Where Value Might Accrue as AI Shifts from Training to Inference
A sample note on how to think about the AI value chain as usage grows: infrastructure, models, and applications, and the questions that separate durable positions from temporary ones.
Cross-industry investment research
SectorVantage connects AI, power, semiconductors, and telecom, and translates each analysis into clear read-throughs for public-market investors, private-market investors, and entrepreneurs.
Our mission
The same shift in technology or policy means different things depending on where you sit. Every SectorVantage piece ends with a structured read-through for each kind of reader.
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Listed equities, credit, and sector positioning.
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Venture, growth, private equity, and infrastructure capital.
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Where to build, what to avoid, and how to position.
Coverage
Models, infrastructure, and the applications built on top of them.
Drives demand for chips, power, and connectivity.
Generation, storage, grid, and the race to power new load.
Powers data centers, fabs, and networks.
Design, manufacturing, packaging, and the supply chain behind compute.
Supplies compute for AI and gear for power and networks.
Fiber, wireless, data center interconnect, and the last mile.
Connects data centers, users, and the grid.
The cross-industry thesis
AI is the demand engine. Semiconductors turn that demand into compute. Compute turns into electrical load, which makes power a gating resource. And none of it creates value until networks move the data and deliver results to users. The data center is where all four collide.
Most research covers these industries in silos. We follow the bottleneck as it moves between them, because that is often where returns, risks, and startup opportunities concentrate.
AI DEMAND ENGINE
↓ Compute demand
Semiconductors COMPUTE SUPPLY
↓ Power-hungry chips
Power ENERGY SUPPLY
↓ Siting near power + fiber
Telecom CONNECTIVITY
↓ Moving data & inference
AI → Semiconductors
Every new model and every new user creates demand for accelerators, memory, and networking silicon. Chip supply, in turn, sets the pace and cost of AI.
Semiconductors → Power
Dense compute concentrates electrical load. Power availability and cooling increasingly decide where, and how fast, compute can be deployed.
Power → Telecom
Data centers cluster where power and fiber meet. Grid interconnection and network routes are becoming joint site-selection decisions.
Telecom → AI
Training needs high-capacity links between facilities; inference has to reach users. Fiber and wireless networks are AI's distribution layer.
Latest analysis
A sample note on how to think about the AI value chain as usage grows: infrastructure, models, and applications, and the questions that separate durable positions from temporary ones.
A sample note on why the right to connect to the grid can matter as much as the generation itself, and what that means for developers, financiers, and large power buyers.
A sample note on advanced packaging, the step that turns chips into systems, and how to evaluate it as a potential constraint and opportunity.
The SectorVantage brief
One email when we publish, with the three read-throughs up front.