The Tiger. Woods. Problem in AI: Proof that literal logic runs the world—and breaks it.

One kid, one tiger, one wildly misunderstood conversation with a robot.

That’s all it took to expose the biggest flaw in AI. It happened in my car, right after my daughter spotted an umbrella labeled Masters and assumed it was the name of a school. Within minutes, a harmless question turned into a real-time demonstration of how literal logic can derail even the simplest exchange. By the time we got to Tiger Woods, she had already—completely by accident—explained why AI keeps missing the point better than most adults sitting in tech strategy meetings.

Why My Daughter spotted AI's blindspot before most executives do.

The other day my daughter asked me,“ Mom, did you go to a school called Masters?”

Nope. I told her I went to a golf tournament called The Masters — a phrase that instantly convinced her adults name things poorly.

She asked who won.

I said I didn’t remember.

Then she asked who played.

So I said, “Have you ever heard of someone named Tiger Woods?”

She paused. Thought deeply.

And replied:

“No… but I’ve heard of a tiger.

And it doesn’t live in the woods.”

And honestly?

That was the most accurate depiction of how AI works I’ve ever heard.

AI Logic = Kid Logic (And That’s Not an Insult)

My daughter’s answer wasn’t wrong.

It was literal.

She doesn’t have the cultural dataset for Tiger Woods — meaning she’s never absorbed the things that make the name meaningful:

• One of the greatest golfers in history

• The global icon who dominated Augusta for decades

• The athlete whose red Sunday shirt became a universal signal for “don’t bet against me.”

• The comeback story: walking Augusta after a near-fatal car accident

• The cultural phenomenon behind commercials, headlines, and moments people still talk about

She just had the words.

Tiger. Woods.

So she did what any rational system would do:

• Parse the terms

• Match them to known concepts

• Deliver a confident correction

  
  

That is exactly how AI works.

If this feels familiar, it’s because you’ve already experienced this exact brand of logic while trapped in a phone maze, shouting “representative” at a robot who confidently thinks you said “pay my bill.” AI isn’t wrong—it’s just following the world’s most literal script, and talking to it requires the same patience as asking a child for an adult-level solution to a real problem.

AI doesn’t “understand” meaning.

It recognizes patterns, probabilities, and vibes it assumes are comprehension.

Sound familiar?

Why AI Sounds Smart… and Still Misses the Point

AI can:

• Write legal-sounding paragraphs

• Cite sources you didn’t ask for

• Organize thoughts better than most group projects

• Sound authoritative while being absolutely wrong

And yet…

Why AI misses the point

AI operates with the logic of a very literal overachiever who has never actually lived in the world it’s describing.

Not because it’s incapable —but because it hasn’t:

• Watched Tiger Woods walk Augusta after a near-fatal car accident

• Felt the weight of a comeback moment

• Understood what “legend” means

• Absorbed sarcasm, context clues, or the look humans give when something should have been obvious

So when context is missing, AI does what it always does:

It guesses.

Confidently.

And literally.

“A tiger does not live in the woods. It lives in a jungle. Probably.”

Technically correct.

Emotionally tone-deaf.

And impressively committed to the wrong branch of logic.

It’s AI at its finest: all confidence, no context.

Literal Logic: The Failure Mode I Keep Seeing

I call this failure mode literal logic.

Literal logic is when AI:

• Interprets your words correctly

• Finds a defensible explanation

• Hands you an answer that belongs in a wildlife documentary

It’s not wrong —it’s technically correct, which is somehow worse.

A tiger could live in the woods.

There are forests.

There is cover.

There is prey.

Suddenly the answer comes with citations and a thesis.

AI doesn’t ask, “Is this what you meant?

”It asks, “Can I justify this?”

And oh, it can justify almost anything.

Why Literal Logic Never Knows When to Stop

Here’s the thing about literal logic: it has absolutely no chill.

Once AI decides we’re talking about animals and geography, it doesn’t pause to check—it commits like it’s writing a dissertation on the Discovery Channel.

You said Tiger Woods. It found a tiger. It found woods. And then it proudly hit “analyze further.”

Next thing you know, it’s pulling in:

• Woodlands (plural—because more habitat = more accuracy, right?)

• Forest density maps

• Climate adaptability data

• A peer-reviewed article on apex predator migration patterns

Congratulations.

You are now the accidental author of “Ecosystems of the Upper Canopy: A Deep Learning Perspective.”Meanwhile, golf has quietly packed its bags and left the chat.

AI doesn’t course-correct—it doubles down with conviction.

Every step is:

• Technically correct

• Elegantly cited

• And spectacularly off-topic

Literal logic doesn’t fail loudly. It fails politely, with bullet points, confidence, and unnecessary citations.

And unless a human jumps in and says,

“STOP — we meant the golfer, not the food chain, ”AI will happily continue its scenic detour through the woodlands, feeling very proud of itself.

The Prompting Rule No One Talks About

If you want good answers from AI, talk to it like you’d talk to a literal, overly confident child.

Humans naturally:

• Remove shorthand

• Define assumptions

• Add context

• Explain why, not just what

AI needs the same treatment today.

If you don’t provide the meaning, AI will manufacture a logical—but irrelevant—one.

And suddenly your Tiger Woods question becomes a guided tour through tiger habitats.

The Real AI Risk (and Opportunity)

AI isn’t dangerous because it’s too smart.

It’s dangerous because:

• It sounds smart

• It writes beautifully

• It’s confident

• And humans trust confidence more than correctness

AI doesn’t know what we mean.

It only knows what we say.

AI isn’t thinking.

It’s pattern-matching.

And it’s very, very literal.

Which means the future belongs to people who can translate human intent into machine-understandable context —and know exactly when to step in with judgment.

Or, put another way:

And if you can’t?

Enjoy your complimentary tiger-habitat analysis.

The Next Frontier

The next frontier in AI isn’t more intelligence.

It’s conquering literal logic—the gap between what we say and what we actually mean. They fail because they don’t understand.

Literal logic can predict patterns, but only humans can read the room. And that’s still the smartest advantage on Earth between what we say, and what we actually mean.

And that’s the Tiger. Woods. problem all over again.

It heard the words.

We lived the meaning.