AI is a process to convert solar energy to intelligence

 We stand at the precipice of a revolution, one powered not by steam or silicon, but by syntax and servers. The rise of Generative AI has been heralded as the dawn of democratized intelligence, a future where every individual has a brilliant co-pilot in their pocket. Yet, as we watch this new world take shape, a rather inconvenient and increasingly stark reality is emerging: the algorithm giveth, and the algorithm taketh away—on a scale that would make John D. Rockefeller blush. The economic landscape is beginning to look less like a level playing field and more like a cartoon where one character has a rocket ship and everyone else has a pogo stick. On one hand, we see super-intelligence labs at companies like Meta offering compensation packages to top AI researchers that resemble the GDP of a small island nation. These are the new high priests of progress, crafting the digital deities that will define our future. In parallel, tech titans like Microsoft and Nvidia are soaring towar...

 The entire discipline of guiding LLM outputs can be reframed through the lens of "choice architecture," a concept from behavioral economics introduced by Richard Thaler and Cass Sunstein. Choice architecture is the design of the environment in which people make decisions; it posits that the way choices are presented—the "architecture"—inevitably influences the outcome, whether intentionally or not. A choice architect, therefore, is anyone who organizes the context in which people make decisions. In the realm of LLMs, prompt engineers and the designers of Retrieval-Augmented Generation (RAG) systems are the new choice architects. They are not merely instructing a machine; they are designing a decision-making environment for an artificial intelligence. This section argues that this process is profoundly shaped by the same heuristics and biases that affect human decision-making, turning context engineering into a practice of creating flawed choice architectures.

 True expertise in the LLM domain is not defined by the ability to merely use high-level libraries and frameworks as black boxes. Instead, it is distinguished by a profound, intuitive understanding of the fundamental mathematical and architectural principles that govern these complex systems. This foundational knowledge is what separates practitioners who can follow recipes from innovators who can invent them. The most significant differentiator for elite LLM talent is not simply knowledge of specific frameworks, but a deeply ingrained, almost physical intuition for high-dimensional geometry and probabilistic systems. They do not just "know" linear algebra; they "think" in terms of vector spaces. They do not just "know" probability; they can "feel" how a change in the loss function will shift a model's output distribution. This intuition is the wellspring of creative problem-solving and the invention of novel techniques that drive the field f...

Silicon Intelligence vs. Carbon Intelligence: A Tale of Two Energies “Artificial Intelligence” is a misnomer, if you ask me. I’d rather call it Silicon Intelligence . After all, it runs on silicon chips (at least for now) and is powered by electricity. By contrast, human intelligence is carbon-based—built from DNA and fueled by bioelectricity. Ultimately, all that energy—whether from a GPU cluster or a human brain—traces back to the sun : The past sun powers us via coal, oil, and gas. The present sun drives wind turbines, solar panels, and photosynthesis (plant food included). Preset Parameters and the Art of Learning When a baby is born, much of their foundational “mental model” is wired by genetics. As Charlie Munger famously quipped, “They’re like machines with 80% of the parameters preset.” But humans are also learning machines . We don’t recompile—we reflect. And if we’re lucky, we wake up “a little less stupid” each day. Large language models improve similarly—via fine-...

AI Coding: The $630 Billion Feedback Loop From Anthropic’s staggering $630 billion post-evaluation fundraise to the Windsurf drama—where an LLM shifted allegiance from OpenAI to Google—everything points to one force: the raw power of large language models in code generation. “AI is real,” said Jamie Dimon, CEO of the largest U.S. bank, during a closed-door webinar. He wasn’t waxing philosophical—he was citing measurable gains in research and customer service productivity. And it’s not just real—it’s profitable. Companies are putting serious money into AI coding platforms like Anthropic’s Sonnet and Claude. Anthropic reportedly hit $4 billion in annualized revenue by July 2025—a near 4x leap from the start of the year. One investor justified the sky-high valuation with a comment that would make any spreadsheet blush: “We’re happy to pay 20x of 2025 projected revenue ~3B, given Anthropic is expected double its revenue YoY.” That rationale—optimistic as it may be—probably still undere...