AI cost crisis hits tech giants as employee ‘tokenmaxxing’ backfires, sparking corporate pullback at Microsoft, Meta, and Amazon — agentic AI eats up to 1000x m

AI cost crisis hits tech giants as employee 'tokenmaxxing' backfires, sparking corporate pullback at Microsoft, Meta, and Amazon — agentic AI eats up to 1000x m

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Microsoft is not alone in this, as Fortune reports that other companies are also pulling back on AI usage. While it’s true that the cost of training AI models is falling, making AI tokens more affordable, people have started using more tokens in their day-to-day tasks. This is particularly true for agentic AI, which can use a thousand times more tokens compared to querying an LLM, depending on the number of steps needed to accomplish your instructions. For example, OpenClaw creator Peter Steinberger claimed that his team spent more than $1.3 million in token costs in just a single month. Because of this, it’s now apparent that using AI is more expensive than hiring people , especially since it offers only limited productivity gains at the moment.

Decreasing token costs, paired with increased usage, reminds us of the Jevons Paradox, in which increased efficiency has led to more people using a particular tool or technology. There are many examples of this throughout history — the introduction of efficient steam engines during the Industrial Revolution led more firms to deploy these tools to increase productivity. This is also evident in the airline industry: as planes became more fuel-efficient, lower ticket prices led to higher demand, and air travel demand is now on track to double by 2050, according to IATA.

Talent over tokens: AI models are becoming more expensive to run, and productivity gains are limited

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