The differentiating factor will be access to proprietary training data. Everyone can scrape the public web and use that to train an LLM. The frontier companies are spending a fortune to buy exclusive licenses to private data sources, and even hiring expert humans specifically to create new training data on priority topics.
It's already come for vast swathes of industries.
Most organizations have already been able to operationalize what are essentially GPT4 and GPT5 wrappers for standard enterprise usecases such as network security (eg. Horizon3) and internal knowledge discovery and synthesis (eg. GleanAI back in 2024-25).
Foundation Models have reached a relative plateau and much of the recent hype wasn't due to enhanced model performance but smart packaging on top of existing capabilities to solve business outcomes (eg. OpenClaw, Antheopic's business suite, etc).
Most foundation model rounds are essentially growth equity rounds (not venture capital) to finance infra/DC buildouts to scale out delivery or custom ASICs to enhance operating margins.
This isn't a bad thing - it means AI in the colloquial definition has matured to the point that it has become reality.