For years, tech giants like Google and startups such as OpenAI have been racing to build ever bigger and costlier artificial intelligence models using a tremendous amount of online data. Deployed in chatbots like ChatGPT, this technology can handle a wide range of complex queries, from writing code and planning trips to drafting Shakespearean sonnets about ice cream.
Mark McQuade is betting on a different strategy. Arcee.AI, the startup he co-founded last year, helps companies train and roll out an increasingly popular — and much tinier — approach to AI: small language models. Rather than try to do everything ChatGPT can, Arcee’s software helps accomplish a more limited set of day-to-day corporate tasks — like building a service that only fields tax-related questions, for example — without requiring as much data. “I say 99% of business use cases, you probably don’t need to know who won an Olympic gold medal in 1968,” McQuade said.
Miami-based Arcee is one of a growing number of companies rethinking the conventional wisdom in the tech industry that bigger is always better for AI. Fueled by billions in venture capital, startups one-upped each other to develop more powerful large language models to support AI chatbots and other services, with Anthropic Chief Executive Officer Dario Amodei predicting it will eventually cost $100 billion to train models compared to $100 million today.
That thinking certainly still exists, but startups like Arcee, Sakana AI and Hugging Face are now attracting investors and customers by embracing a smaller — and more affordable — approach. Big tech companies are learning to think small, too. Alphabet Inc.’s Google, Meta Platforms Inc., OpenAI and Anthropic have all recently released software that is more compact and nimble than their flagship large language models, or LLMs.
The momentum around small models is driven by a number of factors, including new technological improvements, a growing awareness of the immense energy demands associated with large language models and a market opportunity to offer businesses a more diverse range of AI options for different uses. Small language models are not just cheaper for tech companies to build but also for business customers to use, lowering the bar for adoption. Given that investors are increasingly worrying about the high cost and uncertain payoff of AI ventures, more tech companies may choose to go this route.
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