Artificial intelligence is a commodity product

The author argues that Artificial Intelligence models will become interchangeable, low-margin components beneath consumer brands. That shift means brand names and standalone AI apps will matter less than performance, cost and integration.

Every week brings another high-profile launch or announcement from the giants: this week it was Gemini 3 from Google, other weeks have featured GPT-OSS models from OpenAI or large operating agreements among chip makers, AI companies and cloud providers. The flurry of activity raises familiar questions about capacity, investor returns and whether promised productivity gains will materialize. The author notes growing bubble talk and quotes Google’s CEO describing “elements of irrationality” in Artificial Intelligence investment.

The core argument is that, over time, Artificial Intelligence will act like a commodity product. Drawing on experience covering the beverage industry, the author frames a large language model as the aluminum can or the high-fructose corn syrup inside a drink rather than the fizzy branding that differentiates colas. In a commodity market the brand matters less than price and basic functionality. Metrics such as time to first token, tokens per second and output quality will govern purchasing decisions, while loyalty to particular LLM models or versions will evaporate as it becomes trivial to swap models in on-device environments like LM Studio. The piece points to examples such as Qwen and Gemini swapping prominence week to week and mentions that Apple reportedly replaced parts of Siri with Google’s Gemini as evidence of behind-the-scenes provider switching.

The practical implication is that many small and mid-size AI startups are effectively marketing layers around existing models rather than builders of core model technology, because the heavy lifting of model development is a low-margin, high-cost business. Consumer-facing companies such as Apple, Google, Microsoft and Meta may continue to develop in-house systems, but they can also slot in whatever model performs best under their hood with little visible effect for end users. The author concludes that recognizing Artificial Intelligence as a commodity should reframe how investors, founders and product teams think about differentiation, pricing and long-term strategy.

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