Understanding AI as Leverage
When we talk about using AI as leverage rather than as the product itself, we're focusing on how AI can amplify solutions to durable problems. This means our primary concern is identifying a problem that persists over time and then using AI as a tool to enhance the solution. At Inverse Collective, we've found success in building businesses like Elder Voice and JellyPal by following this principle.
Identifying Durable Problems
The first step in our approach is finding a durable problem. These are issues that remain relevant and require ongoing solutions. For instance, consider Elder Voice, where the core problem is loneliness among the elderly. This is a long-standing issue with deep societal roots. By focusing on this, we're addressing a need that isn't going away anytime soon.
Contrast this with a fleeting trend. Say, developing an AI-powered app for a viral dance move. Once the trend dies, so does the product's relevance. When AI simply serves as a flashy feature without a lasting problem to anchor it to, it's more of a novelty than a necessity.
AI as an Engine Room
With our durable problem identified, AI takes on the role of the engine room. It's not the glamorous part that users see, but it's crucial for the system's power and efficiency. For Elder Voice, AI handles the routine yet essential tasks of engaging in meaningful conversations and learning from each interaction to improve over time.
Similarly, JellyPal uses AI to help users track their GLP-1 medications. The core problem here is the complexity of managing medication schedules. AI's role is to simplify this process, providing users with reminders and insights without them needing to understand the intricate workings behind it.
Successful Leverage Examples
A successful example of AI as leverage is in the predictive maintenance industry. Companies use AI to monitor equipment and predict failures before they happen. The core problem is equipment downtime, which directly impacts productivity and revenue. AI improves the efficiency of monitoring and maintenance without being the main product.
Another example is in customer service chatbots. The durable problem is the need for quick and efficient customer support. AI enhances the process by handling common inquiries, freeing up human agents to tackle more complex issues.
Missteps in AI Product Strategy
On the flip side, we've observed missteps where AI is marketed as the product itself, without a clear, enduring problem to solve. Consider some AI art generators. While fascinating, they often lack practical application outside creative experimentation, making it challenging to maintain a user base once the novelty wears off.
Similarly, AI applications that focus on gimmicks, like creating humorous content or predicting trivial personal traits, may capture attention briefly but struggle to sustain engagement without a substantial problem.
Building with AI Leverage
When building a business with AI as leverage, the key is to focus on how AI can amplify the solution to a durable problem. Start with the problem. Understand its nuances and persistence. Then, design AI systems that operate quietly in the background, enhancing the solution while allowing the core product to take center stage.
For us at Inverse Collective, this approach has led to the development of sustainable, capital-efficient companies. We prioritize simplicity in our systems, ensuring that AI enhances rather than complicates our solutions. By doing so, we create businesses that stand the test of time, with AI as a powerful tool rather than the sole attraction.
Conclusion
In building AI evergreen companies, we see AI as the engine room, powering solutions to durable problems. This strategy avoids the pitfalls of chasing trends and focuses on creating value that lasts. For founders and operators, it's a reminder to prioritize the problem first, letting AI serve as a powerful but unobtrusive ally.