Technology thesis · Artificial Intelligence
high conviction growthAI infrastructure
AI infrastructure is the most capital-intensive build since the railroads; its constraints – GPUs, power, packaging and optical interconnect – persist through 2027 and decide who competes in AI.
Position maintained continuously · last reviewed Jun 23, 2026
The thesis
Core thesis
AI infrastructure spending has scaled to a ~$700-725B combined big-four capex run-rate in 2026, but the constraint long ago shifted from chip design to delivery. Grid connection and on-site generation decide when capacity comes online; HBM and CoWoS packaging cap how many accelerators can be built; and optical interconnect is now the third limit as coppers reach fails at cluster bandwidths. Chips can be ordered; the power, packaging and data-movement to deploy them at gigawatt scale are the active limits. Facilities that arent built cant be filled with NVIDIA Blackwell or Rubin.
State of the art (2026)
State of the art (2026). The AI build-out is running at gigawatt scale - OpenAI's Stargate sites and xAI's Colossus expansion are measured in GW, not megawatts - and the binding limits are no longer GPUs alone but power and the data-centre interconnect. Grid connection and on-site generation gate when capacity comes online (see brief 962); HBM and CoWoS packaging cap how many accelerators can be built; and optical interconnect has become the third constraint as copper's reach fails at the bandwidth the clusters now need (brief 973). The competitive question is who can secure all three - power, packaging and optics - not who has the fastest chip.
Power is the new constraint
Each Nvidia B200 rack draws 120kW. A single frontier model training run requires 50-100MW sustained power. Grid interconnection queues stretch 4-7 years. Tech companies are signing nuclear PPAs (Microsoft-Helion, Google-Kairos) but these are 5+ years from delivery. The near-term reality is natural gas peaker plants and power purchase agreements with existing utilities — creating a carbon footprint that conflicts with ESG commitments.
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The full analysis behind the verdict — the structure is real; the content unlocks when you log in.
Signal stack
Evidence stacked leading → lagging
Technology-native KPIs
Metrics that predict trajectory, tracked over time
Landscape map
Who builds what — and who depends on whom
Catalyst calendar
Dated events that will move the position
Technology roadmap
Milestones on the path to maturity
Watchlists
Companies, people and papers — each with a remove-by condition
Decision frameworks
The same call, framed for your desk
Thesis changelog
When our view changed, and why
Change our mind
4 disconfirming conditions
Comparable wave
The historical analogue on the S-curve
Common mistakes
What the market gets wrong right now
The rest is inside
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 the changelog of every time our call on AI infrastructure has changed — all live inside CanaryIQ.