I have been seeing a growing number of posts on LinkedIn and X claiming that AI-generated, “vibe-coded” applications are replacing SaaS products that enterprises have relied on for years. These posts are attention-grabbing, but I am skeptical. I keep asking a basic question: how is this supposed to work in in real life?
In my experience, there is a significant difference between code that can be generated using an LLM and software that can be reliably run, supported, and operated for paying customers. People who claim they have rewritten their favorite SaaS product and eliminated subscription costs often overlook a fundamental reality of software: writing code is only 30% of the work, albeit a fun part of the job for the engineers. Running software in production by supporting customers, handling failures, meeting compliance requirements and maintaining reliability is where a significant part of SaaS value actually lives.
SaaS was never just about the software itself. It is software combined with a strong layer of service. Customers do not care whether a product is written in COBOL, Java, or Rust. What they care about is whether it solves a real business problem. When I look at SaaS from a customer’s perspective, it usually comes down to two questions: does this product meet my needs today, and will it continue to meet my needs as those needs change over time?
Let me explain this with a simple example. Imagine I am part of a small startup with five employees, and we need to set up payroll. The first thing I would do is look for a SaaS platform that can handle this for us. Talking to other founders and doing a quick survey usually gives me a shortlist of commonly used products.
At that point, I would evaluate the product based on a few basic questions:
- Does it meet our immediate payroll requirements?
- Does it integrate cleanly with our banking systems?
- Are compliance requirements handled correctly?
- Is their security model strong enough to protect employee data?
- Is our HR team comfortable adopting and operating it?
Once I pick a product, my expectation is straightforward: payroll should start working. More importantly, when employees have questions, I expect there to be a support team that can respond accurately and on time.
From my point of view, this support function is critical. It is not just about running payroll, but about helping employees navigate taxes, compliance, and edge cases as they arise. The service component is a large part of what I am paying for.
The value of a SaaS product comes from the combination of its features and the consistency of execution over time. As a company grows, complexity increases: headcount grows, contractors are added, employees move across countries, and compliance rules evolve.
A SaaS provider absorbs this complexity and ships updates continuously so I do not have to think about it. Replacing that with a custom, AI-generated solution means I now own long-term maintenance of this product.
Next is accountability. When something breaks, I want a clear escalation path, documented SLAs, and someone whose job it is to resolve the issue. An AI-generated application may work under ideal conditions, but production systems rarely operate under ideal conditions for long. The absence of a support model is often the first thing that surfaces in real-world usage.
Then there is compliance - I expect the vendor to invest in audits, certifications, data protection practices, and incident response. Recreating that posture internally is not just a coding problem; it is an organizational and financial commitment that an engineer wouldnt think about but a manager has to be planning.
This is why I believe many of the “AI replaced SaaS” stories conflate coding with products. Building something that works for a single team for an afternoon demo is very different from delivering a service to the same team year on year in a “it just works” fashion.
To be clear, I am not arguing that AI has no role here. I use AI tools myself, and they are extremely effective at accelerating development, reducing boilerplate, and improving iteration speed. Where I disagree is with the conclusion that faster code generation directly translates into SaaS product replacement.
What I am advocating within my company is a shift in how our products are built. AI reduces the cost of experimentation, lowers the barrier to entry for new features, and allows smaller teams to move faster. That is a meaningful change. But the end product still needs to be operated, supported, secured, and evolved over time that is not a “Vibe coding over the weekend” problem.
Ultimately, when I pay for a SaaS product, I am not paying for code. I am paying for reliability, accountability, and continuity. In my view, AI will continue to compress the cost of development and enable new entrants to move faster. That is valuable and disruptive in its own way. Concepts that define a SaaS product namely feature roadmap, engineering, operations, support, compliance, and security do not disappear. They simply shift to whoever owns the product. Today AI is not owning all of that coherently.