The End of "Buy, Don't Build": How AI Is Flipping the Software Equation

For the better part of two decades, the golden rule of enterprise IT was simple: Buy, don't build. Why reinvent the wheel when you could rent a perfectly good one from Salesforce, Oracle, or SAP? The "Buy" strategy promised speed, maintenance-free updates, and industry-standard best practices. However, the rapid ascent of AI coding assistants and agentic workflows is shattering this decades-old consensus. We are entering an era where building bespoke software is no longer a luxury for the tech elite, but a cost-effective strategy for any company armed with generative AI.

The equation has changed because the cost of writing code is plummeting toward zero. As highlighted by Business Insider, AI coding tools are fundamentally upending the "buy versus build" software equation, threatening the traditional SaaS business model. When a small team—or even a single "vibe coder"—can spin up a lightweight, purpose-built CRM or workflow tool in a weekend using tools like Cursor or Replit, the value proposition of paying millions for bloated, one-size-fits-all SaaS suites begins to evaporate.

What Is "Vibe Coding" and How Is It Democratizing Development?

"Vibe coding" is the colloquial term for a profound shift in software creation: the ability to generate functioning applications through natural language prompts rather than manual syntax entry. This phenomenon is lowering the barrier to entry for building custom software. As reported by CNN, the rise of agentic models that can write and develop code is fueling a narrative that software companies are under pressure because their core product—code—is becoming a commodity. Investors are now questioning whether traditional SaaS seat-counts can survive a world where companies can generate their own internal tools on demand.

The most prominent example of this shift is the "Klarna moment." As detailed in TechCrunch and Diginomica, Klarna CEO Sebastian Siemiatkowski revealed that the company shut down its Salesforce and Workday instances to consolidate data onto an internally developed tech stack. While they didn't simply "replace Salesforce with ChatGPT," they utilized AI coding assistants (specifically citing Cursor) to quickly deploy new interfaces and interactions on top of their own data. This proved that a non-software company could build a bespoke, enterprise-grade solution cheaper and more effectively than renting a legacy suite.

This trend is empowering CIOs to combat "SaaS Sprawl." According to an interview in Computer Weekly with Nintex CEO Amit Mathradas, the proliferation of hundreds of disconnected SaaS apps has become unsustainable. He argues that the convergence of low-code and AI is empowering CIOs to shift back to a "build" mentality. Instead of purchasing yet another niche SaaS tool for every minor problem, organizations can now rapidly spin up their own applications that are perfectly tailored to their specific workflows, eliminating data silos and recurring subscription fees.

Why Are Legacy SaaS Incumbents Struggling with "Installed Base Debt"?

While it has never been easier for companies to build new software, it has never been harder for legacy SaaS vendors to update theirs. This is the "Legacy Debt" trap. SaaStr identifies this as a massive vulnerability for established players. Companies founded before the AI boom are burdened with "installed base debt"—they have thousands of existing customers demanding feature requests and bug fixes on old architecture. They cannot simply tear down their platforms to rebuild them for an AI-native world without disrupting their current revenue stream.

In contrast, startups and companies building internal tools from scratch have a massive speed advantage. Newsweek points out that traditional SaaS platforms run on business rules executed against structured data stores, whereas AI requires a fundamental architectural redesign where models update rules dynamically. Incumbents are forced to "retrofit" AI features onto monolithic codebases, often resulting in clunky "bolted-on" experiences. Meanwhile, AI-native builders can design event-driven architectures optimized for agents from day one, allowing them to move significantly faster.

This architectural ossification is creating a market bifurcation. Citi’s analysis, reported by Futu, suggests the software industry is entering a "winner takes all" era. The report constructs a "Bear Case" scenario where LLMs make the creation of customized applications exceptionally easy, directly replacing existing software vendors. In this scenario, companies providing back-office software with seat-based pricing face the greatest risks, as agile newcomers and internal build teams outmaneuver them with leaner, faster, and cheaper solutions.

Is the Future of Enterprise Software Bespoke?

The resurgence of "building" does not mean every company will become a software engineering firm, but it does mean the end of relying solely on generic off-the-shelf bundles. The AI Journal suggests we are moving toward a world where generative AI empowers users to build software "on demand." Tools like Replit allow individuals to create functional applications without deep technical expertise. This threatens the core SaaS model where value was derived from licensing access to static, pre-built platforms.

We are likely heading toward a hybrid future where "Systems of Record" (like data warehouses) are bought, but "Systems of Engagement" (interfaces and workflows) are built. CIO.com notes that while foundational systems will remain, the "application layer" is becoming fluid. Companies will use AI agents to generate ephemeral, customized interfaces that sit on top of their data, specific to the task at hand.

Ultimately, the flip of the "Build vs. Buy" equation restores power to the buyer. As SaaStr notes, customers are actively shopping again for the first time in decades because the switching costs have plummeted. If a SaaS vendor cannot justify its price against a custom AI-built alternative, they will be replaced. The future belongs to those who can curate and orchestrate their own software stack, rather than just renting it.