Every AI startup is likely to be crushed by rapid expansion of model providers (twitter.com)

Model supply has exploded: large cloud vendors and specialist model shops are rapidly releasing ever-larger, cheaper, and easier-to-access foundation models (open weights, hosted APIs, and turnkey fine-tuning tools). That expansion is driving commoditization of the core LLM/ML stack — inference, embeddings, instruction tuning, and multimodal backbones are becoming utilities. For early-stage AI startups that once relied on proprietary models to defend their value, this means margin pressure, faster feature parity, and the risk that platform providers will absorb valuable use cases into their own product suites. Technically, the shift is driven by practical advances — low‑precision quantization and 4/8-bit inference, parameter‑efficient fine-tuning (LoRA/PEFT), retrieval‑augmented generation (RAG) with vector stores, and broadly available distillation workflows — all of which lower cost and time-to-market for new models and apps. The implication for the community is twofold: commoditization at the base model layer but new differentiation opportunities above it. Surviving startups will need defensible data, specialized vertical models, tight systems engineering (latency, on‑prem/private deployments), compliance tooling, or orchestration/middleware that composes multiple models and retrieval systems. In short, the battleground shifts from “who owns the model” to “who owns the domain, data, and integrations.”