Analysis of AI spending shows users will pay significantly more for faster model inference (e.g., 6x price for 2x speed), prioritizing interactivity over marginal gains in intelligence. This mirrors how e-commerce conversions are highly sensitive to latency, suggesting speed is a critical, high-value feature for AI products.

To build products for a world five years away, IBM commits to hardware designs like embedding AI and quantum-safe security onto chips long before market demand is obvious. This requires deep conviction in long-term trends and having faith that software can be fine-tuned later to meet specific client needs as they emerge.

Previous technology shifts like mobile or client-server were often pushed by technologists onto a hesitant market. In contrast, the current AI trend is being pulled by customers who are actively demanding AI features in their products, creating unprecedented pressure on companies to integrate them quickly.

Historically, software was built for predictable human workflows. Now, with AI agents executing thousands of unpredictable, low-latency queries simultaneously, product design must prioritize their needs. These agents will eventually select their own infrastructure, fundamentally changing the B2B buying process.

Demonstrating long-term strategic foresight, Cloudflare designed its server motherboards with an empty slot for an unknown future use case. This enabled them to rapidly plug in GPUs across their global network to launch AI inference services, turning a hardware decision into a major strategic advantage.

AI models are developed so quickly that there often isn't enough time for full evaluation before release. Faster inference hardware allows researchers to understand a model's full potential intelligence by running extensive tests in a compressed timeframe.

Previously, the biggest constraint in AI was compute for training next-gen models. Now, the critical bottleneck is providing enough compute for *inference*—the real-time processing of queries from a rapidly growing user base.

While training has been the focus, user experience and revenue happen at inference. OpenAI's massive deal with chip startup Cerebrus is for faster inference, showing that response time is a critical competitive vector that determines if AI becomes utility infrastructure or remains a novelty.

The current 2-3 year chip design cycle is a major bottleneck for AI progress, as hardware is always chasing outdated software needs. By using AI to slash this timeline, companies can enable a massive expansion of custom chips, optimizing performance for many at-scale software workloads.