Meta employees consumed over 60 quadrillion tokens within 30 days, sparking widespread debate in Silicon Valley about “Tokenmaxxing,” while Amazon employees have also begun gaming “AI KPIs.” According to data from China’s National Data Bureau, domestic token usage has surged more than a thousandfold over two years, rising from 100 billion tokens per day in early 2024 to 140 trillion tokens per day by March 2026. The industry now faces a triple crisis of uncontrolled usage, costs, and value assessment—tokens have evolved from conversational expenses into production inputs, yet enterprises struggle to quantify their true worth. Superfusion unveiled FusionOne AI at WAIC 2026, creating a “Token Factory” to address the three critical challenges of economics, security, and continuity. It has already been successfully deployed in its own AI coding scenarios and in the healthcare platform Huikang’s medical applications.Article author and source: AI World
Overnight, Tokenmaxxing became a hot topic in Silicon Valley!
Several months ago, Meta set up an internal leaderboard to track the token consumption of over 85,000 employees across the company.
As a result, it's astonishing—
In just 30 days, Meta employees burned over 60 trillion tokens, with the top user spending 281 billion alone—even Zuckerberg didn’t make the top 250.
Source: Information
Similarly, Amazon employees have also begun aggressively gaming "AI KPIs," even running AI agents idle for hours just to boost their rankings.
This seemingly absurd ranking game accidentally stripped away the veil from corporate AI in 2026:
Does burning more tokens really mean creating more value?
No one can answer, because it's impossible to measure.
After the celebration, the industry found itself in an awkward state of "triple loss of control": uncontrolled usage, uncontrolled costs, and uncontrolled value.
Large models went from "not usable" to "usable" in just two to three years. But the problems exposed after becoming "usable" are far more challenging than those when they were "not usable."
The token has become a "means of production."
But it is not equal to value.
To answer this question, we first need to understand what the token has become.
Three years ago, tokens were merely a "cost unit" in casual conversations between humans and AI, with each question and answer costing dozens to hundreds of tokens.
By 2026, the logic is completely reversed.
A single agent autonomously completing one office task can consume tens of thousands of tokens; when multiple agents collaborate on a single task, it can burn through millions of tokens.
Tokens are no longer just an overhead for communication; they have become "means of production" for reengineering business processes.
The scale of the numbers is even more astonishing. According to statistics from the National Data Bureau, domestic token call volumes surged from 100 billion per day at the beginning of 2024 to 140 trillion per day by March 2026.
It has grown more than a thousandfold in two years—this is no longer just a curve, but a towering wall rising from the ground.
Behind the wall are four proven high-value use cases: AI coding, AI for science, multimodal video creation, and general-purpose office agents.
AI coding is the fastest-growing category, accounting for over 50% of global token consumption alone.
Source: OpenRouter and a16z's State of AI Report
But the real key lies in the question posed in the introduction—does burning more tokens equate to creating more value?
The answer is no.
Because the token itself is a "cost," not a "value": it is a new cost center that only transforms into value when truly utilized and integrated into business operations by agents.
The true profit center is the Agent who works with tokens.
A token that merely runs in the background to manipulate rankings is fundamentally different in value from a token that has successfully implemented industrial quality inspection, written product code, and tracked a medical condition—even if their ledger balances appear identical.
The absurdity of Meta and Amazon's ranking lies in their focus on the "output" of tokens, while ignoring the "quality of consumption."
Thus, the ruler used to measure hashing power also changed its unit of measurement.
Previously, we measured "how many FLOPS per kilowatt-hour," assessing hardware performance; today, the basic unit has shifted from FLOPS to Token, and the measuring stick has been extended all the way to the endpoint of value—
WATT → FLOPS → TOKENS → AGENTS → VALUES
The old ruler only measured how much computing power is produced per watt of electricity; the new ruler must measure to the end:
For every unit of electricity you invest, how much is actually utilized by the Agent and converted into valuable tokens?
The conclusion follows naturally: since the token has shifted from a “chat cost” to a “raw material in the value chain,” the entire infrastructure surrounding it—
How to produce, calculate, and manage it all must be completely redone.
It's not that the model is flawed—it's that the foundation hasn't kept up.
The problem lies precisely here: the nature of the token has changed, but the foundation supporting it still follows the old logic.
Over the past decade, businesses viewed purchasing servers as simply "buying hardware, installing an operating system, and making it run."
In this new value chain, this mindset completely fails—
The legacy infrastructure built for a single model cannot meet today’s requirements for orchestrating combinations of multiple models, versions, and scenarios.
Even more unsettling is that theoretical hash power ≠ effective tokens.
GPU utilization is generally below 50%, benchmark scores look great, but the system becomes sluggish under real workloads; when handling long documents or extended contexts, the entire system grinds to a halt.
The widespread adoption of agents has turned long contexts and high concurrency into everyday realities. The performance advertised and the actual experience on the ground are two different worlds.
In short, old infrastructure measures FLOPS but fails to produce stable, effective tokens.
Along the new value chain, companies truly need to account for three books.
A financial account.
Is each conversion efficient? Are all tokens being measured, attributed, and controlled? A GPU utilization rate below 50% means half the electricity is wasted; if you can’t account for what each token has achieved, your spending is unclear.
A secure ledger.
For most enterprise clients, this foundation must also be built within their own premises—data stays inside, models are fully under their control, and agents are confined within secure boundaries.
There is also a continuity ledger that is most easily overlooked.
Since it's a factory, what's most feared is never high electricity costs, but rather shutdowns.
Tokens you buy shouldn't just be about low cost—they must deliver reliable capacity: ensuring critical operations aren't displaced by low-priority tasks, preventing a single lengthy document from crashing the entire system, and keeping pace with weekly model updates without falling behind.
These three ledgers aren’t in the model layer that’s repeatedly discussed—they’re all on the foundational ground beneath us. When applied to specific industries, the pain becomes immediately sharp:
Manufacturing wants AI to integrate into R&D, simulation, quality inspection, and production scheduling, but blueprints and process data are critical assets—requiring on-premises deployment. The result? After the equipment arrives, it takes one to two weeks just to generate the first token, and each model iteration feels like a blood transfusion.
Finance teams want to implement risk control, reporting, and intelligent operations, but compliance and data security are non-negotiable red lines. They can’t benefit from the convenience of public cloud services that are “packaged as APIs and ready to use,” so they must build their own systems—taking full responsibility for selection, optimization, monitoring, and troubleshooting. IT teams without an AI background simply can’t manage it.
Healthcare is the most typical case—interpreting reports, assisting in diagnosis, and tracking patients are all invaluable, but medical records are even more sensitive than gold and must remain within the hospital. Meanwhile, models are updated weekly, while commercial products often lag by a month; neither doctors nor patients can afford to wait.
Three industries, three faces, yet their pain points point to the same issue:
Enterprises no longer need a collection of hardware capable of running models; they need a "production system" that reliably, securely, and measurably converts computing power into valid tokens.
This "production system" already has a name: Token Factory.
A "Token factory" has appeared.
In other words, every business needs its own Token Factory.
At WAIC 2026, Super Fusion delivered a practical response.
Its key action is not to reiterate that value chain, but to turn the ruler into a production line.
Each transformation on the chain is implemented in a specific project:
Compute Infrastructure Layer (WATT → FLOPS): Hardware, liquid cooling, and super nodes determine how much compute power is extracted per watt of electricity.
Model and Computing Power Service Layer (FLOPS → TOKENS): Inference acceleration, concurrent scheduling, and model provisioning determine whether computing power can be transformed into stable and effective tokens;
Token Metrics and Operations Layer (TOKENS → VALUES): Make each token measurable and governable, then empower agents to convert them into business value.
The third layer falling brings an end to that long-neglected black box—
In the past, companies focused only on the two ends of the chain—buying computing power and demanding results—while never questioning the efficiency and cost of every transformation in between.
If Token Factory is the factory to be built, FusionOne AI is the complete "factory construction plan" handed over by Hyperfusion.
FusionOne AI, an integrated AI solution and the vehicle for bringing the enterprise Token Factory from concept to reality.
Its positioning can be summarized as "1+3+N".
- One unified architecture: A complete Token Factory architecture delivering consistent experiences across desktop, office, and data center levels;
- Three Key Value Propositions: Hardware-software synergy unlocking Token efficiency, AI Lab providing T+0 computing power and model supply, and 24/7 continuous self-practice;
- N ecological scenarios: spanning from AI coding and agent development to AI office, AI short videos, government affairs, financial marketing and risk control, and smart healthcare.
In terms of capabilities, the six issues it promises to address correspond exactly to those three ledgers.
On the cost ledger, diversified computing power is uniformly managed as a single production line, free from dependence on any single chip; TokenOps enables every token to be measurable, governable, and optimizable.
On the continuous ledger, the Smart Inference Acceleration Engine maximizes throughput, while the Smart QoS Engine ensures delivery order, preventing critical business operations from being displaced by low-priority requests. Additionally, the 0-Day Model Always Fresh feature enables enterprises to keep pace with changes, deploying new models into production immediately.
On the security side, AgentCare has equipped Agents with a secure sandbox and intelligent memory, enabling agents to be safely integrated into real workflows.
Another point is the replicability of the ecosystem: Superfusion AI Lab and FusionXplay directly adopt paths already paved by others.
Token Factory is going live.
Hardware cannot be avoided
For AI to be truly implemented, it ultimately离不开 hardware.
This is precisely the one point where SuperFusion, as a veteran in computing power, refuses to compromise: no matter how sophisticated the software platform, it ultimately must run on a solid foundation of computing power.
The "1" in the "1+3+N" model, when implemented in practice, refers to three types of Token Factory: desktop-level, office-level, and data center-level.
The all-new TokenBox™ token production platform brings capabilities previously exclusive to data centers into the office for the first time!
The key is plug-and-play.
Data center-grade AI servers are too noisy for office environments and require extremely high facility standards; meanwhile, ordinary workstations lack sufficient computing power to support large models.
TokenBox™ reduces noise to just 35 decibels under mainstream workloads with liquid cooling—library-level quietness—enabling a single unit to run flagship large models like the full-power DeepSeek V4 with 1.6 trillion parameters.
Moreover, TokenFabric™ enables efficient connectivity from a single machine to multiple nodes, supports new models on day zero, and allows hardware to be scaled elastically like building blocks.
For small and medium-sized enterprises and corporate branches that need privatization but cannot afford to maintain data centers or AI operations teams, this essentially turns the dream of using tokens as simply as using water and electricity into a machine that can sit in a corner.
If the Token Factory is a power plant, the "super node" is the actual machine generating the electricity.
It directly addresses the deadlock where "paper computing power ≠ effective tokens."
In traditional architectures, multiple accelerator cards operate in isolation, and communication between nodes is like traffic stuck in rush hour—computing power cannot be fully unleashed, and long-context tasks quickly grind to a halt.
The super-node concept involves integrating an entire rack of multiple GPUs into a tightly interconnected system, using high density, liquid cooling, and a unified architecture to reclaim computing power previously wasted due to communication bottlenecks.
Simply put, with the same set of chips, others can only extract 50%, but it wants you to push it to the limit.
For large enterprises needing to support hundreds or thousands of concurrent users with limited computing power, this is not a luxury—it’s a matter of survival.
From data centers to offices to desktops, Super Fusion’s computing solutions cover all scenarios, from large enterprises to individual power users.
Start by targeting yourself
AI coding becomes the main battlefield
The persuasiveness of Super Fusion lies not in what it says, but in what it has first proven within itself.
In its own words, it’s “sell what I use, use what I sell.” Any product promoted to customers must first be thoroughly and extensively used by us.
It was only after refining and solidifying this set of internal experiences, methodologies, and toolkits that SuperFusion unveiled FusionOne AI to the public.
AI coding is the key area the team will focus on in the second half of the year, and it’s also the most compelling one.
The reason is simple: programming is the fastest among all verified high-value use cases.
What’s remarkable is that Super Fusion didn’t stop at telling stories—it became its own first customer.
The most typical example is a development team that wrote not a single line of code manually—
Deliver product code at the level of 500,000 lines in three months.
More intriguing is its evolution: from a single individual's "super individual" to a "super project" with enterprise-grade processes and automated multi-agent scheduling.
Here, AI coding is no longer just a productivity tool—it has become an integral part of enterprise R&D infrastructure.
A company that has integrated AI into its very core naturally carries more weight when discussing AI implementation.
The practical application of AI has been concretized.
Even more convincing than self-verification are the results others have achieved.
Chuangye Huikang has spent thirty years in healthcare informationization, managing data for 7,000 medical institutions and 250 million resident health records.
There, medical AI has been broken down into numerous "small but excellent" agents—ranging from pre-consultation and ward rounds to pressure ulcer risk assessment and specialty support, with applications multiplying and token demand exploding exponentially.
How to deliver tokens to departments and to the front lines has become a new challenge.
To this end, they searched for such tools for an entire year.
TokenBox™'s solution shrinks that factory down to a size that fits on a desk, giving rise to BsoftGPT + TokenBox™, the local computing foundation for medical AI.
The first half of AI is about getting models up and running; the second half is about continuously delivering intelligence, while keeping costs under control, accounting for expenses, and ensuring reliability for critical operations.
When computing power is no longer scarce, what truly becomes scarce is the ability to organize computing power, models, and business operations into an efficient production line.
AI will truly move from the lab to the production line when every business—whether an office or a data center—can have its own Token Factory.
This, perhaps, is the most straightforward and solid meaning of the four characters "AI implementation."