🤖Revolutionary Computing Pool China

PLUS: India Mandates AI Royalties | Linux Foundation Launches AAIF

December 10, 2025

Reading time: 5 minutes

🗞️In this edition

  • China's 2,000km AI network achieves 98% single-center efficiency

  • India’s AI copyright plan: blanket license, mandatory payment

  • Anthropic, OpenAI donate to new AI agent standards group

  • In other AI news –

    • Mistral AI expands lineup with new coding tools

    • Microsoft plans major 17.5B AI investment in India 

    • Amazon’s ring adds AI facial recognition to doorbells

    • 4 must-try AI tools

Hey there,

China has activated a massive distributed computing network, India is pushing a bold royalty plan for AI training, and the Linux Foundation is uniting major players to set shared standards for AI agents. It’s a snapshot of a global race to build, regulate, and standardize the next wave of AI.

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Let's get into it.

What's happening:

China has activated what may be the world's largest distributed AI computing pool, alongside high-speed data networks developed over more than decade, according to a state newspaper.

Official Science and Technology Daily said optical networks connected distant computing centers, so they could work together almost as efficiently as a single giant computer.

The 2,000km-wide computing power pool formed via this network could achieve 98% of efficiency of a single data center, Liu Yunjie, member of Chinese Academy of Engineering and chief director of the project, was quoted saying.

China's top computing centers are scattered across the country but this pool would allow them to operate as a unified system, working together seamlessly to fast-track development of the most powerful AI models and other cutting-edge technology.

Future Network Test Facility is China's first major national science and technology infrastructure project in the information and communication sector. After more than a decade of development, it officially began operations December 3.

Training a large model with hundreds of billions of parameters typically requires over 500,000 iterations. On a deterministic network, each iteration takes only about 16 seconds. Without this capability, each iteration would take over 20 seconds longer, potentially extending the entire training cycle by several months.

FNTF spans 40 cities with a total optical transmission length exceeding 55,000km, enough to circle the equator 1.5 times. Operating around the clock, the platform can simultaneously support 128 heterogeneous networks and run 4,096 service trials in parallel.

At the launch ceremony, a massive 72 terabyte data set generated by FAST, world's largest single-dish radio telescope, was transmitted across 1,000km in just 1.6 hours. Sending the same volume over the regular internet would have taken about 699 days.

Why this is important:

98% efficiency of a single data center while connecting facilities 2,000km apart solves distributed computing's fundamental challenge: latency and coordination overhead.

Reducing AI training iterations from 20+ seconds to 16 seconds saves months on training cycles for models with hundreds of billions of parameters. That's a massive competitive advantage.

72 terabyte transfer in 1.6 hours versus 699 days on regular internet is 437x speed improvement demonstrating deterministic network capabilities.

55,000km of optical transmission circling the equator 1.5 times is infrastructure scale rivaling or exceeding US and European networks.

Our personal take on it at OpenTools:

This is China solving distributed computing at national scale while US companies build massive single-site data centers.

US approach: concentrate GPUs in a single location (Meta's Colossus, Google's data centers). Advantages: low latency, simple coordination. Disadvantages: power constraints, geographic concentration risk.

China's approach: distribute computing across 2,000km, use a deterministic network to coordinate. Advantages: tap power where available, geographic resilience. Disadvantages: network complexity, latency challenges.

98% efficiency claim is remarkable if accurate. Distributed systems typically suffer 20-40% overhead from coordination. Achieving 98% means network latency is nearly eliminated as a bottleneck.

The 16 seconds per iteration versus 20+ seconds is 20%+ speedup. Over 500,000 iterations, that's months saved. For frontier model training where compute is a bottleneck, that's a meaningful advantage.

This could give China an advantage in distributed AI training if 98% efficiency holds at scale. But single-site data centers with cutting-edge GPUs might still be faster despite distribution inefficiency.

The real test is whether this enables training models competitive with GPT, Claude, Gemini despite China's chip disadvantages. If yes, it's a game-changer. If not, it's impressive infrastructure with limited AI impact.

What's happening:

India proposed a mandatory royalty system for AI companies training models on copyrighted content. The Department for Promotion of Industry and Internal Trade released a framework Tuesday giving AI companies access to all copyrighted works for training in exchange for paying royalties to a new collecting body composed of rights-holding organizations.

The "mandatory blanket license" would lower compliance costs for AI firms while ensuring writers, musicians, artists, and other rights holders are compensated when their work is scrapped to train commercial models, the proposal argues.

India's eight-member committee says the system would avoid years of legal uncertainty while ensuring creators are compensated from the outset. The committee cites OpenAI CEO Sam Altman's remark that India is the company's second-largest market after the US and "may well become our largest" as rationale for establishing a "balanced framework."

Nasscom, representing tech firms including Google and Microsoft, filed formal dissent arguing India should adopt a broad text-and-data-mining exception allowing AI developers to train on copyrighted content as long as material is lawfully accessed. It warned mandatory licensing could slow innovation.

Business Software Alliance, representing Adobe, AWS, and Microsoft, pressed India to avoid a purely licensing-based regime, warning it could reduce model quality and "increase risk that outputs simply reflect trends and biases of limited training data sets."

India opened the proposal for 30-day public consultation.

Why this is important:

India being OpenAI's second-largest market gives this proposal significant leverage. AI companies can't ignore India's demands without sacrificing major revenue sources.

Mandatory blanket license with automatic access and required payment is more interventionist than US or EU approaches still debating fair use boundaries and transparency obligations.

Collecting bodies distributing royalties to creators creates a new revenue stream for rights holders globally if other countries adopt similar frameworks.

Tech industry unified opposition (Nasscom, BSA) signals this will face intense lobbying. Companies arguing for text-and-data-mining exceptions want training without payment.

Our personal take on it at OpenTools:

This is India using market leverage to force payment where the US and EU still debate legality.

Altman calling India OpenAI's second-largest market (potentially largest) means companies can't walk away from this framework without major revenue impact. That's negotiating power.

Mandatory blanket license is a clever mechanism. Gives AI companies what they want (access to all copyrighted works) while ensuring creators get paid. Avoids individual negotiations and litigation but forces payment upfront.

Nasscom and BSA opposing this reveals industry preference: train on everything without payment under "text-and-data-mining exception" framing. They want a fair use carve-out, not a licensing system.

India's 30-day consultation will face intense lobbying from Google, Microsoft, Amazon, Adobe, and others. But unlike the US where tech companies have more political influence, India's government may prioritize domestic creators and revenue generation.

This framework could become a template for other markets. If India successfully implements mandatory licensing, other countries facing similar copyright disputes (EU, Canada, Australia) might adopt comparable systems.

What's happening:

The Linux Foundation launched the Agentic AI Foundation (AAIF), a neutral home for open source projects related to AI agents, aiming to prevent AI agents from splintering into incompatible, locked-down products.

Anthropic is donating MCP (Model Context Protocol), a standard way to connect models and agents to tools and data. Block is contributing to Goose, its open source agent framework. OpenAI is bringing AGENTS.md, its instruction file telling AI coding tools how to behave.

Other members include AWS, Bloomberg, Cloudflare, and Google, signaling industry-level push for shared guardrails so AI agents can be trustworthy at scale.

"By bringing these projects together under the AAIF, we are now able to coordinate interoperability, safety patterns, and best practices specifically for AI agents," said Linux Foundation executive director Jim Zemlin. The goal is to avoid "closed wall" proprietary stacks where tool connections, agent behavior, and orchestration are locked behind a handful of platforms.

OpenAI engineer Nick Cooper said success would look like evolution of standards: "I don't want these protocols to be part of this foundation, and that's where they sat for two years. They should evolve and continually accept further input."

Why this is important:

Anthropic, OpenAI, and Block donating foundational protocols (MCP, AGENTS.md, Goose) to neutral foundation signals industry recognizing need for standards before agent ecosystem fragments into incompatible platforms.

AWS, Google, Bloomberg, Cloudflare joining as members shows broad industry buy-in. That's not guaranteed when competing companies must agree on shared infrastructure.

Preventing "closed wall" proprietary stacks matters because agent interoperability determines whether developers can build once and deploy everywhere, or get locked into a single vendor's ecosystem.

Linux Foundation governance model (technical steering committees, no single member control) addresses concern that donations become vendor control. But history shows fastest-shipping implementation often becomes the de facto standard regardless of governance.

Our personal take on it at OpenTools:

This is preemptive standardization before the market consolidates around proprietary platforms.

Anthropic, OpenAI, and Block donating protocols they developed shows enlightened self-interest. They benefit from open standards preventing competitors (Google, Microsoft) from locking them out through platform control.

MCP, AGENTS.md, and Goose are early-stage protocols. Donating them to the Linux Foundation now establishes these as neutral standards before alternatives gain traction. That's strategic timing.

The "closed wall" framing is the correct threat model. Without shared standards, AI agents become an iOS-versus-Android fragmentation problem: developers build twice, users get locked in, switching costs create moats.

AWS and Google joining as members while also developing their own agent platforms (Bedrock, Vertex AI Agents) creates conflict of interest. They benefit from standards for interoperability but also want proprietary advantages for their platforms.

The short-term appeal (less time building custom connectors, predictable behavior, simpler deployment) is real. But the longer-term question is whether "mix-and-match" agent world actually emerges or whether platforms consolidate around one or two standards, effectively creating new lock-in at the protocol layer.

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