Love life. Bullish on AI. Product at 4x startups. 2x founder.
Seattle, WA
Joined August 2009
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An abundant future does lie ahead. It is a great time to be a builder
I'm excited to launch Relational Capital, a $50m solo GP fund.
I spent the last 8 years as a founder, building @usekojo into the largest US construction procurement platform.
Now I'm backing founders at the earliest stages with $1m-$5m checks.
An abundant future lies ahead 🇺🇸
Jensen Huang's Tiny Microduck Robots Steal the Show at NVIDIA GTC 2026 youtu.be/Nt6d62vN7CU?si=6Rqq… via @YouTube
The challenge with this conversation is that Joe @joerogan is asking the boring question. The question isn’t what’s going to happen to all the jobs. Jobs will evolve alongside the evolving industry.
The more interesting question is: What will change in human needs with the advancement of automation and AI?
That’s the predictor of how the industry will evolve and the jobs will follow
Elon Musk knew it for some time: If your job is on a computer, AI is taking it like lightning.
Not “some jobs.”
Not “in a few decades.”
The digital ones.
Coding.
Email.
Answering the phone.
Anything that is just a person sitting at a screen.
He called it a supersonic tsunami.
If you move atoms, you last longer.
Plumbing. Welding. Farming. Cooking.
If you move pixels, you don’t.
Drivers are next.
Trucks. Ports. Anything a model can steer.
Working becomes optional.
Not universal basic income.
Universal high income.
Anyone can have the products and services they want.
That’s the benign ending.
He also said there will be a lot of trauma on the way there.
That’s the clip.
Not “AI will help you at work.”
AI replaces the work.
“Trying to be rational has helped my luck” - says Charlie Manger. “Rationality is a godsend…” lots of gold in this exchange:
- on @elonmusk and Tesla : “I would never buy it (Tesla stock), and never sell it short..”
- “Never underestimate a man who overestimates himself”
A billionaire sat in a room for 42 minutes and listed every psychological trick that makes people lose money. for free. the finance industry has spent thirty years pretending this recording does not exist.
he didn't sell a course. he didn't write a newsletter. he sat in a chair at 96 years old and explained why brilliant people do the dumbest things with their money. then he explained why they will keep doing it.
MBA programs charge $200,000 to teach behavioral finance. he covered 25 biases in one sitting. some of them are still not in any curriculum. he gave the entire framework away on camera.
the part nobody talks about: he called crypto antisocial. he said index funds will crush most managers. he said private equity is full of wretched excess. he said all of this in a room full of people who manage money for a living. nobody argued.
a hedge fund analyst at a top firm told me this is the first thing they send to anyone who joins the desk. not a book. not a model. a 42-minute video of a 96-year-old man explaining why you will be wrong and how to recognize it before it costs you everything.
40 million people have heard his name. almost none of them have watched him explain the 25 ways their own brain is working against them.
the lecture is free. he died the following year. it is in the video.
Irina Menn retweeted
We’re hiring.. ⛏️
.@travisk's pitch for why young people should join his new startup Atoms:
"I'm not out there pitching some lame-sauce dude who doesn't want to work... This is why Atoms is cool. You're not dropping an app in the App Store. You're automating a 2 million pound machine [in the Amazon] going 35mph carrying gold."
Rejection builds redirection and good things happen. I can relate
We asked Aaron Levie about the 1st time someone tried to buy Box. Box was four guys living in Berkeley when Yahoo's corp dev team called.
They sat around debating what number they'd take. The most they could imagine was $5 to $10 million. They agreed they'd definitely take five.
They drove down to Yahoo headquarters in a Nissan minivan that was falling apart, pitched their whole strategy, and about two weeks later got an email that said "it was really nice meeting you guys." No deal!
Love
Have you heard the term Physical AI yet?
I've just come back from the @AutomateShow with @workr_official and Physical AI was talked about on so many booths.
Every person I met selling it described it differently, which tells you a lot about how fast this space is moving.
In simple terms, Physical AI is AI that doesn't just live on a screen. It acts in the messy real world, where nothing is ever quite where it's supposed to be.
I like to think about it like the person who adapts the recipe on the fly when cooking a meal.
A recipe can't see that your pan is too hot, but a cook adjusts as she goes. Physical AI is like the cook, not the recipe.
This year there were lots of humanoids on site, and while they get all the headlines and the eye-watering valuations, I'm still a sucker for the quiet little robot arm doing one job beautifully in the corner of a factory.
That's what's genuinely working and solving problems for people in factories today.
#PhysicalAI #Robotics #FutureOfWork #Automate
And that is where the job of the Product Manager fits in.
The CEO of Take-Two, the company behind GTA, just said something the entire AI industry doesn't want to hear.
And he said it without being anti-AI.
Strauss Zelnick's argument is precise. AI is built on datasets. Datasets are backward-looking. Creativity is forward-looking. A model trained on everything that already exists cannot, by definition, produce something genuinely unexpected. And all hits, by their very nature, are unexpected.
Asset creation and hit creation are not the same thing. AI is getting very good at the first one. The second one is what actually makes money, builds franchises, and changes culture. Nobody has shown AI can do that yet.
The derivative property problem is real. You can clone GTA with existing technology. You could do it before AI. It would take 3 years and look identical. It still wouldn't sell. Because it isn't GTA. It's a clone of GTA.
And consumers, despite what the industry occasionally pretends, can feel the difference between something genuinely new and something assembled from the residue of things that already worked.
Thousands of mobile games ship every year. 0 to 5 hits get made. The same studios make them every time. The technology to make more games has been commoditized for years. It didn't democratize hit creation. It just flooded the market with more forgettable product.
The Silicon Valley thesis that AI unlocks game creation for everyone is true in the same way that cheap cameras unlocked filmmaking for everyone. They did. And the same 5 studios still make the movies everyone watches.
What Zelnick is saying, without quite saying it, is that the thing AI cannot replicate is taste. The instinct for what hasn't been done yet. The cultural antenna that detects the gap in the market before the data can see it.
Data tells you what people wanted. Hits tell people what they want next.
Those are different jobs.
Irina Menn retweeted
If you've adopted AI at your company but haven't seen any tangible results, read this 1990 article: "The Dynamo and the Computer" by Paul David.
When electricity first arrived, factories that "adopted" it barely got faster. They just swapped the steam engine for an electric one and ran everything else exactly as before: same machine layout, same workflow, same management. Electricity in, no real gains out.
The most common mistake with any new technology is to drop it into the old organization and then declare the transformation done.
The real leap came decades later, when each machine got its own small motor. Suddenly machines no longer had to be lined up around one central drive shaft. They could be rearranged around the actual flow of work.
The productivity gains didn't come from electricity. They came from REDESIGNING THE ENTIRE FACTORY around it.
AI is the same. Bolting it onto your existing process gets you a faster steam engine. The payoff comes when you redesign the work itself.
(link to paper in comments)
Irina Menn retweeted
I just got back from SF and I FEEL INSPIRED.
I spent 5 days with frontier AI model teams, AI startup founders, and 3 billionaires.
My takeaways:
1. I had lunch with 3 billionaires. All of them are buying SaaS companies and rebuilding them agent-first. They were deeply inspired by Bending Spoons and Ryan Cohen's eBay deal. Buy the company, cut the headcount, rebuild the tech, add agents, add features, make more valuable experience, raise prices.
2. The frontier model companies are hungry for usage data from the field. They can see API calls and token counts. They can't see the actual workflows. If you're deep in a niche using these models in ways the model companies haven't seen, that understanding is incredibly valuable. Usage intelligence is the new alpha.
3. Consumer AI is massively underbuilt. Every billboard in SF is either B2B inference infrastructure or vertical agent companies. The entire city is optimized for enterprise. Meanwhile you have companies like Cal AI doing $50M ARR in 18 months as a consumer app. I met with a cool few teams doing consumer AI (@paulscherer / @ekuyda)
4. MCP came up in literally every conversation. The companies exposing their product as MCP endpoints are getting pulled into deals they never pitched for. The ones that aren't are becoming invisible to agents. This is the new SEO. If agents can't find you, you don't exist. Building products for agents is the new zeitgeist in general.
5. Not uncommon for hot seed rounds to be $25-50 million valuations. I saw a Series A at $450 million
6. If I had a dollar every time someone mentioned "forward-deployed engineer" this trip I could have funded a seed round. It's the hottest role in SF right now. The person who sits between the agent and the customer, making sure everything actually works.
7. The mood around open source shifted. A year ago it felt like open source was chasing the frontier models. Now founders are telling me Gemma and DeepSeek are good enough for 80% of what they need at a fraction of the cost. The "which model do you use" conversation is being replaced by "which model for which task." Model loyalty kinda feels dead.
8. Voice agents came up more than I expected. Multiple founders told me voice is the interface for the next billion users. The billion people who will never type a prompt will absolutely talk to one.
9. The Obsidian community in SF is weirdly intense. Multiple founders showed me their vaults unprompted. Like showing someone your home gym. It's a flex now. The quality of your knowledge base (second brain?) is becoming a status symbol among builders.
10. Maybe it was just the people I met but the age of the founders is shifting. I met more founders over 40 this trip than any trip before and more founders under age 21 than ever before. Founders getting older and younger at the same time.
11. I spoke to a lot of fast-growing startups, VCs and frontier models who are hiring content creators right now.
12. The restaurant scene in SF is actually better than it's been in years. Founders are going out more. Alcohol is out, not surprisingly.
13. SF doesn't feel like the only place anymore. We all have access to the same frontier models. We all read the same X feed. A founder in NYC or Lagos is calling the same APIs as a founder in SoMa. So in the past it felt like SF was always lightyears ahead, doesn't feel that way anymore. It's okay not to live in SF and have BIG DREAMS.
14. The coworking spaces in SF are half empty but the coffee shops are packed. People want to be around people. I had a few startup ideas here....
15. Walking around the Mission I noticed something: the street-level businesses, the taquerias, the barbershops, the laundromats, none of them use any AI at all.
16. I heard the phrase "agent debt" for the first time. Like technical debt but for agents. When you hack together an agent workflow fast and never clean it up, the system prompts conflict, the memory gets polluted, the tools overlap. 6 months later the agent is doing weird things and nobody knows why lol.
17. Met a few people who carry two phones now. One for personal. One that's basically an agent terminal running Telegram or iMessage connections to their agent fleet.
It's always amazing to get that dose of inspiration in SF. I FEEL INSPIRED.
But I'm so happy to be back home, locked in and building.
We're 12-18 months into a shift that will take 15 years to play out. The urgency in every conversation was real.
What an incredible time to be building.
Makes sense and a huge opportunity for consumer robotics together with the residential construction industry to rebuild for the next generation of smart homes. Or maybe we should call them intelligent?
.@mcuban says humanoid robots won't last more than 5-10 years.
Instead, we'll "design the house to fit the robot, and design the robot to fit the house."
"You could create a house where the pantry, the refrigerator, and the washing machines were hidden behind the garage, if you even have a garage. That way you could redesign the house so that all the living space was for people."
From his appearance on the show in March.
Crazy talent recognizes crazy talent. The boundary-breakers usually don’t look optimized on paper because their brains are wired for first-principles thinking, not consensus thinking. The hardest part isn’t finding them — it’s creating an environment that doesn’t sand down their edges and lets them thrive
GameStop Makes $56 Billion Offer to Buy eBay - WSJ #bigmove wsj.com/business/deals/games…
For 27 years (or more?) this has been a vision and now it is a reality. When people talk about new category products, here is one
Imagine every pixel on your screen, streamed live directly from a model. No HTML, no layout engine, no code. Just exactly what you want to see.
@eddiejiao_obj, @drewocarr and I built a prototype to see how this could actually work, and set out to make it real. We're calling it Flipbook. (1/5)
Exactly right
Sequoia's thesis that the next $1T company will sell work, not software, is the most important reframe in AI right now.
The argument: if you sell a copilot, you're competing with every new model release. But if you sell the outcome — books closed, contracts reviewed, claims handled — every AI improvement makes your margins better, not your product obsolete.
The key insight most people miss: for every $1 spent on software, ~$6 is spent on services.
The entire SaaS playbook was about capturing the software dollar. The AI playbook is about capturing the services dollar — at software margins.
Not "AI for accountants." The AI accounting firm.
Not "AI for lawyers." The AI law firm.
The companies that figure this out won't look like SaaS companies. They'll look like services firms rebuilt on software infrastructure.
That's a fundamentally different company to build, fund, and scale. And most founders are still building copilots.
100% : “It seems that despite Silicon Valley’s sense that AI has made hard things easy, the most powerful ways to use agents is more “technical” than prior eras of software. Skills, MCP, CLIs, etc. may be simple concepts for tech, but in the real world these are all esoteric concepts that will require technical people to help bring to life in the enterprise” - there is lots to do
Another week on the road meeting with a couple dozen IT and AI leaders from large enterprises across banking, media, retail, healthcare, consulting, tech, and sports, to discuss agents in the enterprise.
Some quick takeaways:
* Clear that we’re moving from chat era of AI to agents that use tools, process data, and start to execute real work in the enterprise. Complementing this, enterprises are often evolving from “let a thousand flowers bloom” approach to adoption to targeted automation efforts applied to specific areas of work and workflow.
* Change management still will remain one of the biggest topics for enterprises. Most workflows aren’t setup to just drop agents directly in, and enterprises will need a ton of help to drive these efforts (both internally and from partners). One company has a head of AI in every business unit that roles up to a central team, just to keep all the functions coordinated.
* Tokenmaxxing! Most companies operate with very strict OpEx budgets get locked in for the year ahead, so they’re going through very real trade-off discussions right now on how to budget for tokens. One company recently had an idea for a “shark tank” style way of pitching for compute budget. Others are trying to figure out how to ration compute to the best use-cases internally through some hierarchy of needs (my words not theirs).
* Fixing fragmented and legacy systems remain a huge priority right now. Most enterprises are dealing with decades of either on-prem systems or systems they moved to the cloud but that still haven’t been modernized in any meaningful way. This means agents can’t easily tap into these data sources in a unified way yet, so companies are focused on how they modernize these.
* Most companies are *not* talking about replacing jobs due to agents. The major use-cases for agents are things that the company wasn’t able to do before or couldn’t prioritize. Software upgrades, automating back office processes that were constraining other workflows, processing large amounts of documents to get new business or client insights, and so on. More emphasis on ways to make money vs. cut costs.
* Headless software dominated my conversations. Enterprises need to be able to ensure all of their software works across any set of agents they choose. They will kick out vendors that don’t make this technically or economically easy.
* Clear sense that it can be hard to standardize on anything right now given how fast things are moving. Blessing and a curse of the innovation curve right now - no one wants to get stuck in a paradigm that locks them into the wrong architecture. One other result of this is that companies realize they’re in a multi-agent world, which means that interoperability becomes paramount across systems.
* Unanimous sense that everyone is working more than ever before. AI is not causing anyone to do less work right now, and similar to Silicon Valley people feel their teams are the busiest they’ve ever been.
One final meta observation not called out explicitly. It seems that despite Silicon Valley’s sense that AI has made hard things easy, the most powerful ways to use agents is more “technical” than prior eras of software. Skills, MCP, CLIs, etc. may be simple concepts for tech, but in the real world these are all esoteric concepts that will require technical people to help bring to life in the enterprise.
This both means diffusion will take real work and time, but also everyone’s estimation of engineering jobs is totally off. Engineers may not be “writing” software, but they will certainly be the ones to setup and operate the systems that actually automate most work in the enterprise.
Irina Menn retweeted
I saw a guy coding today.
Terminal 1: Claude Code
Terminal 2: Codex
He typed the same prompt into both.
Then stared at the screen for 60 minutes.
Like a psychopath.
Opened Cursor.
Read the 10k lines AI generated.
Like a Costco receipt checker pretending to verify every item.