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ITMO · December 6, 2025

Software development in 2030

Testable hypotheses instead of a linear forecast

/ Software development in 2030 · ITMO 2025

Slide contents

  1. 1. Software development in 2030

    Testable hypotheses instead of a linear forecast

  2. 2. Alexander Polomodov

    Technical Director & Fellow, large fintech

    Architecture and engineering practices in fintech

    AI-native development and Platform Engineering

    Code of Leadership podcast, @book_cube

  3. 3. The future resists linear extrapolation

  4. 4. 01. Signal: agents become work units

    But an autonomous task is not autonomous development

  5. 5. From one agent to an agent system

  6. 6. Protocol compatibility does not create trust

  7. 7. Adoption cannot prove that delivery has improved

    Tool popularity is input, not outcome

    No provenance for '70% use it daily'

    Generated code may increase review volume

    Local speed does not guarantee system gains

    We need delivery, quality and outcome metrics

  8. 8. 02. Research offers signals, not dates

    AlphaEvolve, WORKBank, agent economies and SE 3.0

  9. 9. A verifiable evaluator turns generation into search

  10. 10. Algorithm success does not prove full-SDLC autonomy

    DeepMind reports real results and a clear applicability condition

    Data-centre, chip-design and AI-training optimisation

    New matrix-multiplication algorithms

    Evaluation must be objective and automated

    Ambiguous product goals are not scored this way

  11. 11. Automation capability and worker desire: separate axes

  12. 12. Agent economies differ by origin and permeability

  13. 13. 03. Scenario: intent becomes the interface

    A human states the goal; a machine searches the solution space

  14. 14. AI-native shifts focus from code to intent

  15. 15. Measure durable goals before choosing tools

  16. 16. Safe delegation needs a runtime

  17. 17. Agents span the SDLC without transferring accountability

  18. 18. Agent-written code requires scalable verification

    The constraint is proving quality, not generating code

    Evaluators must test functionality and risk

    Review cost belongs in total task cost

    Rising rework falsifies the productivity promise

    Safe rollback limits the blast radius

  19. 19. 04. Metrics connect agents to outcomes

    Acceptance rate helps, but does not show whether the system improved

  20. 20. Agentic development needs multiple metrics

  21. 21. A pilot needs a stop condition before it starts

    Otherwise local acceleration is easy to mistake for impact

    A baseline on comparable tasks

    Task success and human intervention

    Total cost: compute + review + rework

    Escaped defects and delivery outcome

  22. 22. One signal can lead to different futures

  23. 23. By 2030, verifiable systems beat autonomous ones

    What evidence must confirm or falsify

    Intent becomes more important than typing code

    Evaluators become part of development architecture

    The platform defines the agent autonomy boundary

    Outcomes and rework outrank generation volume

  24. 24. Observations are separated from the author's scenarios

    Full links are in the speaker notes

    DeepMind: AlphaEvolve · Virtual Agent Economies

    Stanford: Future of Work with AI Agents

    SE 3.0: AI-native Software Engineering

    Google Research · DORA: developer goals and metrics

  25. 25. Thank you!

    polomodov.tech

    All slides and links — in the Telegram channel

    Alexander Polomodov, Technical Director & Fellow, large fintech

    @book_cube