When Microsoft partners with OpenAI while competing with them, and Amazon doubles down on Google-backed Anthropic, you know the rules have changed.

When a single model like last year's Gemini Ultra costs $190M just to develop, even tech giants with deep pockets can't put all pieces of the puzzle together alone. Just think about all the layers of the stack, product use cases, and distribution channels.

"Cross-ecosystem collaboration is critical to mobilizing the investment and capabilities that different ecosystem participants require to innovate and facilitate broader adoption of gen AI," a recent Accenture analysis reveals.

The telling proof? A striking 65% of $32Bn invested in foundation models (2019 - mid 2024) is partnership-led.

But it's not just about training LLMs or building data centers. Partnerships are critical across all six layers of the AI Stack.

📊 AI Stack's Key Dependencies (examples):

Applications Layer needs:

• Foundation model integration

• Application-specific data

• Distribution channels to reach users

Tooling Layer (model hubs, data orchestration) needs:

• AI frameworks and code libraries

• Synthetic data generation

Foundation Model Layer need:

• Massive compute resources

• Large-scale training datasets

• Distribution channels

Data & Storage Layer needs:

• High-quality structured/structured datasets

• Scalable infrastructure and Interoperability

Cloud & Networking Layer needs:

• High bandwidth networking

• Customer relationships (distribution)

Chips Layer needs:

• Compute for chip design

• Supplier and Customer relationships

4 Key Partnership Types Emerging in the Ecosystem:

1. Chips + Cloud

• Chip makers gain cloud integration expertise and broader market reach

• Cloud providers get access to high-performance AI chips

1. Clouds + Foundation Models

• Cloud providers gain cutting-edge foundation models (eg. GPT)

• Model developers get infrastructure for broader deployment and scaling

1. Data/Storage + Foundation Models

• Data companies expand market reach by supporting AI workloads

• Model developers get access to scalable data and storage solutions

1. Foundation Models + Applications

• Model developers reach new markets through AI-driven applications

• App developers get Foundation model integration capabilities

What makes AI partnerships unique is how they've created incredible incentives for deep collaboration.

Unlike previous tech waves where companies could succeed by controlling key parts of the stack, AI's complexity and resource demands force even the largest players to seek complementary capabilities.

Tech companies are aggressively pursuing multiple parallel alliances rather than (while?) trying to fully vertically integrate. It's about creating an ecosystem where innovation happens through collaboration and co-competition.