Tech giants like Google and Meta are positioned to offer their premium AI models for free, leveraging their massive ad-based business models. This strategy aims to cut off OpenAI's primary revenue stream from $20/month subscriptions. For incumbents, subsidizing AI is a strategic play to acquire users and boost market capitalization.

The primary threat from competitors like Google may not be a superior model, but a more cost-efficient one. Google's Gemini 3 Flash offers "frontier-level intelligence" at a fraction of the cost. This shifts the competitive battleground from pure performance to price-performance, potentially undermining business models built on expensive, large-scale compute.

While US firms lead in cutting-edge AI, the impressive quality of open-source models from China is compressing the market. As these free models improve, more tasks become "good enough" for open source, creating significant pricing pressure on premium, closed-source foundation models from companies like OpenAI and Google.

Google can afford to offer its LLM for free, creating immense pricing pressure on competitors like OpenAI. This strategy aims to eliminate competition by making their business models unprofitable, securing a monopoly for Google before it begins to monetize.

Google's strategy may be to offer its powerful AI models for free or at a significant loss. As a trillion-dollar company, it can sustain these losses indefinitely, forcing smaller competitors like OpenAI into an "endless sea of red ink" until they collapse, thereby securing a market monopoly.

As competitors like Google's Gemini close the quality gap with ChatGPT, OpenAI loses its unique product advantage. This commoditization will force them to adopt advertising sooner than planned to sustain their massive operational costs and offer a competitive free product, despite claims of pausing such efforts.

As the current low-cost producer of AI tokens via its custom TPUs, Google's rational strategy is to operate at low or even negative margins. This "sucks the economic oxygen out of the AI ecosystem," making it difficult for capital-dependent competitors to justify their high costs and raise new funding rounds.

Despite billions in funding, large AI models face a difficult path to profitability. The immense training cost is undercut by competitors creating similar models for a fraction of the price and, more critically, the ability for others to reverse-engineer and extract the weights from existing models, eroding any competitive moat.

Unlike traditional SaaS where high switching costs prevent price wars, the AI market faces a unique threat. The portability of prompts and reliance on interchangeable models could enable rapid commoditization. A price war could be "terrifying" and "brutal" for the entire ecosystem, posing a significant downside risk.