The Monetization Trap: When Profits Come at the Expense of Productivity (greyenlightenment.com)

A recent critique argues that the early promise of generative AI—ChatGPT, Claude, Gemini and the like—will be undermined by commercialization-driven “time sinks” that erode real productivity. Invoking the Solow Paradox and Cory Doctorow’s “enshitification,” the piece claims mainstream AI tools are already being degraded by rate limits, captchas, paywalls, feature bloat and heavy monetization (examples: ChatGPT throttling, pervasive Cloudflare captchas, YouTube autoplay/ads, bloated Gmail). Those product decisions prioritize ROI and abuse mitigation over usability, creating overheads—“AI slop”—like extra verification, double-checking deepfakes, and workflow interruptions that consume the time AI was supposed to save. For the AI/ML community this matters technically and economically: measured productivity gains can be negated by UX friction, latency and verification costs, and winner-take-all/platform lock-in reduces competitive pressure to preserve utility. Practical implications include the need to measure net productivity (not just model performance), design for low-friction verification and robust APIs, and consider open or decentralized alternatives to avoid restrictive monetization patterns. The author predicts that unless businesses accept more abuse risk or regulation/competition shifts incentives, AI’s macroeconomic productivity lift may be far smaller than anticipated.