Demis Hassabis likens current AI models to someone blurting out the first thought they have. To combat hallucinations, models must develop a capacity for 'thinking'—pausing to re-evaluate and check their intended output before delivering it. This reflective step is crucial for achieving true reasoning and reliability.

AI is a 'hands-on revolution,' not a technological shift like the cloud that can be delegated to an IT department. To lead effectively, executives (including non-technical ones) must personally use AI tools. This direct experience is essential for understanding AI's potential and guiding teams through transformation.

AI development is more like farming than engineering. Companies create conditions for models to learn but don't directly code their behaviors. This leads to a lack of deep understanding and results in emergent, unpredictable actions that were never explicitly programmed.

AI's occasional errors ('hallucinations') should be understood as a characteristic of a new, creative type of computer, not a simple flaw. Users must work with it as they would a talented but fallible human: leveraging its creativity while tolerating its occasional incorrectness and using its capacity for self-critique.

It's unsettling to trust an AI that's just predicting the next word. The best approach is to accept this as a functional paradox, similar to how we trust gravity without fully understanding its origins. Maintain healthy skepticism about outputs, but embrace the technology's emergent capabilities to use it as an effective thought partner.

To effectively leverage AI, treat it as a new team member. Take its suggestions seriously and give it the best opportunity to contribute. However, just like with a human colleague, you must apply a critical filter, question its output, and ultimately remain accountable for the final result.

Don't blindly trust AI. The correct mental model is to view it as a super-smart intern fresh out of school. It has vast knowledge but no real-world experience, so its work requires constant verification, code reviews, and a human-in-the-loop process to catch errors.

Alistair Frost suggests we treat AI like a stage magician's trick. We are impressed and want to believe it's real intelligence, but we know it's a clever illusion. This mindset helps us use AI critically, recognizing it's pattern-matching at scale, not genuine thought, preventing over-reliance on its outputs.

GSB professors warn that professionals who merely use AI as a black box—passing queries and returning outputs—risk minimizing their own role. To remain valuable, leaders must understand the underlying models and assumptions to properly evaluate AI-generated solutions and maintain control of the decision-making process.