Thinking Ahead, Building Ahead
Back in 2003, I can remember seeing friends type full questions into Google. Queries like “Where is the best place to take parents to dinner in Manhattan?” would fail completely. We had to learn to bend our thinking around Google’s limitations. Eventually, we became interpreters, converting our questions into strings and syntax.
While all interfaces require translation, early Google Search stood above the rest as a particularly pure example of humans adapting their instincts for the needs of the computer. Reduce the number of keywords. Homogenize your phrasing. Append the word "reddit" for human testimony.
Born out of language itself, LLMs have demolished this translation barrier. Sure, prompting is still an art for crafting specific outputs, but for everyday use, we’re free of syntax. The technology finally allows us to think WITH it.
Embrace the Overwhelming
With this barrier removed, it’s on us to shed old habits. It's actually beneficial to adopt opposite behaviors: To overwhelm AI with information, and overestimate what it’s capable of.
Stop worrying about overwhelming the system. Write whole paragraphs. Throw multiple documents into the context window. The more context you provide, the better the output.
Lose the reflex to homogenize. Build the habit of asking anything, then refine only when necessary. The old rules of careful query construction are artifacts of limited systems.
Assume failures are temporary. When AI stumbles, those who dwell on the failure often miss partially usable output. Don’t let it slow you down. What fails today succeeds tomorrow.
From Thinking Ahead to Building Ahead
It's tempting to focus solely on AI's best capabilities and overlook what it can do imperfectly. A winning strategy is emerging amongst builders: Engage with what AI can do partially and build for when the partial becomes complete.
The alternative is a reactive approach, and one that immediately puts your software in a race against everyone else with the same idea (not to mention, the same frantic refactoring schedule).
If you've placed your bets correctly and found ways to allow AI to scale within your app, you'll already be in the market when a new model release changes the game. This isn't theoretical. Cursor proved it. On a recent episode of the Dwarkesh Patel podcast, , who works for Anthropic, explained that: "Cursor hit PMF with Claude 3.5 Sonnet. They were around for a while before, but then the model was finally good enough that the vision they had of how people would program, hit."
