Working through Karpathy's series builds a foundational understanding of LLMs, providing enough confidence to explore further. A key insight is that LLMs are logit emitters, and their inherent uncertainty compounds dangerously in multi-agent chains, often requiring a human-in-the-loop or a single orchestrator to manage it. Crucially, people confuse word embeddings with the full LLM; embeddings are just the input to a vast, incomprehensible trillion-parameter transformer. The underlying math of these networks is surprisingly simple, built on basic additions and multiplications. The real mystery isn't the math but why they work so well. Ultimately, AI research is a mix of minimal math, extensive data engineering, massive compute power, and significant trial and error.

Companies that promote the "996" culture (working from 9 am to 9 pm, 6 days a week) are a major red flag for any employee. This model might only be justifiable for a founder with a huge equity stake, never for an average employee without extraordinary compensation. Furthermore, these extended hours don't usually translate into greater real productivity.

This debate is part of a critical redefinition of work. Technology has increased productivity, but wages have stagnated, breaking the social contract. As in the past with labor laws, urgent change is needed to avoid a crisis, prioritizing a quality life and a legacy to be proud of, not senseless exploitation.