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The Computomatix Times

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Startups & Products

Launches, founders, growth and building companies

Wallfacer Offers Free Access to 100,000 DTC Ads Analyzed by AI

Wallfacer | Turn the Best Ads Into Your Next Winning Creative

Nikunj Taneja promotes Wallfacer, a free site offering over 100,000 ads from direct-to-consumer brands in India and the US. Frontier AI analyzes hooks, timelines, formats and offers for users.

Original post · 1 min read
ideas ki kami nahi honi chahiye

trywallfacer.com

100k+ ads from top DTC brands in India & US

all for free

hooks, timeline, format, offer everything analyzed by frontier AI
Akshay G Jain @Ajain112
trywallfacer.comWallfacer | Turn the Best Ads Into Your Next Winning CreativeExplore 100,000+ ads from top DTC brands. See what the best brands are doing. Then do it better.
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Profit AI Founder Reports $147K Revenue in Five Months

Profit AI Founder Reports $147K Revenue in Five Months▶

Starter Story features Jack, who built Profit AI, a Shopify profit analytics and marketing automation app launched in December. He says the app reached $30K MRR and services about $40K MRR, totaling $147,000 in revenue, starting from beta testers.

Original post · 1 min read
Jack, who launched Profit AI in December, says his $147K revenue split came from $30K/month on the app and almost $40K/month in services, all in five months.

"I built a profit analytics and marketing automation app for Shopify called Profit AI, which has made a total of $147,000 since launch in December, and is currently at 30K MRR for the app, and just under 40K MRR for services."

"We had no customers for the first couple of weeks, just a handful of brands that I knew that were beta testing the app and getting free access, and yeah, today I'm excited to share more about how I took it from zero to where it is now."
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Mike Mignano Argues Frontier Labs Will Not Win the App Layer

Gokul Rajaram summarizes a 20VC interview with USV General Partner Mike Mignano, who argues AI infrastructure is largely built and value is shifting to specialized applications. He stresses reinventing processes rather than automating them and maximizing token spend.

Original post · 7 min read
THE LABS WON'T WIN THE APP LAYER
@mignano (Mike Mignano), General Partner, Union Square Ventures, interviewed by @HarryStebbings (@20VC)

Summary: Mignano's argument is that the AI infrastructure buildout is largely finished, and value now shifts to the application layer, the way broadband once gave way to internet apps. He thinks the frontier labs cannot capture that layer, because markets rarely crown a single winner and specialized startups keep beating incumbents at the hard, regulated, context-rich problems. The takeaway for builders: move first, stay mission-driven, and spend tokens like the advantage they are.

1. The App Layer's Turn. The infrastructure is built, and now the applications get built on top of it. Mignano compares this moment to the early internet, when fiber and broadband were laid down and then an application layer arrived to use them. Trillions in value came from the labs' buildout, but the next wave is software, and there will be so much of it that you cannot place a bet unless you know exactly what you are looking for. That is the whole case for a thesis-driven fund over a consensus-driven one.

2. Obliterate, Don't Automate. USV backs companies that reinvent how something works, not ones that make an existing process incrementally faster. The example is Doctronic, which USV seeded on the idea of putting an AI doctor in everyone's pocket rather than helping practices process insurance claims. Automating a workflow usually means selling to a middleman and making incumbents a bit faster. Reinventing the model is where the enormous outcomes live.

3. Token Maxxing. If Mignano ran a startup today, he would still pound the table to maximize token spend on the things that matter, especially coding. A great engineer will pick the startup that says spend whatever you need on frontier models over an incumbent that hands them a constrained budget. Big companies like Salesforce, Microsoft, Meta, and Uber have to rein in spend because they carry tens of thousands of employees; a startup does not. Token spend is an advantage, and a small team should use every dollar of it against a giant.

4. The 3.8% Question. The entire bull case for Anthropic comes down to what share of developer salaries gets spent on tokens. Marc Benioff spent $300 million with Anthropic on his dev team, which works out to roughly 3.8% of those salaries. If that figure climbs toward 20% or 100%, Anthropic is wildly undervalued and its exponential revenue holds; if it stalls or spend migrates to open models, the story changes completely. One ratio decides whether the most valuable private company in the world is cheap or expensive.

5. Frontier Only For Code. Roughly 80% of non-coding enterprise tasks can run on models that are nowhere near the frontier. Summarization, drafting docs, and routine operations do not need the best model; coding does. That split creates room for a routing layer that sends each job to the model with the best price-to-capability fit. Open-source models are catching up fast enough that the frontier is only worth paying for when the work demands it.

6. The Rebel Alliance. Mignano is planting USV's flag in open-weight models, open harnesses, distributed compute, and human-aligned agents. Teams go where the incentives are, and as open options become genuinely competitive, smart teams drift toward them. China's open-source ecosystem is evolving at a startling rate, which pulls even more talent into the open camp. Publishing a thesis like this is a bat signal that tells the right founders who to call.

7. Who Is Your Agent Working For. As people hand agents their credit cards, their messages, and their agency, they will start asking whose incentives the agent actually serves. A lab's model is built to make the lab's model smarter, and a user may want a harness aligned with their own goals instead. Not everyone has to care about this for it to matter; enough people caring keeps a few good actors honest and holds the rest in check. Alignment with the user turns into a product feature and a real reason to pick one harness over another.

8. The 30% Rule. Markets almost never hand one company the whole thing; the winner usually takes about 30% and leaves 70% up for grabs. Coding assistants prove it, with Cursor, Lovable at $500 million in revenue, and Cognition all thriving at once. Anthropic put a whole team on design to go at Figma, and Figma still does billions with a trusted brand intact. Mignano changed his mind on this in the past year: even the biggest labs cannot do everything, just as Google and Apple never did.

9. The Context Moat. The durable advantage in AI products is the context they build up once they are inside an organization. Granola wins by doing one thing, meeting notes, and doing it best, which gets its foot in the enterprise door without asking anyone to rip out Gmail or Docs. Once a company's history of notes lives in the product, nobody wants to give that context up. Being first and staying focused is how a startup builds a moat that even Microsoft's bundling struggles to pry loose.

10. The Energy Floor. No matter which model wins, intelligence runs on power, so USV has been betting on energy since 2021. The portfolio includes Radiant's factory-line small nuclear reactors, Fuse, and Rune's micro data centers that sit next to wind farms to solve energy portability. These bets are capital-intensive at scale but cheap in the earliest days, when a team is running science experiments before anyone else is paying attention. The edge of energy innovation is exactly where a venture investor should place early bets.

11. Founder Over Market Over Product. Mignano used to rank product first; now he ranks founder, then market, then product. Early startups almost always pivot, so what matters most is whether the founder is resilient, can execute, and can adapt. The trait he underweighted is communication, which touches recruiting, fundraising, product vision, and storytelling to the market. A founder who cannot communicate cannot align a team or raise the capital to build.

12. Price As A Litmus Test. Fred Wilson's rule is never pass on price, and Mignano now uses price as a test of his own conviction. For the best founders, you would pay double and still feel good about it in hindsight. His hardest lesson as a former operator was to stop projecting his own plan onto founders, because even when your plan is right, it is their company and betting on your version is how you misjudge the team. The discipline is to trust the founder's judgment, and to let price tell you how much you actually believe.
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Runway Reports Media Production Costs Falling Two to Three Orders of Magnitude

Content Factories Don't Have Factories

Cristóbal Valenzuela shares Runway's analysis of hundreds of enterprise customers, reporting that AI-driven media production cuts costs and timelines sharply, with examples including a national broadcast campaign going from over $5M to $3-4K.

Original post · 6 min read
X ArticleContent Factories Don't Have Factories
tl;dr: Your $500K campaign costs $5K now
We now have enough data to create a longitudinal study of Runway’s impact at some of the world’s largest enterprises. We analyzed hundreds of enterprises using Runway for media production and measured the impact of adopting AI from a cost and time savings perspective, as well as from a creative and business impact perspective. Savings figures were reported by the customers themselves, measured against what they spent on the same work the previous year. Examples are anonymized at customers' request.
We define media here as a broad term that encompasses AAA feature films, advertising, brand content, games, editorial and pretty much any category of creation where you are shipping pixels in some concrete way.

The most consistent finding for Runway enterprise customers is that costs of production tend to fall by two to three orders of magnitude and the timeline collapses with it. That pattern holds true across broadcasters, game publishers, retailers, agencies and consulting firms, among others.
A national financial services brand used to spend north of $5M producing a broadcast commercial. Its first AI-generated campaign with Runway cost $3–4K and aired on NFL Sundays. A leading home goods retailer now replicates $800K visual projects for under $10K . A global consulting firm rebuilt a $300–600K client campaign for roughly $3K, inside a 2-day RFP window. At the small end of the scale, a US cable network turned a $10–15K-per-season end-credits process into a ~$10 automated run.

Models are now pixel ready for final frame. Runway-generated work has aired on national television with an on-screen AI credit, run during major sport events, appeared in final trailers for a major IP franchise and won industry awards. In gaming, a 16.5-minute AI-generated story film earned an executive greenlight, and AI-generated hooks beat existing hero creatives in direct A/B tests.
The data shows pretty clearly that usage has transformed the way media is made across industries. A home goods retailer is shifting a $5–6M annual production budget from traditional to AI. A Fortune 50 technology company saves $97K a month on photoshoots alone.
Speed matters as much as costs. We often hear cases like the one from a global agency holding company, that compressed social content production for an iconic US insurance brand's mascot from 2–3 months to 3 hours, covering video generation, voice cloning and lip sync end to end. That's around a 720× compression.

At every scale, we see a similar pattern. A mobile studio licensing major superhero IP replaced a 4–5 person, 2-week asset pipeline with one person working under 3 hours. A AAA game publisher cut concept design from 30 days to 1. A European broadcaster runs VFX for live productions 3× faster than its traditional pipeline.
The best single illustration of what these gains add up to is a major game studio's performance marketing team. They built a fully automated pipeline that runs from trending topic through asset generation and legal review to publish in under one hour. Weekly ad output went from 13 to 75–100 with the same team, and three of the AI-generated creatives directly drove measurable downloads and revenue. Recent AI-made ads for the studio's flagship fantasy title ranked among the top performers in the entire market, per third-party data.
The same decoupling shows up everywhere in our data. A UK broadcaster's 5-person AI team produces 800–1,000 ads a year across 50+ sub-labels, saving 46,000 hours organization-wide. A global travel platform generated roughly 8,000 property videos via API. A global sportswear brand delivered a full back-to-school campaign after its budget was cut 75%, processing 210 products a day at about $13 per product, 9 videos each. Output basically stopped being a function of headcount and became a function of pipeline design.

One interesting behavioral changes is that you might expect deployments to stall after pilots or testing. The opposite is happening. A global consulting firm grew from 20 to 100 seats in a year and now consumes 500K credits every two weeks. A Nordic national broadcaster is expanding from 20 to 50 seats. A global production group hit 96% seat activation at renewal across 70+ production companies. And at the agency holding company, Runway is embedded in a group-wide AI platform serving 5,000 daily active users across 35,000+ employees.

Adoption is also getting written into hiring. The CEO of a leading online education platform, one of the largest US advertisers by asset volume, made Runway a mandatory skill in every creative job description:

In the most mature accounts where AI-generated work has become the primary production mode rather than a supplement. A home goods retailer generates 75% of all visual media with AI. A AAA game publisher produces 50% of all marketing for its flagship shooter franchise in Runway consuming 326K credits in a single month. An independent film studio bui… continue on X ↗
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Solo Founder's Wedding Camera App Reportedly Grosses Over $1 Million Yearly

Solo Founder's Wedding Camera App Reportedly Grosses Over $1 Million Yearly

Marlow describes a wedding camera app built in a month that reportedly earns over $1 million a year with 100,000 downloads and no paid ads. The growth comes from hosts pulling guests into the app at events, which then spread it to their own weddings.

Original post · 1 min read
One guy built a wedding camera app in a month and it now makes him over $1,000,000 a year.

No team, no investors, no marketing budget. 100,000 downloads in the first month. Zero dollars on ads.

He did not bolt marketing onto the product. He made using the product the marketing.

You cannot use the app alone. The host has to pull every guest into it. One wedding is not one user. It is 200 people scanning the same code in one evening.

The guest liked it at someone else's wedding. A month later he throws his own event and brings his own crowd. The loop spins for free.

It does not look like an ad. To the guest it is a gift. So they install it gladly.

$2 to $50 subscriptions. 5% pay. That is $100,000 a month.

He did not win on budget. He sewed the growth into the product itself.

Could you build the loop, or is it just luck?
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AI-Built Shopify App Reaches $30K Monthly With Word-of-Mouth Growth

AI-Built Shopify App Reaches $30K Monthly With Word-of-Mouth Growth▶

Starter Story features Jack, a non-coder who built a Shopify app with AI, reporting $147,000 in lifetime revenue and 122 active users. He says growth came from word of mouth before any hard launch, and cut the paid plan from $5,000 to $800 monthly.

Original post · 1 min read
Jack, who built a $30K/month Shopify app with AI (and can't code), says most of his growth came from word of mouth he hasn't even hard launched yet:

"We have 43 uninstalled and 47 installed. We've made $147,000 to date. We have 122 users of the app right now."

"Most of our growth came from like word of mouth. We haven't hard launched in marketing yet at all, but soon we're going to do a big launch."

"Paid plan used to be $5,000 a month, but we only had a couple of takers… so we dropped it down to $800 a month."
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Founder Shares Mobile App Playbook Reaching $3K MRR in 45 Days

Simone Canc promotes a thread from Frederick James describing a mobile app that scaled to just under $3,000 monthly recurring revenue in 45 days. The post is brief and mostly recommends saving the linked advice.

Original post · 1 min read
this is everything you should know before building an app.

i’m not even joking, save this.
Frederick James @frederickjames
How to Make a Highly Successful Mobile App ($0 to $3k in 45 days) — The product ain't special, but it's important.
I've scaled my mobile app to just under $3k MRR in 45 days.
And just under $4k/m in actual revenue!

There's a lot of sauce here that's not been shared.
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Developer Credits Single Weekly Price for Grandma Sticker App Revenue

Developer Credits Single Weekly Price for Grandma Sticker App Revenue▶

Adrià Martinez describes a sticker app earning about $100K a month on under 5,000 monthly downloads, crediting a $6.99 weekly price point. He also promotes HelloHabit, a daily planner subscription combining four productivity apps.

Original post · 1 min read
This app sells grandma stickers and makes $100K/mo

Under 5000 downloads last month

The whole trick is one price:
- $6.99 a week, not a month
- Looks cheaper than $24.99/month
- People tap it, pay $363 a year

We obsess over the perfect app

The winners obsess over distribution
Adrià Martinez @adriamatz
Everyone keeps building the same habit tracker

This one makes $30K/mo by refusing to stay one

HelloHabit. Daily planner, 10K downloads, $30K/mo

- Most habit apps do one thing: mark a streak
- This one staples 4 apps into one subscription
- To-do list, time-blocked calendar, habits, journal
- $59.99 a year to replace the 4 apps you already pay for

In a market this saturated you don't win by being better

You win by being the one app they never delete
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Tanay Jaipuria Asks Whether Products Should Build or Power AI Agents

Tanay Jaipuria poses the dilemma facing product companies: build the agent users open daily, or power the agents users already run in Claude or Codex via MCP, and links to a fuller essay on headless products.

Original post · 1 min read
Every product company is facing the same dilemma right now: Do you try to be the agent your users open every day, or do you accept they already live in Claude/Codex and power that agent instead?

Wrote about how to think through it and what building an MCP business might entail
Tanay Jaipuria @tanayj
Build the Agent or Power the Agent? — On MCP, headless products, and where your value actually lives.

A question I keep coming back to with founders is some version of this: should you build an agent, or should you power the agent your
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Kunal Bahl Argues Founders Need a Concept of 'Founder Form'

Entrepreneur Kunal Bahl argues that founders, like athletes, move through slumps and flow states, and that startup boards and markets wrongly treat performance dips as permanent failure.

Original post · 5 min read
The Invisible Metric in Startup Building: "Founder Form"

When Messi goes five matches without scoring a goal, or Virat Kohli goes through a dry spell, the sporting world doesn’t assume they’ve suddenly forgotten how to play. We don't demand they be permanently benched or declare their career over.

Instead, we use a very specific word: Form.

We recognise that sports require immense psychological, emotional, and physical intensity. "Form" fluctuates. It has peaks, valleys, slumps, and flow states.

Yet, in the hyper-rational, metric-driven world of startups, we treat founders like machines. We expect them to ingest capital and seamlessly produce flawless strategic execution 365 days a year. If a quarter misses expectations, a product launch bombs, or a key hire walks out, the immediate reaction from boards, markets, and sometimes even fellow co-founders is panic, blame, or a sudden loss of faith.

Over the last 19 years as an entrepreneur and through supporting hundreds of founders in their journeys, I’ve realised one undeniable truth: Founder Form is real. And ignoring it can destroy perfectly good startups.

The Anatomy of the Two States: The Slump vs. The Flow
Building a company demands an elite athlete's intensity. Because it relies heavily on human judgment under extreme uncertainty, founders inevitably move through distinct cycles of form:

When you are in "Bad Form" (The Slump): Every strategic bet feels slightly off. You make a hiring mistake, a key client churns, your pitches don’t quite click, and your decision-making feels sluggish or over-analyzed. You are working just as hard - often harder, out of sheer desperation - but your timing is gone.

When you are in "Good Form" (The Flow State): Everything you touch turns to gold. You make a gut-call on a product pivot and it works flawlessly. A casual coffee meeting scales into a massive strategic partnership. Investor meetings go super smoothly. Your energy is infectious, and the team rallies without effort.

When you are in a slump, it feels like nothing will ever work again. When you are in flow, it feels like you can never fail. Both illusions are dangerous, but the slump is where companies break.

The Danger of Asymmetry in Co-Founder Pairs
The ultimate test of a co-founder relationship isn't how you celebrate wins together; it’s how you handle a divergence in form.

In a multi-founder setup, it is incredibly rare for all partners to be in peak form simultaneously. The danger arises when Co-Founder A is in a legendary flow state, while Co-Founder B is grinding through a deep slump.
If they don't understand the concept of "form," this asymmetry breeds deep resentment. Co-Founder A starts thinking, “I’m carrying the entire weight of this company.” Co-Founder B, already drowning in self-doubt, feels isolated, judged, and increasingly anxious - which only prolongs the slump.

Great co-founder relationships survive because they view performance through the lens of a team sport. When your partner’s timing is off, you don't call them incompetent. You step up, cover the field, take the heavy shots, and give them the breathing room to find their rhythm again. You do it gladly, knowing that next quarter, the roles might be reversed.

A Message to Investors: Stop Managing to the Daily Scorecard
To the investor community: when a founder is in a slump, adding institutional pressure or questioning their fundamental capability is the equivalent of a manager screaming at a struggling athlete from the sidelines. It doesn't fix their footwork; it merely tightens their grip on the bat.

Seasoned investors back the person, understanding that form is temporary, class is permanent.

If a founder who has previously delivered high-quality execution hits a wall, the goal shouldn't be to micro-manage the metrics. The goal should be to help them remove the noise so they can find their baseline.

The Founder’s Playbook for Regaining Your Form
If you are a builder currently grinding through a slump, remember how elite athletes get back into the runs:

Go Back to Basics: When a batsman loses form, they spend hours in the nets focusing on basic footwork, not hitting sixes. Stop trying to solve the entire macro crisis today. Focus on small, undeniable execution wins. Go talk to three customers. Fix one internal bottleneck. Rebuild your momentum incrementally. Confidence will grow with action, shrink further with idleness.

Manage the Fatigue: Founders tend to respond to a slump by doubling down on work, leading straight to burnout. But a chaotic mind cannot produce elite execution. Step back, rest, and disconnect briefly to regain perspective.

Trust the System: Accept that market forces and luck play a massive role in early-stage startups. If the core capability is there and you keep showing up to the crease with clean mechanics, the flow state will return.

Startups are a game of endurance. We can’t expect builders to be flawless algorithms, and we must support them like the elite psychological athletes they are.

Give your co-founders and yourself the grace to find form again. It’s just a matter of time before they find it.
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Vakyam AI Launches Raaga V1 Speech Model for Indian Languages

Vakyam AI Launches Raaga V1 Speech Model for Indian Languages▶

Vakyam AI has launched Raaga V1, a text-to-speech system for Indian languages that it says offers frontier-level quality at ₹0.75 per 1,000 characters, with a public playground for testing.

Original post · 1 min read
Launching Raaga V1 by Vakyam AI.

Natural, expressive speech for Indian languages.

Frontier-level quality at ₹0.75 per 1K characters (~per min).

Try it now on our playground.
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Creator Shares TikTok Farming System Behind Flamme's Growth

Creator Shares TikTok Farming System Behind Flamme's Growth

An Nayal promotes a 15-minute video on a TikTok content system credited with taking Flamme from $0 to $10K MRR with 250K downloads and no paid spend. The post offers a tracker template and a framework for viral content that converts.

Original post · 1 min read
just dropped on @starter_story

flamme: $0 → $10K mrr. 250K downloads. 50M views. zero paid.

one tiktok farming system, 15 min on tape.

→ vsc framework (viral, scalable, convertible)
→ the ugly tracker we run across 50+ accounts
→ why most viral videos don't convert

video link below (vsc tracker template attached) 🫡
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Matthew Prince Shares Venture Capital Anecdotes From Cloudflare's Early Days

Matthew Prince of Cloudflare recounts a Sequoia partner rejecting Cloudflare over gender doubts and a meeting with Marc Andreessen and the a16z team that went poorly. He quotes a related post by Greg Isenberg describing a board pitch where a GP fell asleep.

Original post · 1 min read
Two of our worst VC stories:

1. A Sequoia partner passed on Cloudflare because he didn’t think a woman could lead a security infrastructure company. Seriously. 🙄

2. I got introduced to @pmarca. Meeting got scheduled for a Monday, which should have been a clue. I thought it was just a casual meeting. He thought it was a pitch and brought the whole @a16z partnership team. Hilarity ensued. 🤪 At one point one of them said: “You don’t seem very prepared.” Which was true because I wasn’t. I framed the rejection letter they sent.
GREG ISENBERG @gregisenberg
I was once pitching in a board room at a top 3 VC firm for a $15M Series A.

12 people in the meeting. One of the GPs fully fell asleep. Out cold for 30+ minutes. Nobody acknowledged it. Everyone just kept going.

I kept presenting my Series A slides to an unconscious man in a Herman Miller chair and somehow that was considered normal. That's venture capital.

You might fly across the country to perform for people who may or may not be conscious.

It's a dance.

And sometimes you lead and sometimes you follow and sometimes your partner is unconscious.

If you're raising right now, just know: e…
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Plant Identifier Apps Reportedly Earn Millions Monthly, Says Jacob Rodri

Plant Identifier Apps Reportedly Earn Millions Monthly, Says Jacob Rodri

Jacob Rodri says plant identifier apps can generate substantial revenue, claiming the top app on his list earns $9 million a month. The post includes a photo of the list but offers no source or methodology.

Original post · 1 min read
still surprised by how much money plant identifier apps can make

the first one on this list is making $9,000,000 a MONTH
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Eric Wu Launches NavigateAI to Give Field Workers AI Copilots

Eric Wu Launches NavigateAI to Give Field Workers AI Copilots▶

Eric Wu announces NavigateAI, a new company aiming to provide every field worker an AI copilot to address shortages of hundreds of thousands of skilled workers. A launch blog post and video accompany the announcement.

Original post · 1 min read
Today, I’m launching my newco, NavigateAI. We are short hundreds of thousands of skilled workers and we're on a mission to give every field worker an AI copilot, so they can build faster and better when we need it most. navigate.ai/blog/2026-05-26-launching-navigateai
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Greg Isenberg Lists Opportunities in AI Agent Startups

Greg Isenberg lists over 30 observations on AI agent opportunities, including MCP servers for agent buyers, agent layers for franchises, agent marketplaces and memory as a moat.

Original post · 9 min read
My 30+ observations on the greatest opportunities in AI agents right now:

And some ideas that are keeping me up at night.

1. The new buyer on the internet is an AI agent. Imagine billions of new customers showing up with money to spend but they only shop via MCP. That's what's happening. No MCP server means you're invisible to the fastest growing buyer on the internet.

2. Every franchise system in America (30,000+) needs an agent layer and none of them have one. One founder per franchise vertical. That's 30,000 businesses waiting.

3. Everyone said "distribution is the only moat" a year ago. Now I'd add that the only moat is distribution plus memory. The company that has your audience AND your agent's accumulated context is impossible to leave.

4. Consumer mobile is more interesting than it's been since 2012. Apps can finally DO things for you instead of showing you things. The next wave of $100M apps are being built right now.

5. The most interesting startup nobody has built is an agent marketplace where you rent access to someone else's trained agent. A recruiter spent 6 months training a sourcing agent on healthcare hiring. That agent is worth renting to every other healthcare recruiter on earth. The agent itself becomes the product.

6. A sorta strange phenomenon that's happening right now is agents are developing preferences. Give the same agent the same task 100 times and it starts developing patterns in how it approaches it. Nobody is studying this yet. But the agents that develop good patterns are worth more than the ones that don't. That's a new kind of asset.

7. Dead internet theory is about to become dead SaaS theory. Half the apps you use will quietly replace their support team, their onboarding team, and their content team with agents. You won't notice for months. Then you'll realize you haven't talked to a human at that company in a year.

8. The most valuable data in the world right now is sitting in the support tickets of small or mid tier SaaS companies. Every ticket is a customer telling you exactly what to build next. Mine this.

9. The most interesting pricing problem nobody has solved is how do you price a product when your costs change every time OpenAI or Anthropic updates their model pricing? Your margins can swing 40% overnight based on a decision made in San Francisco. The company that builds dynamic pricing infrastructure for agent-based businesses solves a problem every AI company has.

10. The best AI products feel like they're reading your mind. The worst ones feel like filling out a form with extra steps.

11. An interesting arbitrage I've noticed lately is hiring a human VA for $20/hour to supervise an AI agent that does $200/hour work. The human just checks the output.

12. The managed AI agent business is becoming the new agency model. $5k/month per client. You build it, run it, maintain it. The client gets a digital employee they never have to think about. This will be a $50 B+ category.

13. The first "shadow agent" scandals are about to drop. Employees running personal agents on company infrastructure without telling anyone. Using company API keys. Agents accessing internal docs. IT departments have little visibility into this right now. Lots of opportunity to build companies here. Definitely a painkiller not a vitamin type of business.

14. Right now there are probably millions of agents running on autopilot that their creators forgot about. Still burning tokens. Still sending emails. Still scraping websites. Still costing money. The "find and kill your zombie agents" tool is a product that writes itself.

15. Companies are starting to hire based on someone's agent portfolio instead of their resume. "Show me 3 agents you built that are running right now." It's REALLY early but it's starting.

16. Your Slack archive is a product. Every company's internal Slack has thousands of messages explaining how they actually do things. The company that lets you point an agent at your Slack history and auto-generate SOPs and agents from it will be enormous.

17. We're watching the cost of intelligence fall faster than the cost of distribution. Which means distribution is now the expensive thing.

18. The most underrated asset a human can have in 2026: the ability to sit in a room with another human, make eye contact, and have a real conversation. As AI handles more of the transactional stuff, the humans who can do the relational stuff become disproportionately valuable. The soft skills people used to dismiss as fluffy are becoming the hard skills. The hard skills people spent decades acquiring are becoming the soft ones.

19. There are MANY huge companies to be built around the fact that most people's agents are running on their personal laptops which they also use to browse the internet, check email, and download random files. The attack surface is enormous. One compromised Chrome extension and your agent's API keys, customer data, and workflows are exposed.

20. There's a new type of burnout forming that doesn't have a name. It's not from working too hard. It's from context switching between human work and agent work 50 times a day. Reviewing agent output, correcting it, approving it, reviewing again. The mental load of supervising agents is different from the mental load of doing the work yourself. Some founders are telling me they were less tired when they did everything manually because at least the cognitive pattern was consistent.

21. The cheapest form of market research: search "[your industry] spreadsheet template" on Google. Whatever people are tracking manually is your product.

22. Half the YC companies pivoted within 8 weeks of demo day. Not because they failed. Because agents let them test 5 ideas in the time it used to take to test one. The concept of "committing to an idea" is dissolving. Serial pivoting is becoming the default because 1) AI lets you move fast 2) the world is moving fast.

23. The loneliest job in tech right now is being the only person at your company who understands what the agents are doing. You can't explain it to your boss. You can't hand it off to a colleague. If you leave, everything breaks. You've become a single point of failure for an entire automated system. That person needs a title, a team, and a backup plan. Most companies haven't figured this out yet.

24. Your browser history is the most valuable training data you own and you're giving it away for free. Every site you visit, every product you research, every competitor you study, every pricing page you screenshot. That behavioral data, structured and fed to an agent, would make it understand your business better than any onboarding call. The company that lets you turn your browser history into agent context builds something nobody can replicate.

25. Everyone is building AI wrappers. Nobody is building AI unwrappers. The tool that takes an AI-generated document and tells you which parts a human wrote and which parts were generated.

26. Stripe just became the most important company in the agent economy and they barely had to do anything. Every agent that sells something needs Stripe. Every agent that buys something needs Stripe. They're the payment rail for the entire agentic internet by default.

27. The most undervalued API in the world right now is the US Postal Service address verification API. It's practically free. Every local business lead gen agent needs it. Every real estate agent needs it. Every direct mail agent needs it. Boring government infrastructure is quietly becoming the backbone of agent-native businesses.

28. The concept of "business hours" is for humans. Your agent closed a deal in Tokyo at 3am, processed the payment, sent the onboarding email, and updated the CRM before your alarm went off.

29. What happens when agents start recommending other agents? Your research agent finds that a competitor's sales agent is better and suggests you switch. Agent referral networks are forming organically. The first agent affiliate program is probably 6 months away.

30. Cal dotcom closed their source code. That's the canary. When open source companies start closing up, it means agents were cloning their product too easily. Every open source company is quietly asking the same question right now.

31. "AI for pet groomers" sounds like a joke and that's exactly why it will work. 150,000 of them in America. Zero tech. All scheduling by phone or IG DMs. The joke ideas always win.

32. The thing that will seem most obvious in hindsight: we spent 2025-2026 arguing about which model is best while the entire value was in the orchestration layer. The model is the CPU. Nobody buys a computer based on the CPU anymore. They buy it based on what they can do with it. Makes so much sense in hindsight. What else will be obvious in hindsight?

I'll share more notes soon.

I can't sleep with all that's going on. Maybe you too.

What an incredible time to be building.
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Jaya Gupta Argues Company Institutions Will Be AI's Next Moat

Jaya Gupta argues that as AI products and technical advantages become easy to copy, the enduring moat is the organization itself, including how a company attracts talent, distributes authority and builds compounding systems. She cites OpenAI and Palantir as examples of organizational invention.

Original post · 12 min read
X ArticleThe next biggest moat in AI
It's pretty obvious to everyone that everything in AI is converging. Companies I couldn't have imagined competing with each other are today. The application layer is collapsing into infrastructure, infrastructure companies are moving up into workflows, and almost every startup is rebranding itself as some version of a transformation company. The words change every few months: context graph, system of action, organizational world model. A new category gets named, every website absorbs it, and within weeks the market is filled with companies claiming to be the inevitable platform for how work will change.
When models improve quickly, interfaces converge, and product velocity becomes cheap, the visible parts of company-building get easier to imitate. The harder thing to copy is the institution underneath: the way a company attracts exceptional people, organizes their ambition, concentrates judgment, distributes authority, and turns work into a compounding system no other company can reproduce.
The best companies have always known that people are not an input to the company, but rather are the company. But in AI, that truth becomes sharper because everything else is moving so fast. If products can be copied, categories can be renamed, and technical advantages can collapse in months, then the enduring question is what kind of organization you build around the people capable of building it.
The shape of the company itself is becoming the moat.
Great companies are organizational inventions
The most important companies are actually organizational inventions. They create a new kind of institution around a new kind of work, and in doing so, they make a new kind of person possible.
OpenAI did not look like academia, a corporate research lab, or a traditional software company. At its center was frontier model training as the organizing activity. Safety, policy, product, infrastructure, and deployment all orbited that gravitational center. The structure changed what kind of researcher could exist there: someone who wanted to operate at the edge of science, product, geopolitics, and civilizational risk at the same time.
Palantir invented a new kind of operating institution for broken systems. Forward deployment was not just a go-to-market motion. It was a status hierarchy, a talent model, and a worldview. The company took work that would have been low-status elsewhere, sitting with customers, absorbing institutional mess, translating politics into product and made it central. It created a protagonist who did not fit cleanly into software engineering, consulting, or policy, but could operate across all three.
None of these companies fit the boxes that existed before them. None of the people who built them did either. Great companies are not just places where talented people go. They are structures that let a certain kind of talent finally express themselves.
Shape determines who can exist there
The best companies in the world do not only compete on category, market, or compensation. They compete on identity. Ambitious people tend to value a few things intensely: feeling special, being close to power, becoming undeniable, staying full of optionality, belonging to a mission, being in the room where history bends but they often do not know which of these they are actually optimizing for yet. That is why the strongest institutions find people early and are recruiting at the most top tier universities when they are freshman. They reach them before their self-concept has hardened, before they know what they want to be famous for or what their values are, before they can distinguish between the work they are good at and the person they are trying to become.
A great company gives them a language for their own ambition. It says: the thing you have been circling around but have not known how to name can happen here. You can become the person who moved the Mars timeline, the person who was in the room when the frontier shifted, the person who could operate inside broken institutions, the person whose work became undeniable.
This is why great institutions are wrappers around a kind of person.
Many compete on cash, which is the least interesting form of talent competition for legendary companies (maybe Jane Street or Citadel though). Cash can close people, but it rarely converts them (ask some of the neolabs or Alex Wang). The best people are most loyal when the company can offer something more specific than money: a path to becoming the version of themselves they already wanted to be, or did not yet know they wanted to be.
Each emotional promise is also a structural promise. If the company says customer proximity matters but customer-facing work is low status, the promise is fake. If it says ownership matters but decision rights are centralized, the promise is fake. If it says mission matters but the mission offends no one, selects for no one, and costs nothing, the promise is fake.
So what do people want to feel?
People want to… continue on X ↗
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Whatnot PM Tom Verrilli Critiques the Average Product Manager Role

Building, and Whatnot

Tom Verrilli argues that the product manager role has degraded since the hiring boom, citing 31,832 applicants for one Whatnot PM job. He contends that many PMs became process managers rather than product owners.

Original post · 12 min read
X ArticleBuilding, and Whatnot
In the last two years, 31,832 people applied to be a Product Manager at Whatnot. We hired one. You're twice as likely to hit a hole in one as you are to get a job by simply applying.
That’s not a process failure. I’ve been building products – and product teams – for over a decade and one of the biggest factors in deciding to come to Whatnot ~3 years ago was the very deliberate product culture. No one knows what it means to be a PM in the world of AI, but everything I see says the industry is moving towards us and how we build here - because no tool will make you useful if you aren’t doing the right job.
First we have to acknowledge: the average PM is deeply average.
The product function emerged in response to scale – engineering teams got too big for CEOs or GMs to manage directly, so a business <> tech conduit was needed. Over time we lazily generalized the role to “every time you hire an Engineering Manager you hire a PM”. But where an Eng Director managed 30-40 people through their EMs a PM Director just managed five. Incentives govern the world, so those Director’s jobs became “justify growing my eng partners headcount” so they could, in turn, grow theirs to become a VP. Slowly the role of junior PMs shifted from “CEOs of the product” to “babysitters of buttons” and product-minded engineers to infantilzied order takers.
Then COVID hit and the industry hired a mind-boggling 500,000 new software engineers in just four years and ~80,000 new PMs were minted to match. That’s 80,000 PMs buried within gargantuan teams at FAANG, far from any customer, 50 layers from the zoom where it happens, taught paint-by-number PM’ing at a product school, in an era of unearned engagement growth where seemingly anything worked.
The likelihood of someone emerging from that with great product instincts, experience and grit actually feels less likely than hitting a hole in one.
Second: we made our best, worse.
When your job is supervising five people, all you can do with your day is get in other people's work. They dislike that and label it micromanagement in an anonymous survey so you back off. How then do you spend your time? You story-tell, shepherd things through review so your teams are ‘succeeding’, justify resources. But you don’t know what story to tell so you stand up a user research team to tell you the jobs to be done, then a PMM function to tell that story to customers. The function that came to be strategically important because it gathered context and disseminated clarity abstracted itself out into increasingly ivory towers.
But the actual truth is in the data models of your systems, in sales calls, CX tickets, in the analytics – not in the pretty 2x2 made to simplify it all.
All the time you spend playing management means your innate understanding of the issues is getting stale, your instincts for your customer duller, the likelihood you’re right is dropping.
Our batting average as a function dropped both because the denominator expanded AND because its expansion meant everyone who was good at product seven years ago was promoted out of doing any actual work (or got rich enough that the incentive to stay and play politics was low).
The Whatnot Way
Since its earliest inception, the Whatnot product team has been built on a somewhat simple premise: we regret that product management exists. Sales and engineering got on just fine before we were hired, so where they can, they should just ship without procedural gatekeeping or nonsense paperwork. Product is a trade, not a qualification. Anyone who does it well learned by doing and by being around great people doing.
I was in an interview recently where someone told me Whatnot felt like Twitch and eBay had a baby - culturally it couldn't be more wrong, but in terms of the product span it's a decent comp. A conservative estimate says those two organizations combined have >400 PMs. We have 20. 20 PMs for 1200+ total employees.
Our PMs are mapped to problems, not to EMs. Those two often overlap, but aren’t the same thing. If you’re building a new sales format for fashion sellers you’re going to be pretty hand in glove with the EMs who own how listings and inventory works, but equally with the logistics and payments EMs.
Having to work across multiple stacks and weigh impacts to different customers isn’t easy – it requires broad context of the business, the ability to foresee downstream impacts of changes to any feature, prowess at context switching, the ability to build and spend trust across a whole org rather than with one partner. That’s why we hire almost exclusively senior PMs. PMs who are over endless alignment meetings and itching to build again. Or, we convert promising sales or ops folks and let them learn by doing. We're always looking for the hole-in-one mid-career L5/L6 hire, but the stats don’t lie about how often we find them.
Finally, everybody ships, including me. I am always working directly with a team of engineers and designers to ship features as an … continue on X ↗
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Matt Epstein Outlines Claude Code Formula for Viral Launches

The secret behind every Viral Launch...

Matt Epstein promotes an X article claiming his team has run 30 major launches using a Claude-driven system built around research and a 'bold claim' positioning approach. Mostly promotional with limited concrete detail in the excerpt.

Original post · 9 min read
X ArticleThe secret behind every Viral Launch...
We've done 30 of the largest launches on X.
They all follow a specific formula that can be copied again and again... using Claude code.

In this I will show you how and exactly how to get millions of views for your launch with 95% of the process being done by Claude for you.
Most people use AI the same way they use Google. They open ChatGPT or Claude, type in one prompt, take the first answer, maybe ask it to “make it better,” and then call it done.

That is exactly why most AI-written content sounds like garbage. It has no taste, no structure, no research, no editorial judgment, and no understanding of what actually makes people stop scrolling.
We do not use Claude as a writer. We use Claude as an operating system for launches.

The secret behind every viral launch:

A viral launch s the result of: research, positioning, novelty extraction, hook writing, narrative structure, proof, demo flow, editing, and distribution.

95% of the success of launch comes down to what we call the "BOLD CLAIM"

This is what you are introducing and what makes it different than anything else out there.
For example: If you say "introducing the worlds first AI ad maker" you likely will not go viral in todays landscape, you might have 2 years ago. Why? There are a million AI ad makers out there and the way you are positioning your product is not novel.
If you say "introducing the worlds first AI ad orchestrator that makes ads with your content and kills AI slop" you have a much higher liklihood of going viral because this is NOVEL and it solves a BIG pain point in the market.
Most founders build thier launch positioning based on what THEY think the market wants, rather than relying on first party data to show what the market wants and what they hate to counter position against.

We built a system that forces Claude to do intense research to figure out how to position any product based on: Youtube outliers, deep reddit research, EVERY launch on X + 200 other data sources.
This research creates a 10x HIGHER liklihood that your launch will resonate. A video/product that resonates in market is the difference between a launch that books HUNDREDS of demos and one that books NONE.
The crazy part: Our research has literally uncovered key motivators of market that has changed the development teams of the companies we work with. Knowing what your market DEEPLY wants and VALUES shouldn't only change the way you market, it should change what you build.


Here's how it works:
Inside Claude Code, we run a group of 21 specialized agents. Each agent has one job. One researches the market. One studies viral launches. One finds what customers are already saying. One pulls out the product’s most novel claim. One writes hooks. One critiques tose hooks. One rewrites them. One checks if the narrative is actually interesting. One checks if every line makes the product feel more important.

Each step passed through a Manager step. This agent's job is to check the work, and give feedback to the agent it manages.

The key is that Claude is not allowed to just “write the launch.” That is how you get AI slop. Instead, Claude has to move through a process where every piece of the launch gets attacked, rewritten, scored, and improved before it gets to the final version.
Before a single line is written, the system starts with research. It looks at the company, the product, the market, the competitors, the category, the founder story, and the existing customer language around the problem. Then it studies what has already gone viral in similar categories and starts pulling out patterns.
If you look at the image above, you'll see something called the Mom test agent. We trained this agent on my mom: a 61-year-old woman who only knows how to use Facebook. this agent is trained to call out things that my mom wouldn't understand in the launch script. if you want a super viral launch (say 10 million views) your content needs to be easily understandable by anyone who's extremely non-technical or even extremely low IQ. you could call this a mass market test. I call it the Mom test

This is one of the biggest differences between average launch content and content that actually moves. Most people write from what they want to say. Great launch content starts with what people already care about.
If you are launching a product, nobody cares that you “built a platform.” Nobody cares that you “help teams save time.” Nobody cares that you “streamline workflows.” Those are dead phrases. They sound like every B2B SaaS homepage on the internet.

That is why the research phase matters. Claude is not just looking for information. It is looking for the strongest possible angle. The thing that makes the product feel novel, urgent, obvious, or inevitable.

Once we have that, Claude moves into the hook.
The hook is where almost every launch dies.
Most launch hooks are polite. They sound like the founder is trying to be professional instead of trying to earn a… continue on X ↗
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ixigo Rebuilds Travel Platform Around AI Agent Tara

ixigo Rebuilds Travel Platform Around AI Agent Tara▶

Aloke Bajpai announces ixigo has rebuilt its travel platform to be AI-native, with an agent named Tara integrated into product journeys to understand users and complete tasks.

Original post · 1 min read
We just rebuilt ixigo from the ground-up to become AI-native. Not a redesign. Not an update. A completely new way to experience travel. Tara, our AI agent understands you, assists you and gets things done for you integrated deeply into our product journeys.
#ixigoNEXT #TARA #AI #TravelTech
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Mayank Agarwal Outlines Faceless Instagram Plan for AI Income

Mayank Agarwal shares a numbered plan for quitting a job and using AI to earn money, starting with launching a faceless Instagram page this week. The post is a truncated thread opener.

Original post · 1 min read
If I wanted to quit my job & use AI to get rich by Summer, here's exactly what I'd do:

1. Start a faceless Instagram page before the week is over. Not next month. Not after you "research more." This week.
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Solo AI Founder Tibo Shares Five Lessons From Revid Growth

Peter Yang summarizes five takeaways from solo founder Tibo, who says his AI products reached over $1M monthly revenue. Lessons include charging from day one, following user signals, pricing at $50-100 per month, keeping churn under 20%, and building SEO tool pages.

Original post · 2 min read
My top 5 takeaways from @tibo_maker, a solo AI founder who's making $1M+ a month:

1. Charge money on day one.

Tibo’s first startup failed because he cared more about appearing successful (e.g., I managed a team of 10 and raised $200K) than validating demand with paying customers. “If there is no revenue and no stickiness in the revenue, it’s going to be very hard to build a successful business.” Free signups are easy to mistake for traction.

2. Follow the signal when users surprise you.

Tibo acquired Typeframe ($2K MRR) as a product video tool, but noticed users were hacking it to stitch 5-second AI clips into longer videos with consistent characters and scenes. He pivoted the entire product to meet this need and rebranded it to Revid, which is now making $600K+ MRR.

3. Price your AI SaaS at $50-100/month

Low enough that customers don’t need a sales call and high enough to filter out tire-kickers. “I see so many people charging $10 / month and it puts you into the position of a cheap product.” Tibo picks his price point first, then shapes the product around it.

4. Keep monthly churn below 20%.

If more than 20% of customers cancel each month, stop scaling acquisition and fix the product first. There’s a ceiling (max MRR) on your revenue based on churn vs. acquisition. At 40% churn, customers stay about 2 months and you’ll hit a wall no matter how much you spend.

5. Build tool pages to rank on Google

Revid has 100+ pages each targeting a specific Google search like “turn audio into video” and “YouTube to shorts.” Many AI founders follow a similar model.

📌 Watch our full conversation for more practical tactics like the above: youtu.be/0UnZnonMN9o
Peter Yang @petergyang
"I shipped 9 failed products before one took off...now I'm doing $1M+/month."

Here's my new episode with @tibo_maker, a solo founder who bootstrapped 5 AI products to $1M+ / month.

Tibo walked me through his exact playbook:

✅ How to validate ideas and fail fast
✅ Why his top acquisition channel is still SEO
✅ The pricing sweet spot for AI products

Some quotes from Tibo:

"When people twist your product into something else, that's a very strong signal you have to follow."

"It's easy to lie to yourself [with free users], but if there's no stickiness in the revenue, it's very hard to build a…
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