March 2026 — GoodVision AI, an AI infrastructure company led by former AWS and IBM executives, has introduced an intelligent compute scheduling solution combined with distributed edge inference infrastructure, aimed at addressing rising token consumption, latency, and cost challenges driven by the rapid adoption of AI agents.
At GTC 2026, NVIDIA CEO Jensen Huang noted that AI infrastructure is evolving from traditional data centers into “token factories,” where inference throughput becomes a key metric. He indicated that inference demand could increase by multiple orders of magnitude, potentially reaching a million-fold growth within the next two years.
Systems such as OpenClaw represent a new class of AI agents capable of understanding user intent, maintaining long-term memory, invoking external tools, and autonomously executing multi-step tasks. A single complex task may require hundreds of model calls, substantially increasing token usage compared with traditional prompt-response interactions.
Hyperscalers are increasing capital expenditures to expand AI infrastructure capacity, with combined planned investments exceeding $280 billion in 2026. Yet the rapid increase in demand raises a key question: is scaling centralized compute infrastructure alone sufficient to address real-world efficiency, cost, and latency challenges?
Compute Congestion: Is More Compute Really the Answer?
GoodVision AI CEO David Wang has spent decades in cloud computing. He was a Partner at IBM and former Senior Director at AWS, where he helped scale regional cloud operations from zero to hundreds of millions in revenue.
Through years of involvement in cloud infrastructure, David identified a recurring structural pattern: application demand consistently scales faster than compute supply. This mismatch became a key motivation behind founding GoodVision AI in 2019.
The company says its AI-related revenue reached nearly $10 million in 2025 with more than 100% year-over-year growth. With the rollout of its AI Factory and broader compute infrastructure, it expects AI revenue to scale to hundreds of millions of dollars by 2027.
David’s thesis is simple: “Model training happens once, but inference happens billions of times.”
As agents and applications are invoked simultaneously by millions of users, inference workloads become distributed across geographies, devices, and network conditions. Today’s cloud architecture was not designed for this demand structure. When demand surges faster than supply, rising latency, escalating costs, and degraded reliability become visible.
David argues that infrastructure must evolve toward a distributed and hierarchical architecture:
- Centralized cloud models should handle complex, high-value tasks.
- Edge or localized compute should process high-frequency, latency-sensitive inference.
The key is not merely more compute, but better allocation. An intelligent scheduling system can route tasks of varying complexity to appropriate resources, avoiding congestion, lowering costs, and improving real-time performance.
Distribution Is the Key to Solving AI Compute Constraints
Today’s landscape includes hyperscalers delivering general-purpose IaaS, GPU-native clouds supplying AI compute, and model service platforms offering unified interfaces across models.
Each addresses a layer of the stack, but none fully solves AI at scale. Centralized hyperscalers can be inefficient for geographically distributed, real-time demand. GPU clouds expand supply but often lack intelligent orchestration. API routers provide model flexibility without control of underlying compute.
Agent-driven workflows require coordination across models and compute types while demanding low latency and cost efficiency. The industry therefore needs a distributed compute delivery layer capable of routing each task to the most appropriate resource in real time.
GoodVision AI positions itself not as another compute provider, but as an intelligent compute distribution network designed to orchestrate inference at scale.
Compute Distribution Networks: GoodVision AI’s “AI CDN” Approach
In the early internet, Content Delivery Networks distributed cached content across global nodes to bring data closer to users. A similar shift is unfolding in AI as inference demand spreads across geographies, clouds, data centers, and edge devices.
GoodVision AI calls its vertically integrated stack the AI Factory: GPU resources, a globally distributed node network, and an intelligent scheduling layer capable of orchestrating workloads across heterogeneous environments.
At its core is a proprietary AI agent serving as a control plane for compute orchestration, alongside a token aggregation layer. Unlike pure aggregators, GoodVision AI integrates owned physical infrastructure and deployable private model clusters.
Token-level scheduling allocates workloads based on task complexity, cost sensitivity, and latency requirements. Requests can be routed across AWS, Google Cloud, and private data centers. Control of underlying resources helps stabilize token supply, strengthen pricing control, and improve resource utilization.
The company is also deploying edge nodes. By moving compute closer to users, workloads can avoid distant hyperscale facilities, reducing latency and improving responsiveness — an architectural model conceptually similar to a CDN.
From Bitcoin Mining Sites to AIDCs
As competition intensifies, the decisive factors are power access and deployment speed. Since 2025, GoodVision AI has been building its inference footprint across Asia and globally, with Japan, South Korea, and the United States as strategic hubs. The company says it has secured more than 400 MW of power capacity and designed the network to support up to 400,000 inference GPUs at full buildout.
GoodVision AI’s energy-linked assets include Bitcoin mining facilities. These standardized power-plus-compute environments already provide power access, cooling, and physical space, making them suitable for conversion into AI data centers.
The company is developing its own facilities and partnering with U.S. mining operators to upgrade existing sites and connect them to its scheduling network. The vertically integrated stack spans infrastructure development, operations, and demand-side distribution.
Once converted into AI or high-performance computing facilities, mining sites may generate up to 25 times higher revenue per unit of electricity. While traditional AI data centers can require three to five years to build, mining infrastructure can potentially be repurposed in six to eight months.
Future Vision: When Every City Has Its Own AI Factory
As agents become embedded in everyday workflows, continuous inference demand will span enterprise systems, personal devices, and urban infrastructure. AI infrastructure is evolving toward a global network of nodes that can allocate resources dynamically, much like data flows across the internet.
GoodVision AI’s localized AI Factories are designed to serve regional applications while remaining connected to a global compute network. Each is a modular inference production unit supporting local enterprises and developers while participating in cross-network scheduling.
Because they are deployed closer to users, more real-time inference can be processed at the city level. In existing deployments, the company reports approximately 60% cost reduction, 50% lower latency, and around 50% improvement in platform gross margins.
GoodVision AI is expanding into compute-intensive sectors such as video generation and biotech, where the bottleneck is increasingly the efficiency of matching inference demand with token consumption and supply.
As more cities deploy their own AI Factories, compute may evolve into a foundational utility like electricity or internet connectivity. The mass adoption of AI will not be defined solely by better models, but by a distributed, globally coordinated compute network that makes intelligence universally accessible.