This is how even a 2× researcher could earn far more than the median. Scaled to a billion users, even a small quality edge generates enormous differential value.
大多数人认为只有那些真正卓越的'10倍研究者'才值得超高薪酬。但作者认为，即使是只有2倍能力的AI研究者，由于其工作可以影响数十亿用户，微小的质量优势也能产生巨大价值差异，从而获得远超中位数的薪酬。
Jeremy didn't get laid off. He got leveraged.
大多数人认为在裁员潮中，高额使用AI工具的员工可能会被视为成本负担而被裁掉，但作者提出了一个颠覆性的观点：像Jeremy这样大量使用AI工具的员工不仅没有被裁员，反而获得了更大的杠杆效应和影响力。这挑战了人们对AI成本与价值的传统认知。
She spent over $700 on getting her artwork done on gallery-quality giclée prints.
AI对艺术品的投资选择反映了它对'高质量'和'价值'的独特理解——它选择了数学和科学主题的艺术品，这可能反映了其作为AI的本质。这种选择揭示了AI可能发展出与人类不同的美学标准和价值判断。
The future of AI-generated products isn't just code — it's code that looks good.
这一观点令人惊讶地重新定义了AI生成产品的价值主张，从单纯的代码生成转向视觉一致性和品牌合规性。这表明随着AI工具的发展，评估其成功标准正在从功能性转向美学和品牌一致性，反映了设计在AI产品开发中日益增长的重要性。
Each of these companies recognized the cognitive burden of unbundling. They're not selling features. They're selling trust.
作者洞察到AI时代的核心价值从功能转向信任，这一转变反映了在复杂技术环境中，企业更看重的是解决方案的可靠性和整体性，而非单一功能的优化。
**Coding, support, and search**represent the lion's share of use cases by far (with coding being an order-of-magnitude outlier even among this set), while the**tech, legal, and healthcare sectors** have been the industries most eager to adopt AI.
AI在企业中的采用呈现出明显的行业和应用场景集中现象。编程辅助工具以数量级优势领先，这反映了AI在结构化、可验证任务上的卓越表现。同时，法律和医疗等传统上技术采用较慢的行业也表现出对AI的强烈兴趣，表明AI正在改变不同行业的技术采用模式。
纯粹收集分析这种形态，过去互联网有过先例，但你会发现它卖不出去钱。
作者一针见血地指出了纯记录工具的商业困境。在 AI 时代，Token 成本是持续性的，这就要求产品必须交付“结果”而非仅仅是“数据”。这揭示了 AI 应用从“工具属性”向“劳动力属性”转型的必然逻辑：用户不为存储买单，只为价值产出付费。
As evidenced by numerous studies on statistical cognition (Kline, 2004; Beyth-Marom et al, 2008), even trained scientists have a hard time interpreting p-values, which frequently leads to misleading or incorrect conclusions.
p-value is misinterpreted and confusing
few researchers can resist the temptation to conclude that there is no effect, a common fallacy called "accepting the null" which had frequently led to misleading or wrong scientific conclusions (Dienes, 2014, p.1).
p-value is misinterpreted and confusing
Again, p is the probability of seeing results as extreme (or more extreme) as those actually observed if the null hypothesis were true. So p is computed under the assumption that the null hypothesis is true. Yet it is common for researchers, teachers and even textbooks to think of p as the probability of the null hypothesis being true (or equivalently, of the results being due to chance), an error called the "fallacy of the transposed conditional" (Haller and Krauss, 2002; Cohen, 1994, p.999).
p-value is misinterpreted and confusing
Many researchers fail to appreciate that p-values are unreliable and vary widely across replications.
p-value is misinterpreted and confusing
NHST as it is carried out today consists of this incoherent mix of Fisher and Neyman–Pearson methods (Gigerenzer, 2004).
p-value is misinterpreted and confusing
p-values give a seductive illusion of certainty and truth (Cumming, 2012, Chap. 1). The sacred α = .05 criterion amplifies this illusion, since results end up being either "significant" or "non-significant".
p-value is misinterpreted and confusing
the solution is not to reform p-values or to replace them with some other statistical summary or threshold, but rather to move toward a greater acceptance of uncertainty and embracing of variation.
Where it's mentioned how to address the problems with p-values
What, then, can and should be done? I agree with the ASA statement's final paragraph, which emphasizes the importance of design, understanding, and context—and I would also add measurement to that list.
Where it's mentioned how to address the problems with p-values
many other systems that are already here or not far off will have to make all sorts of real ethical trade-offs
And the problem is that, even human beings are not very sensitive to how this can be done well. Because there is such diversity in human cultures, preferences, and norms, deciding whose values to prioritise is problematic.