Creative Strategies published an expert interview with Billy on the power constraints facing AI data center buildouts, examining behind-the-meter generation, natural gas supply, and contract structuri…
The Diligence Stack reported that the CPU-to-GPU ratio for agentic AI inference will vary by hyperscaler, with no standard configuration, and that the scale-up domain is the priority for serving infer…
Creative Strategies research concludes that the future of compute is fungible, with hyperscalers building data centers around workload fungibility that spans merchant compute, custom silicon, copper, …
Creative Strategies' Diligence Stack research arm rates its conviction high that agentic AI will become an important part of enterprise cyber defense, while holding conviction Medium that restricted f…
Creative Strategies projects that 2027 will be the peak year of constraint for AI infrastructure, with demand running 115-120% above annual capacity and critical manufacturing capacity not fully contr…
Semiconductor test equipment demand is rising as AI accelerators and advanced packaging increase test content per sellable system, according to a report from the GPU Tsunami analysis. The report model…
At Hot Chips 2026, semiconductor vendors presented divergent designs for AI inference, signaling an era of ferment where no dominant architecture has emerged, according to analyst Ben Bajarin. Compani…
Creative Strategies' updated E/AI Index CIO/CTO survey finds enterprise AI has moved from pilots into 'governed production,' where workflows operate under defined rules but cost controls and governanc…
The Diligence Stack's August 2026 State of Models report, based on its CS Bench benchmark, finds that the true cost of AI models includes human review time, with polished but error-filled outputs bein…
Creative Strategies' research, powered by its CS Atlas platform, finds that the GPU tsunami in AI compute is pulling more FPGAs into rack-scale systems, with Lattice Semiconductor's FPGAs appearing on…
Behind-the-meter (BTM) power is becoming a viable option for hyperscalers to accelerate AI infrastructure deployment, driven by a shift from training to inference workloads and improved rack-level pow…