I got triggered enough to write an angry rebuttal. The structure is familiar: first, a summary of the breathless hype that AI will change everything. Then, a c

I got triggered enough to write an angry rebuttal. The structure is familiar: first, a summary of the breathless hype that AI will change everything. Then, a collection of counterpoints suggesting it's all overblown. The grand conclusion? That everything is confusing, nobody knows anything for sure, and the wisest stance is to take it all with a "large grain of salt." The truth is, we know an immense amount about AI, and pretending otherwise is not a sign of wisdom, but an excuse to disengage from the most important technological shift of our time. First, a note on the sloppy analogy of the "economic sacrificial lamb," referring to those first disrupted by AI. This analogy is not just melodramatic; it’s incorrect. Developers aren't being passively offered up to some AI deity. They are the first to feel the effects because they are the ones building, implementing, and integrating the technology. They are not the lamb; they are the blacksmiths forging a new kind of hammer and, in the process, figuring out how it changes their own workshop. Their proximity to the change is a consequence of their agency, not their victimhood. The most frustrating claim, however, is the idea that "we don’t yet know anything for sure." To anyone working with or studying these systems, this is patently absurd. While the long-term societal outcome is uncertain, we have a vast and growing body of practical, empirical knowledge about how these models work. We know, for example, that providing specific context and reference material dramatically improves answer quality and reduces hallucinations—the entire principle behind the now-dominant RAG (Retrieval-Augmented Generation) architecture. We know about scaling laws, prompt engineering best practices, and fine-tuning methodologies. We have gigabytes of data on what tasks AI excels at (boilerplate code, synthesis, translation) and where it fails (complex reasoning, factual accuracy, planning). To dismiss this mountain of hard-won engineering knowledge is to be aggressively ignorant. Then there is the Double Standard. Cal Newport has built his brand as a productivity expert, offering systems for "deep work" and focus. Yet, as any psychologist or organizational behavior expert can attest, human productivity is a field where "moving the needle predictably" is a notoriously difficult, almost impossible task. It’s a domain riddled with individual differences, cultural nuances, and contradictory findings. For a guru from a field that lacks hard empirical certainty to demand it from the nascent field of AI exposes a glaring double standard. He is holding AI to a standard of proof that the behavioral sciences—the foundation of his own work—have never been able to meet. It's a rhetorical move that positions him as a wise skeptic, but to me, it comes off as hypocrisy. And then the final, tired advice: be skeptical. Take it with a grain of salt. This isn't insight; it's a cliché that has been the default take for every technological shift of the last thirty years. It’s safe, lazy, and frankly, boring. We don't need more generic skepticism. We need engaged, critical, hands-on analysis. The interesting work isn't in declaring the future unknowable from an armchair by has-been intellectuals that are unable or unwilling to keep up with recent developments.