And the nice thing about that is that information is really clear to the model, unlike the training data the model was trained on where it's all kind of like trillions of tokens stirred together into a soup of of hundreds of billions or trillions of parameters, but it's all less clear than the actual context uh that the model sees directly for this particular problem or uses use case.
AI flattens everything it touches. The way you stand out is by getting clearer about what you really want, your taste, your perspective, your opinions.
I maintain that in the end there will be a convergence of strategies. So I think there will be a convergence of strategies where at some point as AI becomes more powerful it's going to become more or less clearer to everyone what the strategy should be.
It's easier to even predict whether growth will slow down at a certain point. it's easier to catch these trends earlier. If you don't have good observability over how your business runs and what the company's um key levers are, then you will be scrambling.
A new performance team can likely find enough optimizations to halve infrastructure spend in their first couple of years, even for companies that have been using commercial performance or observability tools.
This is what leads to slop JS/TS software. Treating errors generically like this is a mistake. It leads to fragile software, poor telemetry/observability/debugging, and as a result worse experiences
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