The value logic of AI Token, ecological support, and security challenges

Author: Zhang Feng

1. When every word and sentence has a price tag, how will the AI economy reshape value distribution?

Since 2023, major global large model service providers have almost simultaneously shifted to a token-based pay-as-you-go model—users’ payments are not based on API call counts but mainly on the number of “Tokens,” the smallest semantic units into which models break down text during processing. This seemingly technical change is quietly rewriting the logic of value distribution in the AI industry: from traditional compute resource leasing to a new economic system where Tokens serve as circulation media and inference efficiency is at the core of pricing.

For entrepreneurs, understanding the Token economy model is no longer just a technical team’s pricing question but a strategic issue related to business model design, cost structure optimization, and long-term competitive barriers. When Tokens become the “currency” measuring intelligent consumption, the underlying economic model design and value capture mechanisms become key to whether AI companies can move from “price wars” to “value stratification.”

2. How do Tokens become the standard measurement and circulation unit in the AI economy?

In the AI world, Tokens are both the granularity unit for language processing and the basis for economic exchange measurement. From a business model perspective, the Token economy creates a closed loop: upstream, models split text, images, code, and other data into Tokens during training and inference, processed by neural networks; midstream, cloud service providers and model vendors set the price based on the number of Tokens consumed per inference, with users paying based on total input and output Tokens; downstream, application developers pass Token costs onto end users, forming multi-layered value transfer. The core of this model is standardizing the originally non-standardized computing power into a measurable, tradable, and composable resource unit, analogous to kilowatt-hours in the electricity era or data packages in the communication era.

It’s worth noting that current mainstream mixture-of-experts (MoE) models further change the flow of Tokens—input Tokens are allocated to the most relevant expert modules, causing the same amount of Tokens to consume different amounts of computational power across tasks, thus demanding more refined billing models and resource scheduling.

3. The essence of inference profit is a game of efficiency between per-Token revenue and cost

The underlying logic of profit models is clear and brutal: AI providers profit by lowering the cost per Token while maintaining or increasing the revenue per Token. Studies show that key variables include the relative length of input and output Tokens, KV (Key-Value) cache hit rates, and the type of multimodal inference—all jointly determining the marginal cost of a single inference.

Currently, the industry is shifting from “training-centric compute procurement” to “continuous inference-centric production”—the asset of a Token factory is GPU clusters, which depreciate as long as users invoke them. Some argue that claims of “large models being 10 times cheaper” mask the rising actual costs, as larger model parameters and longer contexts increase the computational power needed per Token during inference. The success or failure of profit models thus hinges on two aspects: first, reducing per-Token costs through architectural optimizations (like MoE, quantization, sparse computation); second, enhancing per-Token pricing power via differentiated services (such as high-priority, low-latency, long-context windows).

Notably, some companies are attempting to tie Token revenue to data contribution, forming incentive mechanisms—like the OPN token economy, which rewards data providers and verification nodes to build data markets—offering possibilities beyond pure traffic-based revenue.

4. Fine-grained measurement, efficient allocation, and ecological incentives form a triangular support

Compared to traditional compute resource sales, the Token economy model has three irreplaceable core advantages.

First, fine-grained measurement makes the costs and value of AI services traceable: users pay only for actual semantic computation consumed, not fixed machine time or API call counts. This greatly lowers the entry barrier for small and medium developers and encourages service providers to continuously optimize inference efficiency.

Second, efficient allocation—by using Tokens as circulation media, compute resources can be dynamically scheduled across different models, users, and tasks. The expert routing in MoE architectures is a typical example, avoiding the inefficiency of “compute islands” in traditional clusters.

Third, ecological incentives—value capture mechanisms based on Tokens can extend to data contributors, model trainers, inference nodes, and other roles, forming a positive growth flywheel. For example, some blockchain projects incentivize data supply and network validation through rewards; transplanting such mechanisms into the AI Token economy could address the scarcity of high-quality data and uneven compute distribution.

These three advantages together underpin the network effects of AI platforms—who can lead in measurement precision, scheduling efficiency, and ecological incentives will hold the pricing power in the next stage of competition.

5. From unified billing to value stratification, how do different players compete for Token premiums?

The current AI Token market competition has evolved from a single “price per million Tokens” to multi-dimensional value stratification, mainly falling into three categories.

The first is the giants of general large models (like OpenAI, Baidu, Alibaba), which maintain high per-Token revenue through scale effects and brand premiums but face challenges from the second group—extreme efficiency players—such as open-source models and specialized inference optimization platforms. These use model quantization, KV cache optimization, and dedicated inference chips to push unit costs to the limit, capturing scale application markets with low-cost Tokens. The third category includes ecosystem integrators—projects combining blockchain tokens and AI Tokens—that do not compete on price directly but build closed loops of data, compute, and applications through Token incentive mechanisms, locking in users via network effects.

Strength does not necessarily last forever. The profit margin per Token heavily depends on inference scenarios; high-value tasks like long-text or multimodal inference yield significantly higher margins than simple single-turn dialogues. This suggests focusing on high-value scenarios may bypass price wars and achieve higher value capture. For Chinese companies, the shift may be from “investing in inference costs” to “optimizing inference profits,” rather than simply following price cuts.

6. Structured cost functions, diversified pricing, and refined ecological incentives

Currently, AI Token costs are decreasing due to model compression, compute efficiency improvements, and open-source competition, though short-term fluctuations remain in multimodal and long-context scenarios. Pricing mechanisms are shifting from single pay-as-you-go to hybrid models: basic calls continue to be token-based, while advanced features add subscription or reserved instance discounts; some platforms experiment with dynamic pricing based on latency or generation quality.

Ecologically, centralized MaaS (Model-as-a-Service) dominates, offering low-entry barriers; decentralized compute networks incentivize idle resources through Token economics, forming community-driven alternative layers. Future workflows and vertical scenarios will drive more refined pricing strategies and interoperability standards, reducing application costs and promoting AI capability commodification.

It is reported that the token price of DeepSeek-V4 may further drop significantly in the second half of this year, likely due to technological innovation and domestic compute substitution. Its sparse attention mechanism has achieved a huge leap in inference efficiency, greatly reducing per-call costs. Additionally, leveraging domestically produced chips like Huawei Ascend 950, which cost over 60% less than NVIDIA solutions, provides ample room for price reductions. For China’s AI industry, this is a key step toward accelerating the domestic compute ecosystem and achieving inclusive deployment. Globally, DeepSeek’s cost advantage and open-source approach position it as a “market cleaner,” shifting industry standards from “burning money” to “extreme efficiency.”

7. Multiple security and compliance challenges

The surge in AI usage brings three major security and compliance challenges. First, in data security, since tokens are the smallest data processing units, their transmission links are vulnerable to sniffing and hijacking, risking user identity theft and sensitive information leaks. Attackers can embed “poison samples” in training data to create backdoors, potentially feeding business secrets into models and causing systemic leaks.

Regarding model security, attackers can craft clever methods using special tokens to bypass defenses and generate harmful or illegal content; improper management of agent permissions can lead to account hijacking and financial losses.

In terms of compliance, cross-border large-scale data flows face high regulatory thresholds, with strict log retention requirements exceeding typical application audits; China’s generative AI registration also demands clear ethical standards. This requires enterprises to deploy data encryption, real-time monitoring, and traceability tools, and for governments, platforms, and individuals to collaborate in establishing comprehensive security barriers and emergency response mechanisms covering the full Token lifecycle.

Additionally, if AI service pricing involves discriminatory or predatory behaviors—such as differential Token prices for certain clients—it could trigger antitrust investigations. Companies need to embed compliance frameworks into their Token economic models: ensuring irreversibility of Token circulation, avoiding violations of financial regulations, and processing data in accordance with the principle of minimal necessary. Practitioners should also closely monitor global trends in the financial recognition of “AI billing units.”

8. From measurement units to value ecosystems, the final vision of the Token economy

Looking ahead, the AI Token economy will undergo three key evolutionary stages.

The first is “standardization and interoperability”—industry efforts to unify Token measurement standards (e.g., standardized Token equivalents based on FLOPS) and develop cross-platform Token exchange mechanisms, reducing switching costs.

The second is “value capture stratification”—model providers will design multi-layer Token pricing based on inference difficulty, timeliness, and data privacy levels. High-value Tokens (like medical diagnosis inference) will command significant premiums, while low-value Tokens (like simple text summarization) may be free or very low cost.

The third is “ecological closed-loop”—AI Tokens may evolve into a “proof of work” collaboration among multiple entities—users not only consume Tokens but also earn them by contributing high-quality feedback data, training compute, or validation, forming a self-sustaining value network.

For strategic decision-makers, the most practical advice now is: don’t just focus on the absolute cost per Token, but on the marginal value each Token creates. Companies that can convert low-cost Tokens into high-value applications will dominate profits in the final stage of the Token economy.