Slide contents

1. 1. From Classic PDLC to AI-Native Development

   How the engineering process changes when AI becomes part of the production loop

2. 2. What This Talk Is About

   Transition strategy

   Part 1: Bimodal IT failure.

   Part 2: SE 1.0 vs 2.0.

   Part 3: DORA, Google, Meta, AWS.

   Parts 4–5: limits and practices.

3. 3. 01. Historical Analogy: Bimodal IT

4. 4. Bimodal IT: Two Gartner Modes

5. 5. Where Bimodal IT Broke in Practice

6. 6. 02. SE 1.0 ↔ SE 2.0

7. 7. Software Engineering 1.0

8. 8. Software Engineering 2.0

9. 9. SE 1.0 vs SE 2.0

10. 10. 03. Industry Signals 2024–2026

11. 11. DORA: Local Productivity ≠ Systemic Acceleration

12. 12. Google: AI as an Engineering Product

13. 13. Meta: Just-in-Time Tests

14. 14. AWS: AI-Driven Development Life Cycle

15. 15. 04. SE 2.0: No Free Acceleration

16. 16. SE 2.0 ≠ Free Acceleration

17. 17. Load-Bearing Elements of SE 2.0

18. 18. 05. Strategy for a Large Company

19. 19. SE 1.0 ↔ SE 2.0 Exchange

20. 20. Three Transition Principles

21. 21. Key Takeaways

    What to take away

    AI changes speed and work organization.

    SE 2.0 is a capabilities factory.

    AI-native rebuilds creation, verification, delivery.

    A unified engineering approach is required.

    tellmeabout.tech

22. 22. References and Materials

    Key sources

    Gartner, McKinsey, NIST SDLC.

    DORA 2024/2025, AI Capabilities.

    Google AI, Meta JIT, AWS DLC.

    Copilot, Amazon Q, AutoGen, SSDF.

23. 23. Thank You!

    From PDLC to AI-Native

    For more materials on this topic, visit the "Book Cube" channel — all links are collected there

    Alexander Polomodov, Technical Director & Fellow, T-Technologies

    @Book_Cube