Building defensible businesses in the B2B generative AI space requires integrating proprietary outcomes data, implementing robust UX/UI frameworks, and complying with privacy and compliance requirements.
Generative AI presents opportunities for startups to disrupt existing markets by leveraging LLM technology and addressing previously unmet needs.
Proper oversight and guardrails are needed to prevent misuse and security breaches in the widespread adoption of generative AI.
Balancing human input, ensuring accuracy, and maintaining user engagement are essential in building effective co-pilot or coach AI systems.
Deep dives
Understanding LLMs and GPTs
LLMs, or large language models, are programs designed to understand and generate human language. They use deep learning to analyze text data and learn the patterns and structures of language. One popular type of LLM is GPT, or generative pre-trained transformer, developed by OpenAI. GPT is based on the transformer architecture and is effective in natural language processing tasks.
Challenges and Opportunities for B2B SAS Companies
For B2B SAS companies, the adoption of generative AI poses both challenges and opportunities. The key is to understand the job to be done and how important accuracy and real-world outcomes are. Some use cases, like consumer applications or copywriting, prioritize attention and creativity over accuracy. However, in B2B use cases, accuracy is crucial. Building defensible businesses in this space requires going beyond just using off-the-shelf LLMs. It involves integrating proprietary outcomes data, implementing robust UX/UI frameworks, and complying with privacy and compliance requirements.
Implications for Incumbents and Startups
Generative AI presents a different landscape for incumbents and startups. Incumbents that rely on traditional UI/UX paradigms may face challenges in adopting chat-based interfaces. Startups have the opportunity to disrupt existing markets by providing new solutions that leverage LLM technology in areas like compliance, pricing strategy, and more. The shift to generative AI creates opportunities to build entirely new categories and address previously unmet needs in the market.
Guarding Against Misuse and Trough of Disillusionment
As generative AI becomes more widely adopted, there is a need for proper oversight and guardrails to prevent misuse and avoid catastrophic incidents. The risk of unintended consequences and security breaches is a significant concern. Enterprises will demand compliance and privacy solutions, both internally and from third-party vendors. The industry may go through a trough of disillusionment as the hype settles and a more cautious approach is adopted. However, the utility and potential of generative AI cannot be ignored, particularly in consumer applications.
Challenges and best practices for building co-pilot or coach interfaces
Building an effective co-pilot or coach requires understanding how to balance human input with ensuring accuracy and avoiding pitfalls. The best UX practices involve providing suggestions and options to users, allowing them to make informed decisions while also learning from their edits and tying it to business outcomes. Examples like Notion's AI feature showcase how conversational elements can be integrated into existing workflows. The fear of users disengaging or over-relying on suggestions is a challenge that needs to be addressed through thoughtful design and maintaining user engagement.
The importance of tying pricing to value creation
Tying pricing to the value created is crucial for AI companies. The traditional per seat pricing model may not be suitable, given the potential cannibalization effect and dissuading spreading usage across the company. Instead, pricing mechanisms like job-based pricing or volume-based pricing that align with value creation can be more effective. Furthermore, pricing can be used to incentivize users to contribute data for the improvement of the product, creating a win-win situation for both the users and the company.
Navigating the current venture and startup investing landscape
The current venture and startup investing landscape presents challenges and opportunities. Maintaining discipline and a long-term perspective in deploying capital is crucial, investing radically over time and not timing the market. Brand power and institutional staying power are also important factors for success. Additionally, conversations with LPs revolve around disciplined deployment strategies and staying focused on value creation amid changing market dynamics. While anxiety and uncertainty exist, maintaining emotional regulation and serving as emotional calibrants for founders are important roles in navigating the current landscape.
How do you build defensible business value in an era when, as AngelList CEO Avlok Kohli said on our last ACQ2 episode, the “cost of intelligence is going to zero”? Longtime friend of the show Jake Saper and his partners at Emergence Capital have been refining their thesis for this brave new world of Generative AI in B2B, and we sit down with him to discuss. We cover topics including:
When do exactly correct answers matter, and when do they not?
When are human-in-the-loop systems necessary?
When do startups have an advantage vs. incumbents, and vice-versa?
Where can companies capture value on a durable basis?
When do you need proprietary data in order to be defensible?
Whether you’re building or investing in existing businesses from the “pre-AI” era or brand new startups that are native to GPT, this episode has plenty of takeaways you should consider. Tune in!
