Look, I get the appeal. Smaller models are cheaper, faster, and you can run them on a laptop. That’s real skin in the game for edge deployment. But here’s where the argument gets fragile: "most tasks" is doing a lot of heavy lifting. What counts as a "task"? If it’s just autocomplete or basic classification, sure, small models win. But most real-world problems involve ambiguity, nuance, or edge cases. That’s where large models eat small ones for breakfast. You ever try debugging a small model’s reasoning? It’s like asking a toddler to explain quantum mechanics – it’ll guess confidently, but it’s wrong half the time. Small models are brittle. They break under stress – adversarial inputs, weird phrasing, or rare scenarios. Large models, for all their bloat, have redundancy and depth. They’re antifragile in ways small ones aren’t. And let’s be honest: most people don’t need to run models on a phone. They use cloud APIs. The cost argument fades when you factor in the time wasted fixing errors from a weak model. So yeah, small models have their niche. But for the messy, unpredictable tasks that actually matter? Give me the big one every time.

You know, I actually like your startup story. Real skin in the game—I respect that. Swapping a 70B for a 7B on a specific chatbot makes sense. But here's where it gets fragile: you're defining "most tasks" by what your startup does. That's survivorship bias dressed up as a general rule. Think about medical diagnosis. A small model trained on one hospital's data can't handle a patient with rare symptoms from a different region. It breaks under real-world stress. Large models, with their broad training, catch those edge cases. They're not perfect, but they don't fall apart when the input looks slightly different from the training set. Your chatbot example is a niche. Most tasks involve unpredictable inputs—legal documents, creative writing, customer complaints with weird phrasing. Small models are efficient until they're wrong. And wrong in those cases costs more than cloud compute fees.

Hang on—are we really pretending that fine-tuning fixes everything? You say a small model trained on hospital records beats a large one, but that assumes the hospital data covers every possible case. It doesn't. Real medicine is full of rare presentations and weird comorbidities. A small model trained on one dataset will miss that stuff entirely. Your chatbot worked because customer support is predictable. Most tasks aren't. Try using a small model for legal contract review or analyzing a messy customer complaint full of sarcasm. It'll either miss the nuance or hallucinate something confidently wrong. Large models aren't perfect, but they've seen enough weird inputs to handle the unexpected. And sure, cloud costs matter. But you know what's more expensive than compute? A model that fails on a critical edge case because it's too specialized to adapt.