It is easier than ever to launch a product from scratch. Today, AI can make your team of two feel like a team of ten almost overnight. Enterprises across the tech industry are completely restructuring engineering teams to double down on AI coding, often incentivizing engineers for the sheer amount of code they push. The AI revolution is incredible. So, you would be crazy not to hop on the vibe coding train right? Well it depends on what exactly you are building.

While AI is a massive productivity booster for writing boilerplate, prompting and pasting until things look right is asking for trouble. Would you want an LLM acting as your final QA manager for a mission-critical system? Probably not. Being thoughtful in how, when and where you use AI should absolutely be the standard approach.

Giving autonomous AI access to code opens the door to a world of scary possibilities.

It takes five minutes to give Claude Code/Cowork access to: your code, your servers, your logs, your environment, and your DNS. 

Chad Arimura, VP of Developer Relations @ Oracle

As Chad recently pointed out on LinkedIn, handing the keys to AI works great for prototypes but engineering like this in a live production environment is asking for issues. "YOLO engineering," as he puts it, or vibe coding is creating serious security vulnerabilities and architectural blind spots in the industry and it's only starting.

Just in the past month or so, the consequences of unchecked AI have made their way into the news, and here are a few of the headlines you can find yourself:

• Amazon Lost 6.3 Million Orders to Vibe Coding
• A Meta agentic AI sparked a security incident by acting without permission
• Vibe Coding Thriving for Prototyping But Loopholes Still Persist Which Makes Developers Irreplaceable
• Hot startup Lovable's security stumble shows one big risk in using AI to code

And those are just the public headlines. You can dive into these post-mortems yourself but the underlying risk is clear. AI isn't perfect. Blindly coding your product with autonomous AI tools and agents, with incentives to push more, introduces risk and inevitably leads to downtime. AI should be thought of as a tool, not the replacement for human architectural intent.

The application layer is just the tip of the iceberg. Companies that are comfortable with vibe coding products are also starting to let AI make critical decisions as it relates to infrastructure and DevOps. Which is the catalyst for the catastrophic downtime. Infrastructure requires intent. AI is powerful but to build a resilient system, you need to know exactly where to draw the line.

The Fallout

Modern infrastructure already comes with plenty of complexity. Between sprawling Kubernetes clusters, bulky Terraform scripts, the infamous AWS UI, the list goes on. And the reality is, developers can easily become overwhelmed. A few years ago a team facing a wall of complexity may have invested heavily in dedicated in-house expertise or expensive managed services. But even for a seasoned Kubernetes expert, orchestrators like ECS & EKS come with their fair share of issues. Today, instead of untangling the web, teams are turning to AI to solve infrastructure problems that they may not have a full understanding of.

The real danger arises when you attach these deeply complex, highly sensitive processes to autonomous AI agents. We all know how easy it is for a human to screw up a deployment, now imagine a machine doing it at scale. AI lacks deep architectural context and it makes mistakes. When you pair this with decisions that must be purposeful and rigorously validated, you get a disaster like the 13-hour outage because an AI agent modified production infrastructure faster than a human could verify it.

Outages and data breaches are happening faster than companies can write the incident reports.

While I'm eager about the advancements across a number of different AI-based infra projects, I'm still wildly hesitant about it being used in production, or being connected to production infrastructure. From the horror stories of agents accidentally deleting production databases, to the vulnerabilities being introduced by AI code that hasn't been thoroughly reviewed, I'm a bit concerned at the amount of freedom teams are giving AI with their most sensitive workloads. 

Jake Warner, CEO & Founder @ Cycle.io

Infrastructure should be boring and predictable. If the biggest players in the space are struggling to navigate AI issues, what hope do smaller teams have when they rely on an LLM to write complex Kubernetes YAML files or long configs with no basic understanding of what they are creating or modifying?

Human-First Approach

There is hope! Even for smaller teams trying to move fast, there is a world where teams can and should be using AI.

Internally at Cycle we practice what we preach, and we want to move as fast and safely as possible. We are incredibly careful how AI is introduced into our own engineering processes.

We don't allow any AI integrations into our IDEs — our engineers need to be deliberate and careful about what context we share with LLMs. In general, we try to generate a single function/component at a time and verify before committing. If a PR is thousands of changes long and that was mostly done by AI, that's a problem. 

Alexander Mattoni, Head of Engineering @ Cycle.io

So how does a team without the resources of Google move faster with this in mind? By using AI as an accelerator for the mundane rather than a substitute for architecture. Offloading the grunt work and using the infrastructure expertise of the team to our advantage we can focus on building Cycle and saving mental bandwidth for the complex problem-solving and intentional design that actually matter.

The overall recommendation is that you don't vibe code your way out of problems, and you know what you are doing from the ground up. AI is an incredible assistant for accelerating specific, isolated tasks, but it should never replace a deep, foundational understanding of the infrastructure you are building on.

How Cycle Stays Secure in the New AI World

It starts from the ground up. We purpose-built Cycle, as a Kubernetes alternative, starting at the kernel to ensure we have absolute control over the entire stack. The same philosophy that we followed a decade ago during our inception and early container days carries over to today's world of agentic workflows: give users the control to do what they need to without introducing unnecessary complexity or risk.

When you're dealing with AI — whether you're hosting resource-heavy models, managing autonomous agents, or simply using AI assistants to accelerate your deployment pipeline — you cannot afford the security blind spots created by endless layers of abstraction. This is where the power of Cycle becomes your greatest super power.

By deploying infrastructure through Cycle, whether it's bare-metal, cloud resources, or from your own data center, you've eliminated the black box virtualization of the hyperscalers. Not only do you get the raw compute that AI demands but you've already reduced your attack surface dramatically.

Once infrastructure is taken care of your team can focus on building products, with or without the assistance of AI.

Conclusion

The team here at Cycle is not anti-AI. We are pro-security, we are pro-developer empowerment and we are pro-product. There's clear evidence that teams are using AI in places that it is just not ready to be used in, and it's causing detrimental issues. Data breaches and downtime are the results and teams are losing real revenue and — more importantly — user trust.

If you take 2 things from this blog, let it be these:

1. Companies shouldn't primarily blindly use AI to code products. If you "vibe code" complex application logic, QA and debugging will easily eat up any time you thought you saved.
2. Don't let AI make critical infrastructure decisions, and have a flexible yet secure environment in place for your team to move fast but stay safe.

We can't all be the next one-person unicorn but we can build solid products on solid foundations that are here for years to come.