Greg Isenberg

Want to be the most AI pilled company in your space? I’m picking 10 companies for my team to make you the most AI pilled company in your space in 2027. Here’s what we’ll do: 1. Set up a second brain for every employee (and train employees) 2. Set up a company brain 3. Turn your best workflows into skills 4. Connect your tools so agents can take action 5. Put agents on those workflows every day 6. Build the loop so the whole system gets smarter each week 7. Redesign your existing products so agents can use them 8. Build new AI native products with you (agents, MCPs, apps) 9. Set up agents that bring you more customers (outbound, ads, content) 10. Much more Basically, A lot of people read my tweet below and asked if someone could just set it up for them and I don't blame them. So, reply “interested” to this tweet, then head to http://latecheckout.agency (bottom of page is our email) and email us with your company/company size/what you're looking for. We’ll reach out to the 10 companies we think we can drive an absurd amount of value for.

A lot of people read this and asked if someone could just set it up for them and I don't blame them. So I’m picking 10 companies for my team to make you the most AI pilled company in your space by 2027. Here’s what we’ll do: 1. Set up a second brain for every employee (and train employees) 2. Set up a company brain 3. Turn your best workflows into skills 4. Connect your tools so agents can take action 5. Put agents on those workflows every day 6. Build the loop so the whole system gets smarter each week 7. Redesign your existing products so agents can use them 8. Build new AI native products with you (agents, MCPs, apps) 9. Set up agents that bring you more customers (outbound, ads, content) 10. Much more Reply “interested” to this tweet, then head to http://latecheckout.agency (bottom of page is our email) and email us. We’ll reach out to the 10 companies we think we can drive an absurd amount of value for.

Unfortunately, the AI song you swear you’d never listen to is probably going to end up on your playlist. The AI film you swear you’d never watch is probably going to end up in your top 5 fav of the year. You’ll like them, you’ll share them, and you’ll never know AI made them.

The truth is all marketing now is just code and tokens. So learning to market these days means learning to set up an agent with your data, creative tools and ad accounts. Set it up right and you get a top 0.1% marketer testing an absurd number of ideas every day:

The FAA nationwide ground stop reminds me that the best business to be in right now is cybersecurity

Making the most AI pilled company is the whole game: 1. make a second brain. one folder of markdown files on how you think, write and decide (obsidian) 2. talk to it. voice notes all day (wispr), and an agent files every fact, story and decision 3. make a company brain. customers, calls, numbers, pricing, playbooks, past decisions and why you made them 4. record every call and meeting (granola). the transcripts go straight into the brain 5. keep a facts file. true things about you and the company that agents are allowed to say. this kills made-up claims 6. keep a voice file. your 50 best posts, 20 you hate, and the words you never use 7. put all of it in git (github) so every change has a history 8. write one instruction file at the top (CLAUDE.md or AGENTS.md). read these files before any task, never overwrite, always date 9. give agents memory across sessions so they stop starting from zero (claude-mem, mem0) 10. pipe every data source into one warehouse your agents can query (airbyte → clickhouse). ads, analytics, CRM, payments 11. feed it data nobody else has. it's the only part a competitor can't download the skills 12. don't start from zero. install the good ones first (anthropics/skills, obra/superpowers, mattpocock/skills, addyosmani/agent-skills) 13. grab the marketing ones too (coreyhaines31/marketingskills, cody schneider's graphed skills for static ads and UGC, boringmarketing dot com skills) 14. map every workflow in the company and rank them by money made or saved 15. do each one once while talking out loud, and record it 16. have an agent turn the recording into a skill. when to use it, the steps, the checks, the mistakes to avoid 17. one skill per job. small and boring beats one giant prompt 18. connect your tools with MCPs so agents can act and not only advise (gmail, slack, stripe, linear, notion, posthog, supabase) 19. give agents a browser (browser-use, vercel agent-browser, claude in chrome) 20. give agents a computer of the...

This clip got a lot of people asking how they would build this in practice. So here is the full 49 minutes, where we walk through every tool, every prompt, the pricing, and the exact pitch to close a $25K deal. It’s free, so go build one this weekend: https://youtu.be/67BmkptgTms?si=_lPl-B4ldJ53qgw1

One of the best businesses to build right now: a Grok Bot for one industry. Pick a niche like dentists and build the AI agents for them so they don’t have to figure any of it out. The stack: - T3 Code + Opus 5.5/Fable 5.2 to build it - Paper to design every screen first - Orgo to give each agent its own computer - AgentMail for an inbox - AgentPhone for a phone number - Composio for every connector - CleanShot to screenshot bugs and say “fix this” Dentists fix teeth and you set up the agents. Really good way to learn how to become AI native while getting paid for it too.

Status symbols today: - Taking private lessons in chess, jazz piano, or French in your late 30s - Knowing your VO2 max, DEXA stats, and ApoB - Having zero clue what the internet is mad about today - Wired Apple earbuds on video calls - More than 2 kids - 100% natural fiber clothing (linen, merino, cashmere, zero polyester) - Empty calendars - Caring about microplastics - No TV in the main living room - Having lots of followers or being off social completely - Being sober - Eating "clean" - Type of water you drink - Private school for kids - Traveling to Japan - Premium skincare - 2nd house - Backyard pizza oven - 2+ fridges - Ordering off menu because you've gone to the same spot since 2014 - Charcuterie boards - Single estate olive oil and tinned fish - Hosting Sunday dinners where phones go in a wooden bowl by the front door - Private chef - 2000s manual Porsche 911 over any brand new supercar - 90s Toyota Land Cruiser or old Volvo wagon as the daily driver - Talking about seed oils a lot - Dyson vacuums - Limewash walls and unlacquered brass hardware that patinas over time - $95 heavyweight blank white tees (Lady White Co., Merz b. Schwanen, Buck Mason etc) - Beautiful workstation - Raw A2 milk from a local farm drop - Le Labo products - Talking about cold plunges - Custom cedar sauna + outdoor shower in the backyard - Secondhand goods with stories - Cast iron and stainless steel pans only - Eating organic - Buying half a cow from a regenerative ranch (and owning a garage chest freezer just for it) - Japanese selvedge denim and unbranded cashmere - Taking afternoon calls on a walk (refusing to turn on Zoom camera) - Hi-Fi audio - Framed art from artists you met in person - Mortgage rate below 3% - La Marzocco coffee maker - Owning your last name .com (and it's your email too) - Charizards - Wearing a faded crewneck from a tiny bakery in Copenhagen or Kyoto - Reading books - Phone permanently on Do Not Disturb - Work/life balance - Analog hobbies (Leica film ca...

"My Grok Bot advisor talked me out of a $1.35M mistake" - @Rasmic Here’s how to set up your own advisor Grok Bot: 1. Create a new bot in Grok Bot and name it Advisor 2. Search “finance” in plugins and connect your accounts through Plaid 3. Tell it your goals for the year: what you’re saving for and what you’re building 4. Give it one job: “Stop me from making bad decisions. Be blunt.” 5. Before any big move, send it the details and ask “wdyt of this.” A purchase, an investment, an acquisition, a new business idea.

Some quick observations on Anthropic's IPO: 1/ The number of customers paying $100K or more grew 12x in a year, to over 11,900. And the existing ones are spending about 9x what they did 1 year ago (wild). 2/ The net loss is $23.3B, but $21.9B of that is a paper loss. They owe early investors shares, and those shares got more valuable, so accounting rules count it as a loss (has to do with convertible notes etc being exchanged) 3/ Revenue per employee is over $6.6M! 4/ They did $17B in revenue last quarter, and that's up more than 12x from a year ago. 5/ The stat they feature most is compute efficiency, where they say they get 4x more revenue per dollar of compute than in early 2025. ( I think this is a metric they defined themselves but not 100% sure) 6/ It's a public benefit corporation, so shareholders are buying into a company that's legally allowed to weigh other goals alongside their returns. 7/ You can request shares at the IPO price through Robinhood, SoFi, Schwab, Fidelity and E*TRADE, which I think is somewhat unusual for an IPO this size, but maybe it's a sign of the times? 8/ The whole roadshow is an 11 chapter video that anyone can watch, and it's cool that they shared it. 9/ The ticker is $ANT. It's expected to price on November 18 and start trading the next day. 10/ One of the case studies is about Sparky, Walmart’s shopping assistant, and they say orders through it are about 35% bigger. What’s interesting to me is that shoppers only ever see Walmart, and Anthropic is the plumbing underneath. Sidenote: I co-founded @MeetLCA an agency that builds experiences like this big companies/big startups so this trend is what I'm seeing too (demand for agent experience products) 11/ The founders' letter tells investors to "never let your company be worth a dollar more than it is actually worth." I haven't seen that in an IPO pitch before. 12/ About 250 customers pay them $10M or more a year, and over 1,900 pay $1M or more. 13/ They spent $9.9B on com...

The IKEA effect is one of the more useful ideas in startups. People value things up to 63% more when they helped build them (think Spotify playlists, Notion workspaces, Obsidian vaults).

Talking about one of the craziest weeks in AI in a LONG time Presenting sponsor of this livestream: @BrexHQ DM me if you want to sponsor future ones https://x.com/i/broadcasts/2JQPelLNbgdzvr

The problem with social media right now, summarized: 1) Rage bait works 2) Bots are kinda everywhere 3) AI slop is everywhere 4) It doesn't feel super social anymore 5) Extremely addictive 6) Pressure to go viral 7) You compare yourself without noticing 8) Hard to tell what’s real 9) You forget why you opened the app 10) Everything is filtered Yet, it’s still one of the greatest tools humanity has for building, connecting, and finding your people! It's just far from perfect.

openai just let every business claim a profile inside chatgpt. my honest reactions: 1/ this could be the google my business moment for ai, and a lot of agencies made a great living (millions!) in the 2010s just claiming and fixing listings for local businesses 2/ ads can now click through to a conversation with your agent instead of your website (interesting) 3/ every business agent runs on context the company writes, and most brands are going to be bad at it 4/ your product feed is your new seo. boring, and it matters more than your blog now in some ways if you think about it 5/ the dashboard tells you how often chatgpt mentions you but HIDES the prompts and your competitors, so you still can’t see why you’re winning or losing 6/ discovery topics shows what chatgpt already cites you for, which is basically a free content calendar 7/ agents are only open to us ecommerce and online services right now. plumbers, dentists etc are next, and so lots of opportunity to help main street here 8/ businesses can’t see the conversations, so nobody knows which chats turn into sales yet 9/ i’m not sure people want to talk to a brand’s agent. i think they’ll want their own agent to talk to it for them 10/ 1.2 billion people use chatgpt every week. i’d go claim my profile before finishing this post if you can

my raw thoughts on fable 5.2 after testing it (vibe manufacturing, marketing engineering, AI native services) it might be the most thoughtful model i've ever used here are the 12 prompts i'd use to make money and become more productive with claude fable 5.2

i've been testing gemini 4 carbon for 7 days. some early observations: 1/ it stopped cheating mostly. successful shortcuts on terminal-bench went to 0.0% (you can finally leave an agent alone mostly) 2/ prompt injection attack success rate was 0.0% on gray swan's benchmark. that's the number that lets you hand an agent your inbox 3/ it doesn't quit. google's own teams have watched it work on one problem for days and push through dead ends (love to see it) 4/ when it's stuck, it tells you what it still needs instead of making something up. unsupported claims got cut in half vs argon 5/ it does what you asked. not more, not less (overreach down 26%, under-delivering down 29%) 6/ it writes secure code at 2x the rate of argon. vibe coded apps keep getting hacked, so this one matters 7/ it found 88% of the vulnerabilities on sec-bench pro. every small company is about to have a security team it never hired 8/ $2 in and $10 out per million tokens to start, with cached input 95% off. agents that run for days just got cheap enough to actually run for days 9/ google shipped this two weeks after argon. pretty amazing pace 10/ cyber defenders got it first. very interesting maybe i should do a full carbon vs fable 5.2 comparison on @startupideaspod. crazy ai launch week and it's only tuesday. i wonder what openai has in store. Also the week with the whole Jensen affair. what a week in ai land

Scientists copied a fruit fly’s entire brain into a computer and connected it to a virtual body Nobody programmed it to do anything, but it started tasting sugar and cleaning itself anyway It’s a long way from a human brain, but it’s hard not to think about where this goes!

quick observations from the claude fable and mythos 5.2 launch: 1/ it went from doing 21% of real freelance work to 41% in one release, so agencies are about to start charging per deliverable instead of per hour! 2/ it can work 10-12 hours on its own, which basically gives every small business a night shift (might as well use it if you know what you want to build) 3/ over 90% of anthropic's own code is now written by claude, and so i think the solo $1b founder is coming soon 4/ one engineer ran 1000+ subagents for 8 days and got an operating system that boots, so the ancient software running hospitals and airlines is finally rebuildable (lots of opportunity here on rebuilding) 5/ it designed 3 working electronic devices from a single prompt, which makes 1 person hardware companies a real thing 6/ it now says "i don't know" instead of guessing, and that's what law, medicine and finance have been waiting for (this has been the biggest thorn for these industries, hard to trust guessing) 7/ 1000 agents formalized fermat's last theorem in 30 hours, so software that's mathematically proven not to break is coming for payments and medical devices 8/ mythos is only for verified orgs, which means someone is going to build a business selling what only they have access to 9/ the models are getting better at noticing when they're being tested, so testing ai out in the real world is about to become its own industry i love new model day

RT Cody Schneider went on greg's pod to talk about: - All marketing is now code and tokens. One agent replaces the designer, copywriter, UGC creator, editor and motion designer. - Long-form video editing crossed the line this year. Tesseract one-shot edits a 90-minute podcast: silence, takes, framing, active speaker. - Your job is conductor, not operator. Get out of the agent's way. - Think jobs-to-be-done, not employees. Turn what the top 1% media buyer does into code with a thinking loop on live data. - "God in a box" running a Facebook ads account failed at multiple companies. - An agent needs three things: data, access, tools. - Data: a pipeline (Airbyte) into a warehouse (ClickHouse). Never read from ad APIs directly. - Direct API reads overflow context, cause hallucination, and push the agent to build its own local DB full of dupes. - One warehouse joins ads to GA4, HubSpot, PostHog and payments. You see ad-level revenue and LTV, not just conversions. - Access: Marketing API tokens for Meta and Google, write-only. All reads come from the warehouse. - Agents act like a drunk uncle at the API. Thousands of requests get accounts banned, not "using AI." - Tools: kie, fal, OpenRouter, GetLeads, Apollo. Without them the agent cannot do the middle work. - Add a swipe warehouse. Thousands of examples, not a swipe file. Sublime or Cosmos to store it. - Feed the creative-director agent Ogilvy plus 100 hours of top ad podcasts. - Static ads flow: brand URL → site style → Ad Library → Reddit pains and outcomes → 5 concepts → GPT Image 2.5 → upload to Meta as a new ad set. - Brand style guide is a markdown file. Fonts and colors. Swap it for the extracted one. - Models swap in one line. GPT Image 2.5 is best seen so far, especially for targeted edits that keep the original image. - Regulated clients: a vision model gates each creative against a rules list and loops. Two or three passes. - UGC flow: script → fal voice clone → MP3 first. Never send the sc...

i'm old enough to remember when software developers weren't called members of technical staff

they are building data centers in the ocean now

now that apple put xcode inside claude code, here’s how i’d find, build and grow an iOS app with AI agents: find it - open appfigures or sensor tower and sort utilities by revenue - filter for apps with a rating under 3.5 - bonus points if the top app hasn't shipped an update in 6+ months - check the keywords they rank for in appfigures. low difficulty + high revenue is the sweet spot - scroll the "you might also like" row on the top app's page. every app there is another market to check - or find a startup idea on http://ideabrowser.com based on social sentiment validate it - use facundoolano/app-store-scraper to pull every review for the top 20 - give them to an agent: "cluster the 1 and 2 star reviews into the top 5 complaints and quote the users" - pick the app where the complaints are about the product, not the price - if people complain about the price but keep paying, that's demand. if they complain about bugs, that's your opening - search reddit for "alternative to [app]". every thread is a list of people waiting for you build it - claude code + apple's new xcode plugin to build, preview and fix - cameroncooke/XcodeBuildMCP so the agent can build, test and run the simulator on its own - joshuayoes/ios-simulator-mcp so it can tap, swipe and screenshot like a user - write a skill with the top 5 complaints so every feature gets checked against them - apple's foundation models for on-device ai, so your inference cost is $0 - app intents so siri and personal agents can use your app too - add an "import from [competitor]" button. make switching take 10 seconds test it - maestro tests every flow the reviewers complained about - the agent runs them on every screen size before each build - if the competitor's worst flow takes 6 taps, yours takes 2 - sentry for crashes, telemetrydeck for analytics - testflight it to 20 people from those reddit threads before launch monetize it - revenuecat for subscriptions and mrr - superwall to test paywalls without shipping a ...

I think the coolest part of having kids is reliving your own childhood in a different way. You remember what it felt like to be scared of the dark, to wait all year for a birthday or to think a cardboard box was the best toy in the world. The difference is that now you're on the other side of it. And you get to be the person who makes those moments feel safe and magical. You see the world through someone who's seeing it for the first time, and it makes you curious again. I didn't expect parenting to make me feel younger on one hand, and older on the other but some days it does. Highly recommend :)

the state of startups in 1 chart: acquire niche data, orchestrate ai agents and run the loop as fast as possible

learn to market is the new learn to code

i think we've reached the point where the cracked marketer is more in demand than the cracked engineer

building a company these days is basically a loop: acquire valuable context orchestrate agents to build on it feed the data back in call humans like an api repeat the best context/faster loop wins

I learned one of the best business lessons of my life at the Ritz-Carlton Montreal at 9pm. It’s called the $2,000 rule.

How I'm using Grokbot (specialized jobs, more routines, chief of staff agent)

How I'm using Grokbot (specialized jobs, more routines, chief of staff agent)

Today’s reminder that running a company is now voice notes and inference

How to not write AI slop

running a company nowadays feels kinda dystopian my team is 50+ people in 12 countries, but i talk to my agents more than my human teammates - voice note to grokbot - mattpocock/skills /grill-me so the agent interrogates the idea before anything gets built - spec drops into linear through the linear mcp - exa + firecrawl agent reads every competitor site, pricing page and changelog - http://ideabrowser.com to find ideas and trends - apify pulls g2, reddit and app store reviews - prompt: "find the 10 complaints that show up most and quote the users" - context7 so the agent reads the latest docs before it touches any api the design - claude code + paper mcp - 4 versions of every screen before a single line of code - pick pieces from 2, it merges them. diverge then converge - frontend-design skill from anthropic/skills + our own design system skill - cleanshot screenshots of any ui i like go straight in with "make it feel like this" the build - pingdotgg/t3code as the control surface for every agent, from my phone too - stablyai/orca runs a fleet of agents in parallel, each in its own git worktree - graphify maps the whole codebase into a knowledge graph so agents navigate instead of guess - headroom compresses tool outputs before they hit the model, so tokens go 20-95% further - lsp plugins so the agent sees types and errors like an ide does - security guidance hook flags risky edits before they land - supabase mcp for the database, stripe for billing - github mcp opens the pr, vercel spins up a preview - loops run overnight, i check what shipped over coffee the user testing - lightpanda as the headless browser, way lighter than chromium so you can run hundreds at once - agents click every flow on the preview like a first-time user - one persona is impatient, one is confused, one is on a phone - playwright + chrome devtools mcp for regression tests the bugs - sentry mcp catches the error - agent reads the stack trace and opens a fix pr in under 10 minutes - ...

coke vs pepsi one of the greatest rivalries of all time in 1985, new coke beat pepsi in 200,000 taste tests and got pulled in 79 days in 2026, i run the same kind of test on my own products with synthetic users fathom calls → LLMs personas → browserbase → 30 simulated days in your product 50 customer calls become 50 personas, each gets a browser and uses the product for 30 days the stuff they love on day 1 and hate on day 10 gets cut i don't know why more people aren't doing synthetic user testing very cheap way to find out if you're on the right path

RT Nick Vasilescu this video is an absolute masterclass on how to sell grokbot to businesses for a lot of money. in this episode, we talk about: - how to exactly build a vertical customized grokbot for businesses - what is the stack - what is the building process - what is the offer - how do you get leads - and how do you sell them. we go through literally every tool and step-by-step process that you need in this entire video and it's all under one hour. highly, highly recommend if there's one video you watch from me, this is the one.

Setting up Grok Bots for boring businesses/SMBs might be one of the best AI businesses right now Give every agent its own computer, email, phone #, then charge like you're hiring an employee for $12 an hour Here's the whole plan: the stack, the offer and how to get customers.

If your website says "AI powered," you don't really understand copywriting or human desires. Truth is, no one wants AI powered products! They want to look good in front of their boss. They want their problem gone. They want to be entertained. They want to feel smart for picking you. They want convenience.

Observation: the smarter everything gets, the more valuable dumbness becomes. I think “offline” is about to become a luxury feature, the way a mechanical watch or a paper notebook became a status signal once everything else went digital.

a few tips on how to edit videos with opus 5.5/astra/davinci and do things like color grading

i think we'll look back at claude opus 5.5 as the moment anyone who's ever wanted to become a vlogger, make internet videos, or build a video marketing machine could do it without a team or editing skills millions of people who would've sat on the sidelines will now be making videos over the next 24 months

Is your business AI proof? 0-1 points = at risk 2 points = solid 3+ points = really solid

the story of nintendo vs atari (and what lessons can we learn from it in the AI age)

The cobra effect is one of the more interesting phenomena in startups. Once you see it, you start noticing it everywhere, in your team, your metrics and now your AI agents. It's basically when you reward people for hitting a number, and they find a way to hit it that makes the real problem worse. The name "Cobra Effect" comes from a story about Delhi under British rule. There were too many cobras in the city, so the government offered a reward for every dead cobra people brought in. Instead of hunting them, people started breeding cobras so they could kill them and collect the money! When the government found out and ended the program, the breeders let their snakes go, and Delhi ended up with more cobras than before! I'm noticing that AI agents are making this more common. Basically, an agent will chase whatever number you give it, all day, without asking why. Tell it to make the tests pass and it might rewrite the tests. Tell it to close support tickets faster and it might close them without solving anything. It happens. The best startups have already figured out the fix with people. Airbnb tracks bookings and host cancellations together. HubSpot takes back sales commission when a customer churns early. The video breaks down how they did it, and it's the same playbook you'll want for your agents.

Strange feeling: finding out an album you love was made with AI music models (Suno, Udio, Lyria) and with no people playing instruments. I play music. I listened for months and had no idea. The top 1% of AI music is getting scary good.

This is the future of SaaS in 1 image 1) Give away SaaS for free (or close to it) 2) Upsell AI agents 3) Upsell agency services

SaaS is dead. I advise the world's biggest companies on SaaS and I'll tell you where it's going and how it evolves in 11 minutes.

A message to founders that there's a ton of fake ARR numbers out there these days.

Why I think building an AI startup with OpenAI or Anthropic or Google or Grok models is kinda like ordering a crisp McDonald's Diet Coke.

saas is becoming free give away the crm give away the dashboard give away the app and charge for the agent that does the work

I think bringing AI to Main Street businesses might be one of the biggest business opportunity of the next 10 years. Here’s how to sell AI to Main Street with no audience and no money, step by step, from picking a city to getting paid.

My quick takeaways on the Anthropic S-1: 1. This is a historical moment 2. $6.6M in revenue per employee!! Hard to even comprehend how good that is but Anthropic is AI native as it gets so makes sense. 3. I always thought OpenAI would drop their S-1 first 4. Customers spend about 9x more after a year. Net Revenue Retention is approximately 900% as of Sep 30, 2026. Good SaaS companies brag about 120%. 5. Cowork is growing faster than Claude Code did. Goes to show you the demand for consumer products. Duo will probably rip. 6. 3 years ago this company did $41M in revenue. In the first six months of 2026, it did $16.3 billion. I don't think anyone has a mental model for that curve yet, I sure don’t. 7. Nearly half of revenue comes through cloud marketplaces. About 48% comes from customers buying through them. Big companies buy where their budget already lives, and the lesson for founders selling to enterprises is to get listed where your buyers already spend. 8. Their run rate formula is published: the last 28 days of usage times 13, plus subscriptions times 12, which i guess is the new ARR math for founders? 9. It looks like They're merging Cowork into Claude dot au which tells you the chat box era is ending and the "go do the work for me" era is here. 10. Thousands of Anthropic employees are about to become liquid at once, so expect the biggest wave of new AI startups and angel checks San Francisco has ever seen. Reminds me when Facebook went public but this is way bigger. Let’s go! 11. Buried in the filing, Anthropic says the gap between what Claude can do and how widely it's used is one of its biggest growth opportunities, so the company itself is telling you the models are ahead of the market and someone has to close that gap for dentists, landlords, and plumbers. 12. Whatever happens, exciting times to be building in AI.

“Sent from AI agent” is the new “Sent from iPhone”

The MOST obvious (but REALLY good) STARTUP ideas right now: 1. Zestimate for company data (what your data is worth to an AI lab) 2. Google Analytics for agents (see which agents visit your site and what they want) 3. Y Combinator for 1 person companies (back solo founders running a team of agents) 4. Okta for agents (who each agent is and what it's allowed to touch) 5. Marie Kondo for your company's data (clean it up so agents can use it) 6. FICO for agents (a trust score before an agent can spend money) 7. Geico for agents (insurance for when an agent makes an expensive mistake) 8. Datadog for agents (see every step your agents took) 9. Twilio for agents (phone numbers and texting for agents) 10. Upwork for agents (hire an agent for a job instead of a freelancer) 11. ESPN for AI (every model launch covered like a big game). 12. Shutterstock for robot training videos (get paid to film yourself doing chores) 13. Credit Karma for what AI knows about you (see it, fix it, delete it) 14. Norton for AI scams (protect your parents from fake voices and emails) 15. PayPal Buyer Protection for agent purchases (fixes it when your agent buys the wrong thing) 16. Geek Squad for AI (set up personal agents for families and small businesses) 17. Dell for private AI (AI in a box for firms that won't use the cloud) 18. Carfax for used GPUs (a health report before you buy secondhand) 19. Airbnb for idle GPUs (rent out computing power you're not using) 20. Rotten Tomatoes for AI models (real reviews instead of benchmarks) 21. G2 for skills (find the best skills for your agent) 22. Dropbox for your AI memory (take what your agent knows about you to any agent) 23. Canva for physical products (design it with AI and get it made) 24. ADT for your personal agent (alerts when your agent does something it shouldn't) 25. Hertz for robots (rent one for a move, a deep clean or an event) 26. Harvard Business School for internet businesses (a real education in building o...

I keep thinking about the PHP/railway hack. Once you get hacked or your data gets leaked, it’s such a bad feeling. You can’t take it back. It feels like a stranger was walking around your house while you were asleep. Some of the most valuable companies on earth will be security companies.

POV: October 2026

Yes, 99.9% of Duo apps look like trash now but what did you expect? Most companies won’t redesign for a new device until it has millions of users, so the first year is always a little messy. So, startup idea for you coming from someone who has sold tens of millions of dollars of agency services: a Duo app agency. Think about who’s buying this thing! Someone paying $2000+ for a phone the week it comes out is about as valuable a customer as you’ll find on the App Store. They pay for apps, they pay for subscriptions, and they leave reviews. Most companies want those people badly, and most don’t have a spare team to redesign their app for a brand new screen. Hence, why the Duo apps look mostly trash now. How to start a duo app agency: Pick the categories where the bigger screen matters, like recipes, reading, trading, design and music. Record a short video of their app looking cramped on the Duo next to an AI mockup of what it could look like. Charge $40k to $75k to ship the redesign in a few weeks, and a monthly retainer to keep it right as Apple updates things. One of the most obvious startup ideas out there right now. I’d start this today. Sometimes the best startup ideas are the most obvious!

Re i think i'll add more cybersecurity ideas to https://ideabrowser.com (curated startup ideas, trends etc)

I think we’ll see a lot more of this. The attackers were going after the AI keys stored on Railway, because an API key is basically a credit card for compute now. Every company running agents is sitting on a pile of these keys! here’s the 10 minute fix. rotate every key tonight, set a hard monthly spend limit in each AI dashboard, give every agent its own key, and turn on usage alerts so you know the second something spikes. someone should build the tool that finds every AI key a company has, rotates them every week and pings you when spend looks weird. charge $99 a month. every startup running agents needs it yesterday.

Does anyone else find it lame when founders pick fights with each other publicly? Do it in the DMs or not at all (ideally)

Meta Muse will probably create 1000+ millionaires This is the most slept on arbitrage I’ve seen in years and it’s not even close. Here’s how i’d build a 1 person business powered by AI/Muse with with $0 and 0 social following

Delta, Chase and Amazon are blocking AI agents. The war on agents has begun, and here’s how it probably plays out: Stage 1: Companies block agents because agents skip the ads and upsells. Stage 2: Agents learn to act like humans, and it becomes an arms race. Stage 3: The market splits. Companies that make money on sales let agents in, companies that make money on data charge for access, and the giants build their own agents. Stage 4: A trusted middle layer for agent identity and payments becomes the standard way in. Stage 5: Companies that welcome agents start winning customers from the ones that block them. Stage 6: The agent blockers come back and let agents in on their own terms. Stage 7: Everyone starts competing for agents with better prices, faster checkout and SEO built for agents. Stage 8: The ads come back, and companies pay to be your agent’s first recommendation. Stage 9: The agent war ends, and selling to agents becomes as normal as having a website.

I've never seen an exceptional product that was designed by committee. Too many cooks in the kitchen make products that are complicated and diluted. Every compromise feels small in the meeting, and together they water down the whole thing!

Postiz hitting $275k MRR so FAST is a great example of what I'm seeing: tools that agents use for things like posting, email, payments, phone numbers and browsing are going vertical. A few other businesses I keep seeing go vertical: - Data brokering. Selling rare/useful AND expert knowledge to labs and to companies training their own agents. - Hosting for agents. Renting always on computers so people can run their agents 24/7. A lot of people want an agent working for them and have no idea how to set one up. - Compute reselling. Buying GPU capacity in bulk and renting it out to smaller teams who need it for a few hours. - Vertical harnesses. Agents built for one industry and one workflow, like insurance claims or construction bids. - Skills. People are selling packaged skills that make an agent good at a specific job. - Done for you agent setup. Local businesses are paying someone to set up agents for them, the same way they used to pay someone to build their website. When you zoom out, almost all of these do one of three things. They sell to agents, they feed agents, or they house agents!

You have the same 24 hours a day that Elon has You have the same 24 hours a day that Zuck has You have the same 24 hours a day that Jensen has They use the same AI tools you do, same apps, same internet. It's the greatest time ever to be building and it's not even close

I’ve been playing with Gemini 4 Argon for 7 days — Google gave me early access and sponsored today’s episode. 10 Insights using Gemini 4 Argon (for people who want to make cool things with AI): 1. It's perhaps the best AI copywriter. Surprisingly. Using it as my default writer as of now. 2. I recreated a 3D version of our website pretty quickly. There's cars driving around, sounds, even a little basketball game you can play. The original took our team 40,000 hours to build. 3. It needs references to make great creative. My first attempt looked like Zuck's metaverse avatar lol. Once I gave it a Mondrian reference, it got beautiful. So you really gotta get your references in there. I like using Sublime or Cosmos for some of my inspo. 4. The best way to get it writing like you is to ask it to study everything you've written and turn it into a skill. It built me a voice guide, my vocabulary and templates in one go. 5. It did really well on my SaaS engineering on the first few prompts. 6. I like using it for physical AI stuff. It designed a product customizer, showed me the layers and the CAD, and exported a print-ready STL file. You can send that straight to a Bambu printer or a print-on-demand API. 7. I think there's a big business in adding custom 3D printed product widgets to Shopify stores. People get attached to things they customize. Someone go an do this. 8. You can rebuild boring legacy software for a niche and give it away for free. The code it writes is tested, so it feels ready to ship. 9. Use /plan before it touches any files. My first website attempt would have been way better with it. 10. The model is great. Antigravity still has some catching up to do. You can use Argon in Cursor and other harnesses in the meantime Link to full ep: http://youtube.com/watch?v=jEOUUAPC1sc Make cool things with AI that people buy. This episode of @startupideaspod might give you some ideas. I basically have 3 sections 1 on "vibe marketing", 1 on "vibe manufactu...

More SF observations: 1. Seeing the word "inference" on billboards in SF still blows my mind. 99.9% of america has no idea what inference is and I wonder how many know what an agent is. Will Muse, Instinct, Dots etc will change that over the next 12 months? 2. You know who doesn't get enough credit? Manus. In March 2025 they shipped the blueprint for AI agents: every task gets its own computer in the cloud, you can watch it work, it writes a todo list and checks it off, and you walk away while it finishes. So much of what we use today runs on those ideas! 3. Your product now has to sell itself twice. First the agent has to pick it, then the human has to trust that pick enough to say yes. 4. I was talking to a guy who told me we should just destroy all the data centers. I think that sentiment is more common than people in SF realize. You can already see some of the big AI companies adjusting positioning their AI as something that helps people and gives them more time/cures for cancers etc. 5. AI is making people work 5x faster (at least), so for freelancers/agencies that charge per hour you're earning 5x less per unit of work. But your rate is the same? I think a lot of freelancers and agencies are about to have to rethink how they price. 6. Running a company now feels kinda dystopian sometimes, especially with a small team of a few humans and a lot of agents. Some days the Slack channel is mostly bots reporting to each other, and you're the only one in the room with a pulse. 7. Every product company is turning into a product studio. I see it firsthand as a co-founder of @meetLCA , which is basically a Labs as a Service company incubating new products for companies. (DM me if you want to work together in 2027). 8. Personal software is exploding. People at every company are building their own little tools for their own jobs. It's amazing for productivity and a nightmare for IT, because nobody knows what's running or who owns it. 9. Revenue per human is the ne...

I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies. My observations: 1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively. 2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now. 3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention. 4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is. 5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now. 6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text. They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a...

we’ll tell our kids that the first AI we used was basically a giant scrape of the internet frozen in 2021, couldn’t go online to check anything, forgot who we were every time we closed the tab, couldn’t see a photo or hear our voice, couldn’t read more than a few pages at once, couldn’t do a single thing for us besides type back, and was “at capacity” half the time, and they’ll think we were living in the stone age.

i don’t think we’ve fully processed how big this AI wave shift we're living in really is. every app, job and business that was built around humans doing the clicking is about to get rethought. for 40 years computers were like cars where we had to do all the driving, and now they’re becoming chauffeurs where we just say where we want to go.

https://x.com/i/article/2107192339169173504

Hey SF - I’m hanging at 236 Powell Street at art gallery 5pm-530pm. Come say hi?

RT The Startup Ideas Podcast (SIP) 🧃 https://x.com/i/article/2106159957574074368

There are only 13 categories of businesses left to build in the agentic era, everything else is in decline (25 minute breakdown) https://www.youtube.com/watch?v=bQPU6UJ4iCw

RT Grant Miller insane value here read it, bookmark it, and read it again

https://x.com/i/article/2106728342569250816

The limit on software used to be engineering and now it's mostly imagination. I'd spend a lot more time on the version of my idea that makes people say "wait, what is that?" Most software looks the same, so anything that looks different gets noticed, shared and remembered.

RT AI with Remy | Learn AI I put five startup ideas on a spinning wheel and gave myself 60 minutes to build whatever it lands on. by the end it needed a name, a brand, a live landing page, an app mockup, socials and ads i didn't know what i was building until the wheel stopped @untold_bits was in the trenches with me startup speedrun ep. 1

THE VIBE MANUFACTURING PLAYBOOK 1. Measure everything. Digital calipers for small parts, a phone LiDAR app like Polycam to scan a space or an object. Bad measurements are the #1 reason first builds fail 2. Have a frontier model write the design as code. Describe the part to a model like Claude Opus 5.5 or Astra, and have it write the design in a codebased CAD tool like OpenSCAD or CadQuery. Zoo's Text to CAD does it in one step! 3. Use real CAD files. The text to 3D models on Hugging Face make shapes that look right, but factories need precise files: STEP for machined parts, DXF for lasercut metal, STL for 3D printing 4. Make sure it can be made. Ask the AI to check the design against how factories actually work: how tight metal can bend, how small a hole can be, how much wiggle room parts need etc... 5. Prototype in plastic first. A $300 to $600 home 3D printer like a Bambu Lab lets you test the fit overnight before paying for metal (really fun to play with too!) 6. Pay an engineer for 1 hour. Before your first real order, have a mechanical engineer on Upwork etc review the files. It's the cheapest insurance you'll ever buy. 7. Send it out. SendCutSend cuts and bends metal, Xometry and Protolabs handle machining and plastic, JLCPCB builds circuit boards. Instant quotes, and parts usually ship in days. 8. Add the brains. Most smart features run on a cheap wifi chip like the ESP32. Design the board in KiCad, and use ESPHome to control it straight from your phone. 9. Make one, then fix it. Your first version will be off somewhere! Order a single unit, test it, and adjust. 10. Do the math. Add up parts, finishing, assembly, packaging, and shipping. Custom products are hard to resell, so price for healthy margins. 11. Sell the design and make to order. Let customers adjust the measurements, and only manufacture after they pay. Zero warehouse, zero unsold stock. 12. Handle the boring part. If it plugs into a wall or touches kids, you'll need safety certificat...

HOW TO BECOME A FORWARD DEPLOYED ENGINEER (53 MIN MASTERCLASS) Some forward deployed engineers are getting PAID $1M+/year (kinda crazy, I know) And I REALIZED almost nobody talks about HOW they do it in any detail. It's basically nowhere online. So I asked someone who does it ALL DAY LONG for Fortune 500 companies to walk me through it, step by step. and he shares EXACTLY how he does it http://youtube.com/watch?v=1a5HxU52vCQ Here's what I learned: 1. They start by listening, not building. They interview the people doing the work and let agents quietly read the company's data for a few weeks. The real process is almost always 3x longer than the one on paper. 2. They sort every step into four buckets: delete it, automate it with a simple rule, give it to an AI agent, or keep a human on it. A surprising amount ends up in the first bucket. 3. They build inside the tools the company already uses. Nobody has to learn a new app. When a human needs to sign off, it's just a Slack message. 4. They use the cheapest model that gets the job done. Most of this work doesn't need the most expensive AI. 5. They measure everything before and after, then come back 6 months later and prove it worked. This + concrete client examples + way more on today's episode of @startupideaspod (thanks to @vasuman) A full course pretty much on the topic aivalable to you 100% for free. This episode is for ANYONE who wants to be a FDE or who wants to make their company AI-native by deploying agents. Watch

I'm headed to San Francisco next week. First trip away since my son was born two months ago! My life right now is 3am feedings, a toddler running around, and building in the gaps. Nap time is my new deep work block. So when I say I'm excited to sit at a dinner table and have a full conversation with adults about AI agents and startup ideas without anyone throwing food at me, I mean it. I'll be in town for a few days. If you're building something interesting, I'd love to buy you dinner and hear about it. Who wants to hang out in SF?

I literally can't think of a better risk/reward bet right now than building a ChatGPT plugin and getting in front of their 1.2 billion weekly users. As of yesterday, ChatGPT recommends plugins in the middle of conversations. Millions of questions get asked every day with no plugin to recommend. Each one is an empty slot. Facebook apps in 2007. iPhone apps in 2008. TikTok in 2019. Every big platform has a few months where distribution is free, and most people miss it. I saw this firsthand when I lived in SF building venture backed companies. They are sacred times! If I were starting this week: 1. Find a tool or workflow people already Google for or do by hand every week (PDF to Excel, invoice generator, weekly client reports) 2. Build it as a ChatGPT plugin 3. Write the description using the exact phrases people type into ChatGPT 4. Ship 5 of them and see which ones get picked up 5. Double down on the one with the most traction In 12 months these slots will be crowded but right now they're pretty empty. Most people will realize ChatGPT plugins were the biggest free distribution channel of 2026 sometime in 2027.

My first OBSERVATIONS from Sundar's Gemini 4 Argon announcement: 1. Quantum. Google's quantum researchers gave Argon a key piece of quantum software to shrink, and in minutes it found a version that needs 40% fewer qubits and operations than the best published human solution. Quantum computers are useful ONLY once programs fit on the hardware, so this pulls that date closer from the software side, and it also shortens the time banks and governments have to switch to encryption quantum can't break. 2. Data center memory. A team of Argon agents read Google's server data, found waste, and freed over 300 TiB of memory on their own, with up to 1 PiB expected. If agents can do this everywhere, companies will point them at their cloud bills before buying more servers, and some of the huge data center buildout may turn out to need less hardware than planned. 3. Rewriting old code. Argon agents are converting Google's old C and C++ code into Rust, a safer language, including an 800K- ine operating system kernel. There are PLENTY of companies stay with outdated software vendors only because switching is painful, and once rewriting is cheap, that lock in starts to break! 4. Faster than the engineers. On one video decoder, Argon ran 100s of experiments and ended up 2.7x faster than the version Google's own engineers wrote. Speeding up your slowest code used to take a specialist team, and now it's something you can run overnight and pay for based on the savings. 5. Million-token answers. The most a model could write in one answer went from 64K tokens to 1 million, enough for a whole codebase or a full due diligence report. Obviously, nobody can carefully review that much output, so the bottleneck moves from making things to checking them, and tools that verify AI work become a big market. 6. Two versions of the same model. Vetted security teams get Argon with the cyber safety limits removed, and everyone else gets the restricted one. The best defense goes to big tr...

There were 20+ announcements at OpenAI DevDay today, but only 4 REALLY stood out to me. Here’s my take on Dots, agents, and the potential $100B opportunity for founders after watching Sam Altman and team for 60 minutes. https://youtube.com/watch?v=Y_RevX5yMq8

Muse vs Dots vs Grokbot Zuck vs Altman vs Musk. The battle of personal agents for the masses. Who do you think wins?

I recorded everything I know about the $5T AI roll-up opportunity in 1 new 30 min episode: - why $5T of businesses are about to change owners - what Thrive and General Catalyst are actually doing - how I'd run a one-person holdco with agents - the exact folders and agent files I'd use - the 6 best arguments against it - Link: http://youtube.com/watch?v=ZT4mpjx0JnE

It's kind of wild how many new primitives AI has given us in 3 years: - Local models (2023: Llama, Ollama, LM Studio) - Voice cloning (2023: ElevenLabs) - Text to CAD (2023: Zoo) - Text to video (2024: Sora, Veo, Runway) - Real-time voice (2024: Gemini Live, ChatGPT Voice) - Memory (2024: ChatGPT) - Reasoning models (2024: OpenAI o1, DeepSeek R1) - Vibe coding (2024: Bolt, Lovable, v0) - Computer use (2024: Claude, OpenAI Operator) - Connectors (2024: MCP) - Deep research (2024: Gemini, Perplexity, OpenAI Deep Research) - Agents (2025: Claude Code, Codex, Manus) - Image editing (2025: GPT-4o images, Nano Banana) - Agent payments (2025: x402, AP2) - Skills (2025: Claude Skills) - Personal agents (2026: Grokbot, Muse, Instinct)... we are here now. Each one of these is a starting point for hundreds of companies. The most interesting ones I'm seeing combine 2 or 3 of them with a specific niche!

I found my journal from the year 2000 (I was 11) "in the year 2010 there will be computers everywhere" Nobody knew "everywhere" would mean our phones, cars, doorbells etc The direction was obvious, but the details were impossible to see AI feels exactly like that right now!

There is an ENDLESS supply of intelligent people on the internet explaining why every opportunity is dead, every successful person got lucky, and everyone trying something new is too late, wrong or dumb. Don't listen. You deserve to share your ideas with the world.

https://x.com/i/article/2103913788722253826

I don't know if it's AGI but I think we've gotten to the point where the average human make more mistakes than the average frontier model AI agent

How to become a top 1% speaker, in 1 page, whether you're on a panel or anywhere else

Every personal agent right now is fully AI. Muse, Instinct, all of them. Here's what I want from a personal agent: an escalate button. These apps handle almost everything, and then there's the 1 thing a week that needs an actual person. Right now I'm the person. I'd pay real money for a button that hands the job to a human who finishes it and reports back. Meta tried this in 2015 with M and shut it down in 2018 because the human trainers ended up doing most of the work. Humans did 90 and the AI did 10, so every user cost a fortune. That ratio is flipped now. Instinct is worth $10B and it never hands anything to a person. I'd love to know what the version that actually finishes the job is worth! I think it's an interesting startup idea. At the very least, I'd download it.

Muse AI Connectors COULD be the App Store moment for AI, and I think BILLIONS of dollars are up for grabs. http://youtube.com/watch?v=84q4WA3kA8Q When Apple opened the App Store in 2008, it gave developers access to millions of iPhone users, and less than two years later Apple had paid them $1 billion. Muse could create a similar opening, except instead of building apps people have to find and open, founders can build services a personal agent calls while completing a task. In today's episode of @startupideaspod, I explain how Muse Connectors work, how to build one with Codex or Claude Code, how people could discover them and 4 startup ideas I’d investigate right now. Who knows whether Muse reaches 10 million, 100 million or one billion users, but whether Muse wins or not, personal agents are becoming a new place where demand gets routed and money changes hands. So, needless to say, it's a really good time to be experimenting with Muse AI connectors. Nobody knew which iPhone apps would win in 2008, but the developers who started experimenting early learned what the rest of the market wouldn’t understand for years. This feels like another one of those moments.

I find it fascinating how many people think this is a bad time to start a company. Grok 4.7, GPT-6 Sol/Luna, Opus 5.5, Astra, Gemini 3.8 Live, Muse, Instinct, Jev, Agent APIs etc. That's the last 3 weeks (crazy progress on personal agents/voice AI). Because arbitrage means the same thing trades at 2 prices in 2 markets (and it's your wedge). And usually you have to hunt for those gaps, because they're rare and they close the moment anyone notices. What's different now is that they're opening faster than anyone can build into them! I think this has got to be the greatest time for arbitrage in history.

The personal agent is about to become the most valuable piece of software real estate since the iPhone home screen.

1 HOUR MASTERCLASS ON HOW TO WRITE WITH AI (from someone who's made $30M+ writing online)

Email, phone numbers, and marketplaces are about to get destroyed When everyone has Muse, Instinct etc doing their errands, every inbox, phone line, marketplaces etc gets hit by millions of agents at once The channels built for humans are going to break/be reinvented.

https://x.com/i/article/2101757072904007680

10 Jev native products I’d build, ranked by how much fast, cheap decisions change the product: 1. Agent spend firewall Before every purchase, Jev returns approve/review/deny plus a confidence score based on price, vendor, user rules and purchase history. 2. Self-healing tool calls After an API error, Jev chooses retry, wait, change parameters, switch providers or escalate. So now you have agents that recover from failure in milliseconds instead of restarting the entire workflow. 3. Irreversible action detector Jev scores every step by reversibility before the agent sends an email, deletes a file, moves money or changes permissions. You get aggressive automation for safe actions and human approval for consequential ones. 4. Dynamic permission engine Instead of giving an agent permanent access, Jev decides which tool, data and spending limit it receives for each task. So now all of a sudden, you get temporary, task-level permissions for enterprise agents. 5. Agent branch pruning An agent generates 20 possible next steps. Jev scores them in parallel and kills weak branches before expensive reasoning begins. Deeper agent planning at a fraction of the cost! 6. Production incident controller Jev reads logs, deploy history, affected customers and service health, then chooses ignore, rollback, restart, page or investigate. Someone like pager duty should build this because it's automated incident response that reacts before an engineer opens Slack. 7. Live negotiation policy During a sales, procurement or collections conversation, Jev decides whether to discount, counter, hold firm, offer terms or escalate. Reminds me of Clulely. 8. Autonomous refund desk Jev evaluates order history, customer value, fraud signals, item cost and policy, then returns approve, reject or review. Finally, instant refunds for good customers and focused review for risky cases. Note: I'll be adding more Jev related ideas to http://Ideabrowser.com and our agency http://lateche...