There's a familiar pattern playing out in the software world right now. In the early 2010s, the prevailing wisdom was that open source would inevitably crush proprietary SaaS. The logic seemed bulletproof: why would anyone pay for expensive, locked-in software when free, customizable alternatives existed?
The prediction wasn't just idle speculation. There were real challengers gaining traction. SugarCRM was positioned to dethrone Salesforce. Mautic would defeat Marketo. Alfresco versus SharePoint. Pentaho versus Tableau. Zimbra versus Exchange. The list of David-versus-Goliath matchups went on and on, with smart money betting on the open source Davids.
But something unexpected happened. Not only did open source fail to kill SaaS, instead it became SaaS instead.
How Open Source Became SaaS
Look at the landscape today. GitLab evolved from an open source project into a publicly-traded SaaS company. Drupal spawned Acquia. WordPress birthed WordPress.com. MongoDB created Atlas. Elasticsearch launched Elastic Cloud. Apache Spark transformed into Databricks.
All this demonstrated was that most organizations don't want to run software themselves, even when it's free.
Now we're seeing the exact same narrative unfold with AI-generated software.
The Same Pitch, Different Technology
The promise sounds compelling: build a custom CRM in a weekend using AI code generation. Create bespoke software for free. Why pay thousands per month for Salesforce when ChatGPT or Claude can scaffold an entire application?
It's déjà vu. The pitch is reminiscent of what we heard about open source, with one crucial difference.
Why Open Source Lost to SaaS
Open source didn't lose because the code wasn't good enough. In many cases, the open source alternatives were technically superior to their proprietary counterparts. It lost because running software, even free software, is infeasible for most organizations.
What actually happens when companies choose the "free" route:
Infrastructure needs constant attention. Server management, scaling, resource allocation, all require dedicated staff.
Downtime becomes an internal crisis. When systems go down at 2 AM, the company needs employees with deep knowledge of the codebase who can diagnose and fix issues under pressure.
Security vulnerabilities demand immediate patches. Attackers don't wait for convenient moments.
Feature development never stops. Competitors keep moving. Standing still means falling behind.
Integrations break. When external systems change their APIs, internal teams scramble to update code.
The "free" software turned out to be extraordinarily expensive when companies accounted for the true total cost of ownership.
But at least open source had communities contributing to the codebase, sharing security patches, building integrations, and solving common problems. There was collective value creation happening.
AI-Generated Software is Even Worse
With AI-generated software, companies get all the problems of open source, amplified.
Not only do they have to build the initial application, they also own everything that comes after. Hosting it. Making it multiplayer and handling concurrent users. Maintaining all integrations with other systems in the stack. Ensuring uptime and implementing monitoring. Patching security holes as they're discovered. Adding features to match evolving business needs.
And unlike open source, there's no community doing any of this work. It's entirely an internal burden.
Sure, teams can generate more code with AI to add features. But now they're managing an ever-growing codebase that they didn't write, don't fully understand, and need to maintain indefinitely. Technical debt accumulates at an accelerated pace.
The AI can scaffold the initial application impressively fast. But software development has never been primarily about the initial build. It's about the long-term maintenance, evolution, and operation. That's where 80% of the cost lies, and AI-generated code doesn't make that easier.
The Real Threat to SaaS Incumbents
So if AI-generated software isn't the threat, what is?
The real danger comes from nimble startups building products from scratch with AI at the core. These startups are designing around intelligent agents rather than creating traditional systems of record with predetermined workflows.
The SaaS model we've known for the past two decades follows a specific pattern. Software provides structured workflows. Users input data into fields. The system processes that data according to predefined rules. Reports get generated. The software is essentially a sophisticated database with a workflow engine on top.
But AI enables a different design. Instead of forcing users to adapt to rigid workflows, these systems can understand intent and dynamically determine the right actions to take. Rather than requiring users to navigate through multiple screens and fields to accomplish a task, they can simply describe what they want to achieve.
These products aren't just existing SaaS with a chatbot bolted on. They're reconsidering the entire user experience and underlying architecture around what becomes possible when software can understand, reason, and act independently.
What This Means for the Industry
The SaaS business model isn't going away. Organizations will continue paying for software as a service because the value proposition remains compelling: someone else handles all the operational complexity while they get reliable, continuously-improving software.
But the companies delivering that SaaS are likely to look very different. The winners will be those that rebuilt their products from scratch with AI at the core, not those that retrofitted AI features onto legacy systems designed for the pre-AI era.
The lesson from the open source era remains relevant: free and customizable sounds great in theory, but managed and reliable wins in practice. AI-generated software, like open source before it, will find its niche. Some developers will use it effectively for specific use cases. Some organizations with unique needs and technical capacity will build custom solutions.
But for the vast majority of businesses, paying for well-designed, well-maintained SaaS will remain the rational choice. The difference is that the best SaaS products of the next decade will be built with AI capabilities at their core, not as an afterthought.
The competitive advantage will be for those who figured out how to build software that understands context, learns from interactions, and acts independently.
