AI Automation
- 8 min read
What are the types of AI (narrow vs general)?
You’ve heard the AI buzz. But what kind of 'intelligence' are we really dealing with? Here’s the difference between Narrow and General AI—and what that means for your business.
Think AI is some futuristic tech thing reserved for startups with VC money and glass-walled offices?
Surprise: If you’ve ever told your inbox to finish your sentence or asked Siri where the nearest coffee shop is, you’ve already been using it.
The difference now? You get to put it to work for _your_ business. This is the behind-the-scenes history—and how to use it without losing your voice (or your mind).
This post lays out the real history of AI in plain English. No sci-fi fluff. No robot overlords. Just the people, ideas, and tools that actually got this whole machine-learning party started—and how it applies to scrappy teams like yours trying to do more with less.
Honestly? The official field of "Artificial Intelligence" was born in 1956, thanks to a workshop that gathered a bunch of sharp humans at Dartmouth College who said, “You know what? Let’s get serious about teaching machines to think.”
But the ideas? The experiments? The philosophical rabbit holes? Those go back centuries before Siri started texting your mom.
Let’s start in 1763, when Thomas Bayes created a mathematical framework called probabilistic reasoning—which is just fancy math for educated guessing. That one idea fuels a massive chunk of today’s AI systems.
Jump to the 1800s, and writers like Jonathan Swift and Samuel Butler were already dreaming up machines that could “think” like humans. These weren’t just wild fiction ideas—they kicked off the bigger question: Can logic and language be programmed into something non-human?
So yes, AI started with math… and pulp fiction.
By the mid-20th century, things got real. Computers were finally capable of doing more than blinking lights. Here's what went down:
Here’s where we drop the mic. In 1956, a summer workshop at Dartmouth brought together four giants: John McCarthy, Marvin Minsky, Claude Shannon, and Nathaniel Rochester. Their job? Outline a strategy for building “thinking machines.”
McCarthy coined the term “artificial intelligence” right there on the seminar notes.
That’s why 1956 gets the credit—even though the ideas had already been evolving for years. It’s like calling a garage jam session the “birth of rock.” The energy was already building. But that was the moment it got a name, a goal, and a (very awkward) research department.
After the Dartmouth workshop, things picked up fast:
So if you're counting, AI has been cooking for over 70+ years.
It wasn’t a solo genius moment. It was a cocktail of brainy folks building on each other’s breakthroughs.
So yeah—AI wasn’t invented by one person. It was crowd-sourced by a small army of mathematicians, programmers, and absolute weirdos with big ambition and zero chill.
Bottom line: AI wasn’t an overnight invention. It was a slow burn—that’s now moving at absolute breakneck speed.
Because understanding where AI actually came from helps you use it better right now.
If you're running a service business, agency, tech startup, or small MSP—you’re almost certainly dealing with:
AI isn’t some magical fix—but it’s also not a mystery anymore. It’s a very real toolset made up of the same logic and machine learning principles built decades ago—now supercharged with better data, faster processors, and terrible LinkedIn memes.
Here's a stat to chew on: 194 million US adults have used generative AI tools as of 2024. That’s over 70% of the population. And marketers and sellers are already building full content sequences, lead scoring, and analytics off AI-first platforms.
If your team is still manually typing out LinkedIn posts, sharing docs over email, or relying on a Zapier string made in a panic last year… you’re operating at a disadvantage.
Early AI focused mostly on symbolic reasoning (logic trees, if-then rules) and neural networks (learning from data). Flash-forward, and those original ideas are alive and well—in your:
Most “generic tools” in the AI space still leave operators guessing—and redoing half the work later.
Honestly? You deserve better than a duct-taped stack and vibe-based decision making.
We build semi-custom and fully tailored AI systems that run in the background and stitch everything together—for sales follow-up, marketing ops, onboarding…all of it.
If your biggest pain right now is time—wasted hours, missed follow-ups, marketing sludge—here’s your move:
Book a free Workflow Optimization Session
We’ll look at what you’ve got, what’s bottlenecked, and map out one or two low-hanging AI wins that could clear up your team’s week without rebuilding your whole setup.
And when you’re ready for more? That’s where our AI automation systems come in.
No fluff. No “guru playbooks.” Just smart workflows designed to get your ops finally clicking.
River Braun, founder of Timebender, is an AI consultant and systems strategist with over a decade of experience helping service-based businesses streamline operations, automate marketing, and scale sustainably. With a background in business law and digital marketing, River blends strategic insight with practical tools—empowering small teams and solopreneurs to reclaim their time and grow without burnout.
Schedule a Timebender Workflow Audit today and get a custom roadmap to run leaner, grow faster, and finally get your weekends back.
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