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Recent events within the SaaS and tech industry stirred things up quite a bit. Journalists from CNBC built a to-do platform clone in less than an hour at a relatively low cost. Anthropic introduced Claude Code and Claude Cowork for building software with AI agents. It all sparked a lot of hype around AI-powered code-generation tools, vibe-coding, introducing the SaaSpocalypse, and the upcoming end of software platforms as we all know them. In a single trading session, around $300 billion vanished from the software stock market. Investors began to question the reliability of the SaaS model.
No doubt, AI agents and their capabilities are improving at lightning speed. But many puzzles are missing from this narrative – security concerns, data privacy, maintenance, and scalability, just to name a few. The reality in software development is much more complicated and nuanced. There are hidden risks that every business leader should consider before jumping into the waters of AI code generation.
In this piece, we’re sharing a different perspective, focusing on the missing parts, their impact on businesses, and how to use AI responsibly.
Artificial Intelligence can “see” things. Beyond the text, it recognises the visual layer of the uploaded photos. If you share a screenshot from a spreadsheet with a table and data, AI will be able to generate a notably similar one.
But what AI can’t see is the underlying logic behind those numbers - what functions were used to calculate them, original data sources, or business meaning - and AI can’t replicate it just based on the picture. The screenshots show only part of the end solution - interface, layouts, and simple components. But without a specified output, the result is likely to miss some crucial functionality.
Within a simple interface, every element connects to others according to specific logic. Yet the UI doesn’t necessarily explain how the data was retrieved, its source, how it should be interpreted, or its constraints. Underneath the visual layer are integrations that connect different data sources or tools, authentication and permissions that distribute the access levels and roles, edge cases, and error handling that enable the platform to function correctly.
For business, data is critical. How it’s synchronised, aggregated, or unified directly impacts the decision-making process.
AI can generate the UI and endpoints, but the data consistency is something more complex. It means data is accurate, consistent, and synchronised across systems or multiple locations. For example, one user has the same userID across all company data sets, such as CRMs, billing, and cloud.
In the AI-generated platforms, the key concern is how this data is actually stored. It can be cloud storage or temporary databases without backups, replication, or disaster-recovery procedures in place when things go wrong. This inconsistency directly impacts business stability, leading to missing data points, reporting issues, or even business-level errors.
Leaders rely on data to support their decisions, and inaccurate data can lead to poor choices, missed market opportunities, or flawed forecasts, resulting in financial losses or even regulatory violations.
Another question that arises is: who’s really accountable for the AI-generated platform? SaaS platforms offer client support, ready to help with any issues. But in the case of a self-built platform, where there’s no one with a technical background, it’s unclear.
In regulated industries, like healthcare or fintech, there’s nothing like “It’s AI that wrote this code.” The responsibility always falls on the company, founders, or technical leaders.
Ensuring the platform remains reliable and fully functional is a crucial step within the software lifecycle. No matter what type of software product it is, it loses compatibility with tools, libraries, or regulatory laws over time if not maintained. There’s also the aspect of fixing issues or adjusting the platform to evolving needs on the fly.
The point is, regular maintenance prevents businesses from costly downtimes, security vulnerabilities, or inconsistent data syncing, and ensures compliance with changing regulations.
For AI-generated platforms, there’s often no support or technical expertise to handle maintenance. And while there are other options, like hiring a technical team or an external vendor, both are time-consuming and costly because they need to understand the code before they can implement the necessary fixes. When used in daily operations, disruptions can have serious consequences: potential security breaches, increased likelihood of downtime, compliance failures, or rising costs.
Between 2010 and 2025, many European startups and companies duplicated features or even entire solutions from American companies, adapting them to the market. The reason behind this was to catch up with innovations, especially in fintech, e-commerce, and SaaS. Some succeeded, including Bolt (Uber’s copy), Klarna (Stripe’s copy), or Zalando (Zappos’ copy).
Yet, it wasn’t the case for many different copies. The reasons behind the failures were quite simple:
Generating solutions with AI is something like that. It copies UI patterns and code, but doesn’t get the full picture of the market, product strategy, or business logic.
Using AI to generate platforms or solutions is not entirely wrong. In some scenarios, it can be an effective, time-efficient way to bring simple ideas to life, such as prototypes, MVPs, or building internal tools. But it’s not a fully fledged digital product, and for now, it doesn’t replace software development as a whole.
Whether using AI tools or agents, they need to be overseen by a person (an AI/LLM expert) who deeply understands how these models work and can review the generated code. This way, companies mitigate the serious business risks and are set to build successful products.
Building a software/SaaS platform has never been about code alone. It’s about architecture, responsibility, and the invisible logic that keeps systems running even when things go wrong. Businesses need stability and resilience, and AI doesn’t necessarily provide these yet.
No doubt, AI is a powerful tool that can enhance workflows, automate repetitive processes, and accelerate development. The thing is, without technical oversight, it can quickly become a liability that drains resources. The line between hype and tangible business value is thin, but the potential risks are real.
