Altman argues that AI model speed is as critical as intelligence for creative work. Using extremely fast models creates an immediate connection between a creator and their creation, shortens the iterative thinking loop, and makes a 'wild difference' in problem-solving. Latency is not just a performance metric; it's a constraint on creativity.

AI tools democratize prototyping, but their true power is in rapidly exploring multiple ideas (divergence) and then testing and refining them (convergence). This dramatically accelerates the creative and validation process before significant engineering resources are committed.

The importance of speed in AI is deeply psychological. Similar to consumer packaged goods where faster-acting ingredients create higher margins and brand affinity, low-latency AI creates a powerful dopamine cycle. This visceral response builds brand loyalty that slower competitors cannot replicate.

As frontier AI models reach a plateau of perceived intelligence, the key differentiator is shifting to user experience. Low-latency, reliable performance is becoming more critical than marginal gains on benchmarks, making speed the next major competitive vector for AI products like ChatGPT.

The speed of models like SWE 1.7 is more than a convenience; it fundamentally changes user behavior. It eliminates the awkward latency gap where tasks are too slow for real-time interaction but too fast to fully context-switch. This enables a new "watch it work" workflow, keeping users in a state of flow.

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 design tools like Claude Design are hampered by slow generation times, credit limits, and high-latency feedback loops. Figma’s advantage lies in its speed, allowing designers to make immediate, drag-and-drop changes without waiting for an LLM call, highlighting the irreplaceable value of rapid iteration in creative work.

Model speed is not just a cost metric; it's a powerful user experience driver. Once users become accustomed to a fast, responsive model, it becomes very difficult for them to tolerate slower ones, creating a sticky product advantage similar to the adoption of high-speed internet.

In AI applications, response speed is a core feature that shapes user perception of accuracy. Users rarely request it, but experiencing a fast model after using a slow one creates an immediate, undeniable sense of value and can be a significant product unlock.