Why Don’t You Have a Custom AI Yet? — Long Version

I use AI every day.

That statement isn't particularly interesting anymore. Millions of people use ChatGPT, Claude, Copilot, Gemini and a growing list of other AI tools every day.

But here's what continues to surprise me:

Most people are still using AI like a chatbot.

Open it. Ask a question. Get an answer. Close it.

Tomorrow, do it again.

There's nothing wrong with that. It's incredibly useful.

But I think we're dramatically underestimating what happens when you stop simply using AI and start building your own AI.

Not building a large language model. I'm certainly not training one in my basement.

I mean creating a customized AI that increasingly understands you.

Your work.
Your priorities.
Your preferences.
Your sense of humor.
Your projects.
Your routines.
Your family.
Your businesses.
Your systems.
Your ideas.

Essentially, you're creating a little mini-me.

And I've learned something interesting along the way:

It takes some time for an AI to know "you" well.

The Part You Can't Pre-Program

You can give an AI instructions.

You can tell it what you do for a living, how you like things written, what businesses you're working on and what applications you use.

That's the easy part.

The really interesting customization happens over time.

My AI knows my slang. It knows my humor. It increasingly recognizes when I'm serious, when I'm brainstorming and when I've wandered into another potentially expensive rabbit hole.

And it isn't limited to work.

Kids. Gift ideas. Gardening. Home projects. Jeep Rubicon upgrades. Technology purchases. Business ideas. Travel. Things I've considered buying and decided against.

That's only naming a few.

Some of that may sound insignificant compared with using AI to analyze data or automate a business process.

I think it's exactly the opposite.

Those hundreds of seemingly insignificant interactions are what create context.

And context compounds.

Eventually, you spend less time explaining yourself.

Instead of:

"I'm a recruiter, and here's some background on what I'm working on..."

it becomes:

"Pull this apart. What am I missing?"

That's a completely different interaction.

I Think There Are Seven Levels

This isn't a scientific framework. It's simply how I've come to think about my own progression with AI.

Level 1 — The Chatbot

This is where most people begin.

Ask questions. Research something. Rewrite an email. Create a job description. Summarize an article. Brainstorm ideas.

It's already incredibly useful.

But the relationship is primarily transactional:

Prompt → Answer → Done.

Level 2 — The Personalized AI

Now it starts learning how you work.

Your preferred writing style. Your terminology. Your role. Your goals. Your preferences.

You stop repeatedly saying:

"I'm a corporate recruiter..."

or:

"Make this sound more like me..."

because increasingly, it already knows.

This seems like a small change.

It isn't.

You're beginning to remove friction from the human-AI interface.

Level 3 — The Working Assistant

Now you stop asking AI only for answers and start giving it responsibilities.

Projects have continuity.

Research from Monday matters on Thursday.

There are recurring routines.

There are content pipelines, project plans, business ideas, candidate searches, follow-ups and things that need revisiting.

Instead of using AI for isolated tasks, you're beginning to work together.

This is where I think the phrase "chatbot" really starts breaking down.

Level 4 — The Connected Assistant

This is where things get much more interesting.

Your AI begins connecting with the systems where your work actually lives.

Email.

Calendar.

Documents.

Project-management systems.

CRM.

ATS.

Databases.

Research sources.

Suddenly the conversation isn't:

"Here's an email. What do you think?"

It's closer to:

"Find the email Jim sent me and tell me what you think."

The difference is enormous.

One requires you to act as the integration layer between AI and everything else.

The other begins removing you from that plumbing.

Level 5 — The Operational AI

Now we're moving beyond assistance.

Your AI can participate in workflows.

Monitor things.

Run recurring research.

Track projects.

Maintain lists.

Watch for changes.

Trigger automations.

Coordinate specialized agents or processes.

Prepare information before you ask for it.

Some actions can happen automatically. Others require approval.

That's an important distinction.

The goal isn't necessarily:

"AI does everything."

It's:

"AI knows what it can do, what it should recommend and what requires me."

At this level, you're beginning to build an operating system around how you work.

Level 6 — The Collaborative AI

This is the level I find especially interesting.

AI stops being something you primarily type to.

It joins the conversation.

Imagine an R&D session with two recruiters discussing a new recruiting technology.

The AI listens.

Someone says:

"What are we missing?"

The AI joins the discussion.

Someone challenges an assumption.

The AI researches it.

Someone wants to see the workflow.

The AI puts it on the conference-room television.

At the end:

"Summarize what we decided, what's unresolved and who's responsible for what."

Now AI isn't sitting outside the meeting waiting for someone to use it.

It's participating in the meeting.

That's a very different computing model.

Level 7 — Embodied AI

And this is where things have apparently gotten a little out of hand.

What happens when your AI isn't confined to a laptop or phone?

I've been working on something I call Jarvina Station 1 — JS1.

It's essentially the beginning of a dedicated physical workspace for my AI assistant: voice interaction, microphones, speaker, visual status indicators and eventually deeper integration with the environment around it.

The objective is ridiculously simple:

Walk into my office and talk.

No opening an application.

No finding the correct chat.

No keyboard required.

Just:

"Good morning, Jarvina."

And we start working.

But then I had another thought.

What happens when I leave the office?

Naturally, this resulted in another drawing and another project.

We're calling that one Jarvina Mobile 1 — JM1.

The concept is a portable AI collaboration device with microphones, camera, speaker, visual status indicators, battery power and its own compute capability.

Put it in the middle of a conference table.

It hears who's speaking.

Potentially turns its camera toward that person.

Participates in the conversation.

Connects to the boardroom television when one is available.

And eventually, perhaps, carries its own small projector when there isn't one.

In other words:

Take the AI that's been learning how you work and give it a physical presence in the room.

And yes, I realize how this sounds.

But the Hardware Isn't Actually the Interesting Part

The more I've thought about this, the more convinced I am that the little robot isn't the breakthrough.

The accumulated relationship is.

You could manufacture thousands of identical devices.

But the intelligence inside each one shouldn't be identical.

A recruiter needs something different from an engineer.

An entrepreneur needs something different from a sales leader.

A family might use one differently from a business.

There can absolutely be prebuilt capabilities:

Meeting routines.

Brainstorming frameworks.

Research processes.

Presentation modes.

Workflow automation.

Follow-up systems.

Project management.

Knowledge retrieval.

Specialized agents.

Approval rules.

But eventually the AI has to learn the person or organization using it.

You can pre-program the foundation.

You can't completely pre-program the relationship.

That's Why I Think We're Asking the Wrong AI Question

The question today is usually:

"Which AI is best?"

ChatGPT?

Claude?

Gemini?

Copilot?

That's a reasonable question.

But I increasingly think another question matters more:

What have you built on top of it?

Have you taught it anything about you?

Does it understand your work?

Does it know what you're trying to accomplish?

Have you given it persistent projects?

Have you developed routines together?

Can it access the information you repeatedly need?

Have you connected it to any of your actual systems?

Does it recognize how you make decisions?

Have you given it responsibilities?

Because if you've been using increasingly powerful AI models for two years but every interaction still begins from zero...

you may own a Ferrari and be using it to drive to the mailbox.

Creating a Mini-Me

I keep coming back to that phrase.

Because that's essentially what I've been doing without realizing it.

Not creating an AI clone of myself.

That would defeat half the purpose. I want something that challenges me when appropriate, sees things I've missed and possesses capabilities I don't.

But it should understand me well enough that we don't have to reintroduce ourselves every morning.

It knows the professional stuff.

Recruiting. Talent intelligence. AI. Technology. Business. The projects I'm working on.

But it also knows enough of the ordinary stuff.

My slang.

My humor.

My family.

What kinds of gifts I'm looking for.

What's happening around the house.

The gardening project.

The Jeep Rubicon upgrade I'm considering.

And approximately seventeen other things I've probably forgotten I'm working on.

That's the point.

Your life isn't organized into separate prompts.

Why should your AI be?

The Real Advantage Is Compounding Context

This is probably the most important thing I've learned.

People naturally focus on how much smarter the next AI model will be.

And that matters.

But there's another curve happening at the same time:

The model gets better.
The tools get better.
The integrations get better.
And your AI gets better at understanding you.

Those curves compound.

Six months of accumulated context isn't the same thing as opening a brand-new chatbot with a slightly better model.

Eventually the question changes from:

"What can AI do?"

to:

"What should my AI do?"

That's when this gets really interesting.

So, Why Don't You Have a Custom AI Yet?

You don't need a dedicated computer.

You definitely don't need to start designing a rotating AI device on a piece of paper.

I appear to have handled that mistake for everyone.

Start smaller.

Give your AI context.

Teach it how you work.

Correct it when it's wrong.

Tell it what you prefer.

Build recurring routines.

Give projects continuity.

Connect useful systems when appropriate.

Give it increasingly meaningful responsibilities.

And see what happens after six months of actually developing an AI instead of merely querying one.

Because I suspect the most valuable AI in your future won't necessarily be whichever company has the highest benchmark score that month.

It may be the one that knows how to work with you.

And, in case anyone is wondering:

I'm apparently on Level 7.

We're currently designing the hardware.

Things have escalated. 🖖