The SaaS Apocalypse That Wasn't: Why Enterprise Software Is More Durable Than You Think
The headlines have been relentless: AI is coming for Salesforce. Your five-person startup vibe-coded their own CRM. Software is dead, and AI agents are the undertakers.
But scratch beneath the hype, and a more complicated picture emerges. Enterprise software isn't dying—it's evolving. And the companies that understand this are positioning themselves for the next decade, not the next quarter.
The Vibe Coding Myth
There's no denying that AI coding tools have gotten remarkably good. A small team can now build functional internal tools in hours, not weeks. The productivity gains are real.
The problem is extrapolating from a five-person startup to a Fortune 100 company.
Consider this: a five-person company might legitimately vibe code a simple CRM. But ask that same engineer to replace a complex Salesforce instance with something they built over a weekend—and then manage change management across a thousand employees, handle security compliance, integrate with existing data pipelines, and support distribution through enterprise sales channels.
It's not happening.
The gap between "useful demo" and "production enterprise software" remains enormous. Legacy vendors have accumulated decades of integrations, security certifications, and institutional knowledge that don't disappear simply because code generation got cheaper.
The Real Threat Isn't Displacement—It's Compression
What AI is actually doing to enterprise software is more subtle than replacement.
Companies are building more internal tools. A marketing team that previously bought a point solution might now build something custom with AI. This is real, and it's happening fast.
But this primarily affects the long tail of niche SaaS products—the obscure tools serving tiny markets. The core enterprise platforms (Salesforce, ServiceNow, Workday, the ERPs) have structural moats that go beyond feature functionality.
The companies to watch aren't the ones being displaced. They're the ones whose customers are building complementary tools that eventually get absorbed back into the platform.
The Economics Are Changing Faster Than Anyone Expected
Here's a number worth considering: in 21 months, the cost of equivalent-model token inference dropped 150X.
That's not a typo. What cost $37 per million tokens in early 2024 now costs around $25 cents. The same collapse is happening with the next generation of models—88X cheaper in just 11 months.
This changes everything about how AI products get built and priced. When your marginal cost approaches zero, traditional SaaS pricing models start to look arbitrary. Usage-based pricing becomes more honest. freemium becomes viable at scale.
The companies navigating this shift successfully are treating it as a pricing and packaging problem, not just a technology problem.
Where Value Actually Accrues
There's a pattern emerging among the most successful AI-native companies: they're building bundles, not point solutions.
The logic is straightforward. If models are becoming commodities—if GPT-5 and Claude 4 and Gemini are all roughly comparable—then differentiation shifts to what's built on top. The moat becomes the workflow, the integration, the data moat.
This means:
- Multi-product companies are outcompeting single-feature startups
- Platforms with network effects are more defensible than ever
- Horizontal point solutions face existential pressure
The defensive play isn't building a better chatbot. It's building a system that locks customers into your workflow through data, integrations, and habit.
What This Means For Startups
The startup calculus has shifted.
Two years ago, the play was: build an AI wrapper around an existing API, ship fast, iterate. That window has largely closed. The incumbents have absorbed the obvious AI enhancements, and the economics don't support as many point solutions.
The new opportunities are in:
- Vertical-specific workflows that require deep domain expertise
- Systems that bridge multiple data sources and create compounding moats
- AI-native products that couldn't exist without the technology—not AI added to existing products
The founders winning now are solving problems that couldn't be solved before, not solving existing problems slightly faster.
The Honest Assessment
Is the SaaS model dead? No.
Is it changing? Dramatically. The companies that will thrive are the ones treating AI as a platform shift, not a feature add. They're rethinking pricing, packaging, and product architecture from first principles.
The hype cycle will continue to oscillate between "software is dead" and "AI was overhyped." The reality is somewhere in between: enterprise software is becoming more capable, more affordable to build, and more competitive to sell.
That's not an apocalypse. It's an evolution.