The New Artificial Intelligence Stack

The convergence of advanced AI models, specialized hardware, and massive datasets is creating a new technology stack where intelligence becomes a core component of applications, workflows and decision-making. This report examines the foundational layers enabling the emergence of agentic AI applications.

We explore the five components of the new AI stack and how their evolution is driving the transition from traditional software to AI-powered, autonomous systems.

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Report Highlights

1. From Software to Service-as-Software

AI is enabling a fundamental shift in business automation. Rather than software tools that augment human work, AI agents can now autonomously complete tasks—moving from Software-as-a-Service to "Service-as-Software." This expansion into services and wage budgets represents a possible multi-trillion dollar opportunity, potentially transforming the landscape of work and labor.

2. The Five Layers of the AI Stack

We outline the core components forming the foundation for next-generation AI applications:

  • Core Compute & Storage: The semiconductors, networking, and storage technologies enabling AI model training and inference at scale.
  • AI & Data Infrastructure: The software platforms, databases, and frameworks developers use to build AI applications.
  • Developer Productivity: AI-enhanced tools that automate and accelerate software development workflows.
  • Cybersecurity: New security tools and practices to protect AI systems and defend against AI-enabled threats
  • Agentic Platforms and Applications: Autonomous AI systems capable of decision-making and action with minimal human intervention.

3. Technical Drivers of AI Progress

Three key technological advances have aligned to enable the current AI revolution:

  • Deep Learning & Transformers: Breakthroughs in AI model design, including LLMs like GPT, that can understand and generate human language.
  • Specialized Hardware: GPUs optimized for the parallel computations required to train large-scale AI models.
  • Massive Training Data: Access to vast amounts of diverse data, with forecasts reaching 394 zettabytes globally by 2028.

Together, these advances have made possible the generative AI capabilities now transforming industries.

4. Agentic AI: The Hidden Requirements

While frontier AI models are advancing rapidly, deploying agentic applications in production requires capabilities across the entire stack. Organizations must address challenges in data processing and storage, cybersecurity threats, model management, compute access, system reliability, and developer productivity.

Many enterprises expecting to simply deploy models are finding it necessary to upgrade practices and tools across multiple layers—creating opportunities for new platforms and solutions.

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