AI at the Edge: Revolution or Illusion?

For years, edge computing has been hyped as the next big thing. Autonomous vehicles, smart cities, and IoT were meant to drive compute to the edge. But if we’re honest… genuine edge use cases have been thin on the ground.

Here’s what actually happened:

• 75% of internet traffic is streaming, like Netflix – and it’s mostly buffered, not real-time. No edge needed.
• Financial trading has long used leased lines, not public edge nodes.
• Industrial IoT often relies on private RF networks – not the 5G+edge combo we were sold.
• Even retail and logistics still rely on centralised systems more than decentralised edge inference.

So the question is: Has edge computing failed? Or have we just been looking in the wrong place?

Enter AI Inference – the First Real Edge Use Case?

We’re now told that AI will finally make edge real.

Why? Because by 2030, 90% of all AI compute will be inference – not training.

Inference happens constantly. It’s scalable. It often runs close to users and sensors. So it sounds perfect for the edge.

But here’s the real question:

How much of that inference actually needs low latency?

Human vs Machine: Who Really Needs the Edge?

Humans don’t notice 300ms. Chatbots, voice assistants, and recommendations can easily run in cloud regions.

It’s when machines talk to machines – in real-time – that latency becomes mission-critical.

Inference Workloads by 2030

Machine-to-Human Use Cases

Most of these can be served from the cloud. And they are.

So How Close Does the Edge Need to Be?

In theory: Speed of light in fibre = 200,000 km/sec → 10ms = 2,000 km.

In practice: That 10ms is eaten up by:

• Routing inefficiencies (+20–30%)
• Switches and hops (+2–5ms)
• Queuing, jitter (+5–20ms)
• Model inference (+5–20ms)
• Security layers (+3–10ms)

Bottom line: To meet a 10ms round-trip, compute needs to be within 100–300km.

Real-World Latency & Distance

Final Thought

AI will deliver real edge use cases - but not where most people expect.

It’s not about chatbots or your Copilot assistant.

It’s about factories, drones, grid control, and autonomous systems - where machines talk to machines, and milliseconds matter.

So yes, AI will validate the edge. But only if we stop confusing “AI we see” with “AI that matters.”

What do you think? Are we finally on the edge of a breakthrough — or still chasing the hype?

References & Sources

1. AI Inference Market Size & Growth (90% of AI by 2030) MarketsandMarkets – AI Inference Market Report 👉 https://www.marketsandmarkets.com/Market-Reports/ai-inference-market-189921964.html
2. Latency Tolerance in AI Applications Telnyx – Understanding AI Latency 👉 https://telnyx.com/learn-ai/ai-latency
3. Speed of Light in Fibre (Latency per km) Stack Overflow / Optical Networking Wiki 👉 https://networkengineering.stackexchange.com/questions/21941/what-is-the-speed-of-light-in-fiber
4. AI Workload Shift to Inference NVIDIA, Jensen Huang commentary on inference vs training 👉 https://www.semianalysis.com/p/nvidia-ai-inference-data-center-projections
5. Edge Computing and AI – Hype vs Reality Gartner Hype Cycle for Emerging Technologies (Edge AI) 👉 https://www.gartner.com/en/articles/what-s-new-in-the-2023-gartner-hype-cycle-for-emerging-technologies
6. Edge AI & Inference Latency Requirements CenturyLink / Lumen – Network Optimization for AI 👉 https://blog.centurylink.com/network-optimization-for-ai-best-practices-and-strategies/