Spent a few hours inside OpenLedger’s ModelFactory and one thing kept bothering me.
Everyone talks about bigger models. More parameters. More general intelligence.
But the models that actually felt useful were the smaller, specialized ones.
Tested a few workflows using narrowly scoped datasets. Nothing massive. In one case, the training set was under 50,000 records. Another was closer to 120,000. The weird part wasn't the accuracy improvement. It was how much less noise showed up in outputs.
The general-purpose model kept giving answers that were technically correct but operationally irrelevant. The specialized version missed some edge cases, sure. But it understood the task.
That tradeoff feels underappreciated.
Right now there's still a tendency to judge AI quality by scale. Yet the difference between a model trained on 100 million generic examples and one trained on 100,000 highly relevant examples can feel larger than people expect when you're actually using them.
ModelFactory makes that tension hard to ignore because the process of creating niche models is much closer to the user than most AI tooling I've touched.
The friction isn't training anymore.
The friction is deciding what data deserves its own model.
I ended up spending more time arguing about dataset boundaries than model architecture.
Which is probably the signal.
Specialized AI might not be limited by compute nearly as much as it's limited by data ownership, curation, and incentives.
Still trying to figure out whether that's a feature or the next bottleneck...