Limits to Modularity -- Reflections on Recent Developments in Chip Design
Bibliographic record
Abstract
Debates on how business organization has moved beyond Chandler’s vertically integrated multi-divisional firm have greatly benefited from the concept of “modularity”. There is however an important tension in the literature between the proponents of modularity and a small, but growing revisionist literature that contends that the enthusiasm for modularity has gone too far. Rather than exploring challenges and difficulties that management is facing in implementing modularity, there is a tendency in the “modularity” literature to generalize empirical observations that are context-specific and to confound them with prescription as well as prediction. This paper sides with the revisionist literature in cautioning against such strong claims of pervasive modularity. The objective is not to propose an alternative theory. More modestly, I am aiming to move the debate away from polemics to a scholarly discourse that explores why and when modularity may have limits, and what management can do to overcome these limits. I examine new evidence from a cutting-edge industry, semiconductors, that is often cited by modularity proponents as an indicator of broader industry trends. The paper shows that, even in this industry, there are powerful counter-forces causing organizational structures to become more integrated, not more arms’ length. Evidence from chip design is used to analyze how competitive dynamics and cognitive complexity create modularity limits, and to examine management responses. I demonstrate that inter-firm collaboration requires more (not less) coordination through corporate management, if codification does not reduce complexity -- which it fails to do when technologies keep changing fast and unpredictably.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".