It was 2024. Everyone and their mother was launching a chatbot. Every pitch deck had the word “GPT” slapped on it like a magic sticker. And founders? Oh, they were busy selling visions of AI-powered empires that didn’t need employees—just prompts.
I remember walking into one boardroom in Dubai where a startup was demoing its “AI-powered legal assistant.” I typed a simple question into their system: “Can you summarize the UAE’s data protection law and identify potential compliance risks for a fintech?”
What came back was a combination of vague generalizations, copy-pasted Wikipedia phrases, and three hallucinated clauses that didn’t even exist. The founders looked proud. I looked pissed.
They called it innovation. I called it autocomplete with a God complex.
That was the first time I realized we had a serious problem on our hands—not with AI, but with how people were selling it. They were selling potential as if it were product. Selling predictive text as if it were intelligence. And burning millions while doing it.
So, I wrote the article: “Entrepreneurs, Don’t Fall for the AI Hype.” It was a war cry against blind adoption. A reminder that no matter how shiny the tool, if it can’t deliver truth—it can’t serve business. And truth, at the time, was nowhere in the picture.
Then Something Quiet Happened: AI Stopped Performing and Started Shipping
Fast forward to may 2025. Something changed.
The AI that once gave philosophical nonsense to technical prompts started writing actual, working code. Not just pretty code. Not just grammatically correct code. But production-ready, testable, functional code. The kind of code that could pass linting tools, talk to APIs, fix broken logic, and do it without asking for equity, benefits, or motivational posters.
At first, I didn’t believe it.
Then I met a solo founder in Riyadh. Built a full appointment system in three days using CLAUDE_CODE as his coding partner.
Then I saw a mid-size logistics firm in Germany replace four legacy systems with one AI-coded internal tool that saved them $900K annually.
Then I watched an engineer in Mumbai—barely two years into his career—ship features five times faster than his peers, thanks to Cline and Cursor helping him refactor code on the fly.
This wasn’t prompt engineering anymore. This was outcome engineering.
The illusion was breaking. And underneath it, something very real was beginning to emerge.
From TED Talk Toy to Developer’s Swiss Army Knife
Let’s be clear: ChatGPT, Claude, Gemini—these tools weren’t made for engineers at first. They were demo products. Novelties. Engines that could help you write poems, brainstorm ideas, simulate Socrates.
But then engineers got curious.
They started feeding these models broken codebases, ugly JSON blobs, unstructured logs. And instead of panicking, the models replied—line by line, fix by fix, suggestion by suggestion.
At first it was assistive. Then it was collaborative. Then it became indispensable.
That was the crossover moment.
You see, the initial use case of generative AI was chatting, writing and ideation. Now? It’s becoming engineering. Quietly. Relentlessly. And unlike writing blog posts or summarizing emails, this use case has stakes. Real economic stakes. Real business leverage.
When AI became good at writing and fixing code, it graduated from “cool toy” to “mission-critical teammate.” Not perfect. Not magical. But freakishly productive.
The In-House Revolution No One’s Talking About
While Twitter debates whether AI can replace copywriters, inside companies—something bigger is happening.
Entire backend systems are getting rewritten with AI’s help.
Legacy infrastructure is being migrated in hours, not weeks.
Customer support workflows are being rebuilt in natural language, tested in sandbox, and deployed through logic chains that used to take teams of devs to wireframe.
You know how many startups I’ve watched skip the $15K MVP dev cost and launch on a weekend now?
Hundreds.
They didn’t hire engineers. They hired an idea. They sat with Claude for 72 hours, got the code snippets, reviewed the logic, and shipped.
These aren’t “hacky demos.” They’re real products with paying users, Stripe dashboards, and working APIs. And they’re outpacing traditionally built companies because they’re not waiting on Jira tickets—they’re iterating with AI like it’s a business partner.
That’s the underground revolution. Not the public hype cycle. Not the next AI conference. The real story is in the backlogs being cleared, the scripts being written, the time being saved—not by automation, but by augmentation.
Economics Still Hurt—but They’re Healing
Let’s talk cost.
AI isn’t cheap. At scale, it’s a monster.
A single query on a large model costs OpenAI 10X more than a Google search. GPUs are still overpriced. Electricity bills are still climbing. And if you don’t manage your AI infrastructure with care, it will eat your profit margins alive.
But guess what?
That’s starting to change.
Open-source models are catching up—fast. Mistral, Meta’s open weights, Every month, something new drops that takes 70% of the functionality at 1% of the cost.
Chip wars are heating up. NVIDIA is no longer the only player in town. Groq_LPU, Cerebras, AMD—they’re all gunning for the same throne, and that’s putting pressure on margins.
And most importantly, AI usage is getting smarter. People aren’t just prompt-spamming anymore. They’re architecting intelligent workflows, batching queries, using hybrid systems, deploying fine-tuned models for specific tasks.
In other words: we’re exiting the “just throw it at GPT” phase and entering the “build with purpose” era.
That’s what real economic maturity looks like.
Welcome to the AI Utility Phase
You won’t hear much about AI in the media in the near future.
That’s a good thing.
Because that’s what always happens when a technology stops being disruptive and starts being useful.
AI tomorrow won’t be sexy. It’ll be silent.
It’ll be inside your CRM, cleaning your data before you send emails.
It’ll be inside your dev stack, debugging the function your junior engineer couldn’t fix.
It’ll be inside your operations team, turning meeting transcripts into workflows.
It won’t be on stage. It’ll be in the engine room.
That’s where the real leverage will live.
Not in hype. In horsepower.
The Solo Entrepreneur Just Got Superpowers
You know what I love most about this shift?
It’s leveling the playing field.
You don’t need to hire a full-stack engineer anymore.
You don’t need to pay $10K for a prototype.
You don’t need to wait six weeks for an agency to mock up your idea.
You just need one thing: clarity.
Clarity about what you want to build.
Clarity about what problem you’re solving.
Clarity about the journey from prompt to product.
The solo founder who understands how to weaponize clarity with the right AI stack? That person is now more dangerous than a 10-person team from 2021.
Because they’re fast.
They’re flexible.
And they don’t need anyone’s permission to launch.
That’s the real disruption. Not replacement. Enablement.
Final Thought: If You’re Still Watching AI from the Sidelines, You’re Already Behind
Here’s the truth I want to say out loud: AI is no longer experimental.
It’s here. It works. It ships.
And it’s not waiting for your opinion to validate it.
If you’re still arguing about hallucinations while others are using it to 10X their output, you’ve already lost.
This is the new stack:
Vision. Velocity. Verifiability.
If your idea is good, AI will help you build it.
If your execution is tight, AI will help you scale it.
If your insight is sharp, AI will help you weaponize it.
But if you’re still debating whether it’s “real,” then you’re not in the game. You’re in the gallery. Watching. Commenting. Critiquing.
And the builders? They’re already gone.
They’re on version four.
They’re shipping.
You?
You’re still writing Medium posts about the risks of AI economics.