Get your free personalized podcast brief

We scan new podcasts and send you the top 5 insights daily.

The availability of lower-cost AI models doesn't subtract from the revenue of frontier models like OpenAI's or Anthropic's. Instead, it adds to the total addressable market for AI intelligence. Demand for high-end tokens remains insatiable and is only limited by physical supply constraints, not price competition from below.

Related Insights

Despite fears that cheaper, open-source models would commoditize the market, the opposite is happening. While token usage for cheaper models is rising, the actual share of economic value (wallet share) is increasingly flowing to expensive frontier labs like Anthropic and OpenAI.

OpenAI's decision to slash prices on its smaller models isn't a discount sale due to struggling sales. It is a strategic maneuver to compete in the increasingly crowded market for more efficient models. This allows them to secure the lower end of the market while demand for their high-priced, frontier models remains incredibly strong.

Contrary to the "bubble pop" narrative, a market shift away from high-margin frontier models toward cheaper alternatives could boost overall AI usage. This would redirect revenue from labs like OpenAI to infrastructure players who provide the most efficient, low-cost compute.

The rise of efficient, cheaper models pressures the profit margins of frontier AI labs. However, this could trigger a Jevon's Paradox effect, where lower costs cause demand to explode. This would dramatically expand the overall market, allowing both frontier and efficient models to thrive in a much larger pie.

Despite powerful open-source AI models, companies like Anthropic post record revenue. This indicates the total addressable market (TAM) is dramatically larger than anticipated, supporting both paid and open-source ecosystems simultaneously rather than one cannibalizing the other.

The trend of some firms seeking cheaper AI options isn't a sign of a bubble bursting but rather healthy market maturation. The most expensive, powerful AI models are being concentrated among firms with the resources and expertise to generate the highest returns—an efficient allocation of scarce compute resources.

The narrative of a zero-sum battle between AI giants is misleading because the market is in its infancy. With less than 3% penetration, there is enormous room for growth for all players. New model releases currently lift the entire ecosystem rather than stealing market share from competitors.

The market isn't a battle between proprietary frontier models and open-source alternatives. Instead, both are seeing parabolic growth. While open-source becomes more capable for simple tasks, the demand for cutting-edge capabilities unlocked by frontier models is also expanding rapidly, creating a positive-sum environment.

The fear that open source will erode the business of OpenAI and Anthropic is misplaced. As open source models make existing solutions cheaper, they compel frontier model providers to tackle the vast number of more complex, unsolved problems, effectively expanding the entire market.

Contrary to fears that cheaper AI models will hurt the market, the opposite is likely true. As the cost of AI tokens and compute drops, it unlocks more use cases and spurs greater demand. This phenomenon, known as Jevon's paradox, suggests total capital expenditure on AI infrastructure will continue to rise despite falling unit costs.