The AI Emperor Has No Clothes (www.jeffgeerling.com)

An industry insider warns we’re in an AI bubble: an avalanche of job pitches, product review requests, paid sponsorships and creator-rep offers signal hype far outpacing real commercial value. The piece argues trillions in startup valuations aren’t supported by plausible future profits — many offerings are either useless or wildly overpriced relative to their benefit — even as genuine, valuable machine‑learning use cases exist but are overshadowed by marketing noise. This matters because overvaluation and hype distort where money and talent flow, risk a credibility crash for AI, and could slow meaningful progress if funding retracts. Technically, the author highlights a growing mismatch: model training demands ever more compute for marginal gains, while hardware improvements are decelerating, making scale‑first approaches increasingly costly and less sustainable. The likely technical implication is a shift toward efficiency — smaller, specialized models, better software/hardware co‑design, and stricter ROI scrutiny — but timing is uncertain; the bubble might deflate this year or in several years. For practitioners and investors, the takeaway is to prioritize demonstrable utility and cost‑efficiency over hype-driven scale.