How to Escape Competition and Build Enduring Value with LLMs: Incorporating Unique Ideas and Insights

How to Escape Competition and Build Enduring Value with LLMs: Incorporating Unique Ideas and Insights

In the world of startups, leveraging LLMs (Language Model Models) has become a popular strategy to gain a competitive edge. Copywriting was the first visible category of work that startups utilizing LLMs went after. Jasper and Copy.ai were the pioneers in this field, and their growth has been impressive. However, these companies have faced criticism regarding their defensibility. The concern is that if anyone with access to ChatGPT or OpenAI's APIs can achieve similar results, there is a constant risk of customers shifting their business to whoever offers the same work product at a lower price.

The question arises: won't the incumbents simply add LLMs to their own products? We have already seen companies like Notion, Hubspot, Canva, and Microsoft quickly announce GPT-driven features in their offerings. This creates a race between the startups that focus on distribution and the incumbents that strive for innovation. The reality is that very few software companies have ever had a technical moat; it has always been about focus and execution.

However, the second critique points to something deeper. We are currently in a "skeuomorphic" generation of LLM-based applications. These applications only scratch the surface of what is possible with this new technology. To truly escape competition, startups should consider narrowing their initial focus and pursuing vertical application opportunities. By tuning a model to a specific use case and integrating it into existing workflows, companies can create a more comprehensive and unique offering that goes beyond a simple API call to a foundation model.

Another crucial aspect of building enduring value with LLMs is the incorporation of feedback loops. If an application can leverage user engagement to improve the accuracy of its model, there are clear advantages to scale. The ability to accrue a valuable data asset through user interactions further strengthens the moat and differentiates the offering from competitors. This externalized moat, which goes beyond the capabilities of LLMs themselves, allows startups to escape competition at scale.

To put these ideas into action, here are three actionable pieces of advice:

1. Focus on a specific vertical: Instead of trying to cater to a broad market, identify a niche where LLMs can provide significant value. By specializing in a particular industry or use case, you can create a more tailored and defensible product.

2. Build feedback loops: Design your application to collect user feedback and engagement data. Use this data to continuously improve the accuracy and performance of your LLM model. This iterative process will create a virtuous cycle that sets your product apart from competitors.

3. Leverage user interactions to create valuable data assets: Explore ways to transform user interactions with your LLM-driven application into a new, useful data asset. This asset should provide unique insights or enable novel functionalities that wouldn't have been possible before at scale. By offering something beyond the capabilities of LLMs themselves, you can establish a strong competitive advantage.

In conclusion, while the rise of LLMs has opened up new opportunities for startups, it has also brought forth challenges in terms of defensibility. To escape competition and build enduring value with LLMs, startups should focus on vertical applications, incorporate feedback loops, and leverage user interactions to create valuable data assets. By adopting these strategies, startups can differentiate themselves in the market and establish a strong position that goes beyond the surface-level functionality of LLMs.