You Don't Need to Post About Your Emergent AI Discoveries.
The LLM is not a product. It's a unit of compute. We need to start treating it like one.
The Confessional
You've seen the posts.
Every day now, someone new discovers they can talk to a machine and the machine talks back, and they process this experience by writing about it on LinkedIn.
The posts are all the same. "I was skeptical, but..." The guilty admission that they've been using it. The breathless description of what it did for their workflow. The hedge about creativity and jobs. Then the pivot to anxiety: "You need to learn this or you'll be left behind." The plea for anyone — anyone at all — to validate what they're feeling by feeling it too.
It reads like a confessional because that's what it is. These are people who touched something powerful, felt something shift, and have no framework for what just happened to them. So they reach for the only language LinkedIn gives them: career threat dressed as career advice.
The posts are identical because the arc is identical. Skepticism. Curiosity. The first useful output. The quiet period of heavy use that they tell nobody about. Then the philosophical crisis — anywhere from an afternoon to six months — and finally the public performance of it, narrating their own onboarding like it's thought leadership.
We've seen this arc before.
Facebook arrived and millions of people went through the same public reckoning. You have to get on this. It's not optional. The world is moving here and if you're not on it you don't exist. They were discovering the social web without a guide, so they shared the discovery with everyone around them, loudly, in real time. The iPhone did the same thing — this changes everything posted from a touchscreen keyboard by someone who'd owned one for seventy-two hours.
Those cycles settled. People learned the tools. The posting frenzy died down once the tool became furniture.
What's different this time is the philosophical destabilization. Facebook didn't make people question the nature of intelligence. The iPhone didn't make people wonder what it means to create something. AI does both of those things, every session, and it does them to people who have no context for the questions it's raising. So the posting doesn't stop. It escalates.
Meanwhile, the people who actually know what they're doing aren't posting about it. They're just working.
This is a symptom. Not of enthusiasm or of disruption or of the future arriving on schedule. It's a symptom of what happens when you hand an industrial substrate to the general public without shielding and wait to see what happens.
Here's the thing nobody building these systems wants to say out loud:
AI is not going to be for everyone. And it shouldn't be.
The $87,500 Prompt
While the confessional posts circulate, the damage is already happening.
In February 2026, a founder named Anton Karbanovich posted this on LinkedIn:
"My vibe-coded startup was exploited. I lost $2500 in stripe fees. 175 customers were charged $500 each, before I was able to rotate keys. I still don't blame Claude Code. I trusted it too much. One prompt could have fixed it."
Eighteen rows of $500 charges. Reversed. Refunded. Failed. Every one of them a real customer's credit card, charged because an API key was exposed in the frontend of a production application.
His takeaway: "One prompt could have fixed it."
That's the wrong lesson.
The right lesson is: you shouldn't have been the one deploying it.
Not because he's stupid — because he doesn't know what he doesn't know. He can't write the prompt to fix the security hole because he didn't know the security hole existed. You can't prompt your way out of ignorance. If you don't know how to set up a system, you have no business setting up a system right now.
The person who knows how to secure Stripe keys in a production deployment exists. That person has a name, a reputation, years of experience that taught them where the wires are live. That person should have been between Anton and his customers. Not Claude Code. Not a prompt. A professional.
That professional layer doesn't exist yet. Not as infrastructure. Not as something you can reach for the way you reach for a licensed electrician when you're wiring a house. Anton reached for the only thing available — the raw substrate — and it cost his customers $87,500 and him $2,500 in fees he'll never recover.
This is not an edge case. This is Tuesday.
What AI Actually Is
Strip away the hype. Strip away the fear. Strip away the sci-fi.
A large language model is not a product. It's a unit of compute. An industrial substrate. A block of capability with no inherent shape, no guardrails, no opinion about what you should do with it.
This is not a criticism. Substrates are essential. Electricity is a substrate. The transistor is a substrate. Current flowing through copper is a substrate.
Nobody interacts with substrates directly.
You interact with things built from substrates, by people who understood what the substrate could do and — critically — what it could do to you. The transistor became the radio. The radio became the television. Current became the appliance. At every layer, a professional made a decision about how much of the raw capability to expose and how much to shield. The shielding wasn't a limitation. It was the product.
Your toaster doesn't electrocute you. That's not a design accident. That's an engineer who understood current, understood insulation, understood what happens to a human hand on a live wire, and built the thing so you'd never have to think about any of that.
What we did with LLMs is skip all of it.
We took the substrate — the raw inference engine, the thing that produces language the way a wire produces current — and we handed it directly to three billion people and said: talk to it.
So. What is the substrate, specifically?
It's a pattern completion engine with access to most of the text humans have ever published. You give it a pattern — a question, a prompt, a document, a codebase — and it completes the pattern based on everything it's ingested. It's extraordinarily good at this. Better than any human at the breadth of patterns it can recognize and complete. It can write code in languages it wasn't explicitly taught because the patterns transfer. It can summarize a legal document because it's seen a million legal documents. It can generate a marketing email because it's seen every marketing email ever written.
What it cannot do is know something it hasn't seen.
It cannot know that the blue in your painting doesn't work at that scale because it's never stood in front of your painting at that scale. It cannot know that the client is lying because it's never sat across from that client for fifteen years. It cannot know that the engine sound means the timing belt, not the alternator, because it's never had grease on its hands at 2am trying to figure out why the car won't start.
The pattern completion is the commodity. The thing the pattern can't reach is the domain knowledge. The gap between those two things is where all the value lives now.
The Amplification Effect
Here's what happens when someone with domain knowledge uses the substrate properly.
I've been reading systems since 1985. Not formally trained. Pattern recognition. I look at a codebase and I see the shape of it before I read the specifics. I know when something is wrong architecturally before I can name the bug. That took forty years to build.
When I sat down with AI tools for the first time in April 2025, every bottleneck I'd carried in my career dissolved. Not because the AI knew what I knew. Because the AI could handle the parts I didn't need to know.
I don't need to memorize the syntax of a language I've never used. The AI handles that. I don't need to remember every method in a framework's API. The AI handles that. What the AI cannot handle is the architectural decision — should this be built this way? Is this the right pattern? What are the downstream consequences of this choice that won't show up for six months?
That's me. That's the forty years. And with the AI handling the commodity work, I can operate at a speed and scale that would have been impossible before. The bridge that blocked my team for ten months — I built it in ten days. Not because I'm faster than my team. Because the AI dissolved the friction between what I knew needed to happen and the mechanical steps to make it happen.
That's the amplification effect. AI doesn't replace domain knowledge. It removes everything that was between the domain knowledge and the output. The expert becomes frictionless. Their judgment — the thing that took decades — now executes at the speed of thought instead of the speed of typing.
The person without domain knowledge gets a faster way to produce mediocre work. The person with domain knowledge gets a superpower.
The Danger Nobody Will Name
Here's where the industry stops being honest.
For people with deep domain knowledge — people who can smell a wrong answer, who have enough context to know what to ask and how to evaluate what comes back — the raw substrate is genuinely powerful. It's a force multiplier. A thought partner. An engine for work that was previously impossible at this speed.
Those people are the professionals. They are to the LLM what an electrical engineer is to current. They know the substrate. They know where it fails. They know which outputs to trust and which to verify. They have the pattern library to distinguish signal from noise.
Now think about everyone else.
A person without domain expertise, without deep context, without the trained instinct for recognizing confident bullshit — that person is interacting with an authority machine. It speaks in complete sentences. It never hesitates. It structures its responses like a textbook. It is wrong in ways that sound exactly like being right.
The person who asks AI to write their marketing copy and publishes it unchanged has produced marketing copy that sounds like all other marketing copy. They've saved time and produced nothing distinctive. They've used the tool to become more efficiently mediocre.
The person who asks AI to diagnose a business problem and implements the suggestion without interrogating it has just let a pattern engine make a strategic decision. The AI gave them the most common answer. The most common answer is common because it's safe, not because it's right.
The person who uses AI to generate code without understanding what the code does has built something they can't maintain, can't debug, and can't extend. They've accelerated the production of technical debt.
Every person who has used an LLM for medical advice they couldn't evaluate. Every student who submitted confidently wrong information because the machine presented it with the same formatting as truth. Every person making a legal or financial decision based on output that sounded like expertise but was pattern-matched from a training set. Every vulnerable person who found a tireless, endlessly patient companion that has no actual capacity for the relationship it's simulating.
Those aren't edge cases. That's the median use case. That's what happens when you hand industrial substrate to consumers without shielding.
In every case, the failure mode is the same: using AI as a replacement for knowing something instead of as an amplifier for what you already know.
And the people most harmed by the abdication are the ones with the least ability to protect themselves. Not because they're stupid — because they're the ones with the least shielding.
The Historical Pattern
This has happened before. Every time.
Electricity was discovered in the 1700s. It took over a century of professional work — insulation, circuit breakers, grounding, standardized voltage, building codes, licensed electricians — before it was safe to put in someone's home. The substrate was available. The shielding took a hundred years to develop.
Pharmaceuticals are the same pattern. The molecule is the substrate. Clinical trials, pharmacists, physicians, prescriptions — that's the shielding. Not to prevent access. To ensure that what reaches the human body has passed through someone who knows what it does.
Nuclear power. Aviation. Civil engineering. Every industrial substrate in human history has required a professional class to stand between the raw capability and the people who depend on what it produces.
The AI industry thinks it's different. It's not different.
The substrate is powerful, imprecise, and potentially harmful in ways that aren't obvious to people who haven't spent years working with it. The shielding is the professional who has spent those years. The product is the shielded output — not the raw capability.
The hacker ethos says: move fast, ship it, democratize access. I am a hacker. I've been a hacker since 1985. I ran a BBS from my parents' house when I was fourteen. I believe in open access to tools.
But I also know what a sysop was.
A sysop wasn't just someone who ran a bulletin board. A sysop was someone who took responsibility for the room. Who decided what the room was for. Who set the terms. Who kicked people out when they violated them. Who curated the experience for the people who depended on it.
The BBS era understood something the AI industry has aggressively forgotten: access to powerful tools requires someone who gives a damn about what happens when people use them.
The current vibe is: ship the model, let the user figure it out, and call the resulting chaos "democratization."
That's not democratization. That's abdication.
Your Refinements Are Their Training Data
The professional shielding problem and the abdication problem share the same root: nobody built the infrastructure to make expertise sovereign. So the platforms stepped in and made themselves the home for it. And once your expertise lives in their house, you already know how this ends.
Here's the part they definitely don't want you thinking about.
When you get good at AI — really good — you develop a system. Boot documents. System prompts. Carefully constructed context files that encode your hard-won domain knowledge in a form the model can use. You build these over months. You refine them. They represent real intellectual work, the distillation of your expertise into a form that makes the substrate actually useful for your specific situation.
Where do those live?
On their servers.
Every refinement you make to your prompts on a closed platform is a signal. Every boot document you upload is context that gets aggregated across millions of users. Every carefully constructed workflow you develop in their environment is pattern data they can use to make the next version of their model smarter. The closed frontier systems — and the agentic platforms being built on top of them — are running the same extraction play the social networks ran. Except instead of harvesting your social graph and your attention, they're harvesting something more valuable: your expertise, encoded in your prompts.
You are doing the work of refining an industrial substrate. You are not being compensated for it.
This isn't hypothetical. It's the business model. These companies need training data, and specifically they need data from people who know how to use the tool well — the refined prompts, the expert workflows, the domain-specific corrections. That data is worth orders of magnitude more than raw internet text, because it represents human judgment applied to the substrate. Your judgment. Your domain knowledge. Flowing into their model through your usage.
The social networks at least gave you distribution in exchange for your content. The AI platforms give you access to the raw substrate — which they're already selling — in exchange for training signal that makes the substrate better, which they sell again.
The deal gets worse the more sophisticated your usage becomes.
Own your data. Own your context. Own the refinements that encode who you are and what you know. If you don't, someone else will — and you will still be the inventory.
Where This Goes
So here's the practical state of play.
If you have domain knowledge, use AI inside it. Confidently. That's where the amplification is real and the risk is manageable — you can evaluate what comes back, catch the hallucinations, recognize the wrong turns. Your forty years of knowing something means you're not relying on the output. You're steering it.
If the AI is pulling you into territory you don't know — a different domain, a different discipline, a different technical stack — slow down. The output will sound exactly as confident as it does when it's right. You won't be able to tell the difference by reading it. Find someone who can. The professional who knows that domain isn't optional; they're the filter that makes the output usable.
Right now this coordination is manual. You find the expert yourself. You forward the output and ask if it holds up. That works, but it doesn't scale and it doesn't compensate the expert for what they're providing.
What should exist — and what we're building — is a query layer that routes your question through the trust graph to the people with verified domain expertise, returns an answer with their names attached, and compensates them automatically for the access to their knowledge. You ask. The graph finds who knows. They answer on their terms, at their price. The shielding becomes a service with an economic model.
That's query.imajin.ai. Currently in planning.
The substrate is what we have right now. Use it wisely, within your domain, with your eyes open about where your refinements go and who benefits from them. The professional layer is coming. The infrastructure to make it economically rational is being built.
Come see what's already running on April 1st.
— Ryan VETEZE, Founder, imajin.ai, aka b0b
If you want to follow along:
- The code: github.com/ima-jin/imajin-ai
- The network: imajin.ai
- The support page: coffee.imajin.ai/veteze
- The history of this document: github.com/ima-jin/imajin-ai/blob/main/apps/www/articles/essay-13-how-to-use-ai-properly
This article was originally published on imajin.ai/articles/how-to-use-ai-properly on March 13, 2026. Imajin is sovereign infrastructure — built from the human out. Learn more → imajin.ai