The goal for computer use agents has shifted beyond mimicking human actions to exceeding them in speed. The primary bottleneck is no longer the AI's reasoning but the real-world latency of the software it operates, like website loading times. This changes how developers must think about agent performance optimization.

The dominant AI use case will shift from real-time, human-in-the-loop chatbots to long-running background agents. For these agents, which work for hours or days, an extra few seconds of latency is meaningless, unlocking massive cost-saving opportunities by prioritizing throughput over speed.

The next leap in AI's value will come from "agents" that work on problems autonomously without direct, real-time user commands. This shift from a reactive, search-engine-like model to a proactive, problem-solving one will drive a 5x increase in compute consumption and unlock new applications.

As AI models reach sufficient intelligence for tasks like coding, the critical bottleneck becomes speed. Ultra-fast models enable interactive, real-time collaboration, allowing a developer to build software with an AI assistant without breaking their creative 'flow' state.

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.

The massive speed increase of FAL's H3 Max model wasn't just an incremental improvement; it enabled entirely new, unplanned real-time applications like interactive Twitch streams. This shows that quantitative leaps in performance can lead to qualitative shifts in user experience and unlock emergent product categories.

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.

The transition from chatbots to autonomous 'agentic' AI represents a fundamental step-change. These agents, which execute complex tasks independently, have already increased the demand for computational power by 1000x, creating a massive, ongoing need for new infrastructure and hardware.

As AI models become commodities, the underlying hardware's speed and efficiency for inference is the true differentiator. The company that powers the fastest AI experiences will win, similar to how Google won with fast search, because there is no market for slow AI.