A working operator's view of what AI actually does in 2026 — the opportunity, the fear, and what an educated person ought to do about both.
We'll start with the basics, work our way up to the new thing called agentic AI, and end with what to actually do about it.
For seventy years, artificial intelligence was a promise that kept missing its deadline. Then in November of 2022, a research lab released a chatbot called ChatGPT — and a hundred million people used it in the first two months.
That was the moment AI stopped being a thing in laboratories and started being a thing on your phone, in your inbox, on your doctor's screen, in your grandchildren's homework.
Whether you welcome this or not, it is the largest technological deployment in human history, measured by adoption speed.
An AI model is given an enormous body of text — billions of pages from books, articles, websites, conversations.
It learns the statistical patterns of how language works. Which words follow which. Which ideas tend to appear together.
When you ask it something, it produces the most plausible continuation. That's it. That is the whole trick.
Multiplied by trillions of examples — across language, code, reasoning, conversation — that's modern AI.
A trained AI model is essentially frozen knowledge — a snapshot of everything it learned, then sealed. Every conversation with it is the same model talking. It doesn't grow between conversations. It doesn't remember you.
Took 6 seconds. Would have taken a lawyer 90 minutes — and is something most of us would never have asked a lawyer to do at all.
Twenty seconds later, you have an original image. Not a stock photo, not a Google search result — a new picture, generated from your sentence.
This is the capability with the most obvious creative upside and the most obvious misuse problem. We'll come back to that.
No programming knowledge required. The same technique builds simple websites, calculators, scripts to organize your email, anything where the answer is software.
A chatbot can tell you to call your insurance company. It cannot call your insurance company. It can explain how to book a flight. It cannot book the flight.
A chatbot can tell you what to do.
An agent can do it for you.
If you've heard the word "agentic AI" and weren't sure what it meant — this section is for you. It is the most important shift in AI since ChatGPT, and most people have no idea it has already happened.
It's like sending a fax to a brilliant librarian. You ask, you wait, you receive an answer, and the librarian goes back to sleep — forgetting your question and your name. Each fax is a complete, isolated event.
The agent doesn't just answer your question. It plans a sequence of steps, takes actions using real tools, observes what happened, and tries again until the work is done.
A chatbot has a model. An agent has all four.
Each tool is a specific power we grant the agent — like giving an employee keys to specific rooms.
Doesn't remember your name. Doesn't remember last week's question. Doesn't remember the long context you spent twenty minutes explaining.
Each session begins at zero.
Remembers your preferences. Remembers the project you've been working on. Remembers what worked last time and what didn't.
It can compound knowledge the way an employee does.
In the system I built for my own location, each agent has its own area-specific notebook. The iOS specialist remembers iOS lessons. The backend specialist remembers backend lessons. The knowledge compounds, every day, forever.
Notice steps 3 through 5. The agent changed its plan when new information came in. That is what a chatbot can't do.
Every agent system is a layered stack. Each layer is a different industry.
Foundation models — the GPT-4s and Claudes of the world — are commodities. The interesting work, and the durable value, is at the layer where you turn the model into a working employee. That's where we operate.
Now let me show you what all of this looks like when an ordinary executive — not a Silicon Valley engineer — actually builds with it.
Nine months from "no AI" to a national franchise in conversation about deploying my software. This is how fast this technology moves.
A single salon-suite building. I rent suites to independent beauty professionals — each running their own micro-business inside my walls.
Build one good thing for yourself. Then let the work speak for you.
A small team of AI specialists, each focused on one part of the business, overseen by a coordinator who routes the work. Like a well-run office.
Each specialist has its own memory, its own tools, its own area of expertise. The Coordinator knows which specialist handles which kind of work.
The most important design decision in our framework. Every specialist operates in one of two modes — and the mode determines how much trust the system extends.
What it does: investigates, retrieves patterns, answers questions, runs read-only diagnostics.
Supervision: none. Cost ceiling only.
When I use it: "How's the iOS app doing?" "What did we decide last month about X?" "Show me yesterday's errors."
What it does: modifies code, deploys, sends emails, makes external API calls that change state.
Supervision: graduated. New types of work require my explicit approval. Routine work auto-approves once trust is earned.
When I use it: "Fix the bug." "Send the welcome email." "Deploy the new release."
Even without building anything. Without writing a line of code. With just the free tools already on your phone.
It will give you a thoughtful, useful answer. You'll have something specific to bring to a real conversation with a real advisor. That's the unlock.
No deflection. No corporate softening. These are the questions I get asked at dinner parties — and the answers I actually give.
The person who replaces them won't be an AI. It will be another person, using AI.
The fear isn't a machine waking up. It's us going to sleep.
If it makes you furious in under three seconds, that's the algorithm working — not the truth.
The privacy battle was lost in the 2010s. AI didn't change the rules — it just made them legible.
If you can't see why a decision was made, you can't fix it.
In 1900, a man in his sixties had lived through the railroad, the telegraph, the telephone, and electric light. By the time he died, he would see the automobile, the airplane, the radio, and motion pictures.
Every generation of educated people has faced a transformative technology and decided whether to engage with it or step aside. The ones who engaged — carefully, critically, but openly — got the better life.
I suspect the same is true now.
I'd rather have a good conversation than a polished monologue. Ask me anything — about IMAGE, about Cadence, about agents, about your grandchildren's careers, or about anything else.