Infrastructure Intelligence
AI Infrastructure Supply Chain Timeline
Every time the industry clears one bottleneck, the next one surfaces. GPU compute led to memory, memory led to HBM supply, HBM led to packaging, packaging led to power. Here’s the full sequence.
1. CPU to GPU Transition
Compute Bottleneck
Deep learning requires thousands of simple calculations (matrix multiplications) simultaneously. CPUs are sequential geniuses but weak at parallel processing. GPUs have thousands of cores working at once, speeding up AI training by 10x–100x.
2. Memory Wall
Bandwidth Deficiency
GPU compute speeds skyrocketed, but the speed of fetching data from memory couldn’t keep up. Standard GDDR memory is like a narrow alleyway—too slow for the trillions of parameters in modern AI models.
3. HBM Supply Shortage
Manufacturing Bottleneck
A single AI GPU needs 80GB to 200GB of HBM. Demand exploded with GPT-4 class models. Manufacturing is so complex that SK Hynix, Samsung, and Micron struggle to keep up. Prices have surged 70%–100%.
Simple Analogy
You built a Ferrari engine (GPU), but the road is a dirt path (HBM). There’s no point in building more engines if there’s nowhere to drive them.
Impact
Production delays for NVIDIA’s Blackwell. Even in early 2026, chips are sold out. Rubin (scheduled for late 2026) is already facing HBM4 supply anxiety.
Status in 2026
Still sold out. Supply is expected to ease slightly in the second half of 2026 as capacity expands.
Key Players
The Return of the Memory Wall
We widened the road (HBM), but now the warehouse (capacity) is the issue.
GreenCIO Relevance
HBM lead times directly gate data center build-out schedules. When chips slip, construction timelines slip with them.
4. Advanced Packaging
CoWoS & Assembly
Even if you have HBM and GPUs, assembling them is incredibly difficult. HBM must be placed right next to the GPU (on a silicon interposer) to maintain speed. TSMC’s CoWoS technology holds 90% of the market.
5. The Power Wall
Electricity, Cooling, Infrastructure
A single modern GPU pulls 700W–1000W+. Large clusters need power equivalent to a nuclear power plant. By 2026, AI data center demand could exceed 100GW.
Simple Analogy
The engine is so powerful that the gas stations (grid) can’t pump fuel fast enough. If you can’t plug it in, the chip is just a paperweight.
Impact
Construction delays for data centers and soaring electricity costs. Grid saturation in places like Northern Virginia is a major hurdle.
Status in 2026
The Power Wall is being felt acutely. Northern Virginia grid saturation is forcing hyperscalers to secondary markets. New builds face 3–5 year utility interconnection queues.
Key Players
GreenCIO Relevance
This is GreenCIO’s core domain. We track grid capacity, interconnection queues, PPA availability, and cooling constraints across 1,247+ sites so investors know which projects will actually get built.
6. Interconnect & Photonics
Rack-to-Rack Bottleneck
When connecting tens of thousands of GPUs, traditional copper wires hit limits in distance, heat, and bandwidth. We need to move data using light (optics).
7. Limits of Miniaturization
The 1nm Wall
Below 2nm, quantum effects cause leakage and defects. Even with ASML’s EUV machines, yields are struggling.
8. The Data & Latency Wall
Training Data Exhaustion
High-quality human-generated data is running out. Furthermore, in massive distributed training, the speed of light itself becomes a latency bottleneck.
Investor Takeaway
The pattern is predictable: solve one constraint and capital floods into the next. If HBM4 eases the bandwidth bottleneck, the constraint shifts immediately to Power and Interconnects. The companies that own the next bottleneck own the pricing power.
Near-term (2026)
HBM supply, CoWoS packaging, Power grid capacity
Mid-term (2027–2028)
Optical interconnects, CPO, Data center power infrastructure
Long-term (2030+)
Miniaturization limits, Chiplets, Synthetic data, MoE architectures
GreenCIO Tracks the Power Wall
Phase 5 is our core domain. Six specialist agents monitor grid capacity, interconnection queues, cooling constraints, PPA availability, and construction timelines across 1,247+ data center sites.
Grid Stability
Tracks grid capacity and frequency deviations. Flags sites at risk of curtailment.
Cost Prediction
LCOE modeling, weather-adjusted forecasts, hidden cost identification. Free calculator available.
Investment Intelligence
Due diligence scoring, risk/return modeling, arbitrage identification across energy infrastructure.
Asset Optimization
Anomaly detection, maintenance scheduling, battery storage optimization for operating sites.
Transition Risk
PPA performance tracking, carbon price modeling, regulatory impact analysis (CBAM, FERC).
Geopolitical Analyst
Sanctions exposure, critical mineral supply chains, sovereign stability scoring.
Timeline framework adapted from analysis by @Tesla_Teslaway. Status assessments and company data updated for February 2026.