There’s a lot of noise right now around vibe coding, model context protocol servers and what open application program interfaces plus large language models might unlock for short-term rental operators.
Connect something like Claude to your property management system (PMS), layer artificial intelligence (AI) on top and build workflows and agents that finally reflect how your business runs.
After years of adapting operations to fit rigid software, you can see why it’s resonating. The ability to move faster, test ideas and shape workflows is genuinely useful.
But this conversation is being framed in the wrong way, because it treats vibe coding as the breakthrough. It isn’t.
For years, operators have adapted to software. Now, for the first time, they’re being told they can shape it. And that shift comes with a consequence. The moment workflows touch operations, guest communication or decision-making, this is no longer just software usage, it becomes system ownership.
In practice, that ownership does not reduce operational load; it relocates it. The complexity that once sat inside software moves closer to the operator, who now has to monitor outputs, validate decisions, troubleshoot issues and adjust systems as conditions change. Rather than simplifying operations, it embeds technical responsibility directly into them.
Property managers are running fast-moving, operationally complex businesses. They are not system architects, data engineers or AI specialists. And yet the industry is increasingly assuming they can step into that role.
A small group will. They’ll build impressive, reliable systems. But they are the exception. For most operators, this is not about using better tools. It is about taking on responsibility for systems they do not fully control, in environments where conditions shift constantly and mistakes have immediate consequences.
It is now relatively easy to connect tools, move data and generate outputs. What’s much harder is making those systems reliable. Short-term rental operations are not clean, static environments. They’re made up of moving parts—bookings, cleaning, maintenance, pricing, guest communication—all interacting in real time, often with incomplete or changing information.
When AI is layered into that, it does not remove complexity. It brings it into whatever is being built. The question stops being whether something works and becomes whether it keeps working under pressure.
A lot of what is being built right now is still experimental. That’s expected at this stage. The issue is how quickly those experiments are being treated as operationally dependable. There is a clear difference between seeing something work in isolation and trusting it to support real decisions inside a business.
This isn’t the shift, it’s the bridge
What’s happening now sits between two models. The software-as-a-service (SaaS) approach, where software supports the business, is starting to give way to one where systems take on execution themselves. That direction is being shaped by agentic AI, which is redefining what software is expected to do.
Vibe coding belongs to this transition. It gives operators more control over how systems are assembled, but it keeps execution tied to that assembly. Systems still need to be built, checked and maintained, and over time that responsibility does not scale.
What’s becoming clear is that vibe coding is not the end state. It moves the industry from adapting to software to assembling it.
The goal is not to give operators more ways to build on top of their PMS, experiment with AI agents, or stitch together workflows themselves. That approach assumes that the operator should take on the responsibility of designing, validating and maintaining systems that are complex by nature.
Operators should not have to become system architects in order to run their business effectively. Instead, the model shifts toward operational systems where automation, decision-making and execution are structured and connected.
The ability to customize how the business runs is there, but it does not come from building agents from scratch or managing layers of AI. It comes from working within a system that already understands the operational complexities and can carry it through reliably.
In other words, the flexibility exists, but without transferring the burden of building and maintaining it onto the operator. Because the real opportunity with AI in this industry is not giving people more tools to assemble, but removing the need to assemble them at all.
Instead of operators adjusting to the system, the system adjusts to how they run their business.
The next phase addresses that directly. It is about systems that already understand how the business operates and can carry execution end-to-end across guest communication, cleaning, maintenance and pricing, without requiring the operator to design, validate and maintain the logic behind it. The operator defines intent, constraints and priorities. The system runs with it.
In that shift, flexibility does not disappear; it becomes embedded. The need to build software starts to fade because it is no longer necessary.
The real shift is not from rigid software to flexible software. It is from software operators use, to systems that operate with them.
About the author
Shahar Goldboim is the CEO and founder of
Boom.