When you type a question into ChatGPT and get back a coherent answer, it feels like you're talking to something intelligent.
You're not.
You're watching pattern completion at industrial scale.
Text In, Text Out
GenAI models are trained on billions of text sequences.
They learn which words tend to follow other words.
When you give it a prompt, it predicts the most likely continuation.
Token by token.
Word by word.
That's it.
No reasoning engine underneath.
No internal spreadsheet of actuarial formulas.
No understanding of what "reserves" actually mean.
Just a very good guesser trained on how actuaries write about reserves.
Pattern Completion, Not Thinking
Imagine you've read every actuarial report ever written.
Every pricing memo.
Every reserving analysis.
Every risk discussion.
Now someone says: "The loss ratio increased because..."
You'd finish that sentence easily.
Not because you analyzed the underlying data.
Because you've seen that pattern hundreds of times.
GenAI works the same way.
It completes patterns it has seen before.
When those patterns are common and well-documented, it performs brilliantly.
When the pattern is rare or the context is novel, it guesses poorly.
Why It Feels Smart
The reason GenAI feels intelligent is that language itself carries compressed knowledge.
When actuaries write, they encode:
- Technical concepts
- Domain logic
- Professional judgment
- Causal relationships
GenAI absorbs all of that through text.
It learns that "adverse selection" appears near "anti-selection spirals" and "risk pool deterioration."
It learns that reserve analyses mention "IBNR" and "development triangles."
It learns how actuaries structure arguments, caveat assumptions, and present results.
So when you ask it to draft a reserve adequacy memo, it reproduces those patterns.
Fluently.
Confidently.
But without actually calculating anything.
The Actuarial Parallel
Think of how you use development factors.
You assume the future will follow historical patterns.
You apply age-to-age factors because that's what the data showed.
GenAI does something similar, but with language instead of loss data.
It assumes the text it should produce will follow the patterns it observed in training.
Sometimes that assumption holds.
Sometimes it doesn't.
Just like your development factors.
What This Means for You
Understanding GenAI as pattern completion changes how you use it.
You stop expecting it to:
- Perform calculations
- Check its own logic
- Know when it's wrong
You start using it for what it actually does well:
- Drafting text that follows professional patterns
- Suggesting structure for common document types
- Translating technical ideas into plain language
The tool hasn't changed.
Your mental model has.
GenAI is autocomplete on an unfathomable scale.
Trained on actuarial language, it reproduces actuarial-sounding text.
That's powerful.
But it's not thinking.
And knowing the difference is everything.