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Record W7106845579 · doi:10.1080/21598282.2025.2579900

DeepSeek Upends Silicon Valley’s Sci-Fin-Fi Business Model

2025· article· en· W7106845579 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Critical Thought · 2025
Typearticle
Languageen
FieldComputer Science
TopicBig Data and Digital Economy
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBusiness modelSilicon valleyProcess (computing)Production (economics)Work (physics)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.314
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it