Compute: A Public-Private Partnership for the AI Era

The AI revolution faces a critical bottleneck: compute infrastructure. As demand for AI computing power grows exponentially, we need a new framework for managing these vital resources, its distribution and making it sustainable. One of the ways I was contemplating is if can treat the “Compute” as a utility akin to Electricity, while Electricity itself can be produced by combination of renewable/ non-renewable resources, the “Compute” will in turn need electricity to power the massive data centres with loads of GPU’s and processors. Lets analyze this “AI generation” utility and how it can be effectively managed

The Challenge We Face

Today's AI compute landscape is dominated by a few major players, creating:

• Limited access to AI capabilities
• Environmental sustainability concerns
• National security risks
• Market competition barriers

But what if we could treat AI compute like electricity or water – as a fundamental utility that powers innovation while serving the public good?

A proposed framework: The Hybrid Utility Model

Imagine AI compute infrastructure that combines the best of public utilities and private enterprise:

Public Foundation

• Government-owned core integrated infrastructure (Data centers and Electricity Generation units)
• Baseline compute access for all especially the Research, educational and public health institutions
• Regulated pricing tiers for safe and compliant usage
• Public oversight to drive accountability and equity

Private Build Layer

• Specialized commercial services including inference and applications
• Advanced hardware development and integrations
• Premium service options to cater to innovation and custom needs
• Market-driven improvements based on feedback, compliance and security considerations

How It Works: The Three Pillars

1. Split Ownership

• Government manages and provisions core infrastructure
• Private companies operate specialized facilities
• Public-private ventures and institutions handle research
• Community cooperatives run local clusters

2. Sustainable Power

• Nuclear power dedicated to AI compute
• Carbon credit trading system between model developers (carbon credit depleting) and inference providers (carbon credit enhancing)
• Renewable energy integration

3. Fair Access

• Utility-style baseline service
• Market-based premium tiers
• Research allocations
• Emergency compute reserves

Real-World Success Stories: There are some prevalent examples of hybrid models which can be studied and leveraged to expand thinking and inspire this framework

CERN's Computing Grid (Source 1)

• Global resource sharing
• Standardized access
• Multi-nation funding
• Proven scalability

Singapore's AI Infrastructure (Source 2)

• Government backbone
• Private sector integration
• Innovation focus
• Tiered access

Making It Happen: A tentative roadmap

Short-term: Foundation

• Establish a robust regulatory framework
• Launch pilot programs to test the model
• Develop initial infrastructure
• Align stakeholders across sectors

Medium-term: Scaling

• Roll out full-scale infrastructure
• Forge international partnerships
• Develop market mechanisms to ensure fair access
• Standardize services across the board

Long-term: Optimization

• Complete the global network
• Introduce advanced service offerings
• Refine policies based on feedback and data
• Accelerate innovation through targeted initiatives

The Path Forward

For Government Leaders:

Initiate policy discussions and allocate pilot funding to kickstart the transformation.

For Industry Partners:

Evaluate partnership opportunities and strategically plan for transitions to the hybrid model.

For Research Institutions:

Set standards, conduct studies, and explore real-world implementations to support the framework.

For the Public:

Engage in consultations, provide feedback, and consider investing in this transformative infrastructure.

What’s Next?

This framework isn’t just a vision – it’s a necessary evolution. As AI becomes increasingly central to our society, we need infrastructure that serves both public and private interests. The question isn’t whether to implement such a system and whether it is the only solution, but how quickly we can agree on a “compute” framework, socialize with stakeholders, gather inputs, mobilize resources and execute to drive outcomes

What do you think ? Share your thoughts:

• How would this framework enable AI evolution and adoption?
• What challenges do you see in implementation?
• How can we accelerate this transition?

About the Author: This article proposes a framework for AI infrastructure governance, drawing on global best practices, research and emerging models in public-private partnerships. The views and opinions expressed are personal.