January has been notable for the variety of essential bulletins in AI. For me, two stand out: the US authorities’s help for the Stargate Mission, an enormous information heart costing $500 billion, with investments coming from Oracle, Softbank, and OpenAI; and DeepSeek’s launch of its R1 reasoning mannequin, skilled at an estimated value of roughly $5 million—a big quantity however a fraction of what it value OpenAI to coach its o1 fashions.
US tradition has lengthy assumed that larger is healthier, and that costlier is healthier. That’s definitely a part of what’s behind the costliest information heart ever conceived. However we now have to ask a really completely different query. If DeepSeek was certainly skilled for roughly a tenth of what it value to coach o1, and if inference (producing solutions) on DeepSeek prices roughly one-thirtieth what it prices on o1 ($2.19 per million output tokens versus $60 per million output tokens), is the US know-how sector headed in the suitable route?
Be taught sooner. Dig deeper. See farther.
It clearly isn’t. Our “larger is healthier” mentality is failing us.
I’ve lengthy believed that the important thing to AI’s success can be minimizing the price of coaching and inference. I don’t imagine there’s actually a race between the US and Chinese language AI communities. But when we settle for that metaphor, the US—and OpenAI specifically—is clearly behind. And a half-trillion-dollar information heart is a part of the issue, not the answer. Higher engineering beats “supersize it.” Technologists within the US must study that lesson.
