Trying to Forecast AI and Geo Performance Over 12 Months — My Experiment

I’ve been curious about whether it’s actually possible to predict AI-driven and geo-specific performance over a full year. Traditional forecasting feels easier because patterns are slower and data is more stable, but AI outputs and regional differences seem way more volatile.

So, I ran a 12-month simulation using historical data and different AI models across multiple regions. At first, the results were messy. Some months, the AI predicted huge spikes that never happened. Other months, it completely missed trends I expected based on seasonal behavior.

Over time, I noticed a few things: AI forecasts are most useful for spotting relative trends rather than exact numbers. Regional differences are huge — the same prompt could lead to very different performance in North America versus APAC or Europe. And even the “best” model occasionally hallucinated metrics, so human sanity-checks were always necessary.

The key lesson for me: forecasting AI and geo performance isn’t about precision, it’s about building a high-resolution trend picture that helps you make decisions. It’s not perfect, but it’s better than flying blind.

Curious if anyone else has tried long-term AI + geo forecasting? How stable were your predictions compared to reality?