European VCs Struggle Under Pressure: Founder and Government Pushes Lead to Investments in Shallow Tech Tools Like Dottxt

Dottxt Raises $11.9 Million in Funding, but Questions Loom Over Long-Term Viability

Dottxt, a Paris-based startup, has managed to raise an impressive $11.9 million in combined pre-seed and seed funding rounds over just seven months, catching the attention of investors eager to capitalize on the AI revolution. The pre-seed round, amounting to $3.2 million, was led by Elaia in December 2023, while the more recent seed round of $8.7 million was backed by EQT Ventures in August 2024. Despite the excitement surrounding this funding achievement, there are questions about whether dottxt’s technology is substantial enough to carve out a meaningful space in the AI landscape long-term.

Transforming LLMs: A Thin Feature with a Thin Future?

Dottxt aims to improve large language model (LLM) capabilities by allowing users to request structured information, such as ensuring output formats like JSON. The platform is designed to transform LLMs from mere conversational tools into reliable, structured computational systems—making it possible for tasks like natural language database queries, CV filtering, and attribute extraction from images. However, a critical issue that emerges is whether this "thin feature" will hold any lasting value in a fast-moving AI environment.

The core value proposition of dottxt lies in its ability to reliably format LLM outputs, turning inherently unpredictable responses into more structured and usable results. While this might have immediate appeal, it is also highly susceptible to being commoditized. Modern LLMs are rapidly evolving to incorporate advanced, customizable output features directly, which means that dottxt's current competitive edge could soon fade away. The problem is compounded by the simplicity of the feature: it’s relatively easy for LLM providers like OpenAI or Google to replicate structured output capabilities, which significantly diminishes the differentiation that dottxt currently offers.

Commoditization and Low Barriers to Entry

The commoditization threat looms large over dottxt. Ensuring LLMs output structured formats is not inherently complex, which means the barriers to entry are quite low. Competing startups or even individual developers can reproduce similar capabilities without the need for significant resources, making it hard for dottxt to maintain a strong market position. Even more concerning is the existence of open-source alternatives that can offer comparable functionality, which further weakens dottxt’s commercial leverage.

The current AI landscape is all about evolving towards broader automation and integration—areas that require multifaceted, deeply integrated solutions. By focusing on such a narrow piece of the AI ecosystem, dottxt risks being outpaced by larger, more versatile platforms that bundle such features into a wider array of tools. Essentially, dottxt's technology might just be too "thin" to sustain interest or generate lasting value.

The European VC Conundrum: Chasing Hype Instead of Depth

The fact that dottxt has managed to secure nearly $12 million in funding has highlighted a broader issue within the European venture capital (VC) ecosystem: a focus on market hype over technological depth. European VCs seem increasingly pressured by both founders and governmental initiatives to demonstrate active investment in the tech space, but they often appear at a loss for how to strategically allocate these funds. This pressure has driven them to fund startups like dottxt, which offer utility-focused but ultimately limited tools.

In dottxt's case, the appeal seems to be primarily driven by the hype surrounding generative AI—despite concerns over the actual scalability and longevity of their core offering. This trend underscores a misallocation of resources in the European tech space. Deep-tech startups that require significant R&D investment—the kind that could drive truly transformative innovation—often struggle to secure funding. These companies, which might push the boundaries of AI far beyond incremental features, are finding themselves sidelined as investors flock to quick wins in buzzy sectors. As a result, thin-featured startups like dottxt that cater to superficial gaps are disproportionately capturing attention and funding.

Limited Scope for Expansion

A significant challenge facing dottxt is its limited potential for product expansion. While the initial traction has been promising—evidenced by their open-source library "Outlines" surpassing 3 million downloads—the feature set itself is unlikely to support a broader product roadmap without significant reinvention. Most successful tech startups grow by broadening their scope and offering diverse functionalities. In contrast, dottxt’s value proposition remains narrowly confined to formatting output, making it tough to evolve into a more comprehensive AI solution.

Over-Reliance on External Platforms and Short Shelf Life

Dottxt’s dependence on external LLM platforms is another major concern. The startup’s technology is entirely contingent on the current limitations of models like GPT. As these LLMs grow increasingly capable of handling their own structured output, the demand for middleware like dottxt could rapidly diminish. This over-reliance puts the company in a precarious position—if the LLM providers themselves close the gap, dottxt’s differentiator will simply vanish.

Moreover, given the rapid pace of AI advancement, there's a real risk that dottxt’s core feature could soon be obsolete. This is particularly concerning for investors who are looking for sustainable growth; while early excitement might drive initial funding rounds, maintaining momentum in subsequent rounds will be far more challenging unless dottxt can broaden its offering and pivot towards more unique, indispensable technology.

Venture Capital’s Hype Problem: Lost in Pressure, Short-Term Wins Over Long-Term Innovation

The juxtaposition of dottxt's funding success against the broader struggle for European startups to secure investment raises important questions about VC priorities. European VCs seem caught between the pressure to invest—from founders, from governments, and from a tech ecosystem eager to emulate Silicon Valley—and a lack of clear direction about where these investments should go. As a result, the current funding climate appears to favor scalable but shallow tech solutions over deep, foundational innovation. Dottxt's promise of quickly monetizing AI's structured outputs appeals to VCs looking for early exits and quick returns, but it doesn't necessarily align with the need for long-term, transformative AI development.

VCs have become enamored with the scalability potential of thin-feature startups. The relatively simple nature of dottxt's technology makes it highly marketable and easy to scale across industries—which seems to be a bigger priority for VCs than genuine innovation. This emphasis on scalability over substance could lead to missed opportunities for supporting more groundbreaking ventures that require a longer runway to mature.

Conclusion: A Thin Feature with a Thin Future

While dottxt’s recent funding rounds demonstrate the appeal of AI-focused startups, the company's core offering—ensuring structured LLM outputs—is too narrow to guarantee long-term success. The risk of commoditization, the low barriers to entry, and a lack of significant product expansion all contribute to doubts about the viability of dottxt as a standalone solution. Unless the company can pivot and significantly expand its capabilities, its journey may be short-lived, despite early enthusiasm.

This funding success also highlights an underlying issue within the European VC landscape: a worrying trend of prioritizing hype-driven investments over deeply innovative projects. Until VCs realign their focus towards fostering genuine technological progress, startups like dottxt—promising quick wins but lacking depth—will continue to dominate, potentially at the expense of more meaningful innovation.