AI Frontier: Hidden Bottlenecks, Blindspots, and the Future of AI Infrastructure
Uses a healthcare system analogy to explain why AI outcomes depend on the full infrastructure stack,
not just frontier models. It highlights bottlenecks in memory, networking, orchestration, tools,
and total cost of outcomes. Companies Mentioned: Anthropic, CoreWeave, DeepSeek, Ericsson, Google, Lambda, Meta,
Nebius, Nokia, NVIDIA, OpenAI, Pinecone, Qdrant, Weaviate, and xAI (Audience: Investors and Practitioners - 19 page PDF)
When Memory, Not Compute, Limits AI Inference
This study explains why AI inference is often constrained by memory bandwidth and KV-cache growth,
not just compute. It shows how these bottlenecks shape performance, cost, and AI infrastructure
strategy. Company Mentioned: Grok (Audience: Investors and Practitioners - 5 page PDF)
When Experts Become the Bottleneck
This study shows how MoE architectures expand model capacity while reducing active computation. It highlights how routing,
networking, and load balancing can create hidden efficiency bottlenecks. Companies Mentioned: DeepSeek, Google, Meta,
and xAI (Audience: Investors and Practitioners - 6 page PDF)
Trading Precision for Efficiency with Quantization
This study shows how quantization cuts AI memory, latency, and cost. It compares FP16, INT8, and INT4
trade-offs in performance and model quality. (Audience: Investors and Practitioners - 5 page PDF)
Distilled AI: Lower Cost, Faster Inference
Model distillation uses larger AI models to train smaller models that are faster and cheaper to run. It supports tiered AI systems
where smaller models handle routine tasks and larger models handle complex cases. Companies Mentioned: Alibaba, DeepSeek, Google,
Meta, and Mistral AI (Audience: Investors and Practitioners - 12 page PDF)
Faster Inference with Speculative Decoding
This study shows how speculative decoding can accelerate AI inference. It highlights why acceptance rate and serving-stack
design determine the real latency and cost gains. Companies Mentioned: AWS, IBM, and Red Hat (Audience: Investors and Practitioners - 5 page PDF and Web Document)
Analyst Briefing
A 1-hour virtual analyst briefing provides a focused walkthrough of the AI Frontier research findings
and their commercial implications. It gives companies the opportunity to promote their own AI capabilities within the context of the research, while giving investors, executives, and practitioners direct access to concise, evidence-based analysis and discussion.
Upcoming Studies
AI Networks: The Connectivity Layer Behind Scalable Intelligence
This study examines AI networking as a core infrastructure layer shaping performance, cost, scale, and competitive advantage in distributed AI systems.
AI Data Management: Memory, Storage, and Sovereignty at Scale
This study examines how AI data management is becoming a critical infrastructure layer, shaping performance, cost, scale, and control as memory, storage, data movement, and governance demands grow.
Agentic AI: Infrastructure, Implementation, and the Economics of Workflows
This study examines how agentic AI shifts infrastructure demands from model inference to coordinating models, tools, data, APIs, and multi-step workflows for AI-enabled execution.
