Don't Be a Mason, Be a Creator

In my last post, I said the harder you hustle with AI, the less you earn.

Because you are using AI to move bricks. You used to move 100 bricks a day; now with AI, you move 10,000.

But bricks are just bricks. No matter how high you stack them, it's just a wall. The wind blows, it might fall. The rain beats, it might erode. You have to stand there, watching it, repairing it.

This is "Dead Matter".

A true AI system should be "Living Matter".

You shouldn't be constructing a building; you should be designing a species.

Buildings need maintenance; species evolve on their own.

Will Your System Die?

The test is simple: when you're not around, does it get stronger or weaker?

In early 2024, independent review site HouseFresh published an article titled "Google is Killing Independent Sites." They spent years writing air purifier reviews, each one based on real testing. But in Google search results, they were outranked by AI-generated content farms—sites that never bought a single purifier, just mass-produced "reviews" with AI and crushed real content through domain authority.

Ironically, these content farms didn't last long either. Google's March 2024 core update sent traffic plummeting 60%-90% for massive numbers of AI content sites. Reddit's r/SEO was filled with site owners saying their sites "went to zero overnight."

That's dead matter. It doesn't improve itself; it just rots.

The opposite example is Intercom's Fin AI. It's a customer service bot, but with a special design: after every response, users can click "Helpful" or "Not Helpful." Helpful answers get flagged as high-quality and prioritized for similar questions. Unhelpful answers get downranked and trigger human agent escalation.

According to Intercom's public data, a year after launch, Fin's automatic resolution rate improved from under 30% to over 50%, with some customers reaching 80%. The system gets smarter with use, not more dependent on human oversight.

That's living matter. It "eats" user feedback and "digests" it into a better version of itself.

Prompts Are DNA

If you want to build a forest, don't plant trees one by one. Design a seed that spreads with the wind, resists drought, and grows extremely fast.

In the AI era, Prompts and Workflows are your DNA.

Pieter Levels (@levelsio) is a legend in the indie developer community—one person running over a dozen products generating millions in annual revenue. He's publicly shared his content strategy on Twitter: instead of manually posting, he built an automated system.

The system generates multiple pieces of content daily in different styles—product updates, startup insights, data shares. After posting, scripts scrape engagement data for each tweet. High-performing content styles get reinforced; poor performers get eliminated.

After a few months, the system "learned" patterns he wasn't even aware of: tweets with specific revenue numbers get the highest engagement, posting during US West Coast mornings works best, short sentences get more retweets than long paragraphs.

He didn't manually derive these rules. The system "figured them out" from the data.

That's the power of DNA. You're not writing an article; you're encoding a set of evolutionary rules.

Don't Hire. Let the System Grow Itself

Traditional customer support faces the trap of linear growth: double the users, double the support team. Costs scale with volume, and you never escape.

Klarna (Swedish payments giant) announced a striking figure in early 2024: their AI customer service handled the equivalent of 700 full-time agents' workload within the first month of launch. Customer satisfaction matched human agents, but average resolution time dropped from 11 minutes to 2 minutes.

The key isn't "replacing humans with AI"—it's how the AI system is designed. After every conversation, user feedback is collected. Conversation patterns that solved problems get reinforced; those that didn't get flagged for improvement. Through processing millions of conversations, the system "grew" the ability to handle all kinds of edge cases on its own.

Klarna's CEO said the system is expected to save $40 million annually.

That's emergence. The system gets smarter with use, not more tired.

Similar logic is everywhere. Notion doesn't produce content—it provides templates, lets users create content, good templates get shared, bringing new users. Calendly doesn't do sales—every meeting invite carries a Calendly link, users spread it through usage.

They're not building products. They're designing self-replicating rules.

You're Fighting Physics

Schrödinger wrote in "What is Life?" that life is "negative entropy."

The Second Law of Thermodynamics says closed systems inevitably drift toward chaos. Houses collapse, code rots, companies become bureaucratic. This is the universe's default state.

But life is different. Life absorbs energy from the outside and converts chaos into order. A seed extracts nutrients from soil, sunlight, and water to become a tree. The tree dies, becomes soil, feeds the next seed.

What does your AI system eat?

If it eats your time and energy—you staring at dashboards, fixing bugs, patching data every day—then it's an energy-draining machine. You're using your own life to fight entropy. You'll burn out eventually.

If it eats user behavior, market feedback, internet data, then it might become a living thing. It's using external energy to fight entropy. You only need to check in occasionally.

Minimum Viable Species

Enough theory. How do you actually build this?

A minimal self-evolving system needs three components:

Nervous System: Workflow automation tools like n8n, Make, or Zapier. Handles scheduled triggers, API calls, and data flow.

Memory: Databases like Airtable, Notion Database, or Supabase. Stores generated content, user feedback, analysis results.

Brain: LLM APIs like Claude, GPT-4, or Gemini. Handles content generation, data analysis, decision-making.

A self-evolving content system workflow looks roughly like this:

Scheduled trigger → Scrape yesterday's content engagement data → Store in database → Call LLM to analyze what performed well → Output strategy adjustments → Call LLM to generate new content based on new strategy → Schedule publishing.

The key is closing the loop: analysis results must influence the next generation cycle. Without this feedback loop, the system is just an automation tool, not a species.

This isn't rocket science. Anyone who can use n8n can build a prototype in a day. The hard part isn't implementation—it's the mental shift: you're not building a tool, you're designing a species that evolves.

No matter how high you stack bricks, they can't leave your hand.

Once a seed sprouts, it no longer belongs to you.

Further Reading

The Underlying Logic of the AI Era

The Harder You Hustle with AI, the Less You Earn: Why traditional "hustle" thinking fails in the AI era.

How to Use Vibe Marketing for AI-Automated Marketing: From Vibe Coding to Vibe Marketing—a practical path.

Systems Theory and Complexity Science

Maxwell's Demon Guide: How to become an information age Maxwell's Demon.

The E^F Law: Value creation in the attention economy.

Tutorial Series

Prompt to Product: Earning USD Overseas: Complete tutorial series on building overseas products with AI.