HomeMajor Firms Curb AI Traffic as Flagship Models Become Luxury Items
A chilling industry trend of "uncontrolled AI costs" is spreading through Silicon Valley and the global tech community. From Atlassian and Adobe to Amazon, more and more top companies are starting to slow down internal AI usage, even banning employees from using the most advanced flagship models, and forcing a switch to lower-cost alternatives.
This collective action was triggered by a drastic change in AI providers' billing models. As companies move from fixed annual fees to expensive pay-as-you-go pricing, AI call costs have skyrocketed. According to leaked internal data, at least one company's monthly AI expenses have surged to three times the original amount, with monthly bills exceeding $15 million. Faced with this heavy financial pressure, companies are now re-evaluating the cost of blindly expanding AI applications.
Amid the wave of refined operations, major companies have adopted different strategies. Citibank took the most direct and firm approach: banning flagship models such as GPT-5.5, Claude Opus 4.6, and 4.7, and requiring employees to "use according to needs." Within Citibank, high-performance models are considered extremely valuable resources, and their use is strictly restricted unless absolutely necessary. Atlassian, on the other hand, introduced a cost dashboard, allowing every employee to clearly see how much money each of their "Prompts" costs. This transparency has proven effective but has also sparked collective anxiety among employees about declining work efficiency.
In contrast, GitHub has a more forward-thinking approach. They plan to shift AI quotas from "department-wide sharing" to "individual pay-as-you-go," and actively transition to open-source models, aiming to find a new balance between performance and cost. Meanwhile, companies like Adobe are no longer renewing unlimited usage agreements, giving employees a final transition period.
Industry insiders point out that this contraction trend driven by "cost economics" marks the end of the wild growth phase of the AI industry. The previous strategy of "investing without considering costs" is no longer effective. As revealed by an internal recording from Accenture, when AI is heavily used for non-core tasks such as generating PowerPoint presentations or predicting World Cup results, the inflated bubble it creates will eventually burst.
As major companies build up computing power "walls," AI developers may need to adapt to a new reality: in the future, high-performance large models will no longer be readily available general tools, but rather expensive assets that require careful budgeting and on-demand allocation. This industry "de-bubble" movement is not only a reallocation of computing resources but also a deep reshaping of the rational return of AI's commercial value.
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A chilling industry trend of "uncontrolled AI costs" is spreading through Silicon Valley and the global tech community. From Atlassian and Adobe to Amazon, more and more top companies are starting to slow down internal AI usage, even banning employees from using the most advanced flagship models, and forcing a switch to lower-cost alternatives.
This collective action was triggered by a drastic change in AI providers' billing models. As companies move from fixed annual fees to expensive pay-as-you-go pricing, AI call costs have skyrocketed. According to leaked internal data, at least one company's monthly AI expenses have surged to three times the original amount, with monthly bills exceeding $15 million. Faced with this heavy financial pressure, companies are now re-evaluating the cost of blindly expanding AI applications.
Amid the wave of refined operations, major companies have adopted different strategies. Citibank took the most direct and firm approach: banning flagship models such as GPT-5.5, Claude Opus 4.6, and 4.7, and requiring employees to "use according to needs." Within Citibank, high-performance models are considered extremely valuable resources, and their use is strictly restricted unless absolutely necessary. Atlassian, on the other hand, introduced a cost dashboard, allowing every employee to clearly see how much money each of their "Prompts" costs. This transparency has proven effective but has also sparked collective anxiety among employees about declining work efficiency.
In contrast, GitHub has a more forward-thinking approach. They plan to shift AI quotas from "department-wide sharing" to "individual pay-as-you-go," and actively transition to open-source models, aiming to find a new balance between performance and cost. Meanwhile, companies like Adobe are no longer renewing unlimited usage agreements, giving employees a final transition period.
Industry insiders point out that this contraction trend driven by "cost economics" marks the end of the wild growth phase of the AI industry. The previous strategy of "investing without considering costs" is no longer effective. As revealed by an internal recording from Accenture, when AI is heavily used for non-core tasks such as generating PowerPoint presentations or predicting World Cup results, the inflated bubble it creates will eventually burst.
As major companies build up computing power "walls," AI developers may need to adapt to a new reality: in the future, high-performance large models will no longer be readily available general tools, but rather expensive assets that require careful budgeting and on-demand allocation. This industry "de-bubble" movement is not only a reallocation of computing resources but also a deep reshaping of the rational return of AI's commercial value.
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