My AI Journey: From ChatGPT to Building an Automated Content Army
I started by talking to AI. Now I’m trying to build businesses where AI actually does the work.
My experience with AI didn’t begin with some grand strategy.
Like a lot of people, I started with GPT.
At first, it was fascinating simply because I could ask questions, brainstorm ideas, rewrite something, research a topic, create a marketing concept, or turn a rough thought into something useful in seconds.
Then I started pushing it.
Could it write an entire article? Could it help build a website? Could it code a game? Could it create a business plan? Could it analyze a market? Could it build a tool instead of just telling me how to build one?
That was when AI stopped feeling like an interesting chatbot and started looking like an entirely new way of working.
And I’m still figuring out how far that goes.
One AI Tool Quickly Became an AI Stack
GPT was the beginning, not the destination.
I added Claude and started using different models for different kinds of work. I experimented with Cowork-style agentic workflows and tools for coding and building. Then came image generation with Midjourney, video with Runway, content and video tools such as OpusClip, and other platforms for taking one piece of media and turning it into many.
The important realization was that there wasn’t going to be one AI that did everything best.
One model might be better at strategy.
Another might be better at grinding through a complicated coding project.
Another creates better images.
Another handles video.
Another is good at turning long-form video into short clips.
And increasingly, the interesting part isn’t the individual tool.
It’s what happens when you connect them.
That’s where my thinking started shifting from using AI to building systems with AI.
I Started Building Instead of Just Asking
One of the biggest changes for me has been how much more I can attempt.
I’ve used AI to help build websites, applications, interactive tools, games, prototypes, content systems and new business concepts.
Ideas that previously would have required finding a developer, designer, copywriter and maybe an entire team can now reach a working prototype incredibly quickly.
That doesn’t mean AI magically produces a perfect application from one sentence.
Far from it.
I’ve had broken layouts.
Broken links.
Code that almost works.
Designs that look great until you use them.
Features that mysteriously disappear when another feature gets fixed.
AI confidently telling me something is complete when five minutes of testing proves otherwise.
Which taught me one of the most important lessons of my entire AI experience:
AI rarely gets the important stuff completely right the first time. The advantage belongs to the person willing to keep working with it.
Prompting Is Becoming a Real Skill
People sometimes talk about prompting as though it’s finding some magical combination of words.
My experience has been different.
Good prompting is increasingly about learning how to think clearly about what you want.
What is the objective?
What are the constraints?
What does success look like?
What should the AI check before declaring the job finished?
What files should it inspect?
What information does it need?
What should it never change?
What should happen when something fails?
And perhaps most importantly:
How do you get the AI to critique its own work?
I’ve learned to treat AI less like Google and more like someone I’m working with.
Give it context.
Give it examples.
Let it build.
Test the result.
Tell it what went wrong.
Have it inspect its own mistakes.
Improve the instructions.
Run it again.
The prompt isn’t necessarily a command anymore.
It’s the beginning of a feedback loop.
Then I Discovered Automation
This is where things became considerably more interesting.
Generating a blog article with AI is useful.
But I started asking a different question:
Why am I manually generating the article at all?
Why couldn’t a system identify an interesting topic, research it, create the article, fact-check it, generate the appropriate image, create social posts, publish everything and measure what happened?
That moves us from AI assistance to AI automation.
And then AI agents push the idea another step further.
Traditional automation says:
If this happens → do that.
Agentic automation says:
Here is the objective. Figure out what needs to happen next.
That distinction has completely changed the way I think about building businesses.
My Goal: An Autonomous Content Engine
One of the systems I most want to build is an almost autonomous content operation across my network of projects.
The vision starts with intelligence.
Agents continuously watch relevant industries, news, search trends, social conversations, competitors, research papers, YouTube, Reddit and other useful sources.
But instead of simply regurgitating what’s trending, the system asks:
Is there actually something worth saying?
A research agent investigates it.
A strategy agent determines which of my projects it belongs to.
A writing agent creates the article in the appropriate brand voice.
A verification agent checks facts, sources, claims and links.
An SEO/GEO agent structures the article for traditional search and AI discovery.
Then the content pipeline begins.
One idea becomes many assets.
Article.
Short social posts.
Long LinkedIn post.
X thread.
Facebook post.
TikTok concept.
YouTube Short.
Full video script.
Quote graphics.
Infographic.
Newsletter section.
Poll.
Question.
Carousel.
Podcast talking points.
Potentially dozens of pieces of content originating from one worthwhile idea.
The important part is that each asset shouldn’t simply be copied everywhere.
The system should understand the medium.
A LinkedIn post shouldn’t read like a TikTok caption.
An X post shouldn’t be a truncated Facebook post.
A YouTube title shouldn’t be written like an SEO title.
Each platform has its own culture, format, length, hooks and audience behavior.
The system should transform the idea—not merely syndicate it.
Publish Once? I’d Rather Publish Intelligently Everywhere.
The next stage is distribution.
When an article belongs on Mass Density, publish it there.
When another piece belongs on one of the other related brands, route it there.
Then automatically create platform-specific versions and distribute them across the appropriate social accounts on Facebook, LinkedIn, X, TikTok, YouTube and other channels.
Potentially hundreds of connected accounts across an entire network.
But I don’t want hundreds of accounts blasting identical AI-generated garbage.
That’s the easy version.
The better version understands:
Which brand should publish this?
Which audience cares?
What format belongs on this platform?
When should it publish?
Which image or video should accompany it?
Which hashtags are appropriate?
What link makes sense?
Should this even be published here?
That last question is important.
Good automation shouldn’t only know how to publish.
It should know when not to.
Then Comes the Part I Find Most Interesting: The Engagement Layer
Publishing content is only half of social media.
The other half is participation.
Ultimately, I’d like agents to help discover relevant public conversations surrounding the subjects my projects genuinely know something about.
Not mindless spam.
Not fake testimonials.
Not bots pretending to be real customers.
And not dropping an unrelated link underneath every popular post.
That’s not guerrilla marketing. That’s just noise.
The interesting version is automated relevance.
An agent discovers a discussion where one of our resources actually adds something.
Another agent understands the conversation.
It finds the appropriate article, statistic, tool, video or resource from our network.
It can prepare a useful response.
Where platform rules permit automation, low-risk interactions can potentially be automated. Where authenticity, disclosure, judgment or platform policy matters, the agent can queue the opportunity for human approval.
That creates a much more powerful loop:
Listen → Understand → Contribute → Link when useful → Engage → Measure → Learn.
Now content marketing stops being broadcasting.
It becomes participation.
From Guerrilla Marketing to Gorilla Marketing
I’ve always liked guerrilla marketing: finding unconventional ways to get attention without simply outspending everyone.
AI takes that concept somewhere new.
Maybe call it Gorilla Marketing.
Not one marketer manually trying to be everywhere.
An intelligent network helping a brand be present wherever it has something worthwhile to contribute.
Imagine hundreds of small, relevant interactions happening around an ecosystem of useful content.
Someone asks about AI agents.
Mass Density has a resource.
Someone discusses creator marketing.
A related project has something useful.
Someone asks a question about SocialFi.
Another brand in the network has research or a tool.
The objective isn’t to manufacture fake popularity.
It’s to dramatically improve the ability to discover opportunities to be genuinely useful at scale.
That’s an important distinction because once AI makes automated content nearly infinite, spam becomes nearly infinite too.
The scarce commodity becomes trust.
The Mac Mini and OpenClaw Experiment
I’m also interested in moving beyond purely cloud-based AI.
A Mac mini running something like OpenClaw opens up another idea: a persistent AI operating environment that can stay active, maintain context, run scheduled jobs, interact with local resources and coordinate agents.
That starts to feel less like opening ChatGPT when I need something and more like having an AI operations center.
Research agents.
Content agents.
Coding agents.
SEO agents.
Publishing agents.
Analytics agents.
Monitoring agents.
Specialized workers with specific permissions and responsibilities.
And potentially a manager agent coordinating them.
That is the direction I find most interesting:
AI teams rather than AI tools.
I’m Still Learning—and That’s the Point
The funny thing is that the deeper I get into AI, the less I feel like I’ve figured it out.
Every answer creates three more possibilities.
You learn prompting and discover automation.
You learn automation and discover agents.
You learn agents and start thinking about memory.
Then orchestration.
Permissions.
RAG.
MCP.
Local models.
Computer use.
Agent teams.
Evaluation.
Observability.
Human approval systems.
The rabbit hole keeps going.
And the technology keeps changing while you’re learning it.
But I think that’s exactly why this period is so interesting.
We aren’t learning a finished technology.
We’re learning while the technology itself is being invented.
My Biggest Lesson So Far
If there’s one thing my experience with AI has taught me, it’s this:
Don’t wait for AI to become perfect before learning how to use it.
It isn’t perfect now.
It misunderstands prompts.
It breaks code.
It hallucinates.
It makes ugly designs.
It creates brilliant designs.
It solves problems you thought would take days.
Then it spends an hour failing at something embarrassingly simple.
That’s AI today.
But focusing on the mistakes misses what is happening.
The models are improving.
The tools are improving.
The agents are improving.
And, perhaps most importantly, we’re improving at using them.
The competitive advantage isn’t simply access to GPT, Claude, Midjourney, Runway or the next breakthrough model.
Millions of people have access to the same tools.
The advantage comes from learning how to combine them into systems that accomplish something useful.
From AI User to AI Architect
That’s where I see my own journey going.
I started as an AI user.
Then I became an AI experimenter.
Now I’m increasingly interested in becoming an AI architect—designing systems where models, agents, automation, software and people work together.
The end goal isn’t:
AI generates a blog post.
It’s closer to:
AI discovers an opportunity → researches it → creates something valuable → verifies it → produces every relevant media format → publishes each version to the right destination → discovers related conversations → participates appropriately → measures what happened → feeds the results back into the system.
Then it does it again.
And gets better.
That is a much bigger idea than content generation.
It’s an autonomous content, distribution and engagement engine.
I’m nowhere near finished building it.
I’m still experimenting. Still prompting. Still breaking things. Still fixing what AI supposedly already fixed. Still finding new tools. Still discovering better workflows.
And that’s probably the most exciting part.
We spent the first few years learning how to talk to AI.
Now we’re learning how to put AI to work.
The next step is learning how to make all those intelligent systems work together.
#ArtificialIntelligence #AIAgents #Automation #ContentAutomation #FutureOfWork
