It is becoming increasingly clear that AI language models are a commodity tool, as the sudden rise of open source offerings like DeepSeek show they can be hacked together on a relatively small budget. A new entrant called S1 is once again reinforcing this idea, as researchers at Stanford and the University of Washington trained the “reasoning” model using less than $50 in cloud compute credits.
S1 is a direct competitor to OpenAI’s o1, which is called a reasoning model because it produces answers to prompts by “thinking” through related questions that might help it check its work. For instance, if the model is asked to determine how much money it might cost to replace all Uber vehicles on the road with Waymo’s fleet, it might break down the question into multiple steps—such as checking how many Ubers are on the road today, and then how much a Waymo vehicle costs to manufacture.
According to TechCrunch, S1 is based on an off-the-shelf language model, which was taught to reason by studying questions and answers from a Google model, Gemini 2.0 Flashing Thinking Experimental. Google’s model shows the thinking process behind each answer it returns, allowing the developers of S1 to give their model a relatively small amount of training data—1,000 carefully curated questions, along with the answers—and teach it to mimic Gemini’s thinking process.
Another interesting detail is how the researchers were able to improve the reasoning performance of S1 using an ingeniously simple method:
The researchers used a nifty trick to get s1 to double-check its work and extend its “thinking” time: They told it to wait. Adding the word “wait” during s1’s reasoning helped the model arrive at slightly more accurate answers, per the paper.
This suggests that, despite worries that AI models are hitting a wall in capabilities, there remains a lot of low-hanging fruit. Some notable improvements to a branch of computer science are coming down to conjuring up the right incantation words.
OpenAI has reportedly cried fowl about the Chinese DeepSeek team training off its model outputs. The irony is not lost on most people. ChatGPT and other major models were trained off data scraped from around the web without permission, an issue still being litigated in the courts as companies like the New York Times seek to protect their work from being used without compensation. Google also technically prohibits competitors like S1 from training on Gemini’s outputs.
Ultimately, the performance of S1 is impressive, but does not suggest that one can train a smaller model from scratch with just $50. The model essentially piggybacked off all the training of Gemini, getting a cheat sheet. A good analogy might be compression in imagery. A distilled version of an AI model might be compared to a JPEG of a photo. Good, but still lossy. And large language models still suffer from a lot of issues with accuracy, especially large-scale general models that search the entire web to produce answers. It seems even leaders at companies like Google skim over text generated by AI without fact-checking it. But a model like S1 could be useful in areas like on-device processing for features like Apple Intelligence.
There has been a lot of debate about what the rise of cheap, open source models might mean for the technology industry writ large. Is OpenAI doomed if its models can easily be copied by anyone? Defenders of the company say that language models were always destined to be commodified. OpenAI, along with Google and others, will succeed building useful applications on top of the models. More than 300 million people use ChatGPT each week, and the product has become synonymous with chatbots and a new form of search. The interface on top of the models, like OpenAI’s Operator that can navigate the web for a user, or a unique data set like xAI’s access to X (formerly Twitter) data, is what will be the ultimate differentiator.
Another thing to consider is that “inference” is expected to remain expensive. Inference is the actual processing of each user query submitted to a model. As AI models become cheaper and more accessible, the thinking goes, AI will infect every facet of our lives, resulting in much greater demand for computing resources, not less. And OpenAI’s $500 billion server farm project will not be a waste. That is so long as all this hype around AI is not just a bubble.
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