Outsourcing thinking

Effects on cognition and learning

• Several commenters report personal changes from AI use: more impatience, skimming, and difficulty sustaining attention.
• A recurring theme: the problem isn’t how much thinking is outsourced but which thinking. Boring, effortful work often builds judgment, intuition, and ownership; removing it may undermine expertise.
• Some fear “Thinking as a Service” will train a generation not to think for themselves, with people unable to judge or edit AI output once hands‑on skills fade.
• Others counter that humans have always happily outsourced thinking (to experts, media, religion), and that the real question is whether this remains adaptive in a future dominated by machine cognition.

Reliability, tools, and tacit knowledge

• Strong distinction is drawn between calculators (deterministic, provably correct) and LLMs (probabilistic, often wrong, opaque biases).
• Using AI in critical tasks without deep understanding is compared to teaching with a calculator that silently returns incorrect values 1–20% of the time.
• Multi‑LLM “cross‑checking” is criticized because models are trained on similar data and share correlated errors.
• Some argue that even flawed tools can lower barriers to entry (e.g., letting novices “vibe-build” software or houses), while others warn this just floods the world with low-quality “slop.”

Historical and technological analogies

• Car‑centric design is a major analogy: cars are useful, but building society around them had large, unintended harms. Many see AI following the same pattern.
• Other comparisons: calculators, Google Maps (loss of navigation skills), industrial food (ultra‑processed “thinking”), 24‑hour news and social media (outsourcing political judgment), and religion (outsourcing moral frameworks and knowledge).
• Some insist past “tech panic” skeptics were often right about real losses, even if net benefits existed.

Power, control, and agendas

• Concern that once dependence on AI is established, providers will bias models for commercial or political agendas—analogous to captured print, broadcast, and search media.
• Competition among model vendors is seen by some as mitigating centralization; others argue compute concentration and regulation will still entrench a few actors.
• Several note that heavy outsourcing makes people prey to those who own the tools, echoing broader worries about offshoring, specialization, and system fragility.

Communication, identity, and accountability

• Many worry that LLM‑mediated writing flattens individuality and removes “human touch,” especially in emotionally meaningful contexts.
• Others like AI as a buffer in hostile or stressful relationships (e.g., with bosses or customers), even if it may preserve relationships that should end.
• A debated issue: AI‑written emails and messages give people a built‑in excuse—“the AI wrote that, not me”—which some see as eroding responsibility and honesty. Several insist senders must still own their words.
• Use of generative replies in everyday communication (e.g., Gmail) is viewed by some as corrosive of authentic connection, by others as reasonable obfuscation in a surveilled world.

Reversibility and long‑term trajectories

• An important distinction: using AI as a visible tool (scratchpad, planner, search) vs. letting it silently shape taste, style, and decisions over years. The latter is seen as harder to reverse and closer to identity change.
• Some believe humans would quickly relearn lost skills if AI vanished; others argue that institutional and educational changes (like with maps and mental math) make certain capacities unlikely to return.
• A few speculative takes suggest we may reclassify “mechanized intelligence” (symbolic, test‑score style) as outsourceable, and lean more into non‑mechanical aspects like intuition, but this remains aspirational and unclear in practice.