Jensen Huang posted about AI factories, and within less than 12 hours the post had received more than one million views. It did not focus on stronger models, but on land, electricity, data centers, and complete computing systems. His judgment was that infrastructure, rather than algorithms, is becoming the main constraint on AI growth.
These challenges shape a practical question: why are model calls becoming cheaper, yet enterprise AI bills can still grow faster than the business itself?
Consider a software company asking an agent to troubleshoot an online system failure. The agent reads alerts and runtime logs, locates abnormal code, searches internal documentation, analyzes dependencies, generates a remediation plan, runs tests, and inspects the results. The engineer may issue one instruction, but the agent executes dozens of steps—and every step may involve a model call.
Those calls are not all the same. Organizing logs does not require the most powerful model, while root-cause analysis may require complex reasoning. Some code can go to a cloud model, while logs containing customer data must remain inside the enterprise. Routine analysis can tolerate delay, while production alerts cannot afford long network round trips.
If every task is sent to the same model and data center, enterprises may not get better results, but will bear higher costs and latency. Competition now happens in every allocation decision: which model should handle which task, where it should run, at what cost, and how quickly it must finish.
GoodVision AI’s modular AI Factories support local and regional inference demand, with a single facility capable of deploying up to 1.5MW of inference compute within 200 square meters. Projects have been planned across Japan, South Korea, and the United States. But the more important question is: as one task is broken into more calls, who decides where each request should go?
Training Is Often a One-Time Investment. Inference Is Forever.
Industry attention has concentrated on training—parameter counts, training clusters, and benchmarks. Training determines the upper limit of model capability, but training and inference represent different ways of spending money.
A major training run is a concentrated investment over a defined period. Inference is an ongoing operational expense. Every call, every step executed by an agent, and every terminal device running AI consumes compute in real time. A model may be trained through major investment cycles, but inference can be executed billions or trillions of times.
GoodVision AI recognized this distinction early. Founder and CEO David Wang established the company in 2019, focusing on the infrastructure gap that would emerge as AI moved from demo-ready to deployable at scale. In his view, infrastructure is shifting from where compute is located toward how every token is generated, routed, and consumed efficiently.
At GTC 2026, Jensen Huang stated that AI inference demand had increased roughly one million times over two years. Global AI infrastructure capital expenditure exceeded $400 billion in 2025 and is forecast to exceed $600 billion in 2026.
Agents are changing both the amount and the pattern of token consumption. A task may require reading materials, calling tools, inspecting results, and continuing based on feedback—breaking one task into dozens or hundreds of model calls. Tokens are shifting from conversation consumption to system consumption.
Once these calls enter core workflows, costs become an operating expense that grows continuously with the business. Enterprises can simultaneously experience oversupply and shortage: high-end resources remain constrained while simple workloads consume expensive infrastructure they do not need. The real problem is allocation.
Every Call Is a Multiple-Choice Question
This is the layer GoodVision AI is building for. David Wang’s experience across IBM, AWS, Alibaba Cloud, and Tencent Cloud made him particularly focused on how AI is actually used inside enterprises: obtaining useful capabilities at lower cost and with greater stability.
The company’s first layer was cloud services, connecting enterprises to resources across AWS, Microsoft Azure, Google Cloud, private clouds, and on-premise data centers. This work gives GoodVision AI visibility into real workloads—where tokens are spent, where latency bottlenecks occur, and where compliance boundaries are drawn.
Once enterprises introduce large models and agents, this capability extends into model invocation, cross-cloud orchestration, and private inference. The decision-making is handled by the Smart Routing Engine.
When a request enters the platform, the system identifies intent and evaluates task complexity, token cost, response speed, data sensitivity, regional compliance, and node availability. Complex reasoning can be routed to flagship models such as ChatGPT, Claude, and Gemini. Translation, summarization, customer service, and repetitive tasks can use lower-cost models. Sensitive or latency-critical requests can run on private models or edge nodes.
The Smart Routing Engine also supports Agent-to-Agent calls, assigning an appropriate model and execution node for each subtask according to objectives, capability, cost, latency, and permissions.
Multi-model routing answers which model should be called. GoodVision AI goes one layer deeper: where should that model run? The same model can have different costs, latency, and data boundaries in a public cloud, enterprise data center, or edge node.
The platform acts as both a unified API gateway and a resource orchestration layer. Simple tasks use lightweight resources; complex tasks retain the capabilities they require. If a cloud, model service, or node fails, requests can automatically switch environments.
In one image and video generation deployment, monthly AI spending had exceeded $500,000 while token consumption grew roughly 50% per month. After relevant models moved to GoodVision AI Factory for private deployment, the customer reduced overall cost by approximately 60% and network latency by roughly 50%. The savings came from rearranging models, compute, and deployment locations together.
Compute Is Moving Closer to Where People Are
Once a request has made its choice, it still has to land somewhere physically. GoodVision AI views today’s AI infrastructure as similar to the internet before CDNs became widespread.
Centralized hyperscale clouds remain ideal for training and complex reasoning. But as agents process translation, customer service, coding, image generation, and video at high frequency, more requests should be handled closer to users, data, and business environments.
AI glasses, robots, autonomous driving, and smart factories are latency-sensitive. Data in finance, healthcare, and government often cannot cross national borders. GoodVision AI’s AI Factory is a modular inference center designed for local and regional demand.
According to company materials, its immersion-cooling architecture targets PUE below 1.2, while up to 1.5MW of inference compute can be deployed within 200 square meters. Each AI Factory connects to the Smart Routing Engine as a local node in the global network.
Projects are advancing in Japan, South Korea, and the United States. In Japan, individual nodes are planned to expand in phases over three years, with total planned capacity reaching 100MW.
GoodVision AI uses existing mining-site and energy infrastructure with North American partners in locations such as Texas and Wisconsin, upgrading already-powered facilities into AI-ready inference infrastructure. Company materials say the first phase of its Fukushima project is expected to enter operation within three months.
The electricity and physical sites left by the previous compute cycle have become strategic assets in the current AI cycle. Their primary advantage is time. GoodVision AI plans to connect nodes across countries and cities into a dynamically orchestrated global AI Compute Grid, with a long-term goal of more than 100 AI Factories across more than 100 cities.
From Selling Resources to Selling Decisions
GoodVision AI’s three businesses—cloud services, the Smart Routing Engine, and AI Factories—form one chain. Cloud services connect fragmented multi-cloud resources. The Smart Routing Engine interprets requests and allocates models and compute in real time. AI Factories provide controllable, low-latency, locally deployable compute.
According to the company’s latest disclosed data, GoodVision AI generated approximately $24 million in revenue during the first nine months of fiscal 2026 ended June 30, 2026, nearly five times the approximately $4.82 million recorded in the same period a year earlier, representing 397.9% year-over-year growth.
The company generated approximately $13.45 million during the three months from April through June 2026, representing 544% year-over-year growth and exceeding the approximately $10.55 million generated during the preceding six months combined.
Management forecasts full-year fiscal 2026 revenue of approximately $38.87 million, roughly five times the $7.74 million generated in the previous fiscal year. The importance is not absolute scale, but evidence that the model is moving from validation to scaling.
What customers buy is also changing. Early customers bought cloud resources. As the Smart Routing Engine handles more requests and AI Factories come online, the company increasingly decides which model should handle each request, where it should run, and at what cost and latency it should be completed. Enterprises are buying not only compute, but the way compute is used.
Being Able to Buy It Is Not the Same as Being Able to Afford to Use It
Return to the incident-troubleshooting scenario. Cost problems arise when model selection and compute orchestration are poorly matched, causing millions of requests with different complexity to be processed in exactly the same way.
As the industry shifts from training toward inference, matching models and compute resources to specific tasks becomes a lesson enterprises can no longer avoid.
Electricity truly changed the world only when the grid was built.
AI’s grid is only now beginning to take shape.
AI may transform the world when intelligence becomes a grid—distributed across devices, edge infrastructure, and the cloud, and intelligently routed to where it is needed most.
That is the infrastructure layer GoodVision AI is building toward.