Application development is moving faster than ever. Between tighter release cadences, exploding generative AI use, and increasing demand for real-time digital experiences, developers are feeling the pressure to deliver more, faster—and with fewer people. But here’s the hard truth: AI doesn’t replace good engineering. If your CI/CD pipelines are brittle, your infrastructure is inconsistent, or your API strategy is undefined, AI will only accelerate chaos.
That’s one of the takeaways from a recent podcast conversation between Paul Nashawaty, Practice Lead at theCUBE Research and Founder at Efficiently Connected, and Richard Seroter, Chief Evangelist at Google Cloud. The discussion zeroed in on where AI helps, where it falls short, and why developers need solid platform engineering more than ever.
“The AI works for me—I don’t work for the AI,” Seroter emphasized. “There’s still ownership we have to take.”
AI-assisted tools like Gemini App Canvas and Code Assist are great for scaffolding interfaces and generating boilerplate—but that speed boost only pays off if your release process is mature. Organizations that lack observability, security scans, and structured deployment models risk pushing unreviewed or unstable code to production, creating bigger problems downstream.
Platforms matter more than personas
The rise of low-code tools and citizen developers has shifted the “who” behind software development—but the fundamentals still apply. Whether you’re building with full-stack engineers or AI copilots, the critical enabler is a resilient platform.
“We’re asking developers to do everything—front-end, back-end, infrastructure, security, even ML engineering,” Seroter said. “That’s not sustainable.”
Instead of overloading generalists, leading organizations are investing in internal developer platforms (IDPs) that abstract complexity and enable safe self-service. These platforms integrate scaffolding tools, policy enforcement, observability hooks, and guardrails—so developers can focus on shipping business logic, not provisioning IAM roles or debugging YAML.
Modernization isn’t always a rewrite
Developers are often asked to “modernize” legacy systems—but that doesn’t always mean replatforming. A growing number of teams are using tools like Gemini App Canvas to build modern interfaces and lightweight APIs that sit on top of heritage apps.
This API-first model makes older systems more accessible to new UIs, mobile apps, and AI agents—without forcing a complete rewrite. It’s also a way to gradually introduce automated pipelines and testing around systems that were never built with DevOps in mind.
“Even buy-first shops will be writing code—to wrap legacy apps, extend SaaS, or create new services,” Seroter noted. “That’s modernization too.”
Don’t skip the fundamentals
The bottom line: you can’t skip steps. AI doesn’t replace the need for testing, release automation, or production-grade monitoring. It accelerates what you already have. If your developer experience is broken, AI will make it faster—but not better.
Because in the end, AI is only as good as the pipeline you plug it into.
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