Token狂飙这一年：巨头踩刹车 打工人用不起｜我们和四位从业者聊了聊

This article focuses on the continuous global growth of AI token consumption, while major tech giants around the world are starting to put the brakes on internal AI usage. It summarizes the current status of corporate AI quota management and actionable solutions, with key takeaways below:

1. Core industry status: Total global large model API calls have risen for nine consecutive weeks, with China’s weekly call volume surpassing the U.S. for eight straight weeks. Doubao large model’s daily average token consumption has exceeded 180 trillion. In the early adoption stage, many companies tied token consumption to employee performance, giving rise to the "Tokenmaxxing"刷量 practice, and industry estimates suggest around 50% of all token consumption is completely useless.

2. Actionable solutions for reference: Companies can start with a low quota of $100-$200 per person per month, set a general company-wide limit, and require supervisor approval for over-quota usage. Do not use token consumption as a KPI; instead, allocate investment based on actual output and set differentiated quotas for different positions.

This article sorts out the current status of internal enterprise AI adoption and common missteps, and offers actionable insights for brands looking to implement AI. Key takeaways are as follows:

1. Common pitfalls in AI implementation: Many brands rushed to follow the trend and mandated AI usage for all employees in the early stage, tying token consumption to performance assessments, which only led to wasteful刷量. Current industry data shows only 39% of enterprise AI applications have delivered a material impact on profits, and just 6% of companies qualify as high-performing AI adopters. Most brands have not seen commensurate returns on their AI investments.

2. Actionable management frameworks for reference: Brands can adopt differentiated quota allocation, where quotas are first pooled at the team level and distributed based on actual demand. Start with low initial quotas, allow additional applications when quotas run out, and require approval for overages. Explicitly exclude consumption volume from performance metrics, and set quotas per role based on input-output ratio.

3. Long-term development direction: Instead of blindly competing on consumption volume, brands should convert token investment into reusable organizational assets such as internal workflows and private knowledge bases to build sustainable AI capabilities.

This article outlines current trends in enterprise AI adoption, and offers risk warnings and actionable guidance for sellers looking to implement AI. Key takeaways are below:

1. Industry status and risk warnings: While AI token consumption continues to rise, the industry has shifted from blind consumption competition to rational cost management. Tying token consumption to KPIs in the early stage will inevitably lead to wasteful刷量, with industry estimates putting useless consumption at over half of total usage. Currently, less than 40% of enterprise AI investments have delivered a material impact on profits, meaning heavy spending does not guarantee commensurate returns.

2. Actionable implementation guidance for reference: Sellers can start with a pilot of $100-$200 per person per month, set a general usage cap, require manager approval for overages, exclude token consumption from performance assessments, allocate investment based on actual output per role, and distribute quotas differently across positions.

3. Opportunity outlook: The core of future industry competition will be accurately matching tasks to appropriate models and building reusable AI assets. Sellers can build these capabilities in advance to establish a competitive advantage in AI adoption.

This article shares lessons from leading companies’ AI implementation journeys, and offers key insights for factories advancing AI-driven digital transformation. Key takeaways are below:

1. Common pitfalls to avoid in transformation: When rolling out AI applications, factories should not blindly chase token consumption or tie usage volume to performance assessments, as this will lead to massive useless consumption and drag down return on investment. Current industry data shows more than half of internal enterprise token consumption is meaningless, and most companies have not generated corresponding profits from their AI investments.

2. Actionable implementation approaches for reference: Factories can start with a low-quota pilot, set differentiated quotas for different positions, centralize quota management at the team level for distribution based on actual demand, implement an over-quota approval mechanism, exclude usage volume from performance assessments, and only measure input-output ratio based on actual output.

3. Long-term transformation direction: The core of AI transformation is not how much you spend on model calls, but building reusable workflows and knowledge bases tailored to your factory’s production and design needs, and developing in-house AI application capabilities. This is what builds long-term competitiveness in AI transformation.

This article clearly lays out current industry trends and core customer pain points in the enterprise AI services space, offering high-value reference for service providers looking to adjust their business strategy. Key takeaways are below:

1. Latest industry trends: Global AI token consumption continues to grow, with China’s call volume ranking first in the world for eight consecutive weeks, and enterprise AI penetration keeps rising. However, the industry has shifted from the early stage of blind expansion and consumption competition to rational management, and enterprises have growing demand for AI cost control and output improvement.

2. Core pain points of enterprise clients: The biggest challenge for enterprises is balancing open AI access with cost control: either unregulated access leads to massive waste and sky-high bills, or tight quotas disrupt daily work. In addition, most enterprises cannot measure the actual output of their AI investments, and cannot distinguish between effective and useless consumption.

3. Market opportunities and solution directions: Service providers can develop targeted tools for dynamic quota management, intelligent task-model matching, and AI asset沉淀, to help enterprises control costs, improve AI return on investment, and meet enterprises’ demand for refined AI usage management.

This article sorts out shifting demand from enterprise clients for large model AI services, offering reference for operation management and risk mitigation for AI platform providers. Key takeaways are below:

1. Shifting demand from enterprise clients: In the early adoption stage, enterprises generally pursued high, unlimited large model call quotas. Now they have shifted to demand for refined quota management and cost control, requiring flexible quota allocation across teams and roles, monitoring of output corresponding to consumption, and elimination of useless waste. This puts forward new requirements for platform function design.

2. Actionable operational directions for reference: Platforms can develop flexible team quota pool management functions that allow enterprises to centrally allocate quotas and approve overages, while adding a consumption-output analysis module to help enterprises distinguish between effective and useless consumption, and intelligently match different tasks to models of appropriate cost-performance, to help enterprises cut costs and improve efficiency.

3. Risk mitigation guidance: Platforms should not overhype consumption scale. Instead, they should guide clients to use AI rationally and focus on actual output, to prevent the industry from falling into a bubble of blind consumption competition. Platforms can also roll out tiered pricing plans to meet the needs of enterprises of different scales.

This article presents the latest industry developments and core issues in the current stage of enterprise AI implementation, backed by a large volume of first-hand industry observations and data, offering high value for AI industry research. Key takeaways are below:

1. Latest industry developments: Global large model API calls have risen for nine consecutive weeks, with China’s weekly token call volume surpassing the U.S. for eight straight weeks to hold the top spot globally. The industry has shifted from the early stage of giants mandating company-wide AI adoption and competing on consumption scale, to the current phase where giants are collectively hitting the brakes to control usage costs, officially entering a new stage of refined operation.

2. Core emerging issues exposed by the industry: When companies include token consumption in performance assessments, goal distortion occurs, giving rise to the "Tokenmaxxing"刷量 behavior. Industry estimates suggest more than 50% of internal token consumption is completely useless. Currently, only 39% of enterprise AI applications deliver a material impact on profits, just 6% of companies qualify as high-performing AI adopters, and most enterprises have not yet sorted out the input-output relationship of their AI investments.

3. New industry trends: The core of future enterprise AI competition will be converting token investment into reusable organizational assets, rather than simply competing on consumption scale. This new industry development is worthy of in-depth research.