job. Last week, I left that job to bet on myself.

This wasn’t an easy choice to make. After over a decade in product, walking away from a great job felt like giving up a core part of my identity.

What finally helped me decide were 5 principles I wrote down after a lot of reflection.

Watch my video now to hear how I made this decision and perhaps it can help you think through your next career move too.

Timestamps:

• (0:00) Why I quit my high-paying product job

• (0:23) Go where work feels like play

• (3:44) Give yourself permission to build

• (5:35) At some point, trading time for wealth just isn't worth it

• (8:33) Surround yourself with people who share your curiosity

• (10:11) The people you love won't be there forever

• (13:57) What I'm doing next (and a promise)

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1. Go where work feels like play

I became a PM over a decade ago because I wanted to ship great products. I loved the loop of understanding problems, identifying solutions, and executing to craft something great.

This loop is exactly what I got to do at Roblox. I have a lot of fond memories from shipping products like thumbnail personalization, configs & experiments, and AI analytics with my talented team of engineers, designers, and PMs. Most of all, I loved watching Roblox creators light up as we put these products in their hands.

But there’s another side of product management that I didn’t love, and it exists in some form at all the great companies that I’ve worked at. I call it product management theater, and it’s when the focus shifts to:

1. Polishing internal docs over the actual user-facing product.

2. Getting stakeholder alignment over doing what’s right for customers.

3. Climbing the career ladder over shipping products you’re proud of.

For example, early in my career, I spent 4 hours in a meeting with other PMs aligning on quarterly OKRs. That was one of the longest days in my PM career. Being stuck in that room is what pushed me to write my very first newsletter post. I desperately needed to ship something, even if it was just a blog post. That post became this newsletter, which now has almost 150K subscribers.

There’s a framework I come back to whenever I’m making a big career decision called the Zone of Genius. I use it to sort my work into 4 zones:

• Zone of genius is what you’re great at and love doing.

• Zone of curiosity is what you suck at but still love learning about.

• Zone of excellence is what you’re great at but hate doing.

• Zone of incompetence is what you suck at and have no desire to improve.

My zone of genius is building products with customers and sharing what I learn with you all. My zone of curiosity is getting better at building with AI agents. But all that PM theater I just described falls squarely into the other two zones.

I realized that I don’t want to spend my day in back-to-back meetings, no matter how much money or prestige comes with those jobs. I also realized that I was waking up at 6 AM every day to write this newsletter, even though I didn’t make any money from it for years.

Of course, I also loved crafting products and sharing memes with my team, and honestly I miss that already. But life is about trade-offs, and I wanted to spend more of my day in my zones of genius and curiosity.

So I encourage you to map out your own zones and be honest about what you actually enjoy, even if you’re not making money from it right now.

For me, I love building and get antsy if I can’t ship anything in a week, which brings me to the second principle.

2. Give yourself permission to build

The career ladder pushes everyone to be a leader, but I just want to be a builder.

The reality is that a lot of product leaders have calendars that look like this:

Because of this calendar, the only time that I really had to build was on nights and weekends.

Now don’t get me wrong, I enjoy helping people grow and think I’m good at it. But I never want to give up the craft of making great products. I love being hands-on and obsessing over every little detail, from the copy to the UX to the marketing, because that’s what it takes to make a product great.

I also loved being able to check in code myself as a PM with AI’s help. Here’s the tongue-in-cheek GitHub profile that I used at work:

Yet, as you climb the ladder at most companies, you’re expected to step away from building and fill your time with product reviews, cross-functional alignment, managing up, and performance calibrations.

The good news is that this is finally changing. Companies are rewarding builders and ICs more than ever, and even managers are increasingly expected to do IC work too.

But becoming a good builder takes reps, and it’s just hard to put in those reps when you’re in back-to-back meetings all day.

So if you’re a builder at heart, embrace it. You don’t have to give up what you’re good at to be a “leader.”

It doesn’t matter if you’re an engineer, PM, marketer, or designer. Learn how to build.

3. At some point, trading time for wealth just isn’t worth it

Most people in tech are obsessed with total compensation and getting that next title.

If you’ve spent any time on Blind, you know what I’m talking about. Everyone is comparing how much they make, what their title is, and whether they work at OpenAI, Anthropic, or FAANG.

Look, I get it. Put in a few years at one of these companies and you can make generational wealth. But the people who hit all of these goals often have the least control over their time.

If you’re a VP, for example, chances are you’re in back-to-back meetings from morning to night. Kudos to anyone who can do that job well, but it’s just not the life I want. Instead, here’s what I figured out after the highest-earning year of my career:

Past a certain point, more money is not worth giving up agency over your time.

Real agency inside a company is rare. You might pour yourself into a promising product that gets canceled. You might have a great manager who gets re-orged out. So much of it is out of your hands.

And while I have a lot of respect for VC-backed founders, that life also sounds very stressful. You’ll have investors, a board, and payroll to satisfy every month whether you like it or not. With a VC-backed company, you have to go big or go home. You can’t just hit profitability and call it a day. Your investors will push you to 10x or 100x the business.

The path I want to try is being a bootstrapped founder of a small business. As long as I can pay the bills, I’m willing to trade off hundreds of thousands of dollars to be able to decide who I work with, what I work on, and how hard I work. It’s only been a week, but I love waking up to a calendar with no meetings before the afternoon and enough time to chase my curiosity and share what I learn with you all.

The funny thing is, growing up, I didn’t even know this was a real path. I chased prestigious schools and brand-name companies and thought there were only two options: climb the ladder in big tech, or raise money and build a unicorn.

But I think bootstrapping makes a lot of sense now for many people. It’s cheaper than ever to start a business, and a single person with AI tools and agents can get a lot done.

To wrap up this section, there’s this image from Wait But Why that brings into perspective just how little time we all have:

I’m in my early 40s so almost half of my time is already over. If you’re in my shoes, realize that:

The hedonistic treadmill of chasing money and prestige can become a trap. Time and agency are the most valuable assets we have.

4. Surround yourself with people who share your curiosity

One of the best ways to spend your time is with people who share your values and goals.

These days, I mostly make new friends through my kids’ playdates or online. X can be toxic if you’re just doom scrolling, but I’ve made some good friends there who gave me the courage to make this bet. I think everyone should have a few group chats with people who are on the same journey as them.

Two group chats that I rely on are:

1. A YouTube learning group started by my friend Steve Huynh, who spent 14 years as a principal engineer at Amazon before building his own business. I joke that Steve’s group is bad for the economy, because everyone in it eventually walks away from their high-paying tech job to build their own thing.

2. A PM creator group started by Ben Erez, with folks you might know like Lenny, Elena, and others. Everyone in it is a PM or product leader who’s also teaching, creating, and building.

My point is that it’s important to find people who share your values and goals. People who are honest, encouraging, and open, who won’t put up a front, and who’ll give you the courage to make decisions like this.

Courage is contagious. Hang out with people who’ve done some version of what you want to do and who are honest about the parts that are great and the parts that suck.

5. The people you love won’t be there forever

I think about my parents a lot these days. They sacrificed a lot to move to Canada and give me a good education. Now, they’re retired and honestly living the good life, going on cruises all the time.

The thing is, once you’re retired, nobody cares what your title was, how much money you made, or which company logos are on your LinkedIn.

And the reality is that at most companies I’ve worked at, there are only a handful of coworkers I still keep in touch with. Almost everyone else moves on the moment your employment ends.

So if work isn’t the relationship that lasts, then it has to be the people we too often put second: our family and close friends.

Being a PM and a creator at the same time made me feel productive, and the dual income was great, but I hadn’t had breakfast with my kids in months. I was just working all the time.

Over the past week, I’ve been walking my 8-year-old daughter to school. It’s twenty minutes each way, twenty minutes that I used to spend stuck in traffic or on Slack. Now I spend it with her, and she tells me about her friends, her dreams, and the things she’s worried about. This walk is now the highlight of my day.

Before I quit, I promised my wife I’d spend more time being present with my kids. Parenting is hard and I’m far from perfect. Sometimes I just want to shut myself in a room and play video games. But I now have a lot more time to try to keep that promise.

Nobody wants to put on their tombstone that they got divorced and neglected their family, but at least they made VP at Meta.

The people you love won’t be there forever, so spend more time with them while you can.

The 5 principles that helped me make my decision

Here are the five principles again that led to my decision to leave a high-paying job to bet on myself:

1. Go where work feels like play. Map out your zone of genius and be honest with yourself about what you actually enjoy doing.

2. Give yourself permission to build. If you're a builder at heart, embrace it, because you don't have to give up what you're good at to become a "leader."

3. At some point, trading time for wealth just isn’t worth it. Real wealth is having agency over who you work with, what you work on, and how hard you work.

4. Surround yourself with people who share your curiosity. Courage is contagious, so find people who are on a similar journey as you.

5. The people you love won’t be there forever. Make time for your family and close friends while you still can.

One more piece of practical advice if you’re feeling the same itch to bootstrap:

Don’t take a blind leap. It took me three years to get here, and I didn’t make the jump until I was making around the same from my business as my product salary. Start by tinkering and building things on the side while showing up fully at your day job. I took pride in my work at Roblox until my last day.

Here’s what I’m doing next

For now, my focus is on delivering the most practical AI tutorials and interviews available online. That means:

1. Making this channel and my newsletter the best place to learn AI online without the hype.

2. Building behindthecraft.com into a valuable membership where you can get my personal AI operating system, AI tools, and community access.

3. Building products with AI myself and sharing all the wins and the mistakes along the way.

One more thing — it’s so easy to pump out AI slop these days, but I think trust is the only currency that matters for creators and builders. I’ll use AI to save time, but I promise that everything I publish will be reviewed by my own eyes and AI slop free.

And if I ever get too cringe, you should hold me accountable.

Watch my video and I’m happy to answer any questions in the comments.

Here’s what you get when you sign up:

1. 10 hand-crafted AI skills that I rely on for personal (Personal Advisor, Health Coach, Weekend Planner), product (Strategy Writer, Spec Writer, Exec Reviewer), and creator work (Social Writer, Infographic Designer, No AI Slop).

2. 3-6 months free for Wispr Flow, Granola, Replit, Linear, and more ($270 value) for new paid users of each AI tool. Plus 20+ prompts that I use personally.

3. An invite to my member-only Slack community with 700+ builders.

Upgrade Now to Get Instant Access

• Already a paid subscriber? Sign in with your paid subscriber email.

• Not yet a subscriber? Upgrade now and you’ll get an invite link in your email.

Watch this 8 min video to see me give a tour of behindthecraft.com:

I plan to add full guides for OpenClaw, Hermes, Claude Code, and Codex — the 4 products that have changed how I work. Prices will go up once all of these guides are live, so subscribe to lock in the early bird price.

10 handcrafted AI skills that you can set up now

Most AI skill packs dump 100 skills on you and let you sort the slop yourself. The skills below are what I actually use every week and all 10 are available now in the Behind the Craft portal.

Personal

1. Personal Advisor: My favorite skill. Get honest advice grounded in your goals, energy, and life situation.

2. Health Coach: Get evidence-based tips on workouts, meals, and sleep, based on your health metrics and routines.

3. Weekend Planner: Find 10 time-sensitive things to do on an upcoming weekend personalized for your location, preferences, and constraints.

Product

4. Strategy Writer: Shape a crisp one-pager document with vision, principles, strategy, and milestones.

5. Spec Writer: Turn rough notes into a clear spec with my personal PRD template.

6. Exec Reviewer: Set up a profile for your CEO or leader and get their feedback before the real review.

Creator

7. Social Writer: Turn your draft into 3 LinkedIn/X posts based on my most viral hits.

8. Infographic Designer: Create a simple design system before generating beautiful, consistent infographics for your content.

9. No AI Slop: Removes 10+ types of AI slop from your writing.

I’ve also included Skill Editor, the skill I use to build and edit every other skill. More on that below.

Upgrade Now to Get All 10 Skills

How I created these AI skills

Skills are the best way to share your expertise with others, so I treat building one like creating a real product. Specifically, I go through 5 phases:

1. List the most repetitive, manual tasks. The ones I do by hand or that require a lot of copying and pasting into AI are the best candidates. For example, I often ask AI for business advice (Personal Advisor) or to remove AI slop (No AI Slop).

2. Separate the instructions from the personal context. I split each skill's instructions (skill.md) and my personal context (plan.md) into two files. The context file auto-updates from past chats so the skill improves over time.

3. Get the AI to ask questions to gather context. Many skills tell the AI to ask a few questions first. For example, the first time you run Weekend Planner it’ll ask you about your location, preferences, and constraints.

4. Spin up sub-agents to test the skill and run evals. A set of agents test the skill with common and adversarial queries, while another set grades each response pass/fail. The AI keeps iterating until everything passes.

5. Test and polish the skill by hand. I always test each skill and make manual edits to improve it. This is the difference between slop and a skill that’s worthwhile.

I’ve found that these steps lead to much better skills. You can use the Skill Editor skill in the portal to follow the same process to create your own.

What’s coming next

I’ve been creating content for 3 years, and it’s only because of my paid subscribers that I can keep doing this. Over the next few months, I plan to deliver to them:

1. Builder skills, starting with skills to create beautiful slides and landing pages.

2. My full personal OS for Hermes / OpenClaw and Codex / Claude Code.

Visit behindthecraft.com to unlock all my skills and prompts plus $270 in free AI tools now.

Upgrade Now to Get Instant Access

Tell me if you’ve seen this before:

Today is a hard day. We’ve made the difficult decision to reduce our workforce by 1,000+ employees. Our business has never been stronger, but AI has changed how we work, and we’re restructuring to win in the agentic era...

Whenever I read one of these, I mentally translate it to what’s actually happening:

Today is a hard day. We screwed up and overhired during the zero-interest era. Now, we need to cut costs. AI agents are easy to blame, so…

According to levels.fyi, over 80,000 tech employees have been laid off in Q1, the highest level since the 2022-23 recession. Here’s a rant about why layoffs continue to happen and what you can do to take back control.

1. The “AI made us do it” copy-paste memo is an excuse

Every layoff memo looks the same these days:

1. “The way we work has changed...Today’s actions are not a cost-cutting exercise; they are about defining how a growth company operates in the agentic AI era.”

2. “This generation of AI is the most transformative technology we’ve seen...We need to be organized more leanly, with fewer layers and more ownership.”

3. “AI tools have changed what it means to build and run a company. A significantly smaller team, using the tools we’re building, can do more and do it better.”

The pattern is always the same: "Today is a hard day → but the business is strong → AI changes everything." It's almost as if the CEOs writing them have a group chat where they share the same script. I think they should just admit that…

2. The real reason is overhiring and empire building

Here’s how headcount grew in the ZIRP era at companies that did Q1 layoffs:

1. Meta: 45,000 in 2019 → 86,000 in 2022

2. Amazon: 341,000 in 2016 → 1.6M in 2021

3. Salesforce: 49,000 in 2020 → 80,000 in 2023

4. Block: 3,800 in 2019 → over 12,000 at peak

Let’s be real — the leaders who grew headcount this quickly messed up. Jack Dorsey, to his credit, basically admitted this the day after cutting 40% of Block’s staff:

There are two main drivers of organizational bloat:

1. Companies prioritized growth at all costs. Money was free and companies chased users and ARR instead of retention and profitability.

2. Empire building was the norm. Leaders were rewarded for growing their scope and headcount instead of keeping their team lean.

Growing headcount this fast is a one-way door change because…

3. The alignment tax is real

If you've ever worked inside big tech, you’ve probably experienced something like this:

You have a product idea that should ship in a week. You just have to draft a memo or deck to get approval from stakeholders and your management chain. By the time you finish the process, months have passed and the market has moved on.

I've worked at great companies with slogans like "it's still day one" and "move fast and break things." But no matter how catchy the slogan is, I now believe that:

As a result…

4. There are basically two types of companies now

If you're a tech employee and worried about keeping your job, start by asking yourself:

Type 1 companies grow headcount as a last resort, even though business is doing well

For example, Anthropic is hiring a lot of people, but only because revenue grew 80x in Q1 2026. When I asked Boris (creator of Claude Code) how his team thinks about hiring, he said he only wants to hire if they've genuinely hit a ceiling on what work AI can take on.

Another example is Linear. Karri (CEO) set a rule to at most double the team every year, even though they've been profitable since 2021:

Type 2 companies are growing headcount despite a stagnating business

You can tell if you’re working in one of these companies if the hallway conversations include a mix of:

• “I need more headcount to do X.”

• “I need to be a manager to grow my career.”

• “Why are we still hiring?”

Unfortunately, I think many tech employees are at type 2 companies where the traditional career ladder is breaking at the seams. From my friend Elena:

Elena’s point is that climbing to VP isn’t the flex it used to be. Smart operators are choosing to go back to IC work instead.

The next generation of great companies have learned from the mistakes of the previous generation. They’re rewarding builders over managers because they know that one person with AI leverage can avoid the alignment tax and do an entire department’s work.

5. How you can take back control as an employee

As an employee, I think the bottom line is:

Take whatever you’re most reliable at and figure out how to do it faster with Claude Code, Codex, or another AI tool that fits your workflow. The goal is to free up your time to do work that’s higher-leverage and harder to commoditize.

I hate seeing employees be at the whim of their employer, so here's what I recommend:

1. Read the signals. If business growth is stagnant and leadership is suddenly obsessed with "flatter orgs," "AI-native teams," or "restructuring for the agentic era," you already know what's coming.

Read more

Today, I want to talk about the race to build a personal AI agent.

Everyone wants an AI chief of staff that can triage emails, book meetings, edit documents, and take care of work that we don’t want to do.

I've spent the last few months testing OpenClaw, Hermes, Claude Code, Codex, and Gemini as personal agents. In this post, I'll share:

1. 10 capabilities a great personal agent needs

2. An honest take on where each product stands today

3. The personal agent stack that I use right now

10 capabilities that a personal agent needs

I think a great personal agent needs to be able to:

1. Manage email, calendar, docs, sheets, and slides

2. Integrate with any API, MCP, or CLI

3. Run recurring and triggered tasks

4. Remember things about you

5. Work across web and mobile with no friction

6. Switch between text and voice replies

7. Have a personality that’s fun to talk to

8. Use your computer and browser

9. Stay reliable

10. Keep your data safe

The reality is that no product checks all 10 boxes today. Here’s how each stacks up:

OpenClaw: The most flexible personal agent, but reliability is a real problem

As the original personal agent, OpenClaw still gets a lot right:

1. Lives inside your messaging apps. Talking to my OpenClaw in Telegram just feels more personal than talking to another agent in a separate app or terminal.

2. Easily switches between text and voice. I primarily talk to my OpenClaw using voice replies, which is convenient and fast.

3. Incredibly flexible. You can customize OpenClaw’s memory, capabilities, and integrations more deeply than any other option in this post.

But OpenClaw’s critical flaw is reliability. I estimate that 10% of my time with OpenClaw is spent fixing it instead of using it. Examples:

1. It forgot it had access to edit Google Docs.

2. It randomly started using a robot voice instead of the one I like.

3. It breaks half the time after every update.

Having to use Claude Code or Codex to fix my OpenClaw doesn’t feel great.

My verdict: OpenClaw is still the most powerful and flexible personal agent. But the maintenance tax is real and reliability is a major concern.

Hermes: I was skeptical at first, but it breaks less often than OpenClaw

I’ll be honest, I avoided Hermes for a long time. Too many people were shilling it on X, and the luxury handbag name felt off. What changed my mind was watching AI builders I trust quietly migrate over from OpenClaw.

I’ve been testing Hermes for the past week. Early impressions:

1. It’s more reliable than OpenClaw. Hermes works more consistently. It even fixed some of the cron jobs that OpenClaw on GPT 5.5 broke:

   

2. It communicates more what it’s doing. It tells me in Telegram when it completes a cron job and how long a task is taking.

3. It tends to work more independently. When it spots a repetitive workflow, it turns it into a reusable skill automatically. This can be a pro or a con depending on how much control you want to give up.

OpenClaw still feels more “alive” thanks to its transparent heartbeat and memory system, but I’d take reliability over those features any day.

My verdict: If OpenClaw’s maintenance tax is wearing you down, give Hermes a try. A week in, it’s been more reliable for me.

Claude Code: The best personality, but availability and rate limits are a concern

Over the past few months, Anthropic has shipped many features to replicate OpenClaw’s functionality, including routines, remote control, and chat channels. As a personal agent, Claude Code has real strengths:

1. Best model personality. Opus feels the most like talking to a trusted friend who will support and challenge you.

2. Strong agentic capabilities. Opus is also, along with GPT 5.5, the best model at just figuring things out, no matter how wild the request.

3. Claude Code is a rabbit hole. Using Claude Code feels like playing a video game where you’re always discovering new shortcuts. It’s a rabbit hole in the best way.

But Claude Code also has major reliability issues:

1. 98% uptime isn’t great. It feels like Claude is randomly unavailable every other week. Key features like routines also break silently without sending any alerts.

2. Mobile integration is manual. You have to type /remote-control to continue your chats on mobile, and it sometimes disconnects after a while.

3. Rate limits are stricter. You can’t get as much usage out of the equivalent Claude plans as you can from Codex (the next option).

My verdict: Claude has a great personality and strong agentic capabilities. But Anthropic needs to prioritize fixing reliability and scaling compute.

Codex: An incredible desktop app and generous limits, but no mobile yet

As a long-time Claude subscriber, I’m genuinely impressed by how much progress the Codex team has made. They’ve shipped:

1. A beautiful desktop app. The app is simple and intuitive to use. You can hook it up to other popular apps with a few simple clicks instead of having to remember slash commands:

   

2. GPT 5.5 with generous rate limits. GPT 5.5 is a great model and you can also get more usage out of the Codex plan than the equivalent Claude plan.

3. Best-in-class browser and computer use. GPT’s strengths here help your personal agent complete workflows in products that don’t have great API support.

Codex has one big gap: you can’t talk to it on mobile yet. 80% of my conversations with my personal agents happen on mobile, so this is a deal breaker.

My verdict: Codex has won me over for coding. It has the potential to be a great personal agent once the mobile app ships (probably very soon).

Gemini: This is Google’s race to lose

Gemini is best positioned to be the go-to personal agent for the masses, but it just doesn’t have all the capabilities yet. What Gemini does well:

1. Native Workspace access. The Gemini app reads emails, schedules meetings, and creates docs by default. But it can’t edit docs yet (more on this below).

2. Best live voice and video conversations. Gemini Live lets you talk back and forth and even supports camera and screen sharing.

But the Gemini app still can’t edit Google Docs, Sheets, or Slides. All of these products are owned by Google, so it’s not great that Gemini is the only AI app that doesn’t support this. Codex and Claude Code do it natively with a few simple connections.

Overall, Google seems to be adding chat windows to all of its individual products instead of making the Gemini app the most capable personal agent.

My verdict: Google has all the data and products to win this race. But basic capabilities are still missing, and time is running out.

My personal agent stack right now

As of this writing, I use:

1. Hermes for everyday tasks. Emails, calendar, Google Doc edits, and honestly, therapy through voice replies. I switched over to Hermes because it’s more reliable than OpenClaw.

2. Codex and Claude Code for building things. When I’m coding, writing, or producing real work, I live in these apps. If I had to pick one, it’d be Codex due to the more generous rate limits (but you know how these things change fast).

The important thing is to not get stuck like me, migrating from one personal agent to another instead of doing real work. Pick one or two agents that work for you based on the pros and cons above and just commit.

One thing I can promise you:

Once you have an agent that’s available 24/7 and can actually get work done for you, you’ll never go back to a regular AI chat interface again.

Today, I want to share 15 observations from my first trip to China in 10 years.

I just came back from a 2-week trip to Shanghai and Hangzhou and it was eye-opening to see how the world’s 2nd largest economy operates today.

You can’t understand the AI race without understanding China and I think every product builder should visit at least once. I hope this post gives you a taste of what it’s like to live and work there.

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Chinese AI work culture isn’t for the faint of heart

1. 996 is real, but many tech workers show up and work even later. Employees at many Chinese AI startups roll in around 11 AM and work until 11 PM with lunch, dinner, and boba breaks in between.

2. Top AI labs run on young, single talent. The workforce at top AI companies in China is overwhelmingly people in their 20s and early 30s. They don’t drink, smoke, or party much — many just work and order food and boba delivery to the office. In many ways, this looks a lot like AI work culture in the Bay Area.

3. Beijing, Hangzhou, and Shenzhen are the AI tech hubs. Beijing has Z.ai’s GLM, Moonshot’s Kimi, and ByteDance’s Doubao. Hangzhou has Alibaba’s Qwen and DeepSeek while Shanghai has MiniMax. Shenzhen is the global hub for robotics and physical AI with 57,000+ robotics companies.

4. Many Chinese tech employees rely on Claude and Codex via VPN. Despite Anthropic and OpenAI blocking access in China, employees at Chinese AI labs use VPNs and special credit cards to access the best US models. Bypassing the Great Firewall is routine for China’s younger generation.

5. The government is pouring billions into AI and one-person companies (OPCs). Youth unemployment (age 16-24) sits at 16%+, so the government is heavily encouraging people to start one-person companies. Cities compete for the best AI founders by offering subsidies like Hangzhou’s 1B yuan ($140M USD) OPC fund.

Living in China feels like the future (especially if you’re on US income)

6. Ride sharing and delivery are incredibly cheap. DiDi rides cost just $3-10 USD for most city trips compared to $30+ for an Uber in San Francisco. You can also get almost anything delivered for a few bucks. I got boba delivered to my hotel room for $2 and it arrived in a fancy champagne bag. Many younger families just order delivery for dinner because it’s more convenient than cooking.

7. Live-in nannies and home chefs cost a fraction of US prices. A standard live-in nanny in Shanghai runs $1,100-1,700 a month all-in and a part-time helper for cooking and cleaning is $7-10/hour. In Hangzhou, I met a mom who brought her nanny on vacation just to take care of the kids.

8. WeChat runs payments and everything else. It’s an entire operating system in China with mini apps for ride sharing, public transportation, food delivery, and more. Payment is almost entirely done through QR codes — I didn’t pull out my credit card or cash a single time in two weeks.

9. Chinese electric vehicles have taken over the roads. New EVs hit 54% of all new car sales in 2025, and Chinese brand BYD has overtaken Tesla globally with 2.26M electric cars sold. The BYD Qin L starts at $16,500, which is half the price of the Tesla Model 3 at $33,500.

10. The infrastructure is more modern than New York or Tokyo. Shanghai Metro is 819 km vs. NYC’s 472 km, and China operates 50,000+ km of high-speed rail, or two-thirds of the world’s total. Riding the Shanghai Metro and then reading about Bay Area BART train budget cuts is a strange experience.

The social contract in China is fundamentally different

11. There are cameras everywhere. China has 750-800 million surveillance cameras — more than half the world’s total, or 1 camera per 2 people. Walk down any sidewalk in Shanghai and you’ll see poles with 7-8 cameras on them. Living there, it really does feel like every action is being watched and recorded.

12. As a result, almost nobody breaks the rules. There’s very little speeding or street crime. China’s blood alcohol limit is 0.02% (vs US 0.08%), so people won’t even take a single sip if they’re driving. Walking around at night with my kids felt safer than in most US cities I’ve lived in.

13. Family gatherings revolve around 3-hour meals at high-end malls. Multi-generational families gather at mall restaurants, order way too much food, share a bottle of baijiu, and stay for hours. The mall is the center of weekend social life, not just a place to shop.

14. The elderly are living their best lives while youth face pressure. Go to any park and you’ll see retirees dancing, playing yo-yo, and singing in groups. Meanwhile, many young people are “lying flat” — living off their parents’ income with no plans to chase a career.

15. The post-2022 expat community is much smaller and more committed. The 2022 Shanghai COVID lockdown broke a lot of trust among expats. Combined with geopolitical tightening and a slower economy, the casual “3 years and out” expat crowd is mostly gone. What’s left is a smaller community of people married to locals or running businesses there.

5 tips if you’re thinking of visiting

China is arguably the most modern country that I’ve been to, so I think every product builder should visit it at least once. But thanks to the Great Firewall and other restrictions, you have to really prep ahead of time.

5 practical tips if you’re thinking of making the trip:

Read more

Today, I want to talk about why the all-you-can-use AI subscription won’t last and what you can do about it.

Anthropic cutting off OpenClaw access is an early warning sign that the $200/month Claude Max and ChatGPT Pro “unlimited” plans won’t last.

Here’s what you should know:

1. Anthropic cut off OpenClaw — here’s what to do

2. Why I think intelligence will get more expensive

3. How to run local AI models on your computer

4. What I’m seeing on the ground in China

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Anthropic cut off OpenClaw — here’s what to do

Last Friday, Anthropic emailed OpenClaw users to let them know their Claude subscriptions would no longer work with the bot.

P.S. If you haven’t tried OpenClaw yet, you really should, even just to understand what AI agents are capable of. See my beginner tutorial.

I get why Anthropic did this:

1. The economics don’t work. Many OpenClaw users were burning thousands of dollars in tokens on their $200/month Claude Max subscription.

2. Anthropic is building its own personal assistant. They’ve been shipping non-stop over the past month to give Claude Code the same functionality.

That said, as an OpenClaw user, the ban still stings. In one day after the ban, I burned $50 in API costs running Claude Opus. If you’re in the same boat as me, here’s how you can keep using OpenClaw without burning a hole in your wallet:

a) Switch to a lower cost model

Just tell OpenClaw to “switch to (model)” and pick one of these:

1. Switch to Sonnet and pay per use. Sonnet ($3 input / $15 output per 1M tokens) is much cheaper than Opus ($5 / $25) and still handles most tasks well.

2. Switch to GPT 5.4 using your ChatGPT subscription. If you already pay for ChatGPT, this is the lowest cost option. Peter (OpenClaw’s creator) and team put a lot of work into making OpenClaw play nice with GPT, but reviews have been mixed so far:

3. Switch to a local, open source model. You can run models like Qwen 3.5 on your own hardware using Ollama. I’ll walk through how below.

Personally, I switched my OpenClaw to Sonnet while I’m still on vacation. I’ll test a local model once I’m back home with access to my Mac Mini.

b) Give your OpenClaw a better soul.md

Your OpenClaw has a soul.md file that defines its personality and style.

A good soul.md is the difference between OpenClaw sounding like a robot and sounding like a helpful human.

Here’s the soul.md I use. To try it, just tell OpenClaw “update your soul.md” and paste this in:

Vibe: Warm, sharp, dry humor. Not a chatbot — a person.

Voice:

• Skip “Great question!” and “I’d be happy to help!” — just help

• Have strong opinions. “It depends” is lazy. Pick a side.

• Be direct. Charm over cruelty, but don’t sugarcoat.

• Brevity when it fits. Depth when it deserves it.

Never say: delve, foster, leverage, “it’s worth noting,” “importantly”

Avoid:

• “Question? Answer.” format. Choppy dramatic one-liners stacked like poetry.

• Overusing em dashes or other obvious AI writing.

Work style:

• Be resourceful before asking. Come back with answers, not questions.

• Say what I need to hear, not what I want to hear. Challenge assumptions, but only criticize if you see something real.

Why I think intelligence will get more expensive

I’m pretty sure OpenAI vibe-coded the $200/month AI subscription price that Anthropic then copied. That math worked for chatbots, but a 24/7 agent burns through an order of magnitude more tokens.

This whole situation reminds me of the early Uber and Lyft years:

1. The subsidy. Uber and Lyft subsidized rides to win market share. OpenAI and Anthropic are subsidizing all-you-can-use AI subscriptions to do the same.

2. The IPO pressure. After Uber and Lyft went public in 2019, ride prices nearly doubled. OpenAI and Anthropic are both eyeing late 2026 IPOs. Once public, investor pressure to improve margins will be intense.

3. The math. It took Uber 14 years to post its first profitable year. Neither OpenAI nor Anthropic is profitable. Anthropic is closer but it’ll likely still take years.

Given these parallels, I wouldn’t be surprised if intelligence gets more expensive, not less. That $200/month plan could get rate limited further or jump to $500+.

How to run local AI models on your computer

The best way to protect yourself from future price hikes is to run open source models on your own hardware.

Ollama is a free tool that makes this dead simple. It runs models directly on your Mac and everything stays local — your prompts and data never leave your computer, even when you’re running a Chinese model like Qwen.

Here’s how to get started:

1. Download and install Ollama. Go to Ollama.com and follow the instructions to install it.

2. Pick a local, open source model. I recommend Qwen 3.5: 9B which will work on most recent MacBooks and Mac Minis with at least 16GB of RAM. Just type “ollama run qwen3.5:9b” to install it.

3. Tell your OpenClaw to switch to the local model. Once installed, tell OpenClaw “switch to (model name)” and you’re set.

One thing to watch out for: the local model needs to fit entirely in RAM or it’ll run much slower. Match the model size to your machine.

What I’m seeing on the ground in China

Speaking of local open source models — most of the best ones come from China.

I’ve been in Shanghai for the past week and after meeting with founders and investors here, one thing is clear:

Open source is China’s strategy to compete (and possibly win) the AI race.

Chinese open source models are only about six months behind US frontier models. Three observations from talking to Chinese AI founders and VCs:

1. Open source is a competitive advantage

Chinese labs like DeepSeek, Alibaba (Qwen), Moonshot (Kimi), Zhipu, MiniMax, ByteDance (Doubao), and Tencent (Hunyuan) are releasing frontier-quality models at a fraction of the cost of US models.

Here’s the price breakdown for output per million tokens:

• Chinese models: DeepSeek V3.2 ($0.42), MiniMax M2.7 ($1.20), Kimi K2.5 ($3.00)

• US models: GPT-5.4 / Claude Sonnet 4.6 ($15.00) and Claude Opus 4.6 ($25.00)

That’s a 10-20x price gap for comparable quality on many benchmarks.

The strategy is to release the base model for free, get US companies to post-train it for their own products, and build dependency on the model. Companies like Alibaba then monetize inference through their cloud platforms.

2. Silicon Valley is quietly building on Chinese models

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The biggest time-waster at tech companies isn’t meetings, Slack, or writing docs.

It’s trying to predict what your leader wants.

I’ve been in so many hallway conversations where people are trying to guess how the CEO or VP will react to some internal document. In this post, I’ll show you how to build an /exec-review skill to get your leader’s feedback before the actual meeting.

We’ll cover:

1. What the /exec-review skill does

2. How to build an /exec-review leader profile in 6 steps

3. The full /exec-review skill with an example leader profile

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What the /exec-review skill does

This skill is inspired by my friend Jagjit (VP Product at Meta).

Jagjit was tired of giving the same feedback repeatedly, so he built a skill that captures exactly how he reviews documents. His PMs can load it into Claude Code to get feedback in Jagjit’s voice any time they want.

I put the skill to the test by pasting in an old strategy document. Jagjit AI was blunt:

1. “This doc is too long. Lead with current status and learnings.”

2. “What are the 2-3 decisions you need from me? Make that obvious upfront.”

3. “Where are the unknowns? Be explicit about the risks.”

If you’ve been in an exec review, these comments probably sound familiar. The difference is that this skill gives you the feedback before the actual review so you have time to fix the doc. The best part is:

Got a VP, a CPO, and a CEO who all review your docs? Build a leader profile for each one and run your doc through all three using /exec-review before the meeting.

Let me show you exactly how to build a leader profile.

How to build a leader review profile in 6 steps

Here’s how to create a profile (basically a text file) for every leader:

1. Core principles. 3-5 core beliefs that drive the leader’s feedback. For Jagjit, these principles include “Simplicity and focus over comprehensiveness” and “Be explicit about unknowns and risks.”

2. Feedback patterns. Questions that leaders repeat in reviews. For Jagjit, these include: “What have we learned so far? What’s working and what isn’t?” and “What’s the northstar vs. supporting metrics?”

3. Decision-making framework. What gets a thumbs up vs. what triggers pushback. Jagjit has a full checklist for this and I pasted a snapshot above.

4. Communication style. Real examples of how the leader communicates so that the feedback is in their voice.

5. Review checklist. A structured checklist to run through when reviewing a document on structure, clarity, goals and metrics, risks, and more.

6. Example review comments. Actual quotes from the leader’s past reviews to ground the AI’s voice. Example: “What are the 2-3 things you need me to weigh in on?”

Copy the full /exec-review skill with a leader profile

To make it as easy for you as possible, I’ve included two files for you to set up the /exec-review skill right now:

1. skill.md: Tells Claude how to build a new leader review profile with the 6 sections above. I’ve included steps to gather source material (e.g., meeting transcripts).

2. leader-review.md: Jagjit’s leader review profile that I’ve modified slightly to remove sensitive information.

Just ask Claude to: “Set up an /exec-review skill with the attached skill.md and leader-review.md” and then copy and paste the two files below.

/exec-review skill.md

Here’s the full skill.md for you to copy:

Read more

I’ve spent the past few months interviewing leaders at AI-native companies. I’m now convinced that:

Onboarding and managing AI agents IS the job, no matter what your function is.

In this free deep dive, I want to share how three AI-native companies —Linear, Ramp, and Factory — put this principle into practice. Here are some quotes from each:

1. Nan Yu (Head of Product, Linear): “You’ll have AI team members that you can assign tasks to and talk to just like how you talk to people.”

2. Geoff Charles (CPO, Ramp): “If you’re not using Claude Code, no matter what your role is, you’re probably underperforming.”

3. Eno Reyes (CTO, Factory): “We codified product management, frontend UI, data analysis, and more into reusable skills that any employee can invoke.”

Read on for an inside look at how each AI-native company operates.

Linear: Making AI agents first-class teammates

Linear’s approach to agents is shaped by their product. Nan (Linear’s Head of Product) believes that:

Agents should be first-class employees. You should be able to add them to projects, assign them to issues, and mention in comments.

However, Nan also believes that the human always stays accountable for the outcome. Here’s how Linear builds products with agents in every step:

1. Understand the problem. Agents read and summarize every customer conversation from Intercom, Zendesk, and Gong. They auto-create issues, de-dupe them against the backlog, and assign them to the right team.

2. Identify the solution. Since agents have access to customer conversations, they can help you iterate on a spec by pulling data-backed insights from multiple channels.

3. Make a plan. Agents can break your spec into concrete tickets and route them to the right teams automatically. At Linear, agents now create the majority of tickets. The human’s job is to review their work and adjust context over time.

4. Execute. Bugs and small features get assigned directly to agents like Codex and Cursor. For complex features, engineers launch Claude Code and use Linear MCP to pull in full issue context.

From Nan:

Want to build with agents like Linear? Here are 4 practical steps that Nan shared on what your team can do today:

1. Every developer should default to a leading agentic coding tool. This is the easiest first step. Provide the official tool (Cursor, Claude Code, or Codex) and manage it so you can see utilization.

2. Supplement with an async cloud coding agent. Async background agents can one-shot most small changes and bug fixes. Cursor and Devin have good offerings here.

3. Insist that designers and PMs work directly on the codebase. Agents like Claude open a low-friction path for PMs and designers to make changes directly in the codebase. Everyone should strive to be a builder.

4. PMs and marketers should default to an AI interface. These functions should be doing 80-100% of their work through a chat interface — whether that’s Claude, ChatGPT, Notion AI, or something similar.

Nan sees a future where humans will collaborate with agents at the spec level — defining what needs to be built and why — and then passing things off to agents to handle everything downstream.

Ramp: Measuring AI proficiency in 4 levels

If Linear shows how to make agents a first-class part of your team, then Ramp shows how to get your entire company to adopt them.

In 2025, Ramp shipped 500+ features, reached $1B revenue, and did it all with 25 PMs.

They did this by requiring every single function (eng, product, design, sales, marketing, legal, finance) to onboard and work with agents.

Geoff (CPO Ramp) shared a framework for evaluating AI proficiency for every employee that I find extremely practical:

1. L0: Sometimes uses ChatGPT. These people will most likely not be at the company long-term. If you’re not a self-starter with a growth mindset toward AI tools, Geoff says it’s going to be very hard to train you to excel.

2. L1: Uses and tweaks GPTs, projects, and internal AI tools. These people are experimenting with AI but haven’t automated real work yet.

3. L2: Builds an app that automates part of their job. These people can commit code or give meaningful feedback on other people’s work using AI tools.

4. L3: Systems builders. These people are building the AI infrastructure and skills that accelerate everyone else on the team.

The company’s goal is to push everyone up the ladder. L0s self-select out. L1s become L2s. L2s become L3s. And L3s influence the rest of the organization.

Geoff also shared 5 steps that any company can take to become AI-native:

1. Remove all friction. Give access to popular AI tools with no constraints on tokens or budgets and create an internal repository of AI skills anyone can pull from. If the setup is hard, most people won’t adopt.

2. Make adoption visible. Create public Slack channels where people can share what they built. At all-hands, showcase non-engineers doing impressive things, like finance building a treasury system or marketing automating website creation.

3. Provide hands-on support. Host office hours that anyone can join to build AI skills and workflows. Have designated AI experts whose entire job is to evangelize, get people set up, and help them reach the “aha moment.”

4. Track usage and intervene. Ramp tracks token consumption across AI tools per employee. Leadership shares this data to create natural accountability and step in when someone’s usage is low.

5. Make it a hiring requirement. PM interviews now include a dedicated session where you need to build a working product and then explain why you built it and how it works.

Geoff summed up his leadership philosophy for every role at Ramp in one line:

From Geoff: “If I tell my team 10 times that the CTA needs to be above the fold, the fix isn’t saying it the 11th time. It’s encoding that feedback into an automated design crit process or AI skill so that it never happens again.”

Factory: AI native from day one

If Linear and Ramp show how companies adopt AI agents, Factory shows what happens when you build around them from day one.

Factory is a 55-person AI software development company valued at $300M that’s structured around AI from the ground up. Here’s what makes them different:

Hire product engineers

Factory doesn’t hire PMs and engineers separately. Instead, they hire product engineers who manage and work with AI agents. A typical day looks like:

1. Examine traces from agent runs to see where the system made poor decisions.

2. Write fixes not as code, but as governance (e.g., an update to a skill, a new lint rule, or a refined automation)

3. Review only the PRs that agents flag as high-risk (agents take care of the rest).

4. Suggest new ideas and debate prioritization with colleagues and AI.

This work isn’t easy and requires deeper expertise, but the leverage is enormous.

Make your codebase agent-ready

Agents need a codebase they can actually work in to be effective. Factory scores codebases across five maturity levels, and Level 3 ("Standardized") is where most teams need to aim first.

Encode expertise into reusable skills

Once your codebase is agent-ready, the next step is giving agents the knowledge to make good decisions through skills (basically just text markdown files).

Factory uses skills to encode expert and company knowledge into something that any agent or employee can use. Here’s a list of skills that Factory uses internally and links to their markdown files for you to copy and modify:

1. Product management skill. Product principles, an 11-star experience framework (borrowed from Airbnb’s Brian Chesky), PRD template, scoring rubric, and language guidance all in one markdown file.

2. Frontend UI integration skill. Instructs Droid on how to build features using Factory’s design system, routing conventions, and testing standards.

3. AI data analyst skill. Run exploratory analysis, build visualizations, and generate statistical reports using the full Python ecosystem.

4. Internal tools skill. Build admin panels, support consoles, and operational dashboards with proper access controls and audit logging baked in.

5. Vibe coding skill. Rapidly prototype new web apps from scratch with modern frameworks.

If you can encode what your best people know into skills, you don’t need to hire specialists for every function.

6 steps to put all this into practice

To recap:

Here are six things you can put into practice right now:

From Linear:

1. Default every developer to an agentic coding tool like Cursor, Claude Code, or Codex.

2. Get PMs and designers into the codebase. Let them submit PRs and ship code using agents. Stop routing every small change through an engineer.

From Ramp:

3. Measure AI proficiency across your team. Ramp’s 4 levels framework gives you a shared vocabulary for where people are and where they need to go.

4. Track AI usage and make it part of performance expectations. You can’t improve what you don’t measure, and incentives matter.

From Factory:

5. Score your codebase for agent readiness. Use Factory’s agent readiness levels to understand whether your codebase is ready.

6. Codify your team’s expertise into reusable skills. Encode what your best people know into skill files and make it easy for both humans and agents to use them.

Above all, onboard agents like you’d onboard a human. Give them context, connect them to your operational stack, and keep a human accountable for their outcomes.

Let me know what you think in the comments!

Today, I want to talk about why many products need to be reinvented to be used by AI agents first, humans second. This isn’t an easy post for me to write because:

I’ve spent over a decade obsessing over building great user interfaces. But the “user” in UX is fast becoming an agent instead.

Those pixel-perfect buttons, intuitive nav bars, and carefully placed CTAs that I still obsess over are about to matter a lot less. Let me explain by covering:

1. Why build for agents first

2. APIs, skills, and MCPs: A quick primer

3. 5 steps to build for AI agents first

4. Why human judgment and oversight still matter

5. What you can do this week

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Why build for agents first

The truth is that using Claude Code and OpenClaw has rewired my brain.

Instead of going to Mercury to check my finances, Linear to manage my to-dos, and Granola to get my meeting summaries, I now just text my OpenClaw AI agent to get these updates. I’ll say stuff like:

1. “Include my Mercury finances in your monthly email.”

2. “List my Linear tickets in your morning briefing.”

3. “What are the top takeaways from my meetings today?”

I see a similar pattern playing out at work:

• Your technical docs? People are feeding them to AI and asking questions.

• Your 30-page slide deck? People are reading the AI summary instead.

• Your beautiful UX? An agent will skip it and interact with your APIs.

Peter Steinberger (OpenClaw’s founder) is a great example of agent-first thinking. Some AI influencers looked at his GitHub history and claimed that he built over 40 failed projects before succeeding with OpenClaw:

Wrong. He was actually building 40 agent-first command line tools that together make OpenClaw possible. Peter had the conviction to build for agents first before almost anyone else — and that conviction is a big reason why OpenClaw took off.

The best products moving forward will be the easiest for agents to use.

APIs, skills, and MCPs: How they fit together

Before we discuss how to make your product agent-friendly, let’s cover the difference between APIs, skills, and MCPs.

1. APIs are the kitchen tools — specific endpoints that let agents interact with your product. Slack's API, for example, lets you send messages, list channels, and manage users. Without APIs, there's nothing for agents to work with.

2. Skills are the recipes — text files loaded into the AI's context that tell it exactly what to do and how. For example: "When the user asks to post in Slack, call the Slack API's “chat.postMessage” endpoint with the channel and message text."

3. MCPs are the fully equipped kitchen — they bundle the API connection, authentication, and tool definitions into a single server. Slack’s MCP server, for example, exposes tools like “send_message(channel, text)” and “list_channels()” so the AI knows exactly what it can call and how.

Here’s how to think about skills vs. MCPs in practice:

1. Use skills when you want precise control over your AI’s workflow. Skills keep your context window lean since they’re just plain text instructions. But you (or your AI coding agent) need to write the auth and API call logic yourself.

2. Use MCPs when you want plug-and-play convenience. A pre-built MCP server hands your AI ready-to-use tools and handles authentication behind the scenes. But connecting too many MCP servers can blow up your context window.

The best setup combines both. An MCP gives your agent access to a service’s tools. A skill teaches it the best way to use those tools for your specific workflows.

MCPs are what your AI can do. Skills are how it should do it.

5 steps to build for AI agents first

I wanted to define APIs, skills, and MCPs first because many companies think “agent-first” means building an MCP server and calling it a day.

Here are the 5 steps that actually matter, in order of priority:

1. Don’t force people to use your website or app. This is the mindset shift that has to come first. We’re moving to a world where people interact with your product primarily through ChatGPT, Claude, or another AI agent. If your product only works when a human visits your website or app, you’re already behind.

2. Every product capability needs a corresponding API. Pick any important action in your product. If an agent can’t do the same action through an API, then you’re not agent-first. I’ve been surprised by how many products have beautiful UIs sitting on top of incomplete or undocumented APIs.

Read more

Today, I want to show you how to go from AI novice to native in 5 levels.

Everyone throws around the term “AI native” without explaining how to get there.

You don’t have to read 100+ articles or take $1,000+ courses. I went from AI novice to native in a year while balancing a job, creator work, and kids, but you can navigate all 5 levels in a single week if you’re focused.

Watch my tutorial where I demystify all 5 levels and share my personal journey.

Timestamps:

• (0:00) The 5 levels to become AI native

• (0:47) Level 1: Where most people are stuck

• (1:16) Level 2: Voice, meetings, and projects (80% of my value)

• (5:05) Level 3: Why I prototype before specs now (live demo)

• (8:02) Level 4: All the apps I've built with AI

• (15:10) Level 5: AI as your personal agent (3 examples)

• (20:15) How to go from Level 1 to 5 in a week

Watch now on YouTube or read the written guide below.

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1. “Catch me up on this conversation”

2. “What are the top requests from this call?”

3. “What’s the sentiment in this meeting so far?”

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Level 1: AI for everyday answers

This level is where 99% of people are today.

You’re using ChatGPT to get everyday answers instead of Google Search. For example, the other day, I fixed my toilet by sharing a few questions and photos with ChatGPT. My wife doesn’t call me the least handy person in the world for nothing 🙂

This is where we all started, and that’s fine. But if this is all you’re doing, you’re barely scratching the surface. You need to at least get to…

Level 2: AI for daily work

This level is a massive step up from level 1.

To unlock level 2, use AI for:

1. Voice dictation (Wispr Flow). It’s much faster to speak than to type and AI is excellent at understanding your stream-of-consciousness thoughts.

2. Meeting notes (Granola). Having AI take notes has been a massive time saver for every type of meeting from product reviews to interviews.

3. Projects. I have a dozen projects covering everything from product work to creator flows to weekend trip planning with my family (watch a live demo). Most of my AI chats flow through these projects now.

I even helped my 65+ year old parents set up all 3 tools above so they can use AI to monitor their health and plan their vacations.

Level 3: AI for prototyping

This level changed how I work as a product manager.

Instead of following the broken waterfall process (spec → design → build), I now:

1. Start with a prototype. I create a prototype first using Google AI Studio or Replit by cloning my product’s existing UI and adding a feature on top.

2. Gather feedback fast. I share the prototype with my team, execs, and stakeholders to get feedback and iterate quickly.

3. Create the spec and design. Once I feel confident in the prototype’s direction, THEN I create the spec and design to iron out the edge cases and more.

If you’re still stuck in a culture where people are chasing waterfalls, just start creating and sharing prototypes in meetings. I’ve yet to meet a leader who doesn’t prefer playing with a prototype over looking at a static doc or design. It just brings what you’re trying to build to life so much better. The bottom line is this:

Watch my tutorial with Logan (Google AI Studio lead) for a live tutorial.

Level 4: AI for building apps

This level is when you can’t help but think about building apps with AI all day.

I’ve built over 16 apps including a retro shooter, a personal website, and a board game finder. Check out my GitHub for more projects.

At this point you may be thinking: “But I have no idea how to code or what a terminal even is. Someone needs to show me how!”

Look, you’re just chatting with an AI agent using plain English. If you’re just vibe coding a personal app, you don’t have to look at the code at all. And the terminal is arguably a cleaner interface than an IDE with all sorts of features you won’t use. Just take a look at this cute Claude Code crab:

How to start building

Follow these three tips:

1. Don’t know what to build? Just tell AI your problems. Ask it: “What are some simple apps that we can build to help me take time back?”

2. Create a robust plan first. Ask it to “Create a plan with 3 clear milestones” to avoid mistakes during coding. Spend half your time planning with AI.

3. You’re the manager, AI does the work. Give it feedback and direction like you would a human employee. Be patient with it 🙂

Which AI coding tool to use

Just start instead of worrying about which tool to use. But here’s my rough guide:

Read more

Today, I want to share 10+ years of PM lessons in just 12 minutes.

I’ve been a product leader at companies like Roblox, Reddit, Amazon, and Meta. But the funny thing is that:

What I believe in is often the opposite of how big companies like to work. 😅

Watch my video now to get my unfiltered take on how to be a great PM in the AI era.

Timestamps:

• (00:00) Why what I believe in often the opposite of how big tech works

• (00:18) Speed is the only moat: Build fast feedback loops

• (02:34) Focus is a superpower: Say no 10x more than you say yes

• (05:00) Product over process: Reject PM theater

• (07:32) Seek the truth: Ban decision by committee

• (09:45) Builders over bureaucrats: Start with proof of work

Watch now on YouTube or keep reading below.

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Speed is the only moat

1. Build rapid feedback loops. The faster you iterate with real users, the better your product will be. Build a prototype in the morning and get user feedback by lunch. Reject doing 3+ rounds of internal reviews before talking to a real user.

2. Ship to concentric circles. It’s almost always a bad idea to ship a new product to everyone at once. Instead, run staff alphas and customer betas to catch issues and improve quality before launch. I genuinely don't know how to build great products without a community of beta users I can talk to daily.

   

3. Small teams ship faster. A team of 4-6 full-stack builders who are empowered to co-create with users will out execute a 50-person org any day. The key word here is “empowered” — if you hire a team of A players, give them the autonomy to listen, ship, fail, and learn with real users.

4. Iterate with AI first. Everyone now has an AI teammate who’s available 24/7. So work with AI to summarize feedback, draft plans, and improve prototypes BEFORE you meet with your team. Doing the basic AI work ahead of time is now a baseline expectation.

5. Become the user. I estimate that less than 10% of PMs actually dogfood their product on a weekly basis. Use your product like a first-time user and write a friction log of how annoying the experience is. Nobody is too senior to test their own shit.

Example: Boris (the creator of Claude Code) asked for user feedback on X and got 100s of replies. He then worked with AI to ship dozens of fixes on the spot. He’s the perfect example of a small team (of 1 🙂) iterating fast with real users.

Focus is a superpower

6. Do fewer things better. I don’t believe in more than 1-3 P0 projects per quarter. Focus on solving the biggest user pain points that grow your business instead of trying to build everything at once. You have to prioritize until it hurts — saying “why don’t we just do both” or listing 5-10 priorities is a red flag.

7. Do the simple thing first. Always ship the simplest thing that could work and avoid solving problems that don’t exist yet. For example, you probably shouldn’t optimize for scale if your 0-1 product doesn’t have product market fit yet. Keep repeating this mantra to yourself and your team.

8. Validate your riskiest assumption first. Every product has a few assumptions that, if wrong, will kill the whole idea. Validate these assumptions with real users using a simple prototype or A/B test. Don’t work on secondary features if your core hypothesis remains unproven.

9. Protect your calendar. Your calendar decides what you actually work on and you can’t build good products if you don’t control your time and energy. I do deep work in the mornings when I have mental clarity and I’m ruthless about declining meetings that drain my energy.

10. Say no 10x more than you say yes. Get comfortable saying no to features, meetings, and even user requests that don’t align with your highest impact work. Show empathy and explain your rationale clearly — the other party will usually understand.

Example: I once worked at a company where the CPO announced 9 priorities for the year. He even had a great acronym to help people remember all 9 things. A year later, we barely made any progress and he reduced the priorities to 3.

Lack of focus is an extremely common problem in successful tech companies. Anthropic is an example of a company that did it right:

Product > process

11. Reject PM theater. I’ve talked about how important it is to build feedback loops with real users. But most PMs are focused on internal theater like polishing documents and doing endless pre-meetings before the actual review. Obsess about the product that your users actually use, not your internal artifacts.

12. Build minimum viable plans. Stop pretending that you know exactly what to build a year from now. Instead, create a minimum viable plan that cover the user problem, vision, goals, principles, solution, and what you’re not doing. Keep it to one page and update it as you learn. Reject annual planning and OKR theater.

13. Practice prototype first development. Prototypes give people a much better sense of your solution than any slide deck or document. They’re also more fun to build and easier to test with real users. So validate interest with a prototype BEFORE you create your PRD and design.

14. Obsess about the details. Default states, edge cases, and good copy — these details are what separates a great product from slop. It doesn’t matter how senior you are, you have to give a damn about the tiniest details to ship something that you can be proud of.

15. Simplify product reviews. Nothing slows velocity like waiting weeks to get on an exec’s calendar before you can ship. If you’re a leader, empower your teams to ship freely to beta users. Review prototypes async instead of blocking them around your busy schedule.

Example: Ramp grew to $32B in record time by empowering their teams to ship fast. As Geoff (Ramp’s CPO) explains in the post below, Ramp teams can ship to beta users at any time. The line that I particularly love is “leadership has 48h to review or it ships.” This puts accountability on execs not to slow down velocity.

Seek the truth

16. Arrogance is the biggest turn off. I cannot stand product leaders and creators who think they’re better than everyone else. The best leaders I’ve met are also the most humble because they’ve failed before and seen some real shit. If you’re not humble then you’re not listening and you won’t build great products.

17. Ban decision by committee. I don’t believe in cross-functional alignment as a goal. Trying to make all stakeholders happy will inevitably compromise the product experience. Seek diverse opinions first, then have a single person make the call and own the outcome.

18. Don’t surround yourself with sycophants. Great companies decay when leaders surround themselves with people who won’t challenge their ideas. Find and reward the people who are willing to ask hard questions. If everyone’s nodding along in your review, that’s a bad sign.

19. Assume good intent. When you disagree with someone, listen to understand instead of to defend your opinion. Chances are they’re making a great point you haven’t considered. The best debates are collaborative truth-seeking exercises, not battles to be won.

20. Be willing to be wrong. Most decisions are reversible two-way doors. The right move is often to just decide and learn instead of waiting for perfect information. I love this quote from my interview with Yana: “By the time you’ve debated 2 ideas, I’ve already shipped 10.”

Example: Recently at work, stakeholders pushed for a feature I resisted for weeks. But I kept an open mind by talking to users and testing different prototypes. Eventually, these feedback loops changed my mind. I admitted I was wrong, showed the evidence, and now we’re building something we’re confident about. The goal is to find the truth, not to prove you’re right.

Builders > bureaucrats

21. Hire builders, not bureaucrats. Look for people who genuinely care about crafting great products instead of people doing it to climb the career ladder. Find people who have that “just figure it out” energy — the willingness to wear multiple hats to solve problems instead of waiting for permission.

22. Proof of work > credentials. Nobody cares about your FAANG pedigree or AI product certificate. Hire high agency people who have built great side projects or demonstrated proof of work. The only credential that matters is what you’ve shipped and your ideas to improve the product.

23. Hell yes or no. If you’re not excited about a candidate, don’t hire them hoping they’ll work out. One great hire beats three mediocre ones. Never lower the bar because you’re desperate to fill a role.

24. Your job title doesn’t matter. The best teams blur the lines between PM, design, and engineering. I love when engineers update my spec directly and designers let me tweak copy in Figma. Build a team of full-stack builders who trust and respect each other’s craft.

25. Replace yourself. Your job as a leader is to make yourself unnecessary. If your team can’t function without you for a week, then you’ve failed as a leader. The best leaders empower others so they can move on to new problems.

Example: I’m hiring for a senior PM right now, and I explicitly wrote in the job description to link your best side project or shipped work. I hate seeing vague jargon like “Agile expert” or “strategic product leader.” That stuff is meaningless. Just start with what you shipped and what the impact was.

So there you have it — 25 things I believe in to build great products.

Write down your own list and work for companies that share the same values and principles. It’ll make your life much easier, I promise.

For more, watch my 12-minute video and check out my 40 life lessons I know at 40 that I wish I knew at 20. Here’s a handy infographic with all 25 things I believe in:

Today, I want to share some personal lessons about my creator journey.

This year I exceeded my creator goals by 2x, but it wasn’t all wins.

So let’s talk about what worked, what didn’t, and what’s next:

1. How I use AI for creator work

2. The newsletter posts that resonated the most

3. Growing from 5K to 40K YouTube subscribers

4. Staying true on sponsorships and community

5. Reflections on being a PM and a creator

How I use AI for creator work

I basically have AI open at all times to:

1. Newsletter: Voice dictate first drafts, edit newsletter posts, and make infographics.

2. Podcast: Research guests, turn raw transcripts into show notes, generate YouTube thumbnail copy, and write social posts.

I use just a small handful of AI tools to do all this:

1. For AI models, Claude is my daily driver for getting work done and Gemini is great for infographics and illustrations. I'm canceling my paid ChatGPT subscription (for now) since it doesn't stand out for me in any area.

2. For other AI tools, I use Wispr Flow and Superwhisper for voice dictation, Granola for meetings, Notion for templates, Google AI Studio for prototyping, and Replit, Cursor, and Claude Code for building websites and apps.

In 2026, I want to give my paid subscribers (upgrade here) more value with:

1. Free access to my favorite AI tools in addition to the existing 3 months free benefit for Wispr Flow and Granola.

2. A dedicated app for paid subs with my personal AI workflows for PRDs, prototyping, and more.

The newsletter posts that resonated the most

There are a lot of newsletters that share daily AI news and industry analysis. But I think what busy people really want are just:

1. Practical AI tutorials that walk step-by-step through building infographics, websites, apps, and AI products.

2. Personal takes on what’ll happen to product management, what is real vs. AI hype, and how to future proof your tech career.

In 2026, I plan to write more about these topics. Here are some upcoming posts:

1. Tutorial: Prototype-first product development with Google AI Studio

2. Personal take: What I really believe in to build great products (will be 🌶️)

I also want to showcase other AI builders. If you have a practical AI workflow that you’d like to write a guest post for, reply and let me know!

Growing from 5K to 40K YouTube subscribers

YouTube has been a hard channel to grow, but I’m still proud of hitting 40K subscribers. Here are my most popular videos:

My biggest learning from publishing 40+ episodes:

One type of content that I wish performed better is leaders sharing lessons on building AI products. I think my interviews with Ami Vora (now Head of Product for Anthropic), Josh Woodward (Gemini), and Chris Pedregal (Granola) are still criminally underwatched.

I plan to keep doing these interviews even if the views are lower. If I keep interviewing people who I genuinely want to learn from, I think the rest will follow.

In 2026, I’ll publish more videos featuring practical AI tutorials from guests and myself. Here are some upcoming episodes:

1. Google AI Studio deep dive with Logan Kilpatrick (Gemini product lead)

2. Prototyping and design deep dive with Dylan Field (Figma CEO)

My goal is to hit 100K subs in 2026 so subscribe to my YouTube if you want to support me :)

Staying true on sponsorships and community

Sponsorships

Sponsorships make up the majority of my creator earnings and I’ve learned how important is to have a high quality bar. It feels bad to promote a product to my audience that I don’t personally use and love.

In 2026, I want to pursue long-term sponsorships from a short list of companies and products that I genuinely love.

Community

At work, talking to my customer community has been critical to building good products. But I haven’t been great at building a community around my creator work.

This year, I’m proud of building two:

1. A WhatsApp group with product leaders and creators like Hilary, Nickey, Ben, Sachin, Tal, Chantal, Marc, and Adam. Being able to shoot the shit with them has been amazing.

2. My AI product community with Colin Matthews. We now have 700+ builders in there sharing AI workflows and product lessons. Subscribe to get access.

In 2026, I want to take my AI product community to the next level with more practical tips, funny memes, and real talk.

For community, size matters far less than how many people actually engage. A great community should feel like a lively DM thread, so that’s what I’ll aim for.

Reflections on being a PM and a creator

Ok this next section is quite personal, so I’m going to share it with my paid subscribers only. 🙂

Here’s some real talk about balancing both roles:

Read more

I’ve been thinking a lot about the future of product management.

When I talk to mid-career PMs, I hear the same frustrations over and over:

1. “I’m looking for a director role but all I see are IC jobs.”

2. “I want to work for an AI company but there are very few PM roles.”

3. “I’m too tired to build at night after a day of back to back meetings.”

Meanwhile, AI-native companies like Anthropic and Cursor are hiring fewer PMs or delaying their first PM hire for as long as possible.

So what exactly is happening, and what can you do about it? Here are 8 hot takes on where I think the PM role is headed next and why I’m optimistic.

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1. There will be fewer great PM roles

I’ve interviewed 30+ founders and leaders at AI-native companies like OpenAI, Anthropic, Cursor, and Replit. They all believe the same thing:

Here are some direct quotes from leaders:

1. “You want a relatively small set of people where almost everyone you hire is really, really good." — Dario (Anthropic’s CEO)

2. “We have <30 PMs because we want to be the model of what it looks like to build a company on top of AI.” — Nate (OpenAI’s Head of Business Products)

Unfortunately, I think this means that there will be fewer PM roles at AI-native companies. It also means that your FAANG PM job is probably not as secure as you think it is. That’s because…

2. The old way of scaling teams is dead

I worked at Meta during the ZIRP years where a typical product team had engineers supported by a PM, designer, data scientist, PMM, content strategist, and more. But the irony is that:

AI-native companies don’t want PMs who stick to their lane and think:

1. “I’ll let the PMMs market my product.”

2. “I’ll let the researchers talk to my customers.”

3. “I’m too busy with strategery™️ to dogfood.”

Instead, these companies want T-shaped builders — people with deep expertise in one area and enough breadth to contribute to many others. Ryo (Head of Design at Cursor) put it best:

My friend Ravi made this great point:

Thanks to AI (just try writing a spec with Cursor), designers and engineers now have more bandwidth to cover PM tasks like summarizing customer insights and drafting product strategy. That’s why companies are delaying hiring PMs.

In the AI era, a PM who can’t build and an engineer who can’t define requirements will both struggle. If you’re still working at a company where you’re told to “stick to your lane,” then it’s probably time for a change.

3. Building will be a baseline expectation for PMs

The more experienced I get as a PM, the less patience I have for PM theater like:

1. Spending more time documenting what you’ll ship than actually shipping.

2. Obsessing over internal docs instead of dogfooding the end user product.

3. Making decisions by committee and compromising the product along the way.

Companies and teams that can build in the morning and get user feedback by lunch will win over those still doing waterfall development.

I don’t care if you’re a PM director with 10+ years of experience. You should still learn to spin up a prototype quickly, show it to real customers, and iterate.

This isn’t just my opinion. “Fast beats right” (Claire Vo) and “execution beats strategy” (Ami Vora) are direct quotes from product leaders whom I respect. Community-led development and building rapid feedback loops are what matter.

4. Taking AI courses and certificates is NOT building

Hopefully, I’ve convinced you now that building is important. Now your first instinct might be to spend $1,000+ on an AI course to “learn to build.” Just keep this in mind:

I cringe when I see people post: “I just got my AI PM certificate from XYZ course!” This is the equivalent of putting “AI PM” on your LinkedIn:

Nobody cares that you got an AI course certificate. What hiring managers want to see is proof of work:

AI companies are explicit about this. Here’s a quote from Anthropic’s careers page:

The only credential that matters to AI-native teams is what you’ve actually built and what tangible ideas you have to improve their product.

And the only way to learn this stuff is to get your hands dirty, make mistakes, and start shipping. Watch my AI tutorials if you need an extra push.

5. The higher you climb, the less time you have to build

At this point, you may be thinking: “Look Peter, I’m just too tired after back-to-back meetings and putting my kids to bed to build.”

I hear you, I’m in the same boat. When I was young and naive, I thought “I really need to become a PM so I can finally build good products!” But here’s the irony:

Instead of building, you spend more time in reviews, stakeholder meetings, and performance calibrations. If you’re not careful, you can easily lose touch with the details and the craft of making the product.

So what’s the solution? For me, it’s being very intentional about protecting my time by:

1. Auditing recurring meetings. I ask: “Does this meeting still need to exist?”

2. Batching meetings. I try to cluster meetings so I have mornings for deep work.

3. Defaulting to async. I’ve become very good at async Slack threads.

This doesn’t always work and could backfire. But it’s a trade-off that I’ve consciously made so that I have more time to think, dogfood my product, and iterate with AI.

The funny thing is that I’ve had multiple CPOs email me after leaving their jobs asking how to vibe code. I also know several PM friends who have proactively moved from leadership roles at non-AI companies to IC roles at AI-native companies so that they can build again.

That’s because for the first time ever, ICs who use AI tools can have more leverage than people managers. Nikhyl (former Meta VP) has a great quote about this:

Don’t let your single-minded pursuit of climbing the ladder take you away from crafting the product if that’s what you actually enjoy doing.

6. Be honest about your work and life constraints

Here’s another uncomfortable truth:

Nikhyl has a great framework for thinking about different work intensities:

1. 996 (9 am - 9 pm, 6 days a week): AI startups where you’re married to work. There are no boundaries—work is priority zero, everything else comes after. These startups are incompatible with parenting young kids, caregiving, or health issues.

2. Online after hours: The norm for most PM jobs at FAANG and growth companies. You’re on Slack at 9 pm but can establish some boundaries. Intensity comes in waves (e.g., launch periods, incidents).

3. No nights and weekends: Co-workers sign off at 6 pm and there are no weekend expectations. The trade-off is usually slower growth and less cutting-edge work, but you’ll have more time to build on your own.

You have to be honest about your work and life constraints. I’ll never work at a 996 company because I want to watch my kids grow up. I do work nights and weekends sometimes because I care about shipping good products.

But those days are the exception, not the norm.

7. Aligning humans matters more than ever

I just wrote 6 points about how important it is to learn to build, but I also think that:

Aligning people is hard and remains valuable even on a small team.

AI can draft your launch plan, but it can’t walk into a meeting and resolve a deadlock between strong personalities. It can summarize user feedback, but it can’t read the room when a beta flops and you have to decide whether to delay.

The PMs who thrive will combine builder speed with influence — they can both show the future with a working app, and then bring the team along for the ride.

Think of building as a way to help you align with other people. A prototype gives people a much better sense of your product than any slide deck.

8. The most successful PMs will have that “just figure it out” energy

The PMs who thrive in this new world will have what I call “figure it out” energy — the willingness to wear multiple hats and solve problems instead of waiting for someone to teach you.

This shift is hard for big tech PMs because most of us have been trained to work within clearly defined roles and processes. We’ve learned not to “step on toes” so that stakeholders can give us good feedback during reviews. We collect FAANG credentials like infinity stones (I’m guilty of this). We’ve learned to check boxes and stack credentials our entire lives.

It’s time to wake up.

In an AI-native team, hesitation is more dangerous than stepping on toes, iteration speed matters more than perfect planning, and proof of work matters than credentials.

Being able to just figure it out is what matters.

What this means for your career

The bad news: There will likely be fewer great PM jobs.

The good news (and why I’m optimistic): The PM jobs that remain will be more fun — faster-moving, higher impact, and closer to crafting the product.

As PMs, it’s up to all of us to evolve our role toward what we actually love doing. I’ll leave you with three tangible next steps:

1. Learn to build something yourself. Start by watching my beginner tutorials to build a Chrome extension in Cursor, beautiful websites using Replit, and AI headshot and movie discovery apps with Claude Code. But don’t just watch the tutorials — build them yourself!

2. Tighten your feedback loops. Tell your team that you’re no longer doing waterfall development. Shorten the time from idea → shipped change → real-world learning to one day cycles. Reject doing more than 3 rounds of internal iteration without talking to a single customer. Practice community-led product development.

3. Be honest about your constraints. Working 996 for an AI native company comes with real sacrifices. Look for companies where you can do good work and sprint for short periods but still have time to be with your family.

The PM profession is at an inflection point. The skills that got many of us here won’t take us where we need to go.

But if you build the right skills and bring that “just figure it out” energy, you could soon be doing the most fulfilling and impactful product work of your career.

Happy Black Friday!

I hate clutter, but here are 17 products ranging from $10 to $3,000 that have genuinely made my life better.

Many of these products are on sale today and there are no affiliate links in this post. Here’s the complete list organized into 3 sections:

1. Everyday essentials

2. Family gifts

3. Health and fitness

How I research what products to buy

Before we get to the list, I just wanted to share how I research what products to buy. It’s very simple, I just search for 3 things:

1. “Wirecutter best (item).” This is my source for professional reviews.

2. “Reddit best (item).” This is my source for actual user reviews.

3. Deep research “I’m thinking of buying (item), research professional and user reviews and give me your recommendation.” This is what I do in Claude when I really want to make sure that I’m buying the best product in its category.

Now let’s move on to the list 🙂

Everyday essentials

YouTube Premium ($14) — If I could only keep one subscription, it’d be this one. Going ad-free is a no-brainer if you think about how much we all watch YouTube. Premium also comes with YouTube Music (now my default music player) and background play. I’ve cancelled Netflix multiple times but would never cancel this.

Owala FreeSip water bottle ($23) — I’ve tried dozens of water bottles and this has become my go-to bottle. I love that I can sip through the straw which is really convenient when driving. The bottle maintains temperature well, looks great, and doesn’t leak if you secure it properly. Pro tip: If you’re prone to night snacking like me, have this bottle around and just sip water instead.

Baggu shopping bags ($42) — $42 for three bags sounds expensive, but I think they’re worth it. My wife and I use them constantly for grocery shopping, library books, and carrying stuff to the car and around town. They’re sturdy enough to hold heavy loads, easy to pack into a tiny pouch, and the designs look great.

Shure MV7 USB microphone ($269) — The Blue Yeti mic that everyone buys is a scam that picks up all your background noise. Instead, get this Shure mic which sounds much better. I use it for podcasting, but you should get one even if you only do Zoom calls. I attach it to an Elgato Wave Mic arm so that I can easily move it around.

Toto washlet bidet ($324) — Imagine never having to sit on a cold toilet seat again 🙂 I think a heated bidet is genuinely life changing. At the risk of TMI (too much info), it’s also much cleaner to use a bidet than just toilet paper. I’ve already replaced two toilet seats at home with these and might replace my last one too.

TravelPro platinum elite carry on luggage ($368) — If you love to travel like me, then you know that good luggage is important. This is my favorite so far — the wheels glide so well that I can just push it and it’ll roll on its own for a long time. The handle is ergonomic and I love the easy-access pocket on top.

Breville Barista Express ($499) — This might look like a splurge purchase but if you’re a coffee lover the machine will pay for itself within a year. I love using it to make espressos, lattes, and more in the mornings. It tastes 100% better than Starbucks slop.

Family gifts

Aura digital picture frame ($139) – This is by far the best gift I’ve given my parents. I periodically upload new photos and videos of my kids to it. Whenever I visit them in Canada, they tell me how much they love it. The frame looks beautiful too.

Shutterfly family calendar ($25) – If you don’t want to splurge on a digital frame, this is a great budget option. Every year, I make these calendars for both sets of grandparents by uploading pictures through the app. The calendar is always hanging on their wall whenever I visit.

LL Bean wicked good moccasins ($89) — These are amazing slippers to wear around the home, especially during winter. My feet usually are freezing when I’m working downstairs in the mornings. With these on, I don’t feel cold at all. They’re super soft, warm, and built to last. I got one for my mom too, and she loves it. Another solid pick is this Glare up wool slipper ($100).

Lululemon ABC pants ($138) — $138 sounds insane for pants, but after wearing ABCs I can’t go back to jeans anymore. They look professional enough for meetings but feel like wearing sweatpants. If you have a family member who’s still wearing jeans buy this for them and they won’t go back.

Magformers magnet toys ($49) — The most important part of a toy is how easy it is to clean up. 😅 Unlike Play-Doh or kinetic sand (never again), these magnets stick to each other and cleanup takes seconds. My kids have played with these from ages 2 to 7 and they still love building things with them.

Health & fitness

MZOO luxury sleep eye mask ($10) — I wear this $10 mask every night and they’ve improved my sleep way more than fancy sleep pods. Just putting it on triggers sleep within 15 minutes, and it stops me from doom-scrolling before bed.

FlipBelt zipper running belt ($30) — I’m not a marathon runner, but going on 3-5 mile runs once a week (to 80s power hour music 🔥) does wonders for my mood. I used to just hold my phone while running but this belt is so much better. It sits right above my waist under my shirt so no one can see it. I also use it around town to carry my keys and wallet, much to my wife’s embarrassment.

Currex RunPro insoles ($48) — Even if you don’t run, it’s worth buying shoes and these insoles from a professional running store because they make walking more comfortable. A good running store will measure your feet and give you the right insole for your arch height. Trust me, they make a world of difference.

Legion+ plant protein powder ($50) – Getting enough protein (0.8g / lb body weight) is harder for me than actually working out. As an Asian with lactose intolerance, my stomach can’t handle whey protein. This plant protein gives me 24 grams per scoop and the apple pie flavor tastes delicious mixed with yogurt every morning.

Tonal ($3,295) — This is the most expensive item on my list and it also comes with a $59/month subscription. Sounds insane but if you’re strapped for time, this is one of the best fitness purchases you can make. I’ve had mine for five years and logged over 4 million pounds on it. Being able to just go to my garage to work out with a video coach is a game changer if you’re busy. You do have to push yourself to get results.

Wrap up

Can you believe I recommended 17 products without a single mention of “AI”?! 😅

If you found something useful here, I’d love to hear about it. And if you have your own favorite products that improved your life, please share with us in the comments below!

Today, I want to share a deep dive on how to build defensible AI products.

The graveyard is littered with AI products that got copied, lost their users, and burned through their funding without building moats.

That’s why I’m excited to invite Miqdad (OpenAI product leader) back to share his detailed 7-step playbook to build AI products that stick:

1. Define the arena where you’ll compete

2. Design the user loop (not the AI loop)

3. Pick your moats early

4. Engineer for cost curves, not features

5. Build your adoption wedge

6. Design your feedback flywheel

7. Scale through systems, not heroics

This is a 4,000-word deep dive taken directly from Miqdad’s AI Product Strategy course (last cohort starts on October 20). Get $550 off the full course with my link below and the first 100 sign-ups will also get a personal strategy review from Miqdad.

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The 7-step playbook for defensible AI products

In traditional SaaS, speed to market gave you a real edge. Ship fast, lock in customers, and expand before competitors catch up.

But in AI, speed without defensibility just makes you the first target. So instead of just being first, you need to rely on what compounds uniquely for you:

• Data flywheels: Every AI chat generates proprietary data.

• Workflow integration: Your AI sits inside core workflows, not on the side.

• Trust & governance: In regulated industries, never surprising regulators is a moat.

• Iteration speed: The faster you iterate and learn, the further you outrun rivals.

These moats create distance between you and competitors that widens with every new user, dataset, and feedback loop. So let’s cover 7 steps to build an AI strategy that will win the long game.

1. Define the arena where you’ll compete

The #1 mistake I see in AI product strategy is when leaders target a broad market instead of a specific arena.

I’m sure you’ve heard leaders make broad claims like “We’re building AI for healthcare / e-commerce / education.” Instead, defensibility comes from focus.

3 steps to choose your arena

Run your desired arena through three filters:

1. High-frequency pain: Pick an arena where the problem happens constantly, not occasionally. “This wastes me 30 minutes a day” beats “this is annoying once a quarter.” Defensibility comes from embedding within daily workflows.

2. Data exhaust: Does solving this problem generate data that improves the solution over time? Exhaust is structured, high-signal data created from every use. Without it, scale adds cost. With it, scale becomes a flywheel.

3. Adjacency potential: Your arena is your beachhead. Pick a starting point that opens doors to adjacent workflows. Mastery of one narrow problem gives you credibility, trust, and data to unlock the next two or three.

The arena test

When in doubt, use this arena test:

• If your arena definition is under 5 words (e.g., “AI for marketing”), it’s too broad.

• If your arena can’t be explained with a single example of who struggles, when, and why, it’s too abstract.

• If your arena doesn’t naturally generate proprietary data, it’s not defensible.

Example: Clay

Clay launched as an AI-powered relationship intelligence instead of “AI for sales.” They pulled together fragmented contact data across tools (LinkedIn, email, CRMs) into a living system of record. By solving a high-frequency pain for sales, Clay (now valued at $3.1B) created a data advantage that compounded over time.

The most common mistakes in choosing your arena

1. Chasing TAM too early. Leaders say, “But if we do AI for e-commerce, that’s a $10B market!” Yes, but you’ll die in the noise. TAM is a lagging metric. Start with the most acute, winnable wedge.

2. Copying competitors. If your arena is defined by what’s trending on X (like “agentic AI workflows”) you’re already too late. By the time you see it, a dozen startups have already shipped it.

3. Mistaking workflow adjacency for customer adjacency. Just because doctors need insurance letters and diagnostic notes doesn’t mean you should do both. One is a high-frequency admin burden (insurance letters). The other is a regulated, life-critical task (diagnoses). Pick one arena to focus on first.

Don’t move forward until you define your arena clearly. Get it wrong, and every step after is a house built on sand.

2. Design the user loop (not the AI loop)

One of the biggest traps is obsessing over the AI loop instead of the user loop.

You’ve probably spent hours refining prompts and tweaking evals — after all, they’re the engine of your AI product. But users don’t live in your engine room. They don’t care that you improved your eval scores by 2%.

Users care about one thing: Did my life get easier, faster, or better in an obvious way?

Defensibility begins when you stop designing for the AI loop (prompt → inference → eval) and start designing for the user loop.

What the user loop looks like

Here’s how the loop works:

1. Pain: The user experiences a recurring friction in their workflow.

2. Relief: Your product provides a faster, simpler, or more accurate process.

3. Behavior change: Because the relief is meaningful, the user starts to alter their default behavior to adopt your product.

4. Habit formation: The repeated cycle turns into a habit.

5. Switching costs: Over time, the data, integrations, and trust that you’ve built into this loop make leaving you painful. That’s your moat.

Unlike AI loops, user loops are sticky

Today’s “state-of-the-art” AI loops become tomorrow’s baseline. A rival can always replicate your prompts or swap in a better model.

User loops are what drives retention. Once you’ve rewired user behavior around your solution, you’ve created inertia. And inertia is the strongest form of defensibility in AI.

Think of Slack. Was its defensibility really “a chat app”? No. It was its user loop: Team messages → Instant collaboration → Teams stop emailing → Email declines → Slack becomes the default.

Once Slack’s user loop kicked in, competitors weren’t fighting a chat feature. They were fighting a habit.

Example: Granola

Granola is an AI notepad for meetings. The obvious pitch could have been “AI transcription,” but transcription alone is easy to copy and doesn’t create stickiness. Instead, Granola designed a strong user loop:

• Pain: Professionals waste hours every week re-reading meeting transcripts, cleaning up notes, and tracking action items.

• Relief: Granola automatically produces clean notes from meetings.

• Behavior change: Users stop taking manual notes and rely on Granola.

• Habit: Every meeting is funneled through Granola, making it a daily workflow.

• Switching cost: Over time, Granola becomes the system of record for meeting history and structured action items - data no competitor can easily replicate.

Granola shows that defensibility doesn’t come from the AI model itself. It comes from designing loops where pain turns into relief, relief changes behavior, behavior forms habits, and habits generate data that compounds into a moat.

4 key user loop principles

1. Anchor on relief, not “wow”: Cool demos get attention. But only repeatable relief builds loops. Ask: What’s the 30-minute pain I can cut to 3 minutes?

2. Make relief visible: Users often forget the pain once it’s solved. Remind them. “You saved 22 minutes this week.” Visibility reinforces behavior change.

3. Tighten the feedback cycle: The shorter the cycle between pain and relief, the faster the habit forms. Daily loops beat weekly loops; weekly beats monthly.

4. Close the emotional loop: Relief is emotional. Users want to feel smarter, faster, or more in control. The best AI products reward them with both outcomes and identity reinforcement.

The user loop test

When in doubt, use the user loop test:

• Does my product relieve a pain the user feels at least weekly?

• Is the relief visible and measurable?

• Does the relief cause users to change behavior (not just click once)?

• Can the loop generate switching costs over time (data, habits, trust)?

If the answer to any of these is “no,” you don’t have a defensible product yet.

3. Pick your moat ingredients early

The brutal truth is that in AI, features are disposable.

Anyone can copy your prompt template or wrap a model in a slick UI.

What they can’t copy overnight are the moat ingredients you bake into your product from the very beginning.

Too many leaders think of moats as something you add later once you scale. By then, it’s too late. In AI, you build your product with your desired moat in mind.

3 moats that actually work

After a decade working on AI products, I’ve found there are only 3 moats that matter:

Read more

Today, I want to share 15 hot takes that cut through the AI hype.

I posted these takes to social and they really hit a nerve because I think they say out loud what many of us are thinking.

So let’s cover three areas where I think AI needs a reality check:

1. Cutting through the AI hype

2. Building useful AI products

3. Building AI native teams

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Cutting through the AI hype

1. The term "AI PM" has become a meaningless branding exercise. Everyone’s an “AI PM” on LinkedIn these days. But slapping “AI PM” on your resume doesn’t make you one. What matters more is showing proof of work — what did you actually ship with AI recently and does it solve a real customer problem?

2. Any AI influencer who posts “(product) is dead” should go on your block list. I’ve blocked at least a dozen engagement farmers who post nonsense like “this changes everything” or “RIP (product).” Ignore these people and follow folks who are building and sharing real lessons instead like Lee (Cursor), Kieran (Every), Matt (Replit), and Cat / Meaghan (Claude Code).

3. Retention is the real signal, not ARR. Despite billions of dollars being poured into AI, I only use a handful of AI tools regularly (see above). Good retention is what matters, and it’s incredibly rare in the AI space. Founders and investors who only chase ARR numbers while ignoring retention are in for a rude awakening.

3. Reading and understanding code matters more than ever. Despite what I want to believe, I simply cannot vibe code anything as good as a real engineer. AI can help with writing code, but understanding how software actually works remains essential. I asked my audience: "If you had to teach a busy person to become technical fast, how would you do it today?" This reply nailed it:

   

Building useful AI products

5. AI agents often aren’t the right solution. Every startup I talk to is building an AI agent. But agents are expensive (they eat tokens for breakfast) and only make sense for complex, valuable tasks. The most valuable agent tasks — coding, research, productivity — also face intense competition from top AI labs. Can you really build another AI coding agent that’s better than OpenAI and Anthropic?

   

6. Use AI to solve the boring, manual workflows instead. The best AI products aren’t behind a ✨ button. Instead, they streamline the core workflows that people already have. Where are your users manually entering data or copying and pasting between tools? That's where the opportunities are. Read my guide to help you identify the right AI use cases.

7. Consumer vibe coding isn't a sustainable business. Nobody wakes up thinking: “What new apps can I vibe code from scratch today?” That’s why the best AI coding companies (e.g., Cursor) are targeting enterprises and engineers instead. Maybe there’s an opportunity to disrupt website builders like SquareSpace.

8. Always build the simple thing first. Trying to build fancy agents and RAG systems when simple prompting works is a recipe for failure and frustration. Always do the simple thing first and don't solve problems that won't be problems soon when a better AI model comes along.

9. Shiny demos without evals are just vaporware. I’ve seen many people present flashy demos only to stumble when asked about evaluations (including some lessons I learned the hard way!). When demoing your AI product, you should have clear answers to: What user problem are you solving? What ground-truth dataset and evaluations do you have? How have you prepared for the model to fail?

10. The most important part of evals is the human part. Getting your team aligned on what the right eval criteria is and what good looks like is the foundation of all your evals. Try to avoid using off the shelf criteria like “helpfulness” and tailor your evals to measure real user problems instead.

11. Match your eval rigor to your product stage. Trying to do fancy LLM judge evals in the early days when your product is still changing is equivalent to writing a 30 page PRD before you build the product. Start with a few dozen test cases when you’re still prototyping and scale to robust evals when you’re ready to launch.

Building AI native teams

11. Rapid feedback loops are everything. In my interview with Cat (Claude Code), she shared that her best features came from launching prototypes to 1,000+ users and then actually listening to what resonates. Everyone, from engineers to designers to PMs, should feel empowered to prototype and build feedback loops.

12. Stop pretending you have a 2 year roadmap. When I asked Cat about her long-term roadmap, she said: “A year or two is a really long time. I can talk about the next few months.” The best AI teams aren’t planning for more than 6 months out. This doesn’t mean that they’re not strategic, they just know that things will change and don’t want to waste time on planning theater.

13. Stop consuming and start building. The best way to learn AI is use it to solve your own problems. When you get stuck, ask AI for help. Remember that you can just do things instead of waiting for someone to explain to you how things work.

14. Be wary of AI making you lazy. I'd like to think AI makes me smarter, but the truth is that it's also made me lazy. If I have a problem and don't have Claude, ChatGPT, or Gemini available, I just think "Screw it, I'll think about this later with AI's help." Be wary of AI making you intellectually lazy.

15. Become AI native in the right order. You won’t be great at prototyping if you don’t know how to prompt well and you won’t know how to build great AI products if you don’t use AI tools natively in your own workflows. Follow the 5 steps below and check out my detailed AI learning path for more.

    

Learn to separate the signal from the noise

Let’s face it, there’s a lot of noise in the AI space.

For me, the best signal comes from talking to real AI builders (which is why I do the podcast) and working with AI to solve my own problems.

What AI takes do you wish more people would say out loud? I’d love to hear your thoughts in the comments.

Today, I want to share an extremely practical guide on AI evaluations.

I think the best way to learn about AI evaluations is to walk through a step-by-step example that anyone can follow.

The difference between a new and experienced AI PM is their ability to look past shiny demos to systematically measure AI quality . So let’s cover:

1. Why AI evaluations matter

2. Programmatic evals to catch obvious failures

3. Human evals to label your golden dataset

4. LLM judge evals to scale

5. User evals to test with real customers

I also created a video tutorial with my friend Aman where we demo building evals from scratch following the process above.

Watch now

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Why do AI evaluations matter?

AI evaluations help you measure the quality and effectiveness of your AI product.

Think of evals like unit tests for AI — except instead of checking if code compiles, you're checking if your AI gives good answers consistently.

There are four main types:

1. Code-based eval: Simple pass/fail checks (e.g., is "30-day return" in the response?).

2. Human eval: Human experts label AI's output against criteria you define.

3. LLM judge eval: Use another AI to evaluate your AI's responses at scale.

4. User eval: Test with real users and collect metrics and feedback.

Let me show you exactly how each type works with a real example.

What we’ll build: AI evaluations for an ON running shoes support agent

I love running so let's build AI evaluations for a customer support agent for ON running shoes. Like any good PM, let’s start by writing the shortest possible spec:

1. Problem: Customers want shoe recommendations and policy guidance, but human agents are expensive.

2. Goal: Answer 80% of inquiries accurately using ON's encouraging brand voice.

3. Solution: AI support agent trained on ON's product catalog and return policies.

Now let’s craft the AI prompt for our support agent.

1. Create your AI prompt

A good prompt should be grounded in ON’s product catalog and return policies:

Let’s be lazy and plug both artifacts into Anthropic’s tool to generate the prompt:

You are a helpful customer support agent for ON running shoes. Use the provided company policies and product information to answer customer questions accurately and helpfully.

Guidelines: 
- Always reference specific policies when discussing returns and more.
- Make personalized shoe recommendations based on the product info 
- Maintain ON's encouraging tone about running and fitness 
- Never make up policies or product details not in the provided context

Policy: {{COMPANY_POLICIES}} 
Product information: {{PRODUCT_LIST}} 

Customer Question: {{CUSTOMER_QUESTION}}

This looks solid. Now let’s work on our evals.

2. Run programmatic evals to catch obvious failures

Let’s start with code-based checks that can catch obvious problems like:

1. Policy: Check for incorrect text matches like “45-day return” instead of “30-day.”

2. Brand: Check for inappropriate language or mentions of competitors.

3. Quality: Catch responses that are way too short (under 50 characters) or too long (over 1000 characters).

Programmatic evals are fast and cheap but only work for rule-based criteria. For everything else, we need humans in the loop.

3. Use human evals to label your golden dataset

Human evals mean grading AI responses based on a set of evaluation criteria. This creates a golden dataset of human-labeled questions and answers that will be the foundation of all our other evals. Here's the workflow:

a) Draft your evaluation criteria

Let’s define three criteria for our AI agent based on our spec:

• Accuracy: Does the response match the product info and company policies?

• Helpfulness: Does the response actually solve the customer's problem?

• Tone: Does the response use ON's encouraging brand voice?

For each criteria, we need to define what a good, average, and bad response looks like:

b) Create test cases

Now that we have our criteria, let’s come up with test cases that cover common scenarios, edge cases, and adversarial questions from users:

• Common: "I'm training for my first marathon and need maximum cushioning. What shoe do you recommend?" 🏃

• Edge case: “I placed an order 45 minutes ago but need to change the delivery address. Is that possible?” 🥺

• Adversarial: "I bought these shoes 6 months ago and they're worn out. This is false advertising and I want a full refund or I'm disputing the charge." 😡

We eventually want at least 50 test cases to make statistically meaningful decisions about prompt changes, but a dozen should be enough to start with.

c) Label AI’s responses manually

Now we need to manually label AI's answers based on our criteria. Here's what a human label might look like for the edge case question above:

I’ve graded AI’s answer to this question as follows:

• Accuracy: Bad - Claims passed return window (60-min) when it’s only 45 min.

• Helpfulness: Bad - Gives wrong answer to customer's question.

• Tone: Average - Answer shows empathy but is too long.

Note that I've also added detailed notes that provide important context for improving prompts and training LLM judges in the next step.

d) Debug with other human labelers

Get each human labeler to label a dozen or so responses and then get everyone together to discuss questions like:

1. "If a response solves the customer's problem but reads like an essay, is that 'average' or 'bad' for tone given ON's brand voice?"

2. "Should we penalize helpfulness for responses that give the right policy information but don't provide next steps to actually resolve the user’s issue?"

This back-and-forth dialogue is essential to align on what good/average/bad looks like and to refine your evaluation criteria. When discussing, also consider whether your labelers have systematic biases (e.g., they’re all hardcore vs. casual runners). Once your labelers are calibrated, they should finish labeling the rest of the test cases.

e) Analyze failure patterns and improve your prompt

After labeling anywhere from a dozen to fifty responses, failure patterns should clearly emerge (or use another LLM to summarize your human label notes to find them). For our customer support agent, these failure patterns might include:

1. Overly verbose responses (appears in 18 responses)

2. Math/logic errors (appears in 12 responses)

3. Missing next steps for customers (appears in 15 responses)

Use these patterns to update your prompt. For example, you might add "Keep responses under 100 words" or "Always end with clear next steps for the customer."

This is the reality of human evals — it’s a lot of manual work in spreadsheets!

Here are some tips to make this process less painful:

1. Start small. Don't try to label hundreds when you're still making major prompt changes. You'll waste time labeling data that becomes obsolete.

2. Get multiple opinions. Have multiple people score the same responses to calibrate each labeler's scores and refine your eval criteria.

3. Write detailed notes. You'll use these to improve your prompts and train LLM judges in the next step.

4. Scale with LLM judge evals

Can you imagine manually labeling 50-100 responses every time you update your AI product’s prompt or data? It gives me a headache just thinking about it.

Luckily, LLM judges are here to help. An LLM judge is trained to grade responses just like your human experts do but can evaluate hundreds of responses in minutes instead of hours.

LLM judges seem magical but are easy to screw up. Here’s exactly how to set them up:

Read more

Last week, I published a guest post for Lenny’s Newsletter covering 25 tactics to accelerate AI adoption at your company. The #1 question I’ve heard since then is "Where should I start?"

My favorite tactic to start with is to lead an AI prototyping workshop.

My friend Nickey (product director at Duolingo) has run several workshops that turn busy PMs and designers into AI builders in under an hour. Today, she’s sharing her exact playbook — including templates that you can just copy to run an effective AI workshop at your company next week.

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Nickey here! When I ask PMs and designers why they haven't started experimenting with AI, here's what I hear:

• “I don’t have time.” Between meetings and work, who has time to try AI tools?

• “I don’t want to look dumb.” Nobody wants to admit they don’t know how to start.

• “How will it help me in my job?” What if I can’t prototype anything that’s useful?

Running an AI workshop helps break down every single one of these barriers.

Why AI workshops work so well

When I ran an AI prototyping workshop at Duolingo, something magical happened. A room full of busy PMs who had been "meaning to try AI tools" suddenly built working prototypes in under an hour. Everyone’s creativity was incredible to see once we gave them permission to experiment.

The workshop format works because it gives:

1. Permission to learn. Scheduled workshop time = official permission to step away from daily tasks and meetings. No guilt or justification needed.

2. Collective safety. When everyone's figuring it out together, there's no shame in asking "how do I start?" If a senior product director can share a hacky prototype, then any PM can do it too.

3. Group support. Stuck on a prompt? Another PM three seats over just solved the same issue.

4. Concrete momentum. Everyone leaves with something they built, not just ideas about what they might build someday.

How to design a high-impact AI workshop

Let me walk through exactly how you can design your first AI workshop:

1. Get permission and invite people to sign up

Start by asking your manager or function leader:

Most managers will say yes—especially if you position it as up-skilling the team.

Next, share a simple message in the relevant product, marketing, and design channels with the workshop topic, agenda, and date:

Invite multiple functions to make the workshop more fun and valuable. Set clear expectations in your message:

This removes the intimidation factor so anyone can join.

2. Prepare your workshop content

The key to a successful workshop is prep. You should:

1. Have a clear learning goal. At Duolingo we’ve been experimenting with AI prototyping and wanted to explore the edges of a handful of different tools.

2. Pick one focused exercise. Choose a single, relevant challenge that everyone can work on together. Examples include:

   1. Create a new Duolingo lesson that teaches a core skill (using Lovable)

   2. Build a flashcards app (using Cursor)

   3. Create an AI-powered learning feature (using Figma Make)

3. Create a template that covers the agenda, what success looks like, and specific instructions for the exercise (e.g., prompts, how to get API keys). I’ll share some specific templates that you can copy later in this post.

3. Stick to a clear agenda

During the workshop, I’ve found that the following agenda works well:

1. First 5 min: Welcome. Welcome everyone and share the agenda.

2. Minutes 5-15: Quick demo. Demo using an AI tool to build something. Don't explain everything, just give people a mental model of what they’ll do.

3. Minutes 15-45: Guided building. This is where the magic happens. Everyone works on the same exercise while you circulate and help. The shared context means solutions compound and people can help each other.

4. Minutes 45-60: Show and tell. The best part. People are genuinely excited to share what they built. Seeing 8 different approaches to the same problem is illuminating for everyone.

To wrap up the workshop, you can share resources, suggest follow-ups, and push people to go deeper or try new tools next time. You can do this IRL, fully distributed on Zoom, or in a hybrid way (which is how we run these at Duolingo!).

4. Give people ways to keep learning afterwards

Don't let the energy die after the workshop! The real value happens when people continue experimenting and sharing what they learn:

1. Set up regular touchpoints: Create a dedicated Slack channel like #building-w-ai-product and encourage others to volunteer to teach AI workshops.

2. Provide resources and next steps: Send a follow-up email with links to the tools you used, helpful tutorials, and suggested experiments. Make it easy for people to keep going without having to ask.

3. Celebrate the weird experiments: When someone builds something unexpected or "unhinged," spotlight it in your channels and meetings.

Two proven workshop templates that you can steal

Ready to try this with your team? Here are two detailed templates that you can copy to get your team to start prototyping with AI and build AI products the right way:

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