The Resilience of the Core: Why the Death of SaaS is Premature in the Era of Vibe Coding

As the “SaaS-pocalypse” narrative continues to dominate market sentiment in 2026, a critical question emerges: Did we actually learn the lessons of the last decade? The sudden volatility in the SaaS sector is driven by the rise of “vibe coding”—the ability for natural language prompts to generate functional code —which has led many to believe that the traditional software moat is dead.

However, history suggests we are on the verge of repeating a classic strategic error: mistaking the ability to create code for the capability to operate a business system. As organizations rush to build homegrown AI solutions, they risk ignoring the hard-won lessons that led to the firing of CEOs at GE, Ford, and P&G during the last wave of digital transformation, where 70% of such initiatives failed. The winners of the AI era will be those who remember the central principle of enterprise IT: Great companies build differentiation and buy commodities.

This rush to build often ignores the “Complexity Tax” inherent in custom software. Gartner research indicates that for custom-built systems, maintenance and personnel costs typically account for 70% to 85% of the Total Cost of Ownership (TCO) over a five-year period. While vibe coding commoditizes the initial creation of a script, it does not solve for the long-term complexity tax that consumes up to 40% of IT budgets, leaving companies with little room for actual innovation. 

The Lesson of Strategic Overreach: CEO Accountability

A look at the recent past reveals a pattern of “big bang” transformations that failed not because the technology didn’t work, but because companies tried to become tech experts in areas that were ultimately commodities.

• General Electric (GE)—The $7 Billion “Platform” Failure: GE’s attempt to build the Predix platform and its own data centers is the definitive cautionary tale. By trying to build an Industrial IoT ecosystem from the ground up, GE effectively built its own commodity cloud infrastructure. The failure—and the subsequent ouster of CEOs Jeff Immelt and John Flannery—stemmed from a lack of focus on GE’s actual industrial core.
• Procter & Gamble (P&G)—Digitizing for the Sake of Digitizing: In 2012, P&G aimed to become “the most digital company on the planet.” By the time the board asked for the CEO’s resignation, the company had spent billions on broad initiatives that lacked clear ROI.
• Ford Smart Mobility—Technology as a “Bolt-On”: Ford’s failed digital transformation was marked by treating its tech division as a separate entity rather than an integrated part of its core business. Chasing “mobility” hype led to a loss of market share and a change in leadership, including the departure of their CEO.
• Volkswagen Group—The CARIAD Capability Trap: CEO Herbert Diess was ousted in 2022 after CARIAD, an ambitious attempt to build a unified, in-house software stack for all VW brands, failed. By trying to build foundational OS and cloud layers—a commodity layer in the modern tech stack—VW faced massive delays in flagship electric vehicle launches, proving that even massive scale cannot overcome the “Complexity Tax” of non-core software development.

Today, the 95% failure rate for generative AI pilots—as reported in the July 2025 MIT NANDA study—suggests that enterprises are once again attempting to build things (e.g., AI infrastructure and AI activation tooling) they should simply buy. Successful companies recognize that external partnerships succeed twice as often (67%) as internal builds (33%).

The Hierarchy of Moats: Stratifying Vulnerability in the AI Era

To understand why the “death of SaaS” is exaggerated, we must stratify the software stack. Not all moats are created equal, and industry-agnostic players often hold the most unbreachable ground. The ordering below follows a “Foundational Resistance” spectrum. The moats at the bottom (Layer 1) are the hardest to cross because they are built on physical assets and capital, while the layers at the top are more susceptible to AI cloning because they rely more on user interfaces and simple logic.

Layer 1: The Infrastructure & Edge Moat

Infrastructure players are the least vulnerable to vibe coding because they provide physical and globally distributed capabilities. Akamai is a prime example. While a developer can vibe code a website, they cannot vibe code a 25-year-old network of edge servers that host content closer to users to reduce latency. Akamai’s “Cloud Inference” service uses this physical footprint to run AI models at the edge, offering triple the throughput of centralized clouds. You cannot prompt your way into a global CDN.

Layer 2: The PaaS & Connective Tissue Moat

Platform-as-a-Service (PaaS) offerings are significantly more resilient than horizontal SaaS because they provide the fabric on which other apps are built.

• Connective Tissue: Boomi manages bidirectional data flows across 300,000+ unique endpoints. Replacing Boomi with vibe-coded scripts ignores the unglamorous “complexity tax” of maintaining over 1,500 pre-built connectors and ensuring real-time data integrity. 
• PaaS Advantage: In a PaaS model, the provider manages the hardware, runtime, and security layers. Vibe coding actually increases the value of PaaS. As more people build custom tools, they require a robust, managed platform to host and secure them. Furthermore, the “Time-to-Value Gap” is stark: Gartner reports that SaaS deployments for standard business processes are typically 4–6x faster than custom builds. While a prompt can generate a function, it cannot automate the SOC2 compliance, security patching, and multi-endpoint maintenance that a platform provides out of the box.

Layer 3: The System of Record & Data Gravity Moat

Systems of Record (SoR) own the “single source of truth” for critical business assets.

• Systems of Record: As the backbone for IT and operational workflows, ServiceNow uses a single data model across modules (IT, HR, Customer Service) that eliminates silos. A vibe-coded tool can’t replicate the decades of integrated business logic that allows ServiceNow to resolve customer issues by automatically triggering back-office IT incidents.
• Data Gravity: The Snowflake AI Data Cloud creates a moat of “Data Gravity” by separating storage from compute and enabling zero-copy cloning. Extracting data from Snowflake or rebuilding its multi-cloud replication and governance (SOC 2, HIPAA, PCI DSS) is “catastrophically risky” for an enterprise.

Layer 4: The Operational Control Plane

As AI-generated code floods production, the need for observability and deep context spikes.

• Dynatrace: AI workloads are probabilistic and non-deterministic, failing in ways traditional monitors can’t see. Dynatrace acts as a “decision fabric,” connecting system telemetry with business outcomes. Vibe coding creates code, but it doesn’t create the system-wide feedback loops required to validate that code.   
• Glean: Glean uses its “Enterprise Graph” to index and understand the context across 100+ disparate enterprise apps. While a startup can build a search tool, it cannot easily bootstrap the “Ground Truth” of a company’s unique internal projects, processes, and relationships that Glean refreshes continuously.

Layer 5: Systems of Engagement

At the top of the stack sit the interfaces where users spend their day. These “Systems of Engagement” are the most vulnerable to vibe coding because their primary value is in the User Interface (UI) and simple collaboration logic. 

• Monday.com: Tools like Monday.com or Trello are highly susceptible to “cloning” because their moats are primarily horizontal and UI-centric. When a prompt can generate a fully functional, collaborative Kanban board or project tracker in seconds, the barrier to entry for bespoke, internal alternatives vanishes. Unlike the layers below, these tools often lack the “extractability risk” of a System of Record, the physical distribution of an Infrastructure player, or the security concerns of systems that handle sensitive financial or operational data.

Vertical Fortresses: The Moats of Authority and Complexity

While agnostic players hold the infrastructure, vertical leaders have built ecosystem offerings with moats constructed from materials that vibe coding cannot touch. 

Cox Automotive: The Integrated Marketplace

Cox Automotive exemplifies the principle of building a moat through an integrated ecosystem of brands like Kelley Blue Book (KBB), Autotrader, and Manheim.

• KBB: Backed by 3.0 trillion data points, KBB provides a “neutral party” valuation that builds immediate credibility with consumers—a brand authority that vibe coding cannot synthesize. 
• Manheim: This moat is tied to physical infrastructure, processing over 5 million wholesale transactions annually. It is a physical-digital flywheel, including reconditioning and logistics that code-only startups cannot replicate. 
• Autotrader: As the industry’s largest automotive listings site, Autotrader creates a “reach moat” powered by two-sided network effects—more buyers attract more sellers, which in turn attracts more buyers. Together with KBB, it attracts over 28 million unique monthly visitors, providing a level of consumer demand that cannot be “prompted” into existence with vibe-coded solutions. 

The true competitive strength of Cox Automotive lies in its cross-platform synergy (fully explained in Unbundling the Enterprise thanks to a revealing conversation with David Rice, Senior Vice President, Product & Engineering at Cox Automotive—shown in the diagram below): A feedback loop where Manheim’s real-time auction data informs KBB’s valuations, which in turn validates the pricing on Autotrader for millions of shoppers. By leveraging 5.1 trillion vehicle insights across its portfolio, Cox has assembled a “System of Intelligence” where the connection between the parts is as valuable as the parts themselves. 

Vibe coding a single functional tool cannot overcome an ecosystem where 60% of automotive shoppers start their journey, and where extracting one component represents a catastrophic loss of liquidity and trust. 

WiseTech Global: The Logistics Complexity Moat

WiseTech Global’s CargoWise platform is used by 47 of the top 50 global 3PL providers. Its moat is the sheer depth of international trade complexity, managing customs clearance across 193 countries. Replacing WiseTech with custom code would require recreating thousands of country-specific regulatory integrations—a “global customs problem” that vibe coding is fundamentally unequipped to solve. 

Conclusion: Buying the Commodity to Build the Difference

The predictions of the “death of SaaS” focus on the production of code while ignoring its operation. Vibe coding has commoditized script writing, but it has not commoditized the global distribution, intelligence, or activation of complex enterprise systems.  To survive the AI era, the modern enterprise stack must be stratified across three distinct, unbreachable layers of infrastructure:

1. Web & Edge Infrastructure (Akamai): The physical foundation of the internet. You buy this because you cannot prompt a 25-year-old network of distributed edge servers or the throughput required to run AI models at the internet’s edge.
2. AI Infrastructure (The Hyperscalers): The foundational layer of intelligence. Giants like Microsoft/OpenAI, Amazon/Anthropic, and Google own the hundred-million-dollar GPU clusters and foundational LLMs that provide raw reasoning capabilities.
3. AI Activation (Boomi): The operational bridge. You buy Boomi not as a commodity pipe, but as the activation platform that embeds governed intelligence into core systems, data, and workflows. AI activation is the process of turning raw model intelligence into business outcomes by orchestrating agents and connecting them to trusted data at scale.

Historically, the CEOs and companies mentioned in the cautionary case studies above failed because they attempted to “build” at Layer 1 and Layer 2—areas where the “Commodity Tax” is highest and the competitive advantage is lowest. Successful companies, such as Cox Automotive or WiseTech Global, focus their “build” energy on the “Vertical Fortress”—integrating complex, industry-specific logic (like global customs clearance or automotive valuation data) that cannot be easily bought or replicated by a generic platform.

The lesson from past failed transformations is that companies that try to build their own commodities eventually fail or face leadership changes under financial pressure. The era of SaaS is simply moving from a system of record to a system of execution. Winners will be those who buy their “commodity” infrastructure from the players who’ve built robust systems of value that extend beyond lines of code, and focus their energy on building only what truly differentiates them in the market.

- About The Authors

Stephen Fishman

Stephen Fishman (Fish) is the NA Field CTO for Boomi. He is a practicing technologist who brings creativity, rigor, and a human-centric lens to problem-solving. Known as an expert in aligning technology and business strategy, Stephen places a premium on pushing business and technology leaders to embrace iteration and the critical need to collaborate across disciplines. Throughout his career, Stephen has consulted with organizations desiring to transform their technology-based offerings to better meet the needs of organizations and the people they serve. In addition to consulting with large organizations, Stephen is an in-demand speaker and advisor. Stephen has led multidisciplinary teams to deliver amazing results at Salesforce, MuleSoft, Cox Automotive, Sapient, Macy's, and multiple public sector institutions including the US Federal Reserve and the CDC. He lives in Atlanta with his family and when he's not working can be found biking on the many trails in Georgia.

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