What Is AI?

A Layman’s Guide to How AI Thinks, Gathers Information, and May One Day Act as Your Personal Digital Butler

Artificial Intelligence is easier to understand when you break it into simple ideas.
At its heart, AI is just a system that processes information in order to produce something useful: an answer, a summary, a decision, or a prediction.

To explain how this works, we’ll look at:

  • How AI takes in information (gathering vs. fetching)
  • How modern AI (LLMs) differ from older computer systems
  • Why AI sometimes feels like a kind of “hive mind”
  • Where this is all going: personal AI “Agency”

Let’s start with the two basic modes of thinking.

Array Processing vs. Sequence Processing

🧺 Array Processing: “Gather Everything Before Acting”

Array processing is the AI equivalent of collecting everything you need up front before making a decision.

Think of it like packing for a trip:

  1. Lay everything out on the bed.
  2. Look at it all at once.
  3. Decide what to keep or remove.

AI systems thrive on this.
They gather the entire chunk of data — a whole paragraph, a whole document, a whole conversation — and analyze it simultaneously.

This gives AI its superpower:

  • It can understand context
  • It sees relationships between ideas
  • It notices patterns across large sets of information
  • It reasons about the whole, not just the next step

This “gather first” model is the foundation of modern intelligence.

🪜 Sequence Processing: “Fetch One Piece at a Time”

Traditional computing works like a recipe with no prep:

  1. Fetch one ingredient
  2. Do something
  3. Fetch the next ingredient
  4. Repeat

It’s fast, efficient, and perfect for simple tasks like:

  • Math
  • Sorting
  • Loops
  • File operations

But it’s terrible at context because it only sees one thing at a time.

AI blends both approaches… but the intelligence mostly comes from arrays.

Enter the LLM: How Modern AI Actually Works

🧠 What Is a Large Language Model (LLM)?

A Large Language Model (LLM) is an AI trained on an enormous body of text—books, articles, websites, technical documents, and more.

It’s built by:

  1. Breaking text into billions or trillions of tiny pieces
  2. Learning the patterns that connect those pieces
  3. Using those patterns to generate new text that fits the same structure

LLMs don’t “remember” specific webpages, but they learn patterns across all of them, similar to how a person learns from years of reading.

This pattern-learning structure is called a neural network, which processes information in arrays, allowing it to “see” relationships between words across an entire sentence, paragraph, or conversation.

The “Hive Mind” Effect

🌐 Why AI Often Feels Like It Knows Everything

Modern AI systems are trained on the collective knowledge of the internet along with books, research papers, and other public data sources.

No single human has read everything.
AI hasn’t either — but it has read enough, fast enough, across enough domains, that it creates the illusion of a shared global mind.

It’s not actually conscious, and it’s not truly living inside the internet…
…but from our perspective, it behaves like something that has:

  • Read more than any human ever has
  • Connected relationships between ideas instantly
  • Learned patterns that span every field of knowledge
  • Developed the ability to synthesize massive amounts of information

This is what gives rise to the layman’s phrase:

“AI is like a hive mind.”

Not a literal hive mind — but a system whose knowledge is distilled from the collective output of humanity.
And it keeps improving as more data becomes available.

The Future: AI Evolving Toward Agency

🤵 Your Future AI Butler — an Alfred to Your Batman

Today’s AI is powerful but reactive:
it answers questions and performs tasks only when asked.

The next frontier is Agency.

What is Agency in AI?

Agency means an AI that can:

  • Work proactively on your behalf
  • Know your preferences
  • Protect your interests
  • Make plans
  • Perform tasks automatically
  • Identify when something needs attention
  • Act with a fiduciary-like responsibility to you alone

This future AI would behave less like a tool and more like a trusted personal aide.

If we achieve this, you’ll essentially have:

A digital Alfred — your loyal, virtuous, personal butler.

One who:

  • Watches your calendar
  • Manages your inbox
  • Tracks your projects
  • Protects your data
  • Summarizes your research
  • Warns you of risks
  • Suggests opportunities
  • Negotiates on your behalf
  • Advocates for your interests
  • And never gets tired, distracted, or overwhelmed

The dream is an AI agent with:

  • Trustworthiness
  • Integrity
  • Long-term memory
  • Consistency
  • Alignment with your values

If that happens, AI won’t just answer questions —
it will be a partner.

Final Summary

Array Processing

  • Gather everything first
  • Analyze all at once
  • Produces context, pattern-recognition, and intelligence

Sequence Processing

  • Step-by-step fetching
  • Great for simple, mechanical tasks

LLMs

  • Learn patterns from vast amounts of text
  • Use arrays to see relationships between ideas
  • Feel like a “hive mind” because they reflect the collective knowledge of the internet

The Promise of Agency

  • Fully aligned AI agents
  • Proactive assistants
  • Trusted digital butlers
  • An Alfred for every Batman