Fordel StudiosShifting to AI model customization is an architectural imperative
What Happened
In the early days of large language models (LLMs), we grew accustomed to massive 10x jumps in reasoning and coding capability with every new model iteration. Today, those jumps have flattened into incremental gains. The exception is domain-specialized intelligence, where true step-function improveme
Our Take
Look, the generic LLM era's over. We've hit diminishing returns on model scale—everyone's chasing the same benchmark gains. The real step function? Domain-specific models. A 7B model trained hard on your industry beats a generic 70B nine times out of ten.
This isn't controversial. It's inevitable. Companies are already waking up to it—fine-tuned models cost less, run faster, and actually solve their problems instead of being mediocre at everything.
The shift means in-house ML expertise becomes table stakes. You can't just prompt-engineer anymore. But here's the thing: that's where the actual moat is.
What To Do
Start building domain-specific fine-tuned models instead of treating Claude/GPT as universal hammers.
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