Today's AI boom is fueled by scaling computation, which is a known engineering challenge. The alternative, embedding nuanced, human-like inductive biases, is far harder as it requires a deep understanding of the problem space. This difficulty gap explains why massive models dominate AI development over more targeted, efficient ones—scaling is simply the more straightforward path.

The focus in AI has evolved from rapid software capability gains to the physical constraints of its adoption. The demand for compute power is expected to significantly outstrip supply, making infrastructure—not algorithms—the defining bottleneck for future growth.

The current focus on building massive, centralized AI training clusters represents the 'mainframe' era of AI. The next three years will see a shift toward a distributed model, similar to computing's move from mainframes to PCs. This involves pushing smaller, efficient inference models out to a wide array of devices.

The current wave of AI companies is growing at unprecedented rates, far outpacing the growth curves of the mobile, social, or SaaS eras. They are becoming larger and more consequential much faster, a phenomenon described as "speed running the process of company growth."

The historical adoption of electricity in factories shows that true productivity gains came from redesigning the factory floor, not simply replacing steam engines. Similarly, companies must fundamentally re-engineer processes around AI to unlock its transformative potential.

Cerebras CEO Andrew Feldman argues that massive speed improvements in AI are not just about reducing latency. Like how fast internet turned Netflix from a DVD mailer into a studio, ultra-fast AI will enable fundamentally new applications and business models that are impossible today.

A key distinction between technological eras: the internet democratized access to information, while AI democratizes access to operational leverage. This fundamentally changes how businesses, especially physical ones, can manage complexity, execute tasks, and ultimately scale their operations.

The common analogy of AI being "like a website" that every company must adopt may be misleading. The real transformative power of AI could be in enabling entirely new, AI-native businesses that leapfrog incumbents, rather than simply being a feature tacked onto existing products.

The recent AI breakthrough wasn't just a new algorithm. It was the result of combining two massive quantitative shifts: internet-scale training data and 80 years of Moore's Law culminating in GPU power. This sheer scale created a qualitative leap in capability.