‘Reasoning’ AI models have become a trend, for better or worse

Call it a reasoning renaissance.

In the wake of the release of OpenAI’s o1, a so-called reasoning model, there’s been an explosion of reasoning models from rival AI labs. In early November, DeepSeek, an AI research company funded by quantitative traders, launched a preview of its first reasoning algorithm, DeepSeek-R1. That same month, Alibaba’s Qwen team unveiled what it claims is the first “open” challenger to o1.

So what opened the floodgates? Well, for one, the search for novel approaches to refine generative AI tech. As my colleague Max Zeff recently reported, “brute force” techniques to scale up models are no longer yielding the improvements they once did.

There’s intense competitive pressure on AI companies to maintain the current pace of innovation. According to one estimate, the global AI market reached $196.63 billion in 2023 and could be worth $1.81 trillion by 2030.

OpenAI, for one, has claimed that reasoning models can “solve harder problems” than previous models and represent a step change in generative AI development. But not everyone’s convinced that reasoning models are the best path forward.

Ameet Talwalkar, an associate professor of machine learning atCarnegie Mellon, says that he finds the initial crop of reasoning models to be “quite impressive.” In the same breath, however, he told me that he’d “question the motives” of anyone claiming with certainty that they know how far reasoning models will take the industry.

“AI companies have financial incentives to offer rosy projections about the capabilities of future versions of their technology,” Talwalkar said. “We run the risk of myopically focusing a single paradigm — which is why it’s crucial for the broader AI research community to avoid blindly believing the hype and marketing efforts of these companies and instead focus on concrete results.”