OpenAI's CEO believes a significant gap exists between what current AI models can do and how people actually use them. He calls this "overhang," suggesting most users still query powerful models with simple tasks, leaving immense economic value untapped because human workflows adapt slowly.
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Eclipse Ventures founder Lior Susan shares a quote from Sam Altman that flips a long-held venture assumption on its head. The massive compute and talent costs for foundational AI models mean that software—specifically AI—has become more capital-intensive than traditional hardware businesses, altering investment theses.
There is a massive gap between what AI models *can* do and how they are *currently* used. This 'capability overhang' exists because unlocking their full potential requires unglamorous 'ugly plumbing' and 'grunty product building.' The real opportunity for founders is in this grind, not just in model innovation.
Sam Altman believes incumbents who just add AI features to existing products (like search or messaging) will lose to new, AI-native products. He argues true value comes not from summarizing messages, but from creating proactive agents that fundamentally change user workflows from the ground up.
Sam Altman confesses he is surprised by how little the core ChatGPT interface has changed. He initially believed the simple chat format was a temporary research preview and would need significant evolution to become a widely used product, but its generality proved far more powerful than he anticipated.
The argument is that "economic diffusion lag" is an excuse for AI's current limitations. If AI models were truly as capable as human employees, they would integrate into companies instantly—far faster than human hiring. The slow rollout proves they still lack core, necessary skills for broad economic value.
The main barrier to AI's impact is not its technical flaws but the fact that most organizations don't understand what it can actually do. Advanced features like 'deep research' and reasoning models remain unused by over 95% of professionals, leaving immense potential and competitive advantage untapped.
The slow adoption of AI isn't due to a natural 'diffusion lag' but is evidence that models still lack core competencies for broad economic value. If AI were as capable as skilled humans, it would integrate into businesses almost instantly.
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.
A theory suggests Sam Altman's massive, multi-trillion dollar spending commitments are a strategic play to incentivize a massive overbuild of AI infrastructure. By driving supply far beyond current demand, OpenAI could create a 'glut,' crashing the price of compute and securing a long-term strategic advantage as the primary consumer.
The most significant recent AI advance is models' ability to use chain-of-thought reasoning, not just retrieve data. However, most business users are unaware of this 'deep research' capability and continue using AI as a simple search tool, missing its transformative potential for complex problem-solving.
