Not everyone is using AI for everything
Claims that “everyone is using AI for everything” clash with developers’ experiences of large language models often making products slower, less reliable, and harder to maintain than deterministic systems. Commenters describe management- and investor-driven pressure to bolt AI onto workflows, uneven productivity gains (especially in coding), and rising dependence on tools whose outputs still need careful review. Statistics on actual usage suggest strong but far-from-universal adoption, while many worry about long‑term effects on software quality, jobs, and the broader information ecosystem as AI-generated content spreads into search, support, and everyday apps.
Overuse and Misfit of LLMs
- Many report companies replacing simple, deterministic workflows (support flows, CI, tooling, code review) with slower, less reliable LLM-based systems, often just to say they “do AI.”
- Some contractors say they regularly implement unnecessary AI because executives insist, even when PoCs show deterministic designs would be cheaper, faster, and more stable.
- A recurring critique: AI is being used to replace tools it should instead be used to build (e.g., generating lint rules or scripts rather than acting as the lint/CI itself).
Deterministic Systems vs Agents
- Strong camp arguing core business logic and customer-facing workflows must be deterministic and testable; LLMs should be wrappers or assistants around robust CLIs/APIs.
- Others promote hybrid patterns: agents reason in natural language but must execute via carefully designed, constrained tools.
LLMs for Coding
- Many developers use LLMs heavily as coding assistants: boilerplate, one-off scripts, refactors, explanations, debugging.
- Supporters claim huge speedups; critics say LLM-generated code is brittle, insecure, overengineered, and harder to understand, often costing more time in review and debugging.
- Several warn of eroding fundamental skills and future codebases that are effectively “software archaeology” projects.
Workplace Pressure and Management FOMO
- Reports of AI hackathons, daily-use mandates, and bonus structures tied to token usage, even where a 1–2% failure rate is unacceptable.
- Engineers often see management chasing investor narratives and “AI adoption” metrics rather than measurable product value.
Who Is Actually Using AI?
- Thread cites studies: ~20–30% of US working-age population using AI tools regularly; far below “everyone,” but high for a new tech.
- Debate over definitions: explicit chatbot use vs. passive use embedded in Google search, recommendation feeds, phone cameras, etc.
- Some argue “everyone uses AI” if you count those background systems; others see that as forced or incidental, not meaningful adoption.
Individual Use Cases and Non-Use
- Heavy users describe substantial real-world benefits (insurance disputes, home repair decisions, garden design, shopping, marketing).
- Others restrict use to programming or search replacement; some avoid LLMs entirely and feel pressured or marginalized.
Hiring and Career Anxiety
- Job seekers see “How do you use LLMs?” as a standard interview question and struggle to answer for both AI-enthusiastic and AI-skeptical employers.
- Some hiring managers now treat lack of agent/LLM experience as a red flag; others value nuanced, critical use over hype.
Quality, Safety, and Societal Concerns
- Recurrent themes: hallucinations, security holes, sloppier software, degraded customer support, and “slopification” of web content.
- Concerns that low literacy and weak critical thinking amplify misuse, while corporate incentives favor cheaper support over better service.
Long-Term Outlook
- Optimists liken this phase to early internet or compilers: rapid growth, eventual ubiquity, and large productivity gains.
- Skeptics foresee permanent niches where AI is ill-suited and warn against assuming future model improvements will automatically fix current structural problems.