prototype stage?
Type a few sentences into a prompt window, press enter, and get a fully working app or website, all within seconds. It’s not hypothetical anymore. It’s what’s available to anyone with an internet connection.
It’s known as vibe coding, but in reality it’s AI-assisted development that turns plain language into working software. It’s been tearing down the barrier between having an idea and having something running in a browser.
Now, anyone with a problem and a prompt can build something that actually works, opening up software development for all. In fact, 63% of active vibe coding users are non-developers.
And while that’s exciting, it’s where the bigger issues lie. Businesses have already started to place concern on AI-developed code, asking questions like whether the code works, or if it’s secure at the moment it’s written.
But that was last year’s problem. We’re now past the point of initial prototyping, where this code is now live in deployment, becoming something businesses are relying upon on a daily basis.
And that’s where the real problems begin.
From prototype to production is a different problem
Generating a working application has never been faster. A short description is often enough to produce something that looks ready to share; complete, functional, polished.
But a live application is not static. It needs to keep running under real traffic, stay patched against new vulnerabilities, and recover when something breaks. All while remaining accessible to the people who depend on it.
And none of that is solved by the initial build, but by whatever sits underneath it. This is where the traditional software development lifecycle starts to break down for AI-built tools and sites. A conventional developer with years of experience instinctively thinks about deployment routes, monitoring and dependency management as part of the job.
Someone building with AI, many for the first time, may never encounter those questions at all and not consider this during the build stage. They built something that works. Great.
Then once it’s live, it’s a different problem. Once deployed, AI-generated sites and apps can connect to third-party services while mishandling credentials. In doing so, they may inadvertently expose sensitive information in raw code, such as source files or client-sidescripts.
Look at Moltbook, the short-lived social platform for AI agents, built using AI-generated code, which exposed 17,000 human users, 35,000 emails, and around 1.5M API keys.
This didn’t happen because the initial build was reckless. It was because nothing in the surrounding environment caught the exposure before it went live.
At the same time, concerns around data sensitivity only get more severe once we’re past the prototype stage. As questions mount over how public cloud providers use data for AI training, larger businesses are moving workloads off the cloud entirely, reverting to bare metal or private infrastructure, further compounding risks.
If you can catch these issues during the prototype stage, then it’s possible to remedy and keep data safe. But once live, remedying only becomes more difficult, and the problem only becomes more severe.
Not everyone understands safe coding
It is no secret that R&D leaders are not fully convinced by the integrity of AI-generated code, with 75% reporting concerns around security and data privacy risks.
Meanwhile, the UK’s NCSC has also published guidance recognizing that AI-assisted software development needs different levels of oversight depending on how it’s built.
Concerns have grown beyond the code at the moment it’s written. Now, a growing number of live applications, tools and websites exist with no clear owner for what happens after launch. For many, no one is responsible for patching, monitoring, scaling, or retiring them when they’re no longer needed.
And for smaller teams and non-specialist builders in particular, this gap is widening. This means we need a serious look at the role of infrastructure to support AI applications once live.
Web hosting has a changing role
As AI expands the existing pool of web builders, it opens up a new role for web hosting.
It is no longer just the place where a product sits. Instead, it’s becoming a core part of the environment that determines whether an AI-built application can be deployed, secured, monitored, maintained and supported once it’s live.
An integrated, managed model can build in security controls, performance tooling and protection against common attacks as baseline safeguards, rather than things a builder has to configure and maintain themselves.
For non-specialists and small teams, this is incredibly important, as they may not have a dedicated security engineer (or even a second developer) to review their work. And the benefits go beyond support too.
One reality of AI-assisted development is that a product may not only be insecure, but also fragile or incomplete in ways its builders do not understand, especially when in application.
For a small team deployed on a major cloud provider with no meaningful spend, there may be no one to call when something goes wrong.
Hosting providers can fill that gap, not just with infrastructure, but with human-backed support that helps builders understand what went wrong and how to fix it.
We need new and old thinking
Experimentation, entrepreneurship and innovation are exactly what AI-enabled development should unlock.
At the same time, the pace at which ideas can now move from concept to working software is genuinely valuable and should be encouraged. It’s a new era after all, and it’s liberating to see software development placed in the hands of those with non-technical backgrounds.
With that in mind, speed at the build stage doesn’t remove the need for what comes after it.
AI-developed code still needs to be maintained, monitored and eventually retired, access controls still need enforcing, and dependencies still need checking.
None of that changes because AI wrote the first version.
What has changed is who is now responsible for making sure it happens. As development, deployment and security become more closely connected, hosting platforms can absorb more of the operational burden.
The advantage will increasingly belong to the platforms and infrastructure providers that can absorb responsibility, making reliability and support part of the foundation, and not just an afterthought.
Seb de Lemos
Seb de Lemos is CEO and co-founder of hosting.com, a global network of hosting brands built on trust, transparency, and long-term value. A technical founder with over two decades in the industry, Seb launched his first hosting company as a teenager.
