The Next Protocol War: Who Owns the Global Scale Computer?

Why the focus moves from GPUs to standards and the Global Scale Computer

We’ve been telling ourselves the wrong story. The winner in AI won’t be the lab with the biggest model or the cloud with the flashiest GPUs. It’ll be whoever defines the protocol that lets data flow across the planet like packets on TCP/IP. The new battleground is a global scale computer—where energy, interconnects, and standardization decided by who actually ships products on time and on budget.

From cloud-as-a-service to Global Scale Computer-as-infrastructure

Today’s AI stacks still treat accelerators as rented snowflakes. Till that’s not scalable. The shift is toward fungible accelerators and globally schedulable workloads: you submit a job, the grid finds the cheapest, cleanest, lowest-latency capacity anywhere—on-prem, colo, public cloud, or sovereign zones—and executes against a universal, chip-agnostic API. When GPUs become anonymous resources behind open abstractions, lock-in weakens and throughput wins.

Energy is the new rate limiter

Unquestionably the constraint isn’t model architecture; it’s electricity. Specifically intermittency, grid congestion, and siting realities will sort pretenders from operators. So the smart money is co-locating data centers with firm power (nuclear, hydro, geothermal) and complementing with renewables. Carbon- and congestion-aware schedulers will move training windows to where juice is cheap and steady. That isn’t virtue signaling; it’s unit economics.

Interconnects beat petaflops

Bandwidth is the tax collector of distributed training. Without standardized, high-throughput interconnects and topology-aware compilers, you’re paying compound interest. Subsequently expect the winners to lean into open fabrics and performance-portable graph compilers that make PCIe vs. NVLink vs. Ethernet a policy choice, not a rewrite. The grid must treat distance, latency, and failure domains as first-class citizens.

Open, chip-agnostic abstractions

This is the contrarian take: GPUs don’t create durable environments anymore—protocols do. Till the spec that normalizes power, placement, and performance across borders is the kingmaker. Think: a universal API that defines capabilities, QoS, reliability guarantees, and cost envelopes, independent of vendor. If your workloads compile once and run everywhere at near-native performance, procurement becomes a price discovery problem.

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A fiscally responsible playbook for founders

• Default to open orchestration and portable IRs to keep switching costs near zero.
• Budget for power first, hardware second. You can hedge supply; you can’t conjure megawatts.
• Treat interconnects as a P&L line item. Optimize for topology before you buy more silicon.
• Use carbon- and congestion-aware scheduling to buy down cost volatility.
• Negotiate capacity across multiple sovereign and commercial zones to de-risk policy shocks.

What to watch next

A recent industry blueprint sketched principles for global-scale data—uniform scheduling, energy-aware placement, and standards that make accelerators fungible. That’s the right direction. The gap now is governance: who certifies compliance, audits performance claims, and arbitrates quality of service when workloads cross jurisdictions? If we don’t answer that, the grid fragments into walled gardens with expensive tolls.

The call: ship the open spec

If TCP/IP standardized packets, we need the equivalent for AI power and placement. Publish a vendor-neutral spec; prove parity across at least three accelerator stacks; lock in a reference scheduler and governance model. Undoubtedly do that, and we turn scarcity theater into real capacity—and shift the conversation from “Who has GPUs?” to “Who has product velocity?”