Edge computing
Edge AI makes most sense where bandwidth is short, a fast response is needed or the device must work offline. NVIDIA's space-computing announcement is one example, pairing local inference with processing on the ground. Nothing yet shows inference leaving the cloud faster than cloud capacity is growing.
Research snapshot · updated Nov 12, 2026
Core thesis
Edge computing places compute closer to data sources – cutting latency and bandwidth cost. The decisive shift is AI inference moving off the central cloud: NVIDIA Jetson Thor anchors the high-power edge tier while Qualcomm pushes on-device inference (Snapdragon X2 Elite) and now contests data-centre inference under its Dragonfly brand. AWS Outposts and Azure Stack/Arc extend cloud to the edge. The genuine accelerator is the data-centre power crunch: if you cannot build centrally, you build at the edge near available power.
State of the art (2026)
Edge in 2026 is defined by AI inference moving off the central cloud. NVIDIA shipped Jetson Thor (Blackwell, 40–130W) into general availability in August 2025, with Boston Dynamics, Figure and Amazon Robotics among early adopters, while Hailo and SiMa.ai contest the low-power inference tier. The serverless edge has scaled fastest: Cloudflare Workers passed three million developers and Workers AI inference requests grew roughly 4,000% year-on-year into Q1 2026, with Akamai, Fastly and Vercel chasing the same compute-edge transition. Hyperscaler edge – AWS Wavelength/Outposts, Azure Stack/Arc, Google Distributed Cloud Edge – plus telco MEC and private 5G now anchor the enterprise build-out. The data-centre power crunch is the genuine structural tailwind pushing workloads outward.
Explore the deeper analysis in CanaryIQ
Explore what is covered below. These previews show the structure; log in to read the full analysis.
Signal stack
Evidence stacked leading → lagging
8 signals
Signal stack
Evidence stacked leading → lagging
Technology-native KPIs
Metrics that predict trajectory, tracked over time
4 tracked
Technology-native KPIs
Metrics that predict trajectory, tracked over time
Landscape map
Who builds what, and who depends on whom
101 players · 6 layers
Landscape map
Who builds what, and who depends on whom
Catalyst calendar
Dated events that will move the position
4 ahead
Catalyst calendar
Dated events that will move the position
Technology roadmap
Milestones on the path to maturity
8 milestones
Technology roadmap
Milestones on the path to maturity
Watchlists
Companies, people and papers, each with a condition for removing it
20 · 20
Watchlists
Companies, people and papers, each with a condition for removing it
Decision frameworks
What our view means for your job
Locked
Decision frameworks
What our view means for your job
Thesis changelog
When our view changed, and why
7 updates
Thesis changelog
When our view changed, and why
Change our mind
3 disconfirming conditions
You've read the verdict. The file is much deeper.
The full signal stack, technology-native KPIs tracked over time, the landscape of who depends on whom, the dated catalyst calendar, decision frameworks for every desk, live watchlists and a record of every time our view on Edge computing has changed. All of it is in CanaryIQ.