AI won't replace entire profession, but it's replacing the bottom 20-80% in {translation, design, coding, content, marketing, sales, design, operation, account, law}

Micro teams like mine beat corporations
because AI brings huge leverage for A players

Real examples of AI: 1. AI replaced junior developers for me.
I used @Replit AI agent to build a real tool I needed. Back in 2022, I'd hire someone on upwork and wait a week; today, it took me an hour of prompting to go from 0 to launch. The link:

seobotai.com/broken-link-ch…

The Time Has Come for Robots.

I build AI Agents to replace office workers, but these demos convince me! All physical labor will be gone to robots, too. (even the world's oldest profession).

Just watch it if you disagree. The biggest robot thread ever (50 demos): 1. Neogamma.

General purpose humanoid robot for home.

Building a “SaaS people wanna pay for” is sooooooooo hard 😩

AI made it easier to build things, but it didn’t really change the game the products people wanna pay for and share with friends.

How I build products people love:
(very different from what you’d expect) 1. I validate the idea first. Sometimes it takes me years to go from ides to star building it, because I wasn’t sure if people actually need this.

I pitch it to people on internet, in DMs, at the dinner table and so on. If I don’t get “wow, yeah, I want that”, I don’t build it

AI Coding is the new No-Code! It's a fact.

Non tech founder, designers, marketers & anyone can build software now.
Zero entry barrier! No limitations! Super cheap!
But complex apps still need real coders; AI won't replace them til 2030.

19 AI Coding Agents (for non coders): 1. .

Can handle full-stack web, mobile, and desktop apps. Can even build games.

Demo: Doordash Website:

blackbox.ai

Chinese AI startups: 1/6th of US funding, bad press, sanctions, brain drain, communism, little English proficiency, and no talent influx..

But after using Manus AI, Deepseek, Trae, Kling, Vidu, & Ying, I think the US is in trouble.

At this pace, China will dominate AI.

Demos: 1. Manus AI.

All-in-one AI Agent (browser operator, deep search/research, MCP, Code Exexution

What is MCP & why it's a big (huge) deal:

(model context protocol)

TLDR: MCP makes it possible for AI Tools to use external tools. E.g. Chatbot/IDE/AI-Agent can use Gmail/GoogleDrive/WeatherApp etc.

Detailed explanation for both, tech & non tech people (+demos):

1) AI Tools (chatbots, wrappers, agents, code generator, etc) wanna talk to external systems.

In pre-MCP world, one would have to write code to connect AI tool to the external system via API. Which meant every connection had to be pre-coded.

It also meant that every AI tool had to hard code its connection to every other tool. So if there are 1000 AI tools and 1000 external tools, then 1000000 hard-coded connections via API.

2) MCP is a standard protocol. This means that every AI tool has to implement this once, and then it can connect to thousands of external tools via this protocol.

3) The same goes for external tools. They all have to create an MCP server just once, and all AI tools that support MCP can connect to them.

4) It's a huge deal. Imagine 10k AI tools and 10k external tools now all have to implement MCP just once each. So it's 20k implementations. Versus 10k*10k=100M implementations.

5) This whole thing can also run on the cloud or on local computer.

See demos: 1. Claude desktop app uses MCP server to make a screenshot of a given website and converts it into HTML.

MCP allows you to simply provide a URL to the chatbot, instead of youtaking a screenshot and pasting it into chatbot.

screenshotone.com

Don’t do this until you made $10k with your startup:

> hire employees > paid google/fb ads
> website redesign > registering a company
> outsource > add more features
> refactoring > optimization
> raise VC funding > start a new project

Why: No employees!

> they can perform well-defined tasks, but at the beginning most tasks need creatively and exploration.
> they don’t care as much as u, they won’t be obsessed by making the product better, listening the users and working their ass off. The customers gonna feel it

We hit the wall with LLMs

> Two years ago, GPT4 made AI wrappers & Agents possible.
> Sonnet 3.5 solved the AI code generation.
> I just tried Sonnet3.7, GPT-4.5, O-3, Grok, and Gemini...
> Not impressive. Not even close to gpt3->gpt4 jump

What's next: 1. These won't lead to ASI:
- scaling & training on more GPUs
- training on more data / synthetic data

More progress will come from the application layer: the wrappers, AI agents, and hacks we all hardcode in our apps to compensate for AI limitations.

What to build (the ideas):

Every profile here with no followers & no reach thinks it's all about hacks, luck, retweets from big accounts, and the number of followers.

I thought the same for 18 years, until I understood what makes content viral.

The Truth (the TLDR is at the end): 1) The content types:

- 99.9% of it is either boring or not useful.
Even the author doesn't wanna reread it.

- 0.09% is useful/valuable content that's boring or difficult to read.

- 0.01% is the viral content. It makes you wanna share it with a friend, bookmark it, or reply.

I'm working on 24 startups simultaneously
(half a million b2b users, multimillion ARR)

No VC funding, employees, calls, office, managers...

Each business is 100% async over chat:
> me: idea, design, UX & marketing.
> comaker: coding, product & support.

Why, how, what: 1. After wasting my best years for a decade in VC-backed space, startup accelerators, conferences, meetings, scrums, I decided to do a hard pivot.

In 2023, I started my bootstrapping journey to build the world's most automated org to democratize startups for everyone

I've tried all (36) AI Coding Agents & IDEs 😵‍💫

[CreateXyz, Cursor, Softgen, Windsurf, Wrapifai, Copilot, Lovable, Bolt, v0, Replit, MarsX, AmazonQ, Pear, Devin, Github Spark, IDX, Webdraw, Claude 3.7 Sonnet & more]

The most complete list ever made: 1.
- it can clone a product when you drop the link to it
- has connections to things like the movie db, hackernews and more
- auth with one prompt

Good if you wanna create functiniing products with just a few prompts.

Create.Xyz

After seeing these humanoid robot demos, I bet you'll be convinced that all manual labor will be gone to robots.

(even the world's oldest profession will be taken by them).

All 26 humanoid robot demos: 1. NEO Gamma (Humanoid for Home)

(black mirror episode vibes)

I extracted the most common failure patterns from my 20-year-long startup journey.

(spoiler: most successful founders have done the same thing, while failed ones did many different things)

Why Startup Founders Fail: 1. Solution looking for a problem.

This one accounts for over 50% of failures I've seen & done. Founders build a solution first and then go to users, pitching it and trying to convince them they have such a problem.

The problem often isn't serious enough to change their habit.

Web Agents are automating the entire class of jobs:

> fill out forms (file complaints)
> signup for things (apply for a visa)
> browse web (buy tickets)
> collect data

These AI agents are actually good for humanity - they remove the most boring jobs.

Tools I use to build them: 1. The agents typically translate user goals into browser automation.

I've tried 30+ tools for this (gonna post a dedicated thread on this topic soon). My fav so far is: .

My @listingbott agent heavily relies on these tools.

stagehand.dev

Predictions for the Future of Startups:

[AI, Education, Parenting, Platforms, Deglobalization, AI Agents, Bio, Agro, Healthcare, Space, Governments, Onpremise, Devices, Migration, Made-by-humans, LLMs, Money, Whitelabeling, Content creators, Startups, VCs, India, Poland] 1. The AI Model race for LLMs will hit the wall.

The application layer (the wrappers and AI agents) will take the stage.

I acquired Unicorn Platform for $0.8M in '22.

I've literally tried every growth method I've heard of.
It all failed in 2023, but in 2024, I found what works & went 25k to 370k users.

Details report on each marketing experiment (failures & successes):

1. Partnerships with incubators.

I contacted all famous startup incubators and signed deals with most to place unicorn as a "perk" there.

I thought it'd become a passive stream of users, but I don't think I got even one paying user out of this. I canceled all these deals.

AI won't replace the entire profession, but it's replacing the bottom 20-80% in {translation, design, coding, content, marketing,sales,design, operation, account, law}

Small teams like mine beat big corporations
because AI brings huge leverage for A players

Real examples of AI: 1. First AI Admaker.

It can create winning ads in minutes.
(and replace $2K-$30K in monthly costs)

Used by $100M+ revenue customers like Ridge, Jones Road, Backbone, & MUD\WTR. I personally hate ads, but I'm giving this AI a try.

This never works:
→ Startup Idea → Plan → Design → Coding → Marketing→ Audience → 😣

This works (sometimes): I launched 20+ startups using the following playbook:

Audience → Problem → Idea → Validation → Waitlist → SEO → One Feature MVP → Iterate → Marketing → Success.

See a full breakdown of each step 🧵 :

If only someone told me this before my 1st startup:

1. Validate.
I wasted at least 5 years building stuff nobody needed.

2. Kill your EGO.
Make your users happy instead of yourself.

3. Don’t chaise investors; chase users, and then investors will chase you.

4... 4. Never hire managers. Only hire doers until PMF.

5. Landing page isn't important.
Go for an average template and edit texts, and that’s it.
The sale happens outside of the website anyway (in the early stages).

Drones, Robots & Humanoids will be absolutely everywhere 10 years from now.

affecting: [nurses, waiters, construction workers, soldiers, cops, janitors, factory workers, gardeners & pretty much every other repetitive manual labor].

Real demos that look like sci-fi: 1. Robots can imitate humans better than humans can do it

I started building an AI Agent for SEO in 2022.

The 1st version took a year to build,
then another year to iterate & reach a near-human level.

Today, @seobotai replaces my entire blog SEO team. An average article takes 2 hours, 700 prompts & 125 tasks.

How it works in detail: 1. The user provides a URL of the site to the agent:
- it scrapes the website
- LLM learns all the details about the biz: pricing, audience, solution, problem..
- It creates a full spec and passes it on to the next model