The AI research paradigm has shifted over the past decade from early reliance on kernel methods, Bayesian approaches, and strict statistical guarantees to the current mainstream dominated by Rich Sutton's "bitter lesson," which states that general methods combined with large-scale computing power surpass carefully designed feature engineering. Funding has shifted from theoretical and algorithmic innovation to large-scale data centers, GPU clusters, and larger datasets. Companies like xAI, OpenAI, and Google have adopted "larger models + more data" as their core competitive strategy, motivated by empirical performance replacing theoretical elegance as the primary evaluation standard. Similar cases include the complete replacement of traditional machine learning by deep learning and the expansion from pure language models to multimodal and world models. The industry is at a stage where "scale remains the most reliable leverage," with theoretical work serving more for post-hoc explanations rather than pre-design. This is essentially a technological substitution: when the costs of computing power and data drop below a certain threshold, the marginal returns of brute-force search and scale expansion exceed those of fine theoretical design, leading to a cultural shift from "proving correctness" to "running it first and explaining later." The industry's true belief has shifted from "proof" to "scale."