"Vibe coding" platforms, which allow users to create apps from natural language, pose a direct threat to the B2B SaaS market. For simple workflows, it is becoming faster for a team to build its own personalized app than to navigate the sales, procurement, and integration process for an existing SaaS product.
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The historical advantage of being first to market has evaporated. It once took years for large companies to clone a successful startup, but AI development tools now enable clones to be built in weeks. This accelerates commoditization, meaning a company's competitive edge is now measured in months, not years, demanding a much faster pace of innovation.
The traditional competitor for B2B tools was an Excel spreadsheet. In the AI era, it's a simple, version-controlled Markdown file within an IDE. If a SaaS offering for documentation or project management can't provide more value than this highly flexible, interoperable setup, it will lose.
By publicizing its internal AI-powered tools for sales, finance, and support, OpenAI signaled its ambition to enter the enterprise application market, directly challenging SaaS incumbents and causing HubSpot's stock to fall.
The ease of building applications on top of powerful LLMs will lead companies to create their own custom software instead of buying third-party SaaS products. This shift, combined with the risk of foundation models moving up the stack, signals the end of the traditional SaaS era.
Dylan Field is skeptical that disposable, AI-generated apps will replace complex SaaS products. Real business software must handle countless edge cases, scale with teams, and support shared workflows—a level of complexity and institutional knowledge that today's agents are far from mastering.
The primary value of AI app builders isn't just for MVPs, but for creating disposable, single-purpose internal tools. For example, automatically generating personalized client summary decks from intake forms, replacing the need for a full-time employee.
Wabi allows users to create and remix personal "mini-apps" that can only be used within its platform. By keeping the content (the apps) self-contained, it aims to build a social graph and network effect around software creation and consumption, analogous to how YouTube became the central hub for user-generated video.
In the future, it will be easier for businesses to build their own custom software (e.g., Salesforce) through prompting than to buy and configure an off-the-shelf solution. This shift towards "liquid software" will fundamentally challenge the one-size-fits-all SaaS model, especially for companies that currently rely on implementation partners.
Instead of building a single-purpose application (first-order thinking), successful AI product strategy involves creating platforms that enable users to build their own solutions (second-order thinking). This approach targets a much larger opportunity by empowering users to create custom workflows.
The existential threat from large language models is greatest for apps that are essentially single-feature utilities (e.g., a keyword recommender). Complex SaaS products that solve a multifaceted "job to be done," like a CRM or error monitoring tool, are far less likely to be fully replaced.
