AI is a floor raiser, not a ceiling raiser

Metaphor debate (floor, ceiling, walls, ladders)

• Many riff on the “floor raiser” idea: AI as shovel breaking the barrel’s bottom, wall raiser, ladder that doesn’t reach the ceiling, or even “false confidence generator.”
• Some argue the OP ignores that most people operate between floor and ceiling, not at extremes.
• A few suggest AI both raises the floor and lowers the ceiling, compressing the skill range.

Floor vs. ceiling in practice

• One camp: AI mainly lets below‑average people reach “average” output, or makes average people faster at low‑level work. This supports the “floor raiser” thesis.
• Another camp: top performers gain the most – AI is a strong productivity multiplier, especially in research, design, and cross‑domain work, thus raising the ceiling too.
• Some note “good enough” is often a low bar; even experts use AI to generate average results that are perfectly acceptable for many tasks.

Learning, mastery, and “cheating”

• Concern: using AI to shortcut hard parts of learning yields an illusion of mastery; you get results without understanding, so your long‑term ceiling drops.
• Others describe workflows where AI is managed like a junior or “pair,” used to clarify concepts, surface terms, and propose directions, while they still drive understanding.
• Several argue AI best helps in “known‑unknowns” (you know what to ask) and is dangerous in “unknown‑unknowns” where you can’t spot its mistakes.

Coding and agents

• AI is widely seen as good for prototyping, boilerplate, and exploring unfamiliar stacks; weaker for deep engineering: edge cases, architecture, safety, and large legacy codebases.
• Some report strong success with agentic tools that can read repos and generate PRs; others find agents drift, forget goals, or degrade complex code and tests.
• There’s debate whether agents are only good on greenfield projects or can already handle real-world issues measured on benchmarks like SWE‑Bench.

Reliability, hallucinations, and search vs. LLMs

• Multiple comments stress that LLMs are extremely convincing but frequently wrong; users often lack the expertise to detect errors.
• Chess and niche company data are cited as domains where LLM outputs can be confidently wrong yet hard to verify.
• Some prefer LLMs as a “better StackOverflow/search” with fewer ads, while others describe concrete failures where classic search quickly outperforms AI answers.

Access, economics, and inequality

• Worry that paid tiers and rising costs will exclude those who most need “floor raising,” while owners of large models and capital capture the upside.
• Cited studies (via The Economist) suggest newer evidence that AI may increase inequality: high performers gain more from AI than low performers in complex tasks.
• Others counter that many APIs are cheap or free and argue that cost trends have been downward so far.

Societal and cognitive effects

• Some fear AI will accelerate wage suppression and “ladder pulling”: junior tasks automated away, making it harder to grow future experts.
• Others note similar trends from earlier automation and the broader digital world; AI is seen as an incremental, not wholly new, shift.
• There’s concern that perfectly fluent AI output erodes media literacy and critical thinking, especially if combined with subtle commercial influence.