Human-Machine Amplification

Human-machine amplification is Wei Qing / 韦青’s claim in E42 孟岩对话韦青：沉默的主角 that technology multiplies the state of the person using it. If the person has judgment, values, curiosity, and self-command, AI can amplify constructive work; if the person is weak, captured, or machine-like, the same technology can amplify dependence, noise, and harm.

The episode uses this frame to shift AI risk from “will machines become human?” to “are humans already becoming machines?” Wei Qing’s human test is deliberately ordinary: can a person still taste sweetness in plain food, feel spring grass, relax enough to move well, and create directional anomalies instead of only repeating learned patterns?

E45 孟岩对话李继刚：人何以自处 adds a complementary split from Li Jigang / 李继刚: body power, brain power, and heart power. If AI amplifies or replaces more brain work, the quality of amplification depends even more on whether the human still has heart power, Wet-State Human Agency, and enough Feed Curation to choose what gets amplified.

Key Claims

• Technology is multiplicative rather than automatically good or bad.
• AI can enlarge the user’s judgment, but it can also enlarge the user’s confusion, addiction, or low-quality intent.
• Machine pattern-matching makes human anomaly, curiosity, and value direction more important.
• Embodied practice matters because not all human capability is explicit, textual, or consciously controlled.
• “High automation, low volition” is a failure state where tools work but the human will weakens.
• When AI takes over more brain work, amplification quality depends on the user’s heart power, taste, body rhythm, and chosen inputs.

Connections

• Human Judgment Under AI — amplification depends on the user’s ability to evaluate and own results.
• Human Agency Under AI — the user still decides purpose, values, and delegation boundaries.
• AI Use Pacing and Attention Industrialization — amplification can become capture if pace and attention are unmanaged.
• Subjectivity As AI Asset — personal taste, values, and point of view become inputs to AI collaboration.
• Domain Expert Alignment — tacit and embodied expertise matter when model knowledge is incomplete.
• Li Jigang / 李继刚, Wet-State Human Agency, and Feed Curation — E45’s heart-power and input-selection extension.