The State of AI 2025: 12 Eye-Opening Graphs

If you read the news about AI, you may feel bombarded with conflicting messages: AI is booming. AI is a bubble. AI’s current techniques and architectures will keep producing breakthroughs. AI is on an unsustainable path and needs radical new ideas. AI is going to take your job. AI is mostly good for turning your family photos into Studio Ghibli-style animated images.

Cutting through the confusion is the 2025 AI Index from Stanford University’s Institute for Human-Centered Artificial Intelligence. The 400+ page report is stuffed with graphs and data on the topics of R&D, technical performance, responsible AI, economic impacts, science and medicine, policy, education, and public opinion. As IEEE Spectrum does every year (see our coverage from 2021, 2022, 2023, and 2024), we’ve read the whole thing and plucked out the graphs that we think tell the real story of AI right now.

1. U.S. Companies Are Out Ahead

While there are many different ways to measure which country is “ahead” in the AI race (journal articles published or cited, patents awarded, etc.), one straightforward metric is who’s putting out models that matter. The research institute Epoch AI has a database of influential and important AI models that extends from 1950 to the present, from which the AI Index drew the information shown in this chart.

Last year, 40 notable models came from the United States, while China had 15 and Europe had 3 (incidentally, all from France). Another chart, not shown here, indicates that almost all of those 2024 models came from industry rather than academia or government. As for the decline in notable models released from 2023 to 2024, the index suggests it may be due to the increasing complexity of the technology and the ever-rising costs of training.

2. Speaking of Training Costs…

Yowee, but it’s expensive! The AI Index doesn’t have precise data, because many leading AI companies have stopped releasing information about their training runs. But the researchers partnered with Epoch AI to estimate the costs of at least some models based on details gleaned about training duration, type and quantity of hardware, and the like. The most expensive model for which they were able to estimate the costs was Google’s Gemini 1.0 Ultra, with a breathtaking cost of about US $192 million. The general scale up in training costs coincided with other findings of the report: Models are also continuing to scale up in parameter count, training time, and amount of training data.

Not included in this chart is the Chinese upstart DeepSeek, which rocked financial markets in January with its claim of training a competitive large language model for just $6 million—a claim that some industry experts have disputed. AI Index steering committee co-director Yolanda Gil tells IEEE Spectrum that she finds DeepSeek “very impressive,” and notes that the history of computer science is rife with examples of early inefficient technologies giving way to more…