DeepSeek Upends Silicon Valley’s Sci-Fin-Fi Business Model
Bibliographic record
Abstract
Commentary on DeepSeek’s release of its R1 model fails to capture three critical points: just how decisively R1’s release has upended the Silicon Valley business model; how decisively China is winning the technological war that the US and the West have been waging; and how the release of R1 demonstrates more clearly than ever that, notwithstanding decades of anti-socialist propaganda to the effect that, for all its faults, capitalism is best at advancing technology and, thus, the forces of production, while socialism has always failed at innovation, capitalism’s capacity to advance the forces of production is manifestly exhausted, while socialism is only now beginning to show its potential in that respect. This article discusses the first point in detail, showing that the vaunted Silicon Valley Model is systematically reliant on hyping what the technologies it offers can deliver in terms of growth or material welfare and on financial hype about the returns it can bring, combining science and financial fiction. It also traces the main academic arguments that have been mustered to support this fictional approach. It touches on the other two points only briefly in the conclusion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.023 | 0.033 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".