Three classifications of artificial intelligence companies
Haebom
1
According to Gil Dibner’s analysis, AI startups today fall into three broad categories: (1) thin and ephemeral; (2) big and weak; and (3) small and strong. This categorization helps us understand the opportunities and challenges faced by startups.
Thin and temporary startups: This type of startup tries to solve a narrow business problem based on AI/LLM. They usually rely on simple GUI and a few prompt calls. This approach can get to market quickly, but it lacks a sustainable business model and runs the risk of being easily replaced.
Big but weak startups: This type is intellectually fascinating, but their ideas tend to be very large and conflated. For example, they claim to “organize all knowledge” or “build/integrate any application.” These startups often have ambitious goals, but they lack a concrete plan for execution, which can make them unconvincing to investors.
Small but strong startups: These types of startups are run by founders with deep expertise in a specific field. They target a clearly defined customer in a specific market and try to solve a specific problem very well using AI. This approach provides a clear market goal and a strong business model.
According to Gil Dibner’s analysis, the current AI startup environment presents various opportunities and challenges. “Thin and temporary” startups aim for rapid growth but lack sustainability. “Big but weak” startups present an ambitious vision but lack a concrete execution plan. “Small but strong” startups, on the other hand, have a clear understanding of a specific market and a strong business model, which increases the possibility of long-term success.
If we think more simply, we can divide it into Service-Driven or Model-Driven. We need model-centered companies that create models and generate revenue by making API calls or basic usage fees for the models, and models that can provide new value to customers as services by utilizing existing services or basic models.
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