The Hardest Part of AI Isn't the Model Anymore.
A year or two ago, if your AI feature wasn't performing well, the answer was usually simple. Try a better model. Teams moved from GPT-3.5 to GPT-4, experimented with Claude, or waited for the next release, and many of their problems disappeared.
That isn't the reality anymore.
Today's frontier models are remarkably capable. For most production applications, the model itself is no longer the biggest obstacle. The real challenges begin after you've chosen the model.
I've seen this happen across multiple teams. The demo comes together quickly, everyone is excited, and it feels like the hardest part is over. Then the real engineering work begins. It's no longer about tweaking prompts. It's about securing permissions, managing memory, controlling costs, investigating unexpected behaviour, and understanding why an agent made a particular decision.
Building a successful AI product today is less about finding a smarter model and more about building a system...
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