The 'Andy Warhol Coke' era, where everyone could access the best AI for a low price, is over. As inference costs for more powerful models rise, companies are introducing expensive tiered access. This will create significant inequality in who can use frontier AI, with implications for transparency and regulation.

Just as standardized tests fail to capture a student's full potential, AI benchmarks often don't reflect real-world performance. The true value comes from the 'last mile' ingenuity of productization and workflow integration, not just raw model scores, which can be misleading.

Non-tech professionals often judge AI by obsolete limitations like six-fingered images or knowledge cutoffs. They don't realize they already consume sophisticated AI content daily, creating a significant perception gap between the technology's actual capabilities and its public reputation.

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.

Judging an AI's capability by its base model alone is misleading. Its effectiveness is significantly amplified by surrounding tooling and frameworks, like developer environments. A good tool harness can make a decent model outperform a superior model that lacks such support.

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.

Users notice AI tools getting worse at simple tasks. This may not be a sign of technological regression, but rather a business decision by AI companies to run less powerful, cheaper models to reduce their astronomical operational costs, especially for free-tier users.

Frontier AI models exhibit 'jagged' capabilities, excelling at highly complex tasks like theoretical physics while failing at basic ones like counting objects. This inconsistent, non-human-like performance profile is a primary reason for polarized public and expert opinions on AI's actual utility.

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.