Generative AI may have just recently hit the mainstream, but the economics of GenAI already point to a shift in the industry’s balance of power, away from the dominant tech giants. Companies like OpenAI, Alphabet’s Google, and Meta continue to plough resources into generalist models of extraordinary power and size (so-called “foundation models”), and they will continue to be at the forefront of technological innovation in GenAI. Yet the driving economic force of the B2B GenAI industry is set to move “downstream,” towards smaller, more cost-efficient models tailored to specific business purposes. The impetus for this shift will be the growing demand for high-performing GenAI systems that are cheaper to use than the large language or multimodal models (LLMs and LMMs) of today, such as OpenAI’s GPT-4 or Google’s Gemini.

What many business leaders don’t fully appreciate is that this shift will open up tremendous opportunity even for companies that, today, are not tech players at all—provided they have the right data. That’s why industry leaders of all types should be asking themselves whether their data might put them in a position to become influential players in the GenAI industry, rather than mere consumers of the technology.

The ‘cost of inference’ problem

Demand for GenAI models has exploded over the last year, so much so that OpenAI’s ChatGPT, in addition to being “the fastest-growing consumer application in history,” is now estimated to have reached over $100 million in run rate revenue from its enterprise service. According to a recent BCG survey of more than 1,400 C-suite executives worldwide, 85% of business leaders plan to increase spending on AI, including GenAI, in 2024. As more companies find helpful applications and make the requisite investments, overall demand for GenAI services faces a serious constraint: the cost of using it.