Anthropic's David Hershey states it's "deeply unsurprising" that AI is great at software engineering because the labs are filled with software engineers. This suggests AI's capabilities are skewed by its creators' expertise, and achieving similar performance in fields like law requires deeper integration with domain experts.

Silicon Valley insiders building AI may overestimate its impact due to self-interest (looming IPOs) and a narrow perspective. Their expertise in AI doesn't translate to economics or labor markets, and their track record of understanding the world outside their bubble is poor, making their job apocalypse predictions unreliable.

When AI systems are trained on historical data, such as past hiring or policing records, they learn and perpetuate existing societal biases. This creates a dangerous illusion of objectivity, where discriminatory outcomes are presented as neutral, data-driven "predictions" by an algorithm.

Leaders often expect AI to magically solve complex issues like data harmonization without considering the foundational work required, such as building an ontology. This shortcut-seeking mindset leads to poor decision-making and ineffective AI deployment, highlighting the need to involve technical experts early.

The tech industry believes better marketing can solve AI's unpopularity. However, the public's negative experiences and the feeling of being dehumanized into data are the real issues. You cannot advertise people out of their own lived experiences, revealing a fundamental disconnect between tech and society.

Tech leaders, while extraordinary technologists and entrepreneurs, are not relationship experts, philosophers, or ethicists. Society shouldn't expect them to arrive at the correct ethical judgments on complex issues, highlighting the need for democratic, regulatory input.

The true danger of AI is not a cinematic robot uprising, but a slow erosion of human agency. As we replace CEOs, military strategists, and other decision-makers with more efficient AIs, we gradually cede control to inscrutable systems we don't understand, rendering humanity powerless.

The builders of AI may have a skewed perspective on its real-world impact. They often extrapolate from their tech-centric experiences and fail to grasp how technology diffuses in the broader economy. Their predictions about societal consequences, such as mass job displacement, should therefore be viewed with healthy skepticism.

AI systems often collapse because they are built on the flawed assumption that humans are logical and society is static. Real-world failures, from Soviet economic planning to modern systems, stem from an inability to model human behavior, data manipulation, and unexpected events.