While often discussed for privacy, running models on-device eliminates API latency and costs. This allows for near-instant, high-volume processing for free, a key advantage over cloud-based AI services.

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 ultimate winner in the AI race may not be the most advanced model, but the most seamless, low-friction user interface. Since most queries are simple, the battle is shifting to hardware that is 'closest to the person's face,' like glasses or ambient devices, where distribution is king.

Models that generate "chain-of-thought" text before providing an answer are powerful but slow and computationally expensive. For tuned business workflows, the latency from waiting for these extra reasoning tokens is a major, often overlooked, drawback that impacts user experience and increases costs.

Tasklet's CEO reports that when AI agents fail at using a computer GUI, it's rarely due to a lack of intelligence. The real bottlenecks are the high cost and slow speed of the screenshot-and-reason process, which causes agents to hit usage or budget limits before completing complex tasks.

A 'GenAI solves everything' mindset is flawed. High-latency models are unsuitable for real-time operational needs, like optimizing a warehouse worker's scanning path, which requires millisecond responses. The key is to apply the right tool—be it an optimizer, machine learning, or GenAI—to the specific business problem.

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.

AI struggles to provide truly useful, serendipitous recommendations because it lacks any understanding of the real world. It excels at predicting the next word or pixel based on its training data, but it can't grasp concepts like gravity or deep user intent, a prerequisite for truly personalized suggestions.

The perceived limits of today's AI are not inherent to the models themselves but to our failure to build the right "agentic scaffold" around them. There's a "model capability overhang" where much more potential can be unlocked with better prompting, context engineering, and tool integrations.