Open-Source; Open-Season; Open-Fire: <i>Google v. Oracle</i> and the Vulnerability of Code to Copyright Infringement by AI Harvesting
Bibliographic record
Abstract
In Google v. Oracle, the Supreme Court was forced to decide if Google’s copying of 11,500 lines of computer code from Oracle without permission constituted copyright infringement. Much was on the line, including precedent concerning the copyright status of millions of lines of code nation-wide. In the lengthy decision, the Supreme Court avoided the central issue of holding whether the copied “declaring code” could be protected by copyright or instead was a functional tool outside of the Copyright Act. Instead, it punted the issue, assuming for the sake of argument that the declaring code that was taken was in fact copyrightable material. This forced the Supreme Court to review Google’s copying under a skewed fair use analysis and find that Google’s copying was a fair use. In the end, the Supreme Court came to a narrow decision: Google had done nothing wrong, and it did not owe Oracle anything. While its holding was intended to be narrow, the Court did not anticipate or account for the parabolic rise of generative artificial intelligence (“AI”) and the potential misuse of its dicta against computer code copyright holders. The lack of guidance on the copyrightability of code leaves intellectual property law in a period of purgatory as generative AI picks the pocket of protectable software left and right.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".