AI could be the end of the digital wave, not the next big thing

Impact on creativity, skills, and learning

• Several developers report feeling less creative and more dependent on AI, with fewer original project ideas and difficulty coding without AI (e.g., on planes / offline).
• Others say their “muscle memory” hasn’t degraded despite heavy AI use; they see skill loss as normal “use it or lose it,” not AI-specific.
• Mention of cognitive offloading: when a tool is trusted, humans remember less and think less deeply; studies cited showing lower ownership and weaker recall when using AI for writing.
• Some argue not remembering boilerplate is fine and even desirable; the important part is understanding logic and architecture.

Online experience degradation & retreat to offline

• Many complain that search and shopping are being flooded with “AI slop” (bad images, SEO text), making it harder to learn or buy reliable products.
• This drives people back to physical spaces: clothing stores, libraries, film rental shops, social clubs, and in‑person services.
• Some foresee a partial “rewind to 1990”: in‑person shopping, paper exams, proctored tests, and distrust of online text/images.

Economic & labor implications

• Strong expectation that AI will be used first for cost-cutting and layoffs, especially in back-office and low–medium complexity white-collar work.
• Concern that even if society benefits overall, individual workers (especially mid‑career engineers) may be “the horses” in the car analogy.
• Others note historical patterns: tech often lowers costs, compresses margins, increases competition, and benefits consumers more than workers.

Is AI the end of the digital wave or a new surge?

• One camp: AI/LLMs are late-stage digital optimization; software has already “eaten the world,” and AI just accelerates commoditization and margin collapse.
• Another camp: current investment behavior (massive, speculative, infrastructure-heavy) looks like an “installation phase” of a new techno‑economic surge, not an endgame.
• Some view AI as fundamentally new because it can “do” rather than merely “help,” potentially clashing with existing economic and social structures.

Alternative “next waves”

• Robotics, renewable energy (solar + batteries + EVs), biotech (protein folding, mRNA), and space are proposed as deeper or more important transformations than LLMs.
• Debate over how tightly robotics is tied to LLMs; some see them as largely separate, others see LLMs as a coordination layer for robot behaviors.

Adoption patterns, UX, and infrastructure

• Rapid mainstream uptake of chatbots is contrasted with the slower PC adoption curve; non‑technical users often adopted ChatGPT early.
• Disagreement over whether a “chatbox for everything” is actually a better UI than traditional interfaces; sometimes doing the task directly is quicker than describing it.
• Some predict ubiquitous on-device models (even in appliances), but others question cost, accuracy, hallucinations, and user tolerance.
• Concerns about hyperscale data centers: seen locally as symbols of job loss, high energy use, and “your livelihood being replaced.”

Open vs proprietary models and moats

• Open-weight, local models already handle many everyday tasks well for some users.
• Skepticism that proprietary AI firms have durable moats if “good enough” local models catch up; AI might erode existing software moats rather than create new ones.