Love life. Bullish on AI. Product at 4x startups. 2x founder.
Seattle, WA
Joined August 2009
- Tweets4.2K
- Following655
- Followers777
- Likes1.8K
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."
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.
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.
Irina Menn retweeted
We are witnessing the nervous system of software development waking up. When agents handle the coordination tax of merging, humans reclaim cognitive bandwidth for high-level strategy. True leverage is automating the handshake so judgment can focus on the blueprint.
Irina Menn retweeted
TL;DR: Technology increases work, and AI is no exception.
A super interesting new study from Harvard Business Review.
A 8-month field study at a US tech company with about 200 employees found that AI use did not shrink work, it intensified it, and made employees busier.
Task expansion happened because AI filled in gaps in knowledge, so people started doing work that used to belong to other roles or would have been outsourced or deferred.
That shift created extra coordination and review work for specialists, including fixing AI-assisted drafts and coaching colleagues whose work was only partly correct or complete.
Boundaries blurred because starting became as easy as writing a prompt, so work slipped into lunch, meetings, and the minutes right before stepping away.
Multitasking rose because people ran multiple AI threads at once and kept checking outputs, which increased attention switching and mental load.
Over time, this faster rhythm raised expectations for speed through what became visible and normal, even without explicit pressure from managers.
Irina Menn retweeted
for me the odds that AI is a bubble declined significantly in the last 3 weeks and the odds that we’re actually quite under-built for the necessary levels of inference/usage went significantly up in that period
basically I think AI is going to become the home screen of a ludicrously high percentage of white collar workers in the next two years and parallel agents will be deployed in the battlefield of knowledge work at downright Soviet levels
Irina Menn retweeted
Being a VC looks great on paper. Until you actually try it.
Cristina Cordova spent a year at First Round Capital and realized it wasn't for her.
The job is fundamentally different from operating. You're battling other firms to lead deals. You give advice, then watch from the sidelines while founders do the actual work.
For someone who loves executing? That's not enough.
"Good luck implementing that" isn't where she wanted to be. She wanted to be the one implementing.
A lot of great operators can become great investors. But not everyone should.
Some people are wired to be in the arena. To do the work, not just advise.
Are you an operator or an advisor at heart?
Irina Menn retweeted
AI is becoming the first place people go to research products. Not Google. Not Amazon. AI.
If your brand doesn’t show up in AI answers, you don’t exist in the buying decision.
Here’s the opportunity most brands are missing. AI shopping research often skips Amazon, which means smaller brands can get recommended without fighting massive marketplaces.
But only if AI can understand you.
That means clear product data, real customer reviews, accurate specs, and up to date pricing and availability.
AI is the new distribution channel. Either your brand is legible to it, or you’re invisible.
#AISearch #AIShopping #FutureOfSearch #DigitalMarketing #BrandVisibility
Irina Menn retweeted
There’s never been a more exciting time to be in venture capital - not just because of the amazing companies getting started, but also because the way venture is done is changing completely.
We're hiring a product engineer to join @GreylockVC's investment team and build an AI system to drive VC decision-making.
The role is perfect for a self-directed product manager who thrives on building and coding, or a lead software engineer.
You’ll have the opportunity to join the investment team at Greylock and build an advanced AI decisioning system with multiple inputs including people, markets, technology, and business.
This is an in-person role based out of Greylock’s San Francisco office. Please fill out this form if interested:
lnkd.in/guJNZxpj
Irina Menn retweeted
Sequoia just called the end of an entire go-to-market era and most SaaS companies won’t realize what hit them for 18 months.
Product-led growth was built on one assumption: humans would try the software. The entire playbook since 2010 optimized for human discovery. Beautiful landing pages. Frictionless free trials. Viral invite loops. Slack, Dropbox, Zoom, Calendly. $200B+ in market cap created by winning the user’s first 5 minutes.
None of that matters if an agent is picking the software.
Claude doesn’t care about your hero image. It can’t be impressed by your Dribbble awards. It’s reading documentation, parsing user reviews, checking API reliability, and matching features to use case. All the surface-level polish that convinced lazy humans to click “sign up” becomes irrelevant.
The new PLG funnel isn’t landing page → free trial → activation → conversion.
It’s agent query → documentation scan → feature match → recommendation.
Which means the new moat looks completely different. You don’t need the best onboarding. You need the best documentation. You don’t need viral loops. You need structured data that agents can parse. You don’t need a beautiful UI for the first session. You need an API that an agent can actually call.
The companies that won PLG hired designers and growth hackers. The companies that win agent-led growth will hire technical writers and developer relations engineers.
And here’s the part nobody’s pricing in yet: agents don’t have loyalty. They don’t have switching costs. They’ll recommend Supabase today and something better tomorrow if the documentation is cleaner or the pricing is more transparent. The stickiness that made PLG so powerful, the network effects and learned behavior, doesn’t transfer.
Sequoia is telling you the entire distribution layer is being rewritten. The question is whether your product is optimized for human attention or machine parsing. Most are built for the wrong audience.
Sequoia partner @sonyatweetybird says we're going from the age of product-led growth to the age of agent-led growth.
"You see this most clearly if you're using Claude Code actively. It says, 'Hey, for a database, you should use Supabase. For hosting, use Vercel.' It's choosing for you, the stuff you should be using."
"Product-led growth brought us closer to the vision of 'best product wins,' but ultimately people are still lazy. They can't read all the reviews, and they kind of default to what looks cool on the website."
"Whereas your agent has infinite time to go and make these choices for you. It can go and read all the documentation, read all the user comments, and figure out [what you need] for your use case."
Irina Menn retweeted
AI agents will be a big part of how we shop in the not-so-distant future.
To help lay the groundwork, we partnered with Shopify, Etsy, Wayfair, Target and Walmart to create the Universal Commerce Protocol, a new open standard for agents and systems to talk to each other across every step of the shopping journey.
And coming soon, UCP will power native checkout so you can buy directly on AI Mode and the @Geminiapp.
Irina Menn retweeted
The entire robotics industry is about to compress a decade of progress into 18 months, and nobody’s pricing it in.
The hardware has been ready for years. Boston Dynamics had Atlas doing backflips in 2018. The bottleneck was never motors or actuators. It was that every robot behavior had to be hand-coded. Pick up a box? That’s one program. Pick up a bottle? Different program. Move the box from shelf A to shelf B in a warehouse with slightly different lighting? Start over.
Foundation models broke this completely.
Before VLAs, teaching a robot one skill gave you exactly one skill. Zero compounding. Zero transfer. A robot trained to fold shirts couldn’t fold towels without starting from scratch. The labor intensity of data generation meant robotics datasets stayed narrow, robots overfit, and small variations like object weight or table height caused failures.
Now a single Gemini Robotics model handles tasks it has never seen in training. Google’s On-Device model learns new behaviors with 50-100 demonstrations. Not 50,000. Fifty. That’s a 1000x reduction in the data requirement for new capabilities.
The speed implications cascade through everything.
First order: deployment timelines collapse. What took robotics teams 6-12 months of custom programming now takes days of fine-tuning. Second order: the addressable market explodes. Tasks that were never economical to automate suddenly are, because the integration cost dropped by orders of magnitude. Third order: the data flywheel accelerates. Every robot running Gemini Robotics feeds learning back into the foundation model. More deployments means faster improvement means more deployments.
Physical Intelligence raised at $2.4B because investors finally understood this. Boston Dynamics partnered with Toyota Research Institute to bolt Large Behavior Models onto Atlas. Every humanoid company is scrambling to either build or license the intelligence layer they don’t have.
The market is still valuing robotics companies on their hardware differentiation. But hardware is commoditizing. Boston Dynamics spent a decade perfecting locomotion, and now that’s table stakes. The value is migrating entirely to whoever owns the foundation model that generalizes across embodiments.
Google trained Gemini on the largest multimodal corpus ever assembled. Then they added physical actions as an output modality. That’s not a robotics company bolting on AI. That’s an AI company whose models now output motor commands.
The companies pricing this correctly are building around foundation model access, not around proprietary hardware. The companies pricing this wrong are still acting like the moat is in the mechanical engineering.
AGI moving into the physical world isn’t a 10-year prediction. Gemini Robotics shipped in March. The 1.5 version with chain-of-thought reasoning shipped in September. They’re iterating on a 6-month release cycle while hardware companies iterate on 3-year cycles.
The gap between software intelligence timelines and hardware development timelines is the entire trade.
Irina Menn retweeted
Founders underestimate how much narrative work the product needs. Shipping creates change. Marketing explains change. When explanation lags, customers experience motion as noise. Confusion quietly erodes trust even when the product improves.
Irina Menn retweeted
I was also a perfect parent before I had kids.
No parent of actual kids would ever make a post like this.
This quoted post is unavailable.
Irina Menn retweeted
1. Maximize who you are. Lean into all the aspects that make you different
2. Build relationships with winners
3. Solve problems that matter to you
Irina Menn retweeted
OK, hear me out on this one.
10 hour days. 100 minutes / hour. 100 seconds / minute.
Noon is 5:00. Midnight = 0:00 or 10:00.
Base-12/60 time is a Babylonian fossil.
Just because they didn't figure it out during the French Revolution doesn't mean it can't be done.
