Unlocking VC Investment: The Key Element For Large Language Model-Backed Startups

As a seasoned entrepreneur and academician, venture capitalists (VCs) and startups frequently approach me with a burning question: "How can a startup stay competitive and defensible when building its core based on large language models (LLMs) such as ChatGPT?"

My typical response is one of careful consideration. While low intellectual property (IP) companies may raise concerns, a pivotal factor demands our utmost attention. In this article, we delve into the significance of this critical element.

The Rise Of Large Language Model-Backed Startups

LLMs have revolutionized the way we interact with technology, enabling unprecedented advancements in natural language processing and understanding. This cutting-edge technology has found its applications across various sectors, ranging from customer service and content generation to data analysis and decision-making. As a result, the startup landscape has witnessed a surge in ventures harnessing the potential of these powerful language models.

In the wake of OpenAI's groundbreaking release of ChatGPT, the widespread interest generated by VCs across the globe has been nothing short of remarkable. While commonly seen as a lesser disruption, the enthusiasm surrounding LLMs has caught the attention of renowned investors.

In a mere 61 days, Sequoia took the lead by investing $10 million in a promising startup known as "ChatGPT Tips." This is just one of the thousands of startups that have received billions of investment dollars. PitchBook expects the global generative AI market to reach $42.6 billion in 2023.

Navigating The Vulnerabilities Of Nonproprietary Tech In Startups

While the emergence of transformative language models has sparked immense interest and potential in the startup landscape, it's crucial to recognize the inherent challenges associated with leveraging nonproprietary technology. Startups, unlike industry giants such as Google, often lack the necessary resources and infrastructure to fine-tune, retrain, or even fully exploit the capabilities of LLMs.

This reliance on third-party technology not only leaves them vulnerable to replication by rival startups but also exposes them to the possibility of being overshadowed by larger corporations seeking to capitalize on burgeoning markets. With their well-established technologies and a constant hunger for new applications, these industry giants can swiftly deploy their own resources at scale, potentially eroding the competitive edge of smaller ventures.

Safeguarding Success Through Innovation And IP

To mitigate the risks posed by technological replication, startups must proactively seek ways to differentiate themselves beyond the mere application of LLMs. The key lies in fostering a culture of innovation and IP protection. By nurturing a team that not only understands the nuances of the technology but also possesses domain expertise and creative problem-solving skills, startups can augment their offerings and cultivate a competitive advantage that extends beyond the underlying model itself.

Coupling this with the strategic acquisition and protection of IP rights can fortify a startup's market position and shield it from potential imitators.

Data Is King—Its Utilization Is The Key

We all know that in the realm of venture capital, the ownership of unique data stands as a proven method to protect technological defensiveness. Within the VC community, it is acknowledged that possessing proprietary datasets can effectively deter competition, preventing rivals from merely copying ideas with superior funding or technology teams.

The value of unique data extends beyond its role in impeding replication, as it provides startups with invaluable insights—enabling them to identify hidden patterns, understand customer behaviors and stay ahead of market trends. By harnessing the power of proprietary data, startups can solidify their competitive edge and drive innovation in today's fiercely competitive landscape.

One way for founders to stay ahead of their competition in the eyes of VCs is to amplify their proprietary data and technology with the power of modern generative networks and, once the product market fit is proved, further look into in-house LLM implementation.

For example, when building our competitor analysis suite, "SoMonitor," and its LLM-powered analytics assistant, "SoDa," we had technology defensiveness as one of the top priorities in our minds.

Particularly at the core, we set a vast trove of anonymized data encompassing five years' worth of advertising campaigns run on the Meta AI platform for our clientele. Leveraging proprietary AI tech we've built, this data is leveraged to accurately predict click-through rates (CTRs) and conversion rates (CRs) of advertising banners while also providing a heatmap that highlights the key visual components driving customer engagement and product purchases.

By integrating visual and textual data we have (our in-house multi-modal models for ad creative performance prediction with the power of LLMs), we've made the system generate actionable recommendations for clients covering competitor brand archetypes, audience outreach approach, content, and promotional snapshots in the form of a real-time digest. This enables our clients to refine their strategies, enhance creative assets, and stay ahead in a fiercely competitive market.

In such a way, this kind of approach of combining LLMs and in-house AI allowed us to come up with a novel application in the Martech industry that otherwise wouldn't have been possible without the existence of LLMs. Additionally, these capabilities cannot be easily replicated. We effectively employ LLMs to provide a comprehensive description of the digital marketing industry, and the amalgamation of proprietary data and CTR prediction algorithms render the system highly distinctive and challenging to replicate—setting it apart from competitors.

Raising VC Funds As An LLM-Backed Startup

The decision of whether to invest in the flourishing realm of LLMs remains a subjective one, with considerations varying among investors. However, one enduring piece of advice remains relevant: In the world of startup pitching, substantiate your fluff with cutting-edge proprietary tech, valuable IP and a skilled team committed to success—especially if your stage is early.

Delving beyond the surface is crucial. If the team exhibits promising expertise and demonstrates a vision that extends beyond a mere API call, VCs will certainly be keen to explore your company more deeply.