I'll be honest: I don't fully know what AI is at its core. I didn't invent it. But I've been building software long enough to recognize where a lot of its DNA came from — and once you see it, you can't unsee it.
The Data Dictionary Was the First Draft
Back when I was first getting into programming and SQL, "smart" software didn't exist yet — not really. What we had instead was the data dictionary.
A plain database match is dumb by design. You ask it a yes-or-no question — does this value equal that value? — and it gives you a yes-or-no answer. Nothing in between. So we built ways around that. LIKE and the % wildcard let a query catch close enough instead of demanding exact. And beyond that, we built entire tables whose only job was to relate one word to its variants — misspellings, synonyms, alternate phrasings — so a single search could reach further than the literal string a person typed.
That's it. That's the whole trick behind "Did you mean?" It's not magic. It's a data dictionary somebody built, patiently, word by word, so the software could interpret intent instead of just checking equality.
I think that matters, because it means "smart" software isn't a new invention. It's an old idea that just kept getting more capable.
So What Is AI, Really?
Strip away the hype and here's my honest definition: AI is very advanced interpretation software. It can analyze, interpret, respond, connect, and act.
Notice the common thread in all of that: capability. It can get a lot. It can send a lot. It can do a lot. Every one of those is an action — and a person operates the same way. We take an action, interpret what came back from it, analyze the response, and decide the next action from there. AI just does that loop with more data, more speed, and more consistency than we can.
This Is Where the Agent Comes In
If AI is the interpretation engine, the agent is the one who acts like the person — the one who chains a string of individual actions into an entire process, start to finish.
Here's what I mean. Say you go to a dinner and meet a potential new business partner. Normally, that's a string of follow-up chores that land entirely on you: remember the details of the conversation, get them into your CRM, set a reminder to follow up, capture the deal information before it goes stale in your memory. Every one of those is a separate task you'd have to sit down and do.
An agent does all of it as one instruction. You tell it what happened, and it notes the details, loads them into the system, creates the follow-up reminder, and logs the deal — the entire process a person would've done step by step, done at once, and done faster than you could've typed it yourself.
That's the shift. It's not that the AI got smarter than a data dictionary. It's that we finally built something that can act like the person operating the data dictionary — not just answer a question, but carry out the whole chain of decisions that used to require you.
Where This Leaves Us
Imagine having a dozen of these running for you at once — not answering questions, but actually doing the follow-through. The note-taking, the data entry, the reminders, the busywork that eats an afternoon.
It makes me wonder: what's going to be considered a "normal" workload for a top performer in 2 years? In 5? In 10? Because if the busywork stops being the bottleneck, the bar for what "getting a lot done" looks like isn't going to hold still. And I don't think most people have really sat with that yet.