Introducing Waves of Innovation

On This Podcast

In this conversation, we examine Pini Reznik's model for how technology waves force organisational change, drawing on Conway's Law and the pattern he first documented in Cloud Native Transformation. He explains the disciplined sequence that successful transformations follow: constant exploration of new ideas, a deliberate refusal to commit until a technology proves itself, then a small core team building an MVP before scaling gradually across the organisation. We also discuss why this rarely happens cleanly in practice, with a maturity matrix revealing that most companies adopting Kubernetes and cloud native tooling never reach the point of real value, instead getting stuck babysitting complex platforms without the observability or delivery practices needed to benefit from them. Pini argues the same trap now looms for AI native adoption, where organisations hope AI will simply fix broken processes rather than requiring the same organisational rebuild cloud native demanded.

In this conversation, we examine where Pini and Daniel believe the real disruption from AI native technology will land, and it is not primarily inside existing software teams. They point to small and medium-sized enterprises, such as a manufacturing company they had recently spoken with, that have never had software development capability but suddenly gain access to automation through natural-language tools. We also discuss how this represents a genuine democratisation of software development, letting non-technical domain experts automate the toil of spreadsheets, documentation and government forms without needing to learn to code. Pini argues this will not eliminate developer jobs so much as expand the pyramid of people creating demand for software, fuelling more specialisation rather than less.

In this conversation, we examine what happens to the value of technical skill once AI can generate much of the code itself. Daniel draws a parallel with desktop publishing's paste-up artists, who lost their trade almost overnight because they held no domain knowledge to fall back on, and Pini agrees that software development has always really been a supporting function for converting domain expertise into automation. We also discuss the implication that domain experts, from bankers to logistics specialists, will increasingly be able to build software themselves, while engineers who understand a specific business domain become more valuable than generalists. The pair conclude that this shift could finally deliver the productivity gains long promised by the cloud and computing revolutions, but only once these tools genuinely reach everyday people in sales, marketing and operations rather than staying confined to engineering teams.