Current LLMs are intelligent enough for many tasks but fail because they lack access to complete context—emails, Slack messages, past data. The next step is building products that ingest this real-world context, making it available for the model to act upon.

A profoundly underutilized feature of AI is its ability to teach. Instead of just delegating tasks, professionals should ask LLMs to train them in new skills, create practice assignments, and evaluate their performance, unlocking rapid personal development.

Generative AI can be used as a conversational expert to quickly gain deep domain knowledge in new industries. By engaging in long dialogues about market trends, regulations, and business models (e.g., value-based medicine), a leader can compress months of research into a single afternoon.

A remarkable feature of the current LLM era is that AI researchers can contribute to solving grand challenges in highly specialized domains, such as winning an IMO Gold medal, without possessing deep personal knowledge of that field. The model acts as a universal tool that transcends the operator's expertise.

High productivity isn't about using AI for everything. It's a disciplined workflow: breaking a task into sub-problems, using an LLM for high-leverage parts like scaffolding and tests, and reserving human focus for the core implementation. This avoids the sunk cost of forcing AI on unsuitable tasks.

A powerful, under-explored use of LLMs is as a tool to enhance human cognition. Rather than simply generating answers, one can interact with them to challenge, validate, and improve one's own mental models of a system or problem, creating a valuable learning loop.

To master a new skill like creating a sales offer, first command an LLM to outline the framework of a known expert (e.g., Alex Hormozi). Then, have it generate interview questions based on that framework. Answering these allows the LLM to apply the expert's model directly to your specific situation.

For experienced coders, LLMs eliminate the steep learning curve for new platforms. Max Levchin, who had never built an iOS app, created a custom A/V remote control app for his home because an AI agent handled the research and setup, letting him focus on the core logic.

To improve LLM reasoning, researchers feed them data that inherently contains structured logic. Training on computer code was an early breakthrough, as it teaches patterns of reasoning far beyond coding itself. Textbooks are another key source for building smaller, effective models.