“Get Rich, or Die Trying”: Lessons from Rambus' High‐Risk Predatory Litigation in the Semiconductor Industry
Bibliographic record
Abstract
Patent litigation is a visible and widespread feature of the semiconductor industry, as firms pursue judicial mechanisms to defend, or promote, their intellectual property portfolios. This study highlights the antecedents, strategic goals, tactics and outcomes of the most significant US trial of this type in the last decade, namely Rambus v. Infineon, whereby a smaller company (Rambus) successfully pursued a “do or die” litigation campaign against a larger rival, thus changing the rules of engagement for the semiconductor industry as a whole. This campaign is notable, not just because of its undoubted effects on the semiconductor industry, but because of the innovative nature of Rambus' strategy, which was extremely risky both in terms of its prospects of success and its potential damage to the company if it failed. Arguing that dominant logic and operating rules are important antecedents in the development and pursuit of patent litigation strategies, this paper analyses the Rambus case using a “dominant logic” and “effectuation” framework. Doing so demonstrates the innovative nature of Rambus' “high‐risk predatory strategy”, the outcome of a dominant logic sustained by effectuation principles. The paper discusses the impact and significance of this new strategic form.
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 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".