AI powered platform or platform for AI? It’s not just a matter of word order. It’s a matter of perspective. It’s the kind of question that makes you stop for a second and wonder which direction you’re actually heading. Are we building platforms to enable AI? Or are we injecting AI into platforms to make them smarter, smoother, and more human-like? The truth, of course, is that it’s both. It’s always been both. But we haven’t really paused long enough to unpack what that means.
Let’s talk about the first path: the platform for AI. The foundation. The engine room. The complex machinery humming quietly behind every large language model or deep learning pipeline. Because let’s face it, all that AI magic doesn’t float on air. It lives somewhere. It’s deployed, tested, scaled, and observed somewhere. And that somewhere is the platform. Not a mythical, vague “cloud” platform, but a very real, carefully designed, opinionated software ecosystem built by Platform Engineering teams. These platforms do the dirty work. Infrastructure as Code, Kubernetes orchestration, CI/CD pipelines, observability stacks, data pipelines that feed the beast. And when you’re trying to go from AI prototype to AI product, this is where the rubber meets the road.
Building a platform for AI means understanding that machine learning and GenAI workloads are not just “apps with more compute”. They come with baggage. GPUs, tensor runtimes, massive parallel processing. They don’t play nicely with traditional DevOps pipelines out of the box. They need their own flavors of automation, data versioning, experiment tracking, model registry integrations. And the kicker? They still need to run reliably, securely, and reproducibly at scale. So yes, the platform for AI is a different animal. And the teams building it? They’re not just DevOps engineers anymore. They’re something new. A hybrid breed of infra-savvy, data-aware, automation-addicted builders who can make AI production-ready without burning the house down.
But then, flip the script. AI powered platform. Now we’re not talking about AI as the goal. We’re talking about AI as the secret sauce. The embedded intelligence. The superpower that turns an ordinary developer platform into something almost…aware. And this is where things get really interesting. Because developers today? They’re drowning. In YAML. In dashboards. In choices. Too many tools, too many options, too much noise. Platform Engineering was supposed to fix this. It promised golden paths and paved roads. It gave us Internal Developer Platforms, Portals, automated scaffolding. But even those started to feel heavy. Static. Dumb. Enter AI.
Imagine a platform that doesn’t just offer you a golden path but builds one for you based on what you’re trying to do. Imagine a CLI that talks back to you like a senior engineer would, not with cryptic error codes but with helpful nudges, context-aware suggestions, even dry humor when needed. Picture dashboards that summarize themselves, alert systems that don’t cry wolf, knowledge bases that answer in full sentences instead of sending you on a Google scavenger hunt. This isn’t sci-fi. This is happening. GenAI is seeping into the platform itself. And it’s not just making it cooler. It’s making it usable again.
This is where devex gets its groove back. Developer experience isn’t about adding more UI layers or slapping a chatbot onto your console. It’s about reducing friction, cognitive load, and unnecessary ceremony. AI can learn from usage patterns. It can adapt. Recommend. Auto-complete not just your code, but your deployment configs. It can explain why your pipeline failed in plain English. It can help you debug Kubernetes issues without sending you to page 3 of Stack Overflow. It can even watch for anti-patterns and suggest improvements before you hit deploy. AI isn’t just in the platform. It is the platform. Or at least, it’s becoming the soul of it.
So where does this leave us? Stuck between two revolutions that are actually just one. Because whether you’re building a platform for AI or infusing your platform with AI, you’re still playing the same game: abstraction. Acceleration. Making the complex simple and the slow fast. One side lays the groundwork; the other builds the interface. But they’re converging. Fast.
This convergence is redefining what it means to “build software”. It used to be that you wrote code. Now, you curate behavior. You prompt. You configure. You orchestrate systems that learn, adapt, and occasionally hallucinate. The new platform engineer? They don’t just write Terraform scripts. They manage ML lifecycle workflows. They fine-tune LLMs to answer internal queries. They build internal copilots. They think in terms of embeddings and context windows, not just CPU and RAM. The line between dev and data is blurring. The line between infra and intelligence is gone.
Of course, this isn’t all rainbows. The risks are real. AI in the platform can lead to over-reliance, explainability issues, and new forms of tech debt. We need guardrails. We need transparency. But let’s not kid ourselves: we’re not going back. We’ve tasted what’s possible. And the developer of the future? They won’t want to go back to a world of static docs and brittle scripts. They’ll expect copilots that scaffold projects, diagnose issues, and even write a solid README while they grab coffee.
So here’s the takeaway. Stop asking whether you’re building an AI-powered platform or a platform for AI. Start asking how both can feed into each other. How your platform team can partner with your AI team. How your pipelines can support both training and serving. How your developer portal can become a living, learning interface, not a graveyard of links. Start thinking in loops, not layers. Because the future isn’t just platform or AI. It’s platform as AI.
And if that doesn’t light a fire under your roadmap, maybe it’s time to ask whether your platform is actually enabling innovation or just hosting it. The best platforms don’t just support intelligence. They embody it. The question isn’t whether the AI is in your stack. The question is whether it knows your developers better than you do. And if it doesn’t yet? Give it a sprint or two.
Welcome to the age of symbiosis. Build accordingly.