AI Is Changing Who Builds Software. Platforms Must Catch Up.

I recently had the opportunity to share some of my perspectives on AI, platform engineering, Kubernetes, and the changing nature of enterprise software development in two different publications: CIOnews and AI Data Press.




While the conversations approached the topic from different angles, they connect around one theme: AI is dramatically increasing who can build software and how much software gets created. The platform underneath has to evolve accordingly.

CIOnews: When Everyone Becomes a Developer

Platform Engineering Becomes The Enterprise Control Plane For Employee-Built Software

My TL;DR:

AI-assisted development is expanding software creation beyond traditional engineering teams. When business users and AI agents can create applications, platform engineering becomes the guardrail.

The idea I explored is a two-lane model:

Vibe coding / experimentation → governed sandbox → bridge → production engineering

Give people freedom to experiment, but make security, dependencies, network access, data access, and blast-radius controls defaults of the platform rather than something every new developer needs to understand.

Another point close to my heart: don't over-index on today's AI model. Invest in your knowledge graphs, ontology, semantics, and enterprise context. Models will change. Your organizational knowledge is the durable asset.

Read the CIOnews article

AI Data Press: When AI Creates 10x More Workloads

As AI Applications Multiply, Kubernetes Takes on a Larger Governance Role

My TL;DR:

The next challenge isn't simply running bigger AI models. It's dealing with the enormous number of small, short-lived applications AI makes practical.

Many of these applications aren't creating huge amounts of new data. They're enthusiastically reading and recombining existing enterprise data.

That changes the platform problem.

When applications can appear in seconds and disappear a few months later, governance can't live inside every application. Controls such as egress, read-only data access, rate limits, resource allocation, and identity-based policies increasingly need to be inherited from the platform.

Kubernetes therefore becomes more than an application orchestrator. It becomes an important enforcement layer between rapidly created workloads and long-lived enterprise data.

Read the AI Data Press article

The Common Thread

Put the two articles together and the evolution looks something like this:

AI → More Builders → More Apps → More Data Consumption → More Need for Platform-Level Governance

The interesting part of enterprise AI may ultimately be less about which model wins and more about how safely we enable thousands of new ideas to become applications without creating thousands of new ways to cause problems.

The views and opinions expressed here are my own and do not represent the official policy or position of any organization.

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