New technologies perceived as job-destroying, like AI, face significant public and regulatory risk. A powerful defense is to make the general public owners of the technology. When people have a financial stake in a technology's success, they are far more likely to defend it than fight against it.

Initial public fear over new technologies like AI therapy, while seemingly negative, is actually productive. It creates the social and political pressure needed to establish essential safety guardrails and regulations, ultimately leading to safer long-term adoption.

The rhetoric around AI's existential risks is framed as a competitive tactic. Some labs used these narratives to scare investors, regulators, and potential competitors away, effectively 'pulling up the ladder' to cement their market lead under the guise of safety.

Since AI can deliver results instantly, customers may perceive the output as low-effort and thus low-quality. To combat this, shift the focus from the speed of delivery to the immense effort, experience, and investment required to build the underlying AI system in the first place.

To get mainstream users to adopt AI, you can't ask them to learn a new workflow. The key is to integrate AI capabilities directly into the tools and processes they already use. AI should augment their current job, not feel like a separate, new task they have to perform.

Despite broad, bipartisan public opposition to AI due to fears of job loss and misinformation, corporations and investors are rushing to adopt it. This push is not fueled by consumer demand but by a 'FOMO-driven gold rush' for profits, creating a dangerous disconnect between the technology's backers and the society it impacts.

AI's contribution to US economic growth is immense, accounting for ~60% via direct spending and indirect wealth effects. However, unlike past tech booms that inspired optimism, public sentiment is largely fearful, with most citizens wanting regulation due to job security concerns, creating a unique tension.

Despite the hype, AI's impact on daily life remains minimal because most consumer apps haven't changed. The true societal shift will occur when new, AI-native applications are built from the ground up, much like the iPhone enabled a new class of apps, rather than just bolting AI features onto old frameworks.

Contrary to expectations, wider AI adoption isn't automatically building trust. User distrust has surged from 19% to 50% in recent years. This counterintuitive trend means that failing to proactively implement trust mechanisms is a direct path to product failure as the market matures.