The 7 AI Bottlenecks
The Stocks That Will Solve Them: MRVL, AVGO, VRT, ETN, CEG, MU, TSM, AMAT, PLTR
1. Photonics: The Speed of Light
The Bottleneck:
Data centers can’t move data fast enough between chips using copper wires. AI training racks now pull 300-600 kilowatts versus 10-25 kilowatts for traditional servers, and electrical signals can’t keep up with the bandwidth demands. The transition from copper to optical (light-based) interconnects is the only solution.
The Stocks:
Marvell Technology (MRVL) - Acquired Celestial AI for $5.5B in January 2026 to dominate optical interconnects. Their “Photonic Fabric” embeds light-based connections directly into silicon, bypassing the electrical bottleneck entirely. Datacenter business is now their core revenue driver.
Broadcom (AVGO) - Leading competitor in Co-Packaged Optics (CPO), which reduces power consumption from 15 to 5 picojoules per bit. Generated $20B in AI hardware revenue in fiscal 2025.
2. Data Centers: Not Enough Buildings
The Bottleneck:
There aren’t enough buildings to house AI computers. Construction timelines run 18-24 months, and hyperscalers are committing $200B annually, but physical real estate, cooling requirements, and construction backlogs are creating multi-year delays.
The Stocks:
Vertiv Holdings (VRT) - Core business is power distribution and thermal management between the grid and servers. Their infrastructure solutions address five critical shifts: higher-voltage DC power, on-site energy generation, digital twins for facility design, and liquid cooling adoption.
Eaton Corp (ETN) - Their “grid-to-chip” strategy integrates power from utility interconnects down to GPU-level distribution. Acquired Fibrebond (modular deployment) and Resilient Power (solid-state transformers) to accelerate buildout speed.
3. Grid Power: Not Enough Electricity
The Bottleneck:
AI data centers need massive amounts of reliable electricity 24/7, but power grids can’t expand fast enough. Data center power demand is growing 165% by 2030. Each AI training rack requires 20-40x more electricity than traditional servers in the same square footage.
The Stocks:
Eaton Corp (ETN) - Captures both the data center build-out and the grid upgrades required to feed them with their electrical infrastructure solutions.
Constellation Energy (CEG) - Nuclear power provider. AI’s need for 24/7 “firm power” (not intermittent solar/wind) is driving nuclear renaissance. Hyperscalers are signing long-term nuclear power purchase agreements.
4. Memory: Only Three Companies Make It
The Bottleneck:
AI chips need ultra-fast memory (HBM) to feed data to processors. Only three companies globally produce High Bandwidth Memory, and they can’t produce enough. A single NVIDIA Rubin R100 superchip requires up to 288GB of HBM4. Prices surged 170% in 2025.
The Stocks:
Micron Technology (MU) - Entire 2026 HBM supply is sold out. Company is phasing out consumer Crucial brand to free manufacturing capacity for AI memory. Controls 21% of HBM market. Stock surged 18% to $346 in January 2026, with some analysts setting targets above $400.
5. Chip Manufacturing: Fabs Can’t Keep Up
The Bottleneck:
Companies can design advanced chips quickly, but manufacturing them in fabs (fabrication plants) takes much longer than the design process. New fabs require $20B+ investments and 3-5 year build times. Geopolitical supply chain shifts (reshoring to US) are adding complexity. Advanced packaging (stacking chips and memory together) is becoming the new constraint.
The Stocks:
Taiwan Semiconductor (TSM) - Dominant foundry with 60%+ market share in advanced nodes. Building $40B Arizona fabs to address reshoring. Risk: geopolitical exposure to Taiwan-China tensions.
Applied Materials (AMAT) - Semiconductor equipment maker. Provides the tools that build fabs. Benefits from both new fab construction and advanced packaging equipment demand.
6. Talent & Skills: Nobody to Build It
The Bottleneck:
Tech companies can’t find enough skilled workers to build and maintain AI infrastructure. This isn’t software engineers; it’s electrical engineers, data center technicians, HVAC specialists, and semiconductor process engineers. Training programs lag years behind industry needs.
The Stocks:
Quanta Services (PWR) - The contractors who actually build data centers and grid infrastructure. Their $39B+ backlog and “self-perform” model (manages entire projects in-house) solves the skilled labor shortage by aggregating specialized workforce at scale. U.S. data center power demand forecast to nearly double from 2025 to 2026.
Vertiv (VRT) and Eaton (ETN) - Their modular data center solutions reduce on-site labor requirements by pre-fabricating components.
7. Integration: Systems Don’t Talk to Each Other
The Bottleneck:
Companies have AI tools, but their existing computer systems don’t talk to each other. Connecting everything takes weeks or months of custom work. Legacy enterprise IT infrastructure wasn’t designed for AI workloads. Data pipelines, API integrations, and middleware are becoming critical bottlenecks for actual AI deployment (versus proof-of-concept demos).
The Stocks:
Palantir Technologies (PLTR) - Specializes in integrating fragmented data systems across enterprise and government. Their Foundry and Apollo platforms solve the “data plumbing” problem that prevents AI deployment at scale.
Snowflake (SNOW) - Cloud data warehouse that unifies siloed data sources. AI models need clean, accessible data; Snowflake provides the infrastructure layer to make that possible without custom ETL work.
The Edge
Most equity analysts are modeling AI as software/GPU capex. They’re missing the physical infrastructure layer: the power systems, cooling, memory supply chains, and data plumbing required to actually run AI at scale. These bottlenecks have multi-year lead times and create structural advantages for companies positioned to solve them.
The companies listed above aren’t “AI plays” in the traditional sense. They’re the picks and shovels. And in 2026, the shovels are in short supply.