Deep domain expertise can be self-taught by finding a single compelling "nerd-snipe" company (like ASML for semiconductors) and relentlessly following the knowledge trail from there, reading textbooks and exploring the entire downstream industry through passionate, independent study.
Current AI excels at information gathering, similar to a junior analyst. However, it lacks the meta-level learning to develop true expertise from repeated tasks. This makes it a powerful tool for amplifying existing experts by handling tedious work, not replacing their decision-making capabilities.
Legacy business software like Excel are "IDEs for analysts" and are doomed. The core abstraction layer is shifting from graphical interfaces with complex, hard-to-discover functions to direct, natural language interaction with agents like Claude Code, which is a fundamentally superior workflow.
To evaluate candidates, run the same case study through an AI agent like Claude. This creates an objective performance floor; if a human candidate cannot outperform the AI's output, they fail to meet the minimum standard for the role, providing a practical filter in the hiring process.
Experts develop a "meta-level" understanding by repeatedly performing tedious, manual information-gathering tasks. By automating this foundational work, companies risk denying junior employees the very experience needed to build true expertise and judgment, potentially creating a future leadership and skills gap.
Microsoft's strategy of renting its Azure cloud to AI leaders like OpenAI is akin to Rome paying barbarians at the gate. They are funding the very entities that will inevitably build products to disrupt their core Office and Windows software businesses, creating a fatal strategic conflict.
To prevent an LLM's performance from degrading in a long conversation, a phenomenon called "context rot," it is best to separate tasks. Use one context window for content generation and a new, fresh window for evaluation tasks like applying a rubric. This avoids bias and improves output quality.
Ambitious AI projects may fail their primary goal but still produce valuable secondary assets. An attempt to predict memory prices with an LLM failed, but the automated data gathering process created a first-of-its-kind historical analysis dashboard, which proved to be a more valuable outcome.
Integrating AI into legacy software like Excel is a suboptimal, backward-compatible approach akin to putting a car engine in a horse carriage. The more powerful workflow is to use a native AI coding environment to generate final outputs like Matplotlib charts directly, bypassing the constraints of old UIs.
Success in tech investing can come from a radical, top-level thesis that challenges core industry assumptions. The belief that Moore's Law was ending provided a powerful lens to re-evaluate the semiconductor industry, correctly predicting that pricing power would shift to innovators like Nvidia.
Forcing a draft when tired is counterproductive. Instead, do all research and outlining, then go to sleep. Writing the full draft first thing in the morning leverages the brain's clarity after rest for a much higher quality output, much like starting a new context window with an LLM.
True financial alpha lies in identifying technological inflection points with billion-dollar impacts, such as a product's on-time delivery. This focus on qualitative, high-impact events is superior to the traditional sell-side's broken model of chasing commoditized one-cent earnings-per-share differences.
AI's true productivity leverage is not just speed but enabling more attempts. A human might get one shot at a complex task, whereas an AI-assisted workflow allows for three or more "turns at the wheel." The critical human skill shifts from initial creation to rapid review and refinement of these iterations.
Modern coding agents can now execute entire data analysis workflows in a single request. This includes scraping public data via custom queries, performing analysis, and generating publication-ready visualizations based on provided style guides and theoretical principles, collapsing a multi-day task into minutes.
The demand for HBM memory for AI is causing a global shortage because of a ~4:1 manufacturing trade-off: each bit of HBM produced consumes capacity that could have made four bits of standard DRAM. This supply crunch will raise prices for all electronics, from phones to PCs.
Benchmarks like GDPVal show models like GPT-4 consistently outperform human experts on professional tasks, meeting the practical definition of AGI for knowledge work. The public discourse, however, has prematurely shifted the goalposts to sci-fi concepts of Artificial Superintelligence (ASI), obscuring the revolution already underway.
Google is offering its TPUs externally for the first time as a strategic move to gain market share while it has a temporary hardware advantage over Nvidia. This classic tactic aims to build a crucial install base that can be upgraded later, even after its competitive performance edge inevitably narrows.