Smarter at the Edge: Why the Future of AI Leadership Depends on Efficiency
Hello, I’m Ylli Bajraktari, CEO of the Special Competitive Studies Project. The race for AI leadership is no longer just about who trains the biggest models — it’s about who can deploy them smarter.
In today’s newsletter, we’ve partnered with Arm to launch a new report which argues that the next phase of U.S. AI competitiveness will hinge on efficiency — in both energy and architecture. Below are some of the highlights, but click here to download the full report, Smarter at the Edge: How Edge Computing Can Advance U.S. AI Leadership and Energy Security.
Today’s AI systems rely on massive, centralized data centers that already consume 4% of U.S. electricity. On the current trajectory, that figure could rise to 20 to 25% by 2030. The energy demands of compute are becoming a strategic bottleneck — not only for utilities but for national security and innovation itself.
The paper makes a clear case: AI’s future won’t live solely in the cloud. It will unfold across a hybrid architecture where data centers handle the heaviest training workloads, and the edge — billions of connected devices, local networks, and embedded processors — handles the rest. This shift is not only a technical necessity; it’s a strategic advantage.
The Edge Advantage
Edge computing brings AI closer to where data is generated, enabling faster, more secure, and vastly more efficient processing. Inference — the phase where AI models actually “do work” — can consume up to 75% of total compute demand. Shifting these workloads to optimized edge devices can cut energy use by up to 60% while improving responsiveness for real-time applications like autonomous systems, smart grids, and healthcare devices.
In effect, edge AI turns a potential crisis of energy demand into an opportunity for innovation — one where hardware and software advances reinforce U.S. competitiveness.
A Global Competition in Efficiency
China has already recognized the strategic stakes. Through its “AI+” initiative and massive new government-backed investment funds, Beijing is building a distributed AI ecosystem designed to scale efficiently — coupling cloud infrastructure with plans for local edge deployment across strategic sectors such as energy, manufacturing, and transportation.
The U.S. has made important moves of its own, from the CHIPS and Science Act to the Department of Energy’s Energy Efficiency Scaling for Two Decades (EES2) initiative and the National Science Foundation’s National AI Research Resource (NAIRR) pilot. But these programs still fall short of the coordinated investment and procurement incentives needed to ensure the U.S. leads not only in AI capability, but in sustainable deployment.
A Blueprint for Leadership
The white paper lays out a roadmap for what comes next:
Invest in Efficient Infrastructure: Expand R&D to target performance-per-watt gains and heterogeneous computing architectures.
Use Federal Procurement as a Lever: Reward efficient AI systems in government purchasing to drive private-sector innovation.
Launch Public Edge Testbeds: Pilot edge AI in high-value missions — wildfire monitoring, critical infrastructure protection, and emergency response.
Align Industry and Policy: Foster co-design between hardware, software, and governance to make efficiency the default, not the exception.
The Strategic Imperative
The next wave of AI will not be defined exclusively by the largest models, but by the smartest and most efficient ones — and the U.S. should lead in making that future a reality.
Arm’s press release on the report: https://newsroom.arm.com/blog/how-efficient-ai-can-power-us-competitiveness