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Ivan Ilin

What really differentiats LLM-powered products today - features or marketing positioning?

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LLMs or AI, as you call it, are changing the software market. Thousands of new LLM-powered products are launched, but the core tech is pretty much similar as everyone is using foundational models, so the architectures & application cases are pretty much limited to: - RAG (search + reasoning + text generation) - summarization - entities extraction (names, organisations, locations, etc) - text classfification (tone of voice, positive / negative, etc) - chit-chat with dialogue history and maybe RAG for chatbots - agnetic pipelines (this one is pretty flexible) - image recognition (quite recent thing with GTP-4o) This pretty short tech stack is applied to thousands of usecases and datasets giving birth to all the new apps & services. The big difference is that before we normally had some custom fine-tuned models to solve particular problems, we needed ML Engineers, some domain specific data and this was our moat. Today all the apps can be powered by GPT-4o and they will be fine. It's ok to have specific tools for specific problems, market niches and audiences (cause audience is also a moat), but is that a sustainable business model or would only giants survive eventually? Curious of your thoughts.

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Julia Zakharova
Hi. The products that survive are the ones that make the longest and loudest public statements. Exactly those who find their audience and communicate with it (perhaps without building up through advertising). I recently dealt with one LLM (it is still being finalized, but here is the link https://metrasensor.com/). And the essence of it is a little further than GPT - in prediction. This is an area that I think is not as widely discussed among the masses as just data processing.
Ivan Ilin
@julia_zakharova2 Good point! You can still make custom models, my take was that most of the startups are using good enough vanilla SOTA LLMs for solving all the variety of tasks
Arthur Leclercq
You make valid points about the similarities in core technologies across LLM-powered products due to foundational models like GPT-4o. However, differentiation now comes from implementation and solving specific problems. While tech giants benefit from scale, specialized tools for niche markets offer unique value. Success depends on understanding user needs, delivering excellent user experience, and continuous innovation. Small players can thrive by focusing on specific applications and user engagement, ensuring their solutions meet particular needs better than generic ones.
Gurkaran Singh
What sets LLM-powered products apart today is not just the features but also how they skillfully dance the marketing tango, showcasing the same tech stack in unique, alluring ways. It's like giving the same ingredients to different chefs and seeing who serves up the most delightful dish!
Novicto H
@ivan_ilin Hi Ivan, you’ve touched on some key points about the current landscape of AI-powered products. It’s true that many new applications are leveraging foundational models like GPT-4, which provides a versatile yet somewhat homogeneous tech stack for various use cases. At StockLibrary.ai, we’re certainly using AI in a way that aligns with some of the categories you mentioned, specifically in generating custom stock photos based on user prompts. While our core technology leverages existing models, we focus heavily on fine-tuning our approach to meet specific market needs. Here’s how we see it: Specialization and Niche Focus: While the foundational tech might be similar across many applications, the specialization in addressing specific market niches can still be a significant differentiator. For example, our emphasis on high-quality, AI-generated stock photos tailored for different creative and marketing needs sets us apart from more generalized AI tools. User Experience and Accessibility: Making AI tools user-friendly and accessible without requiring deep technical expertise can also be a key differentiator. Our goal is to enable users to generate professional-grade images without needing to master complex prompt engineering. Sustainability of Business Models: As you pointed out, having a specific audience and addressing niche problems can be sustainable, but it does require continuous innovation and staying closely aligned with user needs. Giants will undoubtedly dominate certain aspects of the market, but there’s plenty of room for specialized services that offer unique value propositions. Evolving Use Cases: The versatility of foundational models means there’s always room for new and innovative applications. As technology evolves, so will the opportunities to refine and expand what these models can do, keeping the market dynamic and full of potential. In summary, while the core technology might be shared, how we apply it and the specific problems we solve can create sustainable business models. It’s an exciting time to be in the AI space, and I’m curious to see how these dynamics continue to evolve. What are your thoughts on the sustainability of niche-focused AI tools versus generalized AI applications?