AI competition is no longer about models alone. It is about full-stack control: hardware, infrastructure, platforms, models, services, and applications. The companies that own more layers compound advantages faster and escape the constraints that slow everyone else down.
1. Hardware Layer — The Foundation of AI Economics
Google / Alphabet: Complete
Google controls its silicon with TPUs, edge devices, and vertical integration from chip design to deployment. This gives Google a structural cost advantage explored in detail in The Business Engineer: https://businessengineer.ai/
OpenAI: Limited
OpenAI relies entirely on NVIDIA hardware. No custom silicon means no hardware–software optimization loop.
Microsoft: Moderate
Microsoft has emerging silicon initiatives and owns devices like Surface and Xbox, but still depends on partners.
Meta: Emerging
Meta invests in research chips and edge devices (VR/AR), but silicon control is still early.
Amazon: Selective
Inferentia and Trainium represent AWS’s push into custom AI hardware.
Apple’s chip strategy is deeply vertical, optimized for privacy and on-device AI.
NVIDIA: Dominant
NVIDIA owns the global GPU market, CUDA ecosystem, and GPU software stack. This dominance is discussed within the broader stack analysis in The Business Engineer: https://businessengineer.ai/
2. Infrastructure Layer — The Compute Backbone
Google: Complete
Massive cloud footprint, global data centers, and Kubernetes across the stack.
OpenAI: Partial
Microsoft Azure partnership provides infrastructure but limits autonomy.
Microsoft: Complete
Global cloud presence combined with enterprise security and integration.
Meta: Strong
Large-scale infrastructure optimized for social platforms and AI research.
Amazon: Dominant
AWS remains the global cloud leader, with unmatched enterprise reliability.
Partners provide infrastructure; NVIDIA supplies the hardware.
3. Platforms Layer — ML Frameworks, Dev Tools, APIs
Google: Complete
TensorFlow, JAX, Keras API, and Colab give Google one of the strongest platform layers. Platform leverage is a key vector analyzed in The Business Engineer: https://businessengineer.ai/
OpenAI: Strong
GPT APIs, developer tools, Playground, and fine-tuning.
Microsoft: Strong
Azure AI, Cognitive Services, ML Studio, and Bot Framework.
Meta: Open
PyTorch and open-source frameworks dominate research.
Amazon: Growing
SageMaker and Bedrock are expanding rapidly.
Anthropic: Focused
Claude API, simple tools, research focus.
Apple: Ecosystem
Core ML, Create ML, and MLX.
NVIDIA: Foundational
CUDA, cuDNN, TensorRT, Triton — the software backbone of GPU computing.
4. Models Layer — Foundation and Specialized Models
Google: Complete
Gemini, PaLM 2, Imagen, and DeepMind research.
OpenAI: Leading
GPT-4o, DALL·E 3, Sora, Whisper.
Microsoft: Partnership
Top-tier access to OpenAI models plus internal research.
Meta: Competitive
Llama 3.1 and FAIR research.
Amazon: Emerging
Nova models, partner models, and Titan.
Anthropic: Excellent
Claude 3.5, Sonnet/Opus, and Constitutional AI research.
Conclusion — Vertical Integration Is the Real Battleground
The companies competing in AI are not fighting over models alone. They are fighting over entire stacks. The ones that own more layers gain powerful self-reinforcing loops:
This is why Google, Amazon, Microsoft, Meta, Apple, and NVIDIA look so different across the map. Their strengths and weaknesses emerge from the layers they control and the dependencies they cannot escape.
To explore the full strategic logic behind vertical integration and AI moats, see The Business Engineer: https://businessengineer.ai/
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
