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.

Analysis of Anthropix's OPUS model reveals a strong user preference for speed, with customers willing to pay six times more for a model that is only two times faster. This disproportionate willingness to pay for performance validates the market for specialized, high-speed inference chips like those from Cerebras.

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.

Despite perceptions of LLMs as interchangeable commodities, user behavior shows significant stickiness. This loyalty isn't just about model performance; it's driven by the overall product experience, workflow integrations (like Claude Code), and agentic capabilities, which make users reluctant to switch even with service interruptions.

Companies like OpenAI and Anthropic are intentionally shrinking their flagship models (e.g., GPT-4.0 is smaller than GPT-4). The biggest constraint isn't creating more powerful models, but serving them at a speed users will tolerate. Slow models kill adoption, regardless of their intelligence.

As AI model capabilities become easily replicable, the key differentiator for giants like Anthropic isn't the tech itself, but the speed at which they can innovate and launch new products. This creates a flywheel of data, improvement, and market capture that outpaces slower competitors.

While many AI models compete on technical benchmarks, Mykhailo argues ChatGPT's dominance comes from superior product execution. Its user interface, responsiveness, and fast 'time to interaction' create a user experience that is incredibly difficult to replicate, giving it a powerful moat beyond just model quality.