The investment logic in the AI field is currently undergoing a transformation

Please act as a professional industry analyst, conducting an in-depth analysis of the core bottlenecks, the weakest links, and the critical issues that urgently need to be addressed in the U.S. AI supply chain, and recommend relevant listed companies.

by Gemini

The current market consensus often stops at the superficial phenomenon of "chip shortage (GPU)." In reality, with the advancement of Scaling Law, the exponential growth in AI computing power demand is hitting the triple limit wall of physics and infrastructure.

Below is an in-depth research report on the core bottlenecks, weakest links, and investment opportunities in the U.S. AI supply chain.

Core Summary: From "Computing Power Shortage" to "Power Shortage" and "Memory Wall"

If we compare the AI supply chain to a supercar, the current situation is: the engine (GPU) is already powerful enough, but the fuel tank (memory) is too slow to supply fuel, the transmission system (advanced packaging and interconnects) can't keep up with the speed, and the gas station (power infrastructure) where this car is parked is at risk of a blackout.

The three core bottlenecks in the current U.S. AI supply chain are, in order: Energy and Cooling (Thermodynamics), Advanced Packaging & Memory, and Interconnect Bandwidth.

I. Analysis of Core Bottlenecks and Weakest Links

1. Energy and Infrastructure: The Fundamental Physical Bottleneck (The Energy Wall)

This is currently and will remain the weakest and most challenging link to address over the next 3-5 years.

• Bottleneck Logic: AI training and inference require staggering amounts of electricity. A large data center's energy consumption is already comparable to that of a small city. The U.S. power grid infrastructure is aging, and the delivery cycle for transformers and power generation equipment is as long as 2-3 years.

• Weakest Link: "Last Mile" Power Delivery and Cooling.

• Power: The issue isn't generating electricity but delivering sufficient and stable high-voltage power to specific locations (data center clusters).

• Cooling: With next-gen chips like Blackwell exceeding 1000W in power consumption, traditional air cooling is no longer effective, necessitating a full transition to Liquid Cooling. Liquid cooling retrofits involve complex engineering and operational challenges due to leakage risks.

2. Advanced Packaging and Memory: The "Short Board" of Computing Efficiency (The Memory Wall)

The speed of GPU computation is growing much faster than the speed at which memory can transfer data, causing GPUs to frequently "idle" while waiting for data.

• Bottleneck Logic: What often limits NVIDIA's shipments isn't TSMC's logic process (5nm/4nm) but the CoWoS (Chip on Wafer on Substrate) advanced packaging capacity and HBM (High Bandwidth Memory) yield.

• Critical Issue to Address: How to break the "Memory Wall." HBM3e and future HBM4 are the battlegrounds. Whoever can provide higher-stacked, higher-bandwidth memory will dominate the market.

3. Interconnect Bandwidth: The Limit of Cluster Scaling (The Scale-out Wall)

When training clusters scale from 10,000 cards to 100,000 cards, single-card performance becomes less important; what matters is the communication speed between cards.

• Bottleneck Logic: In ultra-large clusters, the bandwidth and latency of optical modules and switches become the core bottlenecks. Electrical signal transmission suffers from high losses, necessitating a shift to **Silicon Photonics** and CPO (Co-Packaged Optics) technologies.

• Weakest Link: The production ramp-up and yield stability of high-speed optical modules (800G/1.6T).

II. Critical Issues to Address

1. Inference Cost: The biggest risk in the current AI business model is "expensive training, loss-making inference." To truly popularize AI applications (e.g., Sora, Agent), inference costs must be reduced by 10-100x through algorithm optimization, dedicated ASIC chips, or quantization techniques.

2. Data Scarcity: High-quality public internet text data has been exhausted for training. How to leverage **Synthetic Data** while avoiding "model collapse" is the core challenge for the next phase of model iteration.

III. Investment Recommendations and Related Listed Companies

Based on the above analysis, the investment logic should shift from simply "buying GPUs" to "buying the infrastructure around them" and "technologies that solve bottlenecks."

1. Solving the "Power & Cooling Wall"

This is currently the sector with the highest certainty but also significant valuation volatility.

• Vertiv (VRT): Strong Focus. The global leader in data center thermal management (liquid cooling) and power management systems. It is a core partner for NVIDIA's Blackwell architecture liquid cooling systems.

• Eaton (ETN): An electrification giant. Benefits from the massive demand for power distribution, transformers, and backup power in data centers.

• Constellation Energy (CEG): Top Energy Pick. The largest nuclear power operator in the U.S. AI giants (e.g., Microsoft) are turning to nuclear energy for stable zero-carbon power (refer to Microsoft's Three Mile Island nuclear plant revival agreement).

2. Solving the "Memory & Networking Wall"

A high-elasticity segment in hardware.

• Micron Technology (MU): Top Memory Pick. Micron is a core supplier of HBM3e, gradually capturing market share from SK Hynix. As long as AI runs, HBM is a necessity.

• Arista Networks (ANET): Top Networking Pick. A leader in data center Ethernet switches. As AI clusters shift (InfiniBand vs. Ethernet), Arista is gaining more market share with its ultra-low-latency Ethernet solutions.

• Marvell Technology (MRVL): Optical connectivity and ASICs. Provides high-speed PAM4 DSP chips (the heart of optical modules) and customized AI chip services, directly benefiting from optical communication bandwidth upgrades (from 400G to 800G/1.6T).

• Broadcom (AVGO): King of Communication & ASICs. Beyond providing top-tier switch chips (Tomahawk series) for GPU connectivity, it is also the primary design partner for Google's TPU and Meta's MTIA custom chips.

3. Solving the "Data & Deployment Bottlenecks"

Beyond hardware, software infrastructure is also evolving.

• Oracle (ORCL): Dark Horse in Cloud Services. Though an old-school company, Oracle has solved network congestion issues in GPU cloud leasing (OCI), securing orders from NVIDIA and becoming a supplementary compute provider for OpenAI/Microsoft.

• Palantir (PLTR): Application side. If the previous picks are about building infrastructure, Palantir is one of the few companies proven to help enterprises deploy AI in real-world operations and generate value (AIP platform).

Summary and Next Steps

Summary: The investment logic in the U.S. AI supply chain is shifting. While the first phase (buying NVIDIA) isn't over, its marginal effects are diminishing. The second phase's excess returns will come from "bottleneck-solving" companies—those providing power, liquid cooling, HBM, and high-speed interconnects.

Next Steps You Can Take:

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