Understanding the architecture behind artificial intelligence and the companies powering it
If AI is the defining technology of this era, then the AI stack is the system that makes it all work. From electricity and cooling to semiconductors, networks, cloud services, models, and applications, every layer has its champions, its bottlenecks, and its leverage points.
Too often, AI is discussed as if it were a monolith rather than what it actually is: a layered system of interdependent technologies. For investors, builders, and curious professionals, it pays to understand how the AI stack works and who’s operating where.
This article breaks down the AI stack into five functional layers: Compute & Silicon, Power & Cooling, Networking, Data & Orchestration, and Applications. For each, we highlight the key players, their role in the system, and why it matters now.
Layer 1: Compute and Silicon
The hardware backbone, custom-built for intelligence
At the foundation of the AI stack lies a brutally competitive race in silicon. Building and running modern AI models requires extraordinary compute power, and only a handful of companies can deliver it at scale.
Nvidia dominates the space, with its H100 and the upcoming B200 GPUs becoming the de facto standard for training large language models and running inference at scale. The company’s architectural advantage in parallel processing has made it indispensable to the AI revolution.
AMD is gaining ground with its MI300 series of accelerators, though adoption remains modest compared to Nvidia’s H100 dominance. While still playing catch-up, AMD represents the primary competitive threat to Nvidia’s market position.
TSMC (Taiwan Semiconductor Manufacturing Company) is the foundry where most of these chips are actually fabricated. Its ability to produce chips at 3nm and below is critical to AI progress, making it perhaps the most strategically important company in the entire stack.
ASML, a Dutch company, holds a near-monopoly as the sole supplier of EUV lithography machines. This is the technology required for producing the most advanced chips, making ASML a critical chokepoint in the global semiconductor supply chain.
Broadcom designs high-performance custom silicon used in hyperscale environments, especially for networking and AI workload acceleration. While less visible than GPU manufacturers, Broadcom’s infrastructure chips are essential to scaling AI systems.
Why it matters: Every generational leap in AI, from GPT-2 to GPT-4, from narrow tasks to multi-modal agents, has required more powerful and efficient silicon. Bottlenecks at this level can slow progress throughout the entire AI stack.
Layer 2: Power and Cooling
The overlooked constraint: without energy, nothing runs
AI isn’t just compute-intensive. It’s power-hungry. A single training run of a large model can consume megawatt-hours of energy, and that energy generates tremendous heat. Power provisioning and thermal management are fast becoming critical constraints in AI deployment.
Schneider Electric provides energy distribution and grid integration systems for data centers. As AI workloads strain electrical grids, Schneider’s infrastructure becomes increasingly vital.
Eaton supplies industrial-scale power protection, backup systems, and management platforms. Reliable power delivery is non-negotiable when training runs cost millions of dollars, particularly for cutting-edge models from major labs.
Vertiv specializes in thermal management and high-density cooling systems, particularly liquid cooling, which is becoming a necessity for modern AI clusters. Traditional air cooling simply cannot handle the heat generated by dense GPU installations.
Why it matters: As demand grows, so does strain on electrical grids and environmental systems. Companies unable to solve power and cooling challenges at scale will be unable to compete in the coming wave of AI deployment.
Layer 3: Networking
Connecting the stack at speed and across distance
AI systems aren’t just heavy lifters; they’re communicators. Training large models involves constant coordination between thousands of GPUs across a data center. These connections must be lightning-fast and low-latency.
Arista Networks provides Ethernet switching solutions that enable rapid communication between GPUs and servers within data centers. This east-west traffic is the nervous system of AI training clusters.
Ciena supplies optical transport systems for long-distance, high-capacity connections between data centers. This north-south traffic enables distributed training and global AI infrastructure.
Why it matters: AI isn’t just about crunching numbers; it’s about moving data fast. Networking is the silent workhorse of the stack, and as workloads scale, so does the importance of fiber optics and low-latency design.
Layer 4: Data and Orchestration
From infrastructure to intelligence: bridging systems with insight
This is the layer where enterprise systems meet AI models. It’s the connective tissue that brings real-world data into the stack, adds guardrails and governance, and enables usable outputs across workflows.
Palantir offers robust tools for securely integrating AI into sensitive workflows, especially in defense and regulated industries. Its platforms enable real-time decision-making with AI embedded throughout complex operations.
Snowflake supports large-scale data management and pipelines, enabling secure access to enterprise data for AI model training and inference. For many organizations, this becomes the effective front-end for AI integration.
Why it matters: Without high-quality data and structured orchestration, even the most advanced models become useless. This layer turns AI capability into business value, and it’s where many traditional enterprises are investing first.
Layer 5: Applications and Interfaces
The user-facing frontier where AI becomes experience
At the top of the AI stack is the part most people actually interact with. These are the tools, features, and assistants that bring AI into daily workflows and consumer experiences.
Some are integrated into existing platforms:
- Microsoft Copilot in Office and Teams
- Google Gemini across Gmail, Docs, and Search
- Amazon Q, the enterprise assistant built into AWS and gradually rolling out across enterprise services
- Meta’s suite of AI-powered features across Facebook, Instagram, and WhatsApp
Others are standalone AI interfaces:
- ChatGPT (OpenAI), Claude (Anthropic), and Perplexity all offer direct access to powerful foundation models via conversational or search-based interfaces
These tools often rely on foundation models developed by specialized AI labs such as OpenAI, Anthropic, and others like Mistral and Cohere.
Why it matters: This layer is where value is realized, where AI earns its keep. It also serves as the interface through which user data is collected, model feedback is gathered, and brand loyalty is won.
Why This Structure Matters
The AI landscape is not a vertical market. It’s horizontal and interconnected. Advancements in one layer reshape what’s possible in another.
For instance, improvements in chip efficiency enable larger and more responsive foundation models. Innovations in cooling technology allow hyperscalers to run denser GPU clusters in the same footprint. Bottlenecks in grid access or network throughput can delay entire AI deployments. And at the top, new applications raise the stakes for performance and reliability all the way down.
Understanding the stack also helps clarify where value accrues. While application companies may get the spotlight, much of the defensible value and long-term leverage may sit deeper in the stack. Data center infrastructure, chip design, and power provisioning are becoming increasingly strategic assets. Meanwhile, hyperscale cloud providers like AWS, Microsoft Azure, and Google Cloud Platform serve as the critical bridge between raw infrastructure and enterprise applications.
Not Quite a Supply Chain
This isn’t a classic supply chain. It’s a technology stack: a layered system with interdependencies, but not a linear flow. Some companies play in multiple layers (Microsoft builds chips, runs Azure, and offers Copilot), while others are highly specialized.
And while the phrase “AI race” often conjures images of model leaders like OpenAI or Anthropic, the real competition may be more diffuse, playing out across thermal engineers, switch designers, power consultants, and optical transport vendors.
Looking Ahead
The future of AI isn’t just about who builds the smartest model. It’s about who enables it at scale, reliably, and profitably.
The AI stack offers a clearer lens into that future. It’s a map of where we are, where constraints lie, and where investment is likely to flow next. Whether you’re in tech, finance, policy, or operations, understanding the AI stack is no longer optional.
Understanding the AI stack reveals something important: the companies building the smartest models are just one part of a much larger system. The real work of AI happens across every layer, from silicon to software, power to platforms.
