Early startups that attempted to wrap access to large language models like ChatGPT and sell them as standalone products are quickly losing relevance. Developers of foundational AI systems are now integrating the very capabilities that once formed the core of these young companies' offerings.
According to the venture capital firm NFX, startups should abandon simple overlays on third-party models and instead seize control of entire industry processes. Rather than building narrow tools for lawyers, companies are creating comprehensive AI-powered legal services, and instead of isolated procurement applications, they aim to manage full supply chains.
The first wave of such wrappers emerged shortly after the launch of ChatGPT. These tools generated advertising copy, responded to customers, supported sales teams, and extracted information from legal documents. The companies behind them assumed that base models would remain unable to handle specialized tasks independently for a long time.
That calculation did not hold. OpenAI, Anthropic, and other model developers began transforming their systems into complete working products. Features that assist with programming, document work, and content creation have become native parts of major platforms, stripping smaller single-task companies of their primary differentiation.
One of the most prominent examples is Jasper. In 2022 the company raised $125 million at a $1.5 billion valuation and was viewed as a leader in AI advertising copy generation. It later lowered its annual revenue forecast, reduced headcount, and shifted direction, now attempting to evolve into a full marketing platform.
More durable approaches have come from companies that treat AI as the foundation of an entire service rather than a single feature. The legal platform EvenUp focuses on personal injury cases and has expanded to prepare documents, process case materials, and manage additional workflow stages. NFX reports that more than 2,000 law firms now use the service.
Another example is Blitzy, which builds enterprise software to replace contractor teams. The system analyzes a customer's entire codebase, breaks large projects into smaller tasks, and selects appropriate models for each step, capturing internal dependencies that generic models might overlook without domain-specific preparation.
The mortgage service Tomo has gone further by automating sales, borrower verification, and daily operations. The company states that 77% of its clients receive better interest rates than those offered by traditional mortgage providers, demonstrating a strategy of replacing legacy processes rather than layering new software on top of them.
Seso has adopted a similar model for managing seasonal agricultural workers in the United States. Previously, employee records, visa information, and documentation were scattered across law firms, office software, and paper files. With more advanced models, Seso now also handles transportation, housing, business insights, and personnel decision support.
Large organizations can also integrate AI directly with model developers. The law firm Freshfields, for instance, has partnered with Anthropic to build specialized tools, although such initiatives demand substantial compute spending and dedicated technical staff, and automation remains only one part of their traditional business.
NFX believes younger companies can still win by specializing deeply in narrow niches. Industry expertise, accumulated data, established sales relationships, and control over every stage of a service are significantly harder to copy than isolated software functions. As universal models grow stronger, the value of simple wrappers diminishes, making the ability to convert AI capabilities into complete, operational businesses increasingly critical.
