Start-Up War and the world as platform: venture capital, the U.S. military, & Silicon Valley
Bibliographic record
Abstract
The past decade has seen a set of transformations in the institutions of the U.S. Department of Defense towards financial and organizational logics drawn from Silicon Valley and the tech industry. In turn, Silicon Valley and tech industry start-ups and giants have pursued valuable defence contracts expanding their footholds in the defence economy. This article argues that these recent collaborations mark a convergence of the political economy and geopolitical ideologies of the U.S. military and tech industry. I articulate the concept of ‘Start-Up War’ in this article to both describe the constellation of these political, economic and ideological relationships and as an analytical lens for understanding the politics produced in these relations. In tracing this constellation through defence institutions, defence contracts and military strategy, this article argues for an important connection to be drawn between U.S. imperial geopolitics and struggles against precaritization and exploitation in platform and gig work economies. This connection is illustrated through the lens of the ‘platform’ and its uptake as a common logic between Silicon Valley tech firms, venture capital funds and the U.S. military.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".