Beyond AGI: Why Specialization Is the Real AI Breakthrough

RunLLM co-founders Joey Gonzalez and Chenggang Wu spoke at Data Council 2025 in Oakland, CA, an event known for its "no bullsh*t technical talks from the brightest minds in data & AI."

They argued that Artificial General Intelligence (AGI) is already here. While definitions of AGI differ, today's general-purpose AI models clearly handle diverse tasks such as answering questions, writing code, or analyzing images. This broad adaptability distinguishes them from earlier, narrowly specialized AI systems and demonstrates that general intelligence has arrived.

But AGI isn't actually the ultimate goal in the field. And now that we've reached it, we've entered a new phase of specialization. Specialization means that AI systems are trained on specific tasks or domains to perform better compared to general-purpose AI (which is focused on trying to do everything well). Think about tailoring a model for medical diagnosis, natural language processing, or advanced technical support. And now think about how that might compare to the performance of a generalized model like ChatGPT or Gemini. Adapting from general to specialized unlocks AI's practical value to solve more complex, real-world challenges.

Three Keys to Achieving Specialization

• Proper data use is key: Specialization starts with carefully selecting, curating, and preparing relevant data. RunLLM achieves these goals by ingesting and organizing data to create domain-specific expertise. In the case of RunLLM, it ingests data related to one company — all of its technical documentation, internal wikis, Slack conversations, Zendesk tickets, GitHub issues, and even source code—giving it the data to build deep understanding and insights.
• Fine-tuning for deeper expertise: Refining general models with targeted fine-tuning and synthetic data helps it grasp domain-specific contexts. RunLLM builds a finely tuned model for each customer’s products, making the agent a deep expert capable of understanding and using proper terminology, as well as grasping even nuanced corner cases. Doing so ensures that answers aren’t vague or show signs of hallucination. Users build trust through receiving highly accurate answers, even for complicated technical scenarios.
• Decomposition to manage complexity: Complex problems must be broken down into smaller, manageable tasks that specialized AI can effectively handle. RunLLM uses a multi-step reasoning pipeline, involving classification, precise retrieval, tool-based augmentation, and iterative refinement. This minimizes errors, ensures the agent doesn’t answer things for which it doesn’t have enough information, and knows when and how to hand off to humans.

Specialization is how we bridge the gap between general intelligence and real-world value, enabling AI to solve specific, meaningful problems. Achieving this goal effectively will shape the future of how businesses and users trust and interact with AI.

Explore these ideas further by reviewing Joey and Chenggang's presentation deck from Data Council 2025 here.

We welcome your thoughts. Let us know what you think!

‍