Hello and welcome to Eye on AI. In this edition…Nvidia snags the team and tech from AI chip startup Groq…Meta buys Manus AI…AI gets better at improving AI…but we might not know enough about the brain to reach AGI.
Happy New Year! A lot has happened in AI since we signed off for the year just before Christmas Eve. We’ll aim to catch you up in the Eye on AI News section below.
Meanwhile, as I’ve noted here before, 2025 was supposed to be the year of AI agents, but most companies struggled to implement them. As the year drew to a close, most companies were stuck in the pilot phase of experimenting with AI agents. I think that’s going to change this year, and one reason is that tech vendors are figuring out that simply offering AI models with agentic capabilities is not enough. They have to help their customers engineer the entire work flow around the AI agent—either directly, through forward deployed engineers who act as consultants and “customer success” sherpas; or through software solutions that make it super easy for customers to do this work on their own.
A key step in getting these workflows right is making sure AI agents have access to the right information. Since 2023, the standard way to do this has been with some kind of RAG, or retrieval augmented generation, process. Essentially, the idea is that the AI system has access to some kind of search engine that allows it to retrieve the most relevant documents or data from either internal corporate sources or the public internet and then the AI model bases its response or takes action based on that data, rather than relying on anything it learned during its training process. There are many different search tools that can be used for a RAG system—and many companies use a hybrid approach that combines vector databases, particularly for unstructured documents, as well as more traditional keyword search or even old-fashioned Boolean search.
