Moore's Law (Contextual): What It Means for Business and AI Strategy

Overview

Moore’s Law (contextual) refers to the historical observation that compute density doubled periodically, driving down cost per computation and boosting AI capacity. While the exact cadence has slowed and shifted to new technologies (GPUs, AI accelerators, packaging, software efficiency), the business impact remains clear: more compute becomes available per dollar over time, enabling more ambitious AI use cases—if leaders plan for it strategically.

Key Characteristics

Performance and Cost Dynamics

• Density doubles → capability rises: More transistors per chip historically led to higher performance and lower cost per FLOP, expanding feasible AI workloads.
• Energy efficiency matters: Gains increasingly come from performance per watt, not just raw speed—critical for data center costs and sustainability targets.
• Diminishing “free lunch”: Traditional scaling has slowed; improvements now depend on specialized hardware, parallelism, and software optimization.

Beyond Transistors: The New Stack

• Accelerators over CPUs: GPUs and custom AI chips (ASICs) drive modern AI capacity due to parallelism advantages.
• Packaging and interconnects: Technologies like chiplets and high-bandwidth memory are as important as transistor counts.
• Software leverage: Compiler optimizations, quantization, sparsity, and caching can yield 2–10x gains without new hardware.

Economic and Supply Realities

• Capex to Opex shift: Cloud-based AI turns hardware cycles into consumption decisions, with variable pricing for cutting-edge chips.
• Supply chain volatility: Geopolitics and foundry capacity can affect availability and price; multi-source strategies reduce risk.
• Scaling laws for AI: Model performance often improves predictably with more compute and data—guiding investment pacing.

Business Applications

Product and Roadmap Strategy

• Unlock new features: As compute-per-dollar improves, real-time translation, multimodal assistants, and advanced personalization become viable at scale.
• Time features to cost curves: Plan launches to align with expected hardware and cloud price drops.

Cost Management and ROI

• Right-size models: Use smaller, optimized models for most tasks; reserve frontier models for high-value cases.
• Exploit price/performance windows: Revisit TCO quarterly; renegotiate cloud commitments as new accelerators roll out.

Infrastructure and Cloud Strategy

• Hybrid approaches: Combine cloud for bursty training with on-prem or colocation for steady inference to control cost and latency.
• Regionalization: Place compute near customers or data to reduce latency and comply with data residency rules.

Talent and Operating Model

• Cross-functional optimization: Pair ML engineers with systems and FinOps to capture software and procurement gains.
• Continuous benchmarking: Establish a performance council to evaluate hardware, frameworks, and model efficiency quarterly.

Risk and Sustainability

• Avoid lock-in: Use containerized runtimes and open standards (ONNX, OpenXLA) to maintain portability across vendors.
• Track carbon and watts: Factor performance-per-watt into SLAs and procurement; align AI growth with sustainability goals.

Implementation Considerations

Capacity Planning and Forecasting

• Scenario models: Build 12–24 month forecasts for compute needs under conservative, base, and aggressive adoption scenarios.
• Trigger-based scaling: Define budget and performance triggers to shift workloads between CPU, GPU, and specialized accelerators.

Build vs. Buy

• Cloud-first for agility; on-prem for steady state: Start in cloud to learn quickly; consider on-prem for predictable, high-volume inference.
• Evaluate hidden costs: Include engineering time, networking, storage, reliability, and deprecation in TCO.

Architecture Choices

• Optimize the full stack: Use quantization (e.g., INT8/FP8), sparsity, knowledge distillation, and caching to cut costs without hurting quality.
• RAG and hybrid models: Combine retrieval-augmented generation with smaller models to reduce dependency on frontier compute.

Procurement and Vendor Management

• Flexible contracts: Seek committed-use discounts with flexibility, multi-region capacity, and step-down clauses as hardware evolves.
• Diversify supply: Maintain at least two viable providers per critical workload to mitigate shortages and pricing pressure.

Governance, Security, and Compliance

• Data gravity and privacy: Keep sensitive workloads close to data; use confidential computing for regulated sectors.
• Model governance: Track lineage, datasets, and inference logs to meet audit requirements as capacity and model updates accelerate.

Measurement and Benchmarking

• KPIs to track:
  • Cost per 1K tokens / inference
  • Latency and throughput under load
  • Performance per watt and carbon intensity
  • Quality vs. cost trade-off (business metrics tied to model performance)

Conclusion

Treat Moore’s Law contextually: not as a guarantee of effortless speedups, but as a strategic trend you can harness through smart timing, architecture, and procurement. Companies that align product roadmaps, infrastructure choices, and governance with the evolving compute landscape will ship more capable AI at lower unit cost—and convert hardware progress into durable business value.