“Their valuation is based on bigger is better, which is not necessarily the case,” Babak Hodjat, chief AI officer at Cognizant told Fortune.
“We do use large language models. We don’t need the biggest ones. There’s a threshold at which point a large language model is able to follow instructions in a limited domain, and is able to use tools and actually communicate with other agents,” he said. “If that threshold is passed, that’s sufficient.”
A number of companies are orienting their services around AI agents or apps, on the assumption that users will want specific apps to do specific things. Superhuman—formerly Grammarly—runs an app store full of “AI agents that can sit in-browser or in any of the thousands of apps where Grammarly already has permission to run,” according to CEO Shishir Mehrotra.
At Mozilla, CEO Laura Chambers has a similar strategy for the Firefox browser. “We have a few AI features, like a ‘shake to summarize’ feature, mobile smart tab grouping, link previews, translations that all use AI. What we do with them is that we run them all locally, so the data never leaves your device. It isn’t shared with the models, it isn’t shared with the LLMs. We also have a little slideout where you can choose your own model that you want to work with and use AI in that way,” she said.
At chipmaker ARM, head of strategy/CMO Ami Badani told Fortune the company was model-agnostic. “What we do is we create custom extensions on top of the LLM for very specific use cases. Because, obviously, those use cases did vary quite dramatically from company to company,” she said.
This approach—highly focused AI agents run like separate businesses—stands in contrast to the massive, general-purpose AI platforms. In the future, one source asked Fortune, will you use ChatGPT to book a hotel room that fits your specific needs—perhaps you want a room with a bathtub instead of a shower, or a view facing west?—or would you use a specialized agent that has a mile-deep database beneath it that only contains hotel data?
“You need someone to help you figure that out. We at IBM believe in a fit-for-purpose model strategy, meaning you need the right model for the right workload. When you have a model router that’s able to help you do that, it makes a huge difference,” Emily Fontaine, IBM’s venture chief, told Fortune.
