Shiny New Tools Won't Fix Your Problem

Generative AI can help us produce code faster than ever before, but faster code generation does not automatically translate into faster or safer delivery. In practice, AI acts as an amplifier: teams with strong engineering fundamentals improve, while teams with existing bottlenecks and weak software development practices feel those problems more acutely.

Writing code is cheaper. Testing, troubleshooting, building, deploying, understanding and changing it is not.

In this talk, we’ll look past the hype and focus on what actually drives productivity in an AI-accelerated world. We’ll explore why shiny new tools don’t fix broken systems, why optimising individual steps rarely improves end-to-end flow, and why software engineering fundamentals matter more — not less — when code is easy to generate.

We’ll also look at how observability and measurement help teams understand where time is really being spent, and why accelerating feedback is essential if AI is going to help rather than hurt.

You’ll leave with a clear understanding of:

- Which engineering practices consistently improve outcomes in an AI world and provide vital safety nets
- Why good design and readable code still matter
- What to measure to understand whether AI is really making your team more productive

This is not an anti-AI talk. It’s a reminder that the hard parts of software didn’t get easier, and that fundamentals determine whether AI makes us faster, or breaks us faster.

Transcript

1. Shiny New Tools Won’t Fix Your Problem Trisha Gee AI, productivity, and the parts of software that didn’t get easier
2. • Java Champion • Author, speaker & YouTuber • 20+ years hands-on experience Trisha Gee Practical engineering over hype
3. Tools Rock!
4. Tools allow you to stop thinking
5. “Real stupidity beats artificial intelligence every time.” Terry Pratchett
6. You’ve bought the tool, so you’re doing the practice
7. • UML - Software design • Scrum - Agile • Cucumber - BDD • Jenkins - CI • Puppet/Chef - DevOps • Docker - Microservices • GitHub Actions - CD Pipeline
8. • UML != Software design • Scrum != Agile • Cucumber != BDD • Jenkins != CI • Puppet/Chef != DevOps • Docker != Microservices • GitHub Actions != CD Pipeline
9. The tool is not the practice
10. Agentic coding != Software engineering
11. We see no silver bullet. There is no single development… which by itself promises even one order of magnitude improvement in productivity, in reliability, in simplicity. The Mythical Man-Month Frederick P. Brooks Jr.
12. But AI is Special
16. “Automation doesn’t replace thinking. It amplifies it.” Angie Jones
17. AI is special because it works like us
18. With one superpower: it shows you where your systems are failing
19. Writing code was never the bottleneck
20. Downstream: Building the Thing Right
21. Code Reviews adding friction
22. More tests…
23. …taking longer to run…
24. …and giving less reliable results
25. Out of control dependencies
26. More, longer builds. Less trustworthy results.
28. AI exposed a different bottleneck
29. Upstream: Building the Right Thing
30. “The hardest single part of building a software system is deciding precisely what to build.” The Mythical Man-Month Frederick P. Brooks Jr.
32. What’s the business impact?
33. What does good look like?
34. “It’s OK, just throw more tokens at it”
35. But are we learning from it?
36. Because writing code is easy and painless now!
37. Only now we’re…
38. Breaking work into smaller pieces…
39. …specifying how to solve the problem…
40. …clarifying the acceptance criteria…
41. …and creating A Plan
42. We’re also…
43. Documenting standards and decisions…
44. …making sure we have tests…
45. …constantly reviewing code and providing feedback…
46. …and training a team member.
47. Yes, all the stuff engineers used to avoid like The Plague
48. Most of all…
49. …we’re communicating…
50. …reducing ambiguity and reliance on unspoken tribal knowledge because it improves the outcome
51. Who Knew?
53. The fundamentals are the same
54. The speed of code generation is faster, but that doesn’t automatically translate to higher quality software or better business outcomes. The fundamental challenges of good software… remain the same.” The LeadDev AI Impact Report 2025 Laura Tacho
55. I’m going to give you some books to read
56. Software Development
57. “Tools are useful, but they are not the point. The point is the practices that allow us to create software reliably.” Modern Software Engineering Dave Farley
58. • Working Iteratively • Being Experimental • Modularity • Cohesion • Separation of Concerns • Abstraction • Managing Coupling 2019
59. “Use the model as the backbone of a language. Commit the team to exercising that language relentlessly in all communication within the team and in the code.” Domain Driven Design Eric Evans
60. • Create a Domain Model • Ubiquitous Language • Bounded Contexts • Make implicit concepts explicit 2003
61. “Improving the design of code makes it easier to understand and cheaper to modify.” Refactoring Martin Fowler
62. • Bad smells in code • …and what to do about them 1999, 2019
63. “Tidying is software design addressing you, your relationship to your code, and ultimately your relationship with yourself…. Tidying is geek self-care.” Tidy First? Kent Beck
64. • First, After, Later, Never • Optionality • What is software design? • How software design drives the cost of software development • What are the trade-offs? • What principles can we use to inform whether and how to change the structure of software? • …and specific tips for tidying code 2023
65. “We recommend that the developers, who are good at writing efficient, maintainable code, work together with the testers, who are good at specifying test cases, to automate tests.” Agile Testing Condensed: A Brief Introduction Lisa Crispin, Janet Gregory
66. • Provide Continuous Feedback • Deliver Value to the Customer • Have Courage • Keep It Simple • Practice Continuous Improvement • Respond to Change • Self-Organize 2008
67. “Design patterns help you communicate with other developers.” Design Patterns: Elements of Reusable Object-Oriented Software Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides (1994)
68. • Observer Pattern • Decorator Pattern • Factory Pattern • Singleton Pattern • Command Pattern • Adapter and Facade Patterns • Template Method Pattern • Iterator and Composite Patterns • State Pattern • Proxy Pattern • Compound Patterns • Better Living with Patterns 2004, 2020
69. • Fundamentals applied to working with the genie • (Plus loads of other tips) 2025
70. Software Delivery
71. “High performers… deploy more frequently, have shorter lead times for changes, have lower change failure rates, and recover from failures more quickly.” Accelerate Nicole Forsgren, Jez Humble, Gene Kim
72. • Deployment automation • Continuous integration • Trunk-based development • Test automation • Shift left on security • Continuous delivery (CD) • Loosely coupled architecture • Empowered teams • Working in small batches • Team experimentation • Monitoring • WIP limits • Visualizing work • Supporting learning • Collaboration among teams • Job satisfaction • Transformational leadership 2018
73. “The key to successful software delivery is fast feedback on the quality of the software being delivered.” Continuous Delivery Jez Humble and David Farley
74. • Continuous Integration • Automated Testing • Managing Data • Deployment Pipelines • Deploying and Releasing • Managing Infrastructure 2010
75. “The tools, services, and environments that developers need to do their jobs should be treated with production-level SLAs. The development platform is the production environment for the job of creating software” Release It! Michael Nygard
76. • Create Stability • Design for Production • Deliver your System • Chaos Engineering 2007, 2018
77. “We are way behind where we ought to be as an industry. We are shipping code we don’t understand, to systems we have never understood.” https://charity.wtf/2020/03/03/observability-is-a-many-splendored-thing Charity Majors
78. Monitoring is about known-unknowns and actionable alerts, observability is about unknown-unknowns and empowering you to ask arbitrary new questions and explore where the cookie crumbs take you.” Observability: A Manifesto Charity Majors
79. • Instrumentation • Structured Events • Observability Analysis • Observability-Driven Development • Efficient Data Storage • Sampling • Ontologies as a Shared Language for Humans and AI • Observability for CI/CD Pipelines • Performance Engineering 2022
80. Process, People and Teams
81. “We needed to demonstrate that our product development efforts were leading us toward massive success without giving in to the temptation to fall back on vanity metrics and “success theater”—the work we do to make ourselves look successful.” The Lean Startup Eric Ries
82. • Build–Measure–Learn • Treat everything as a hypothesis • Pivot or persevere • Small batches & iteration • Nurturing innovation • Minimum Viable Product 2011
83. “I’m not a great programmer; I’m just a good programmer with great habits.” Kent Beck
84. • Communication • Simplicity • Feedback • Courage • Respect • Humanity • Economics • Improvement • Diversity • Reflection • Flow • Failure • Quality • Baby Steps • Accepted Responsibility 1999
85. “This then is programming, both a tar pit in which many efforts have floundered and a creative activity with joys and woes all its own. For many, the joys far outweigh the woes, and for them the remainder of this book will attempt to lay some boardwalks across the tar.” The Mythical Man-Month Frederick P. Brooks Jr.
86. • Adding people makes a project later • Communication complexity dominates • Conceptual integrity matters • Estimation is hard • Integration is where things break • No silver bullet 1975, 1995
87. Notes for Leadership
88. “Efficiency means to do things in the right way (repeatable, controllable, scalable). Effectiveness means to do the right thing (trial and error, risk taking, adaptability). Unless you design against it, efficiency will tend to overpower and snuff out effectiveness." Organizational Physics: The Science of Growing a Business Lex Sisney
89. Hiring hasn’t fundamentally changed
90. You’re still looking for engineers with solid foundations who can learn
91. “What they've learned is not just the specific rules intrinsic to a particular system; they've learned abstract principles that can be applied when approaching any complicated system.” Everything Bad is Good for You Steven Johnson
92. Some Common Themes
93. Scientific Method • Hypothesis • Experiment & Measure • Conclusions
94. Fast Feedback & Iteration Fail fast, learn fast
95. Common language Communication is Really Important
96. Ease of Change Developers and AI slow down when things are hard to change
97. The people matter
98. • Courage • Empower teams • Job satisfaction • Nuture innovation & support learning • Responsibility • Respect The people matter
99. In Summary
100. “We are stuck with technology when what we really want is just stuff that works.” Douglas Adams
101. AI is an amplifier
102. Good Software Engineering practices matter more than ever
103. Seniors: Your knowledge is more valuable than it's ever been
104. Juniors: The tools will keep changing. The fundamentals won't.
105. “Don’t Panic” The Hitchhiker’s Guide to the Galaxy Douglas Adams
106. Keep learning
107. Questions? https://trishagee.com
108. “Testing is an activity, not a phase.” Lisa Crispin
109. “I’ve calculated your chance of survival, but I don’t think you’ll like it.” Marvin, Hitchhiker’s guide
110. But people are having fun
111. Building stuff faster
112. Building things they didn’t have time for
113. Learning as they go
114. Making changes they never dared to make before
115. Doing acceptance tests
116. Taking on small projects
117. Hiring is broken
118. “The saddest aspect of life right now is that science gathers knowledge faster than society gathers wisdom.” Isaac Asimov
119. It always was. It got worse though
120. Tell me your stories
121. AI changes the balance, but not the fundamentals
122. AI can help us write code faster, but doesn't change the fundamentals of software engineering. The hard problems are: understanding the problems to solve; creating the right teams and processes to produce solutions; and delivering those solutions” Me