Bibliographic record
Abstract
The introduction of AI into the developer workflow has fundamentally changed the conversation around productivity, but merely adopting tools is not enough. Drawing on insights from the 2025 DORA State of AI-assisted Software Development report, this keynote argues that AI is an amplifier that magnifies an organization's pre-existing technical and cultural capabilities. It will show why the greatest returns on AI investment come not from the models themselves, but from the maturity of the underlying software delivery system. We will explore a real-world transformation, using the journey of Unico IDTech https://unico.io as a prime example. This story demonstrates how creating a robust and consistent engineering structure Dev Prime coupled with a radical shift to user-centric monitoring SLOs and reliability engineering created the necessary high-quality Platform foundation. When this foundation is present-characterized by clear governance, healthy data ecosystems, and foundational practices-AI's benefits such as throughput and organizational performance gains are dramatically amplified. This session provides technology leaders with a clear, data-driven mandate: to maximize the value of AI, you must first invest in and rationalize your foundational structure, treating your internal platform as the critical enabler for sustainable AI-driven excellence.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.329 | 0.203 |
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".