The 'Intelligence Stack' Redefines AI Value

- The "Intelligence Stack" is not a formally defined framework but rather a conceptual model describing how value is shifting in the AI industry. While foundation models provide the base layer, the real-world application and economic value are increasingly captured by the layers built on top. - Proprietary data is a key differentiator in the intelligence stack, as it provides the unique context that general-purpose models lack. Companies with exclusive access to domain-specific data can create more accurate and relevant AI systems, establishing a competitive moat. For instance, a healthcare AI model trained on proprietary clinical trial data will outperform a general model. - Workflow automation platforms like Zapier, Workato, and Microsoft Power Automate represent a critical integration layer. These tools allow businesses to embed AI into their existing processes, connecting various applications and automating tasks, which is where much of the practical value of AI is realized. - Agentic AI systems are a significant evolution beyond simple retrieval-augmented generation (RAG). While RAG retrieves information to answer queries, agentic systems can perform multi-step reasoning, use various tools, and take autonomous actions to achieve a goal, representing a move from "information access" to "problem-solving." - The pricing models for enterprise AI are shifting from simple subscription fees to more dynamic, value-aligned structures. Common models now include usage-based pricing (per API call or token), outcome-based pricing (payment for successful results), and hybrid approaches that combine a base subscription with variable charges. - For ML platform engineering, cost optimization of GPU infrastructure is a major focus. Strategies include using on-demand or spot instances for non-urgent workloads, which can reduce costs by 70-90%, and right-sizing GPU selection for specific tasks like training versus inference. For instance, using smaller GPUs for development and reserving high-performance ones like H100s for production training can significantly lower burn rates. - The commoditization of foundation models is evidenced by the falling prices for API access, with some providers dropping rates by over 80% between 2023 and 2024. This trend is compelling companies to focus on building differentiated products and enterprise services on top of these models rather than relying on the models themselves as a competitive advantage. - Looking ahead to 2026, the convergence of AI with agentic platforms is expected to be a major driver of enterprise performance. This will involve integrating multiple AI-as-a-Service (AIaaS) and Software-as-a-Service (SaaS) systems, requiring disciplined orchestration and standardization to manage complexity.