There’s a common claim that in the age of AI, original thinking no longer matters, that everything worth saying has already been said, and machines can now say it better.
AI is very good at synthesis. It can summarise, connect, and re-express existing knowledge at scale. Best practices are now cheap. Competent execution is table stakes.
But synthesis is not origination.
Original thinking doesn’t come from summarising and synthesising what already exists on the web, in books, or in training data. It comes from lived experience, systematic experimentation, and pattern recognition across domains that don’t usually speak to each other.
My interests sit at these intersections.
Whether in enterprise architecture, personal finance, health, or productivity, the same pattern keeps repeating: systems are designed for averages, and built on assumptions we no longer even see and therefore don’t question. The only way to see that clearly is to have skin in the game, gather your own data, and be willing to challenge orthodoxy. To return to first principles and question what we’ve learned to take for granted.
AI can help articulate insights once they exist. It can help structure thinking, test language, and scale communication.
What it can’t do is:
- have lived experience
- generate new data through personal experimentation
- notice patterns across a specific combination of domains
- challenge established assumptions
In other words, AI can amplify original thinking — it can’t create it.
As generic synthesis becomes abundant, original insight becomes rarer, more valuable, and more interesting by contrast. The bottleneck isn’t access to information anymore. It’s the willingness to do the work that produces something genuinely new.
That’s the kind of thinking I’ll be documenting here.
— Charlotte
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