Everyone’s Using AI. Almost Nobody’s Using It Right. [Part 1]

Something quietly crossed a threshold in the last year.

I’ve been in tech since I was twelve – taking apart computers before I understood what a processor did, writing code before I understood what an algorithm was. I’ve watched a lot of “this changes everything” moments from up close. Most of them changed some things.

This one is different.

But here’s what I keep noticing: the people getting the most from AI right now aren’t using it the way most people think they are. And the gap between those two groups is about to get very wide.

The Way Most People Use AI

Ask a question. Get an answer. Copy it. Maybe clean it up. Done.

That’s a genuinely useful workflow. It saves real time. I use it myself. But it’s roughly the equivalent of having a world-class expert available at any moment – and only ever using them to proofread your emails.

The interaction is linear. You prompt, it responds, you take over. You are doing the work, with AI as a capable assistant. Every step still runs through you.

For most things, that’s fine. For the things that actually move the needle in your life and business – it’s leaving almost everything on the table.

What’s Actually Happening in the Background

Here’s what most people don’t realize: the version of AI that’s generating real leverage isn’t a chatbot. It’s an agent.

The distinction matters.

A chatbot responds. An agent acts. You give it a goal – not a prompt, a goal – and it figures out the steps, uses the tools it has access to, makes decisions along the way, and comes back when it’s done. Sometimes it handles the whole thing without checking in at all.

You’ve already used agents, you just might not have called them that. The AI in your email client that auto-drafts replies? Agent. Claude Code, which can open your codebase, understand what you’re trying to build, write the code, test it, and fix errors on its own? Agent. The Notion AI that can scan your entire workspace and surface what’s relevant? Agent.

The difference between ‘using AI’ and ‘using AI agents’ is the difference between asking someone for directions and hiring a driver.

Why This Isn’t a Small Distinction

Linear AI – the prompt-and-response loop – still requires you to be the coordinator. You think of the task, frame the request, evaluate the output, decide what comes next. The AI handles one step at a time, and you manage the sequence.

Agents remove you from that loop. Or more accurately – they move you up that loop. Instead of managing each step, you state the outcome you want, and the system figures out how to get there.

This changes what’s possible. Not incrementally. Fundamentally.

I’ve been running Plujo, a venture studio, through multiple products and markets for years. The single biggest constraint on what we can do has never been ideas. It’s been bandwidth. The number of things we can actually execute, track, follow up on, and iterate – that’s the ceiling.

Agents push that ceiling up in ways I haven’t seen any technology do before. And we’re still in the early stages of what’s coming.

The Part That Most People Haven’t Seen Yet

Here’s where it gets interesting – and where I’ll leave you for now, because this deserves its own conversation.

Agents don’t just work alone. The more advanced implementations have agents working with other agents. A system that coordinates, delegates, and synthesizes. Where different agents are brilliant at specific things – and something above them decides which one gets called and when.

It’s less like AI, and more like building a team.

In Part 2, I’m going to show you what that team actually looks like. What happens when you stop thinking about AI as a tool you use – and start thinking about it as a workforce you direct. The analogy isn’t complicated. In fact, you’ve seen this model work in the real world for centuries.

You just haven’t seen it run at this speed, at this cost, in the palm of your hand.

Part 2 drops next week. If you don’t want to miss it, bookmark this page or follow Plujo for updates.