The AI industry has reached the stage where it can't just be exciting and new anymore. It has to prove its worth.

Whether extreme spend pays off comes down to the ultimate business value of shipped code (e.g. revenue), which most companies still can't measure.

Model companies must now compete on both dimensions. The application layer will compete one level up, on dollars per outcome.

Every layer in the stack now has to price the same way the customer thinks : per result, not per token.

This dynamic UI management is the future of software value : the harness to control the interface/ensure it's correct & the knowledge management to rationalize all the AI products over time

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.

Jeremy didn't get laid off. He got leveraged.

She spent over $700 on getting her artwork done on gallery-quality giclée prints.

The future of AI-generated products isn't just code — it's code that looks good.

Each of these companies recognized the cognitive burden of unbundling. They're not selling features. They're selling trust.

**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.

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。

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.

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).

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).

Many researchers fail to appreciate that p-values are unreliable and vary widely across replications.

NHST as it is carried out today consists of this incoherent mix of Fisher and Neyman–Pearson methods (Gigerenzer, 2004).

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".

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.

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.

many other systems that are already here or not far off will have to make all sorts of real ethical trade-offs