15 Graphs That Explain the State of AI in 2024

The AI Index tracks the generative AI boom, model costs, and responsible AI use

6 min read

AI spelled on graph paper
IEEE Spectrum

Each year, the AI Index lands on virtual desks with a louder virtual thud—this year, its 393 pages are a testament to the fact that AI is coming off a really big year in 2023. For the past three years, IEEE Spectrum has read the whole damn thing and pulled out a selection of charts that sum up the current state of AI (see our coverage from 2021, 2022, and 2023).

This year’s report, published by the Stanford Institute for Human-Centered Artificial Intelligence (HAI), has an expanded chapter on responsible AI and new chapters on AI in science and medicine, as well as its usual roundups of R&D, technical performance, the economy, education, policy and governance, diversity, and public opinion. This year is also the first time that Spectrum has figured into the report, with a citation of an article published here about generative AI’s visual plagiarism problem.

1. Generative AI investment skyrockets

While corporate investment was down overall last year, investment in generative AI went through the roof. Nestor Maslej, editor-in-chief of this year’s report, tells Spectrum that the boom is indicative of a broader trend in 2023, as the world grappled with the new capabilities and risks of generative AI systems like ChatGPT and the image-generating DALL-E 2. “The story in the last year has been about people responding [to generative AI],” says Maslej, “whether it’s in policy, whether it’s in public opinion, or whether it’s in industry with a lot more investment.” Another chart in the report shows that most of that private investment in generative AI is happening in the United States.

2. Google is dominating the foundation model race

Foundation models are big multipurpose models—for example, OpenAI’s GPT-3 and GPT-4 are the foundation model that enable ChatGPT users to write code or Shakespearean sonnets. Since training these models typically requires vast resources, Industry now makes most of them, with academia only putting out a few. Companies release foundation models both to push the state-of-the-art forward and to give developers a foundation on which to build products and services. Google released the most in 2023.

3. Closed models outperform open ones

One of the hot debates in AI right now is whether foundation models should be open or closed, with some arguing passionately that open models are dangerous and others maintaining that open models drive innovation. The AI Index doesn’t wade into that debate, but instead looks at trends such as how many open and closed models have been released (another chart, not included here, shows that of the 149 foundation models released in 2023, 98 were open, 23 gave partial access through an API, and 28 were closed).

The chart above reveals another aspect: Closed models outperform open ones on a host of commonly used benchmarks. Maslej says the debate about open versus closed “usually centers around risk concerns, but there’s less discussion about whether there are meaningful performance trade-offs.”

4. Foundation models have gotten super expensive

Here’s why industry is dominating the foundation model scene: Training a big one takes very deep pockets. But exactly how deep? AI companies rarely reveal the expenses involved in training their models, but the AI Index went beyond the typical speculation by collaborating with the AI research organization Epoch AI. To come up with their cost estimates, the report explains, the Epoch team “analyzed training duration, as well as the type, quantity, and utilization rate of the training hardware” using information gleaned from publications, press releases, and technical reports.

It’s interesting to note that Google’s 2017 transformer model, which introduced the architecture that underpins almost all of today’s large language models, was trained for only US $930.

5. And they have a hefty carbon footprint

The AI Index team also estimated the carbon footprint of certain large language models. The report notes that the variance between models is due to factors including model size, data center energy efficiency, and the carbon intensity of energy grids. Another chart in the report (not included here) shows a first guess at emissions related to inference—when a model is doing the work it was trained for—and calls for more disclosures on this topic. As the report notes: “While the per-query emissions of inference may be relatively low, the total impact can surpass that of training when models are queried thousands, if not millions, of times daily.”

6. The United States leads in foundation models

While Maslej says the report isn’t trying to “declare a winner to this race,” he does note that the United States is leading in several categories, including number of foundation models released (above) and number of AI systems deemed significant technical advances. However, he notes that China leads in other categories including AI patents granted and installation of industrial robots.

7. Industry calls new PhDs

This one is hardly a surprise, given the previously discussed data about industry getting lots of investment for generative AI and releasing lots of exciting models. In 2022 (the most recent year for which the Index has data), 70 precent of new AI PhDs in North America took jobs in industry. It’s a continuation of a trend that’s been playing out over the last few years.

8. Some progress on diversity

For years, there’s been little progress on making AI less white and less male. But this year’s report offers a few hopeful signs. For example, the number of non-white and female students taking the AP computer science exam is on the rise. The graph above shows the trends for ethnicity, while another graph, not included here, shows that 30 percent of the students taking the exam are now girls.

Another graph in the report shows that at the undergraduate level, there’s also a positive trend in increasing ethnic diversity among North American students earning bachelor degrees in computer science, although the number of women earning CS bachelor degrees has barely budged over the last five years. Says Maslej, “it’s important to know that there’s still a lot of work to be done here.”

9. Chatter in earnings calls

Businesses are awake to the possibilities of AI. The Index got data about Fortune 500 companies’ earnings calls from Quid, a market intelligence firm that used natural language processing tools to scan for all mentions of “artificial intelligence,” “AI,” “machine learning,” “ML,” and “deep learning.” Nearly 80 percent of the companies included discussion of AI in their calls. “I think there’s a fear in business leaders that if they don’t use this technology, they’re going to miss out,” Maslej says.

And while some of that chatter is likely just CEOs bandying about buzzwords, another graph in the report shows that 55 percent of companies included in a McKinsey survey have implemented AI in at least one business unit.

10. Costs go down, revenues go up

And here’s why AI isn’t just a corporate buzzword: The same McKinsey survey showed that the integration of AI has caused companies’ costs to go down and their revenues go up. Overall, 42 percent of respondents said they’d seen reduced costs, and 59 percent claimed increased revenue.

Other charts in the report suggest that this impact on the bottom line reflects efficiency gains and better worker productivity. In 2023, a number of studies in different fields showed that AI enabled workers to complete tasks more quickly and produce better quality work. One study looked at coders using Copilot, while others looked at consultants, call center agents, and law students. “These studies also show that although every worker benefits, AI helps lower-skilled workers more than it does high-skilled workers,” says Maslej.

11. Corporations do perceive risks

This year, the AI Index team ran a global survey of 1,000 corporations with revenues of at least $500 million to understand how businesses are thinking about responsible AI. The results showed that privacy and data governance is perceived as the greatest risk across the globe, while fairness (often discussed in terms of algorithmic bias) still hasn’t registered with most companies. Another chart in the report shows that companies are taking action on their perceived risks: The majority of organizations across regions have implemented at least one responsible AI measure in response to relevant risks.

12. AI can’t beat humans at everything... yet

In recent years, AI systems have outperformed humans on a range of tasks, including reading comprehension and visual reasoning, and Maslej notes that the pace of AI performance improvement has also picked up. “A decade ago, with a benchmark like ImageNet, you could rely on that to challenge AI researchers for for five or six years,” he says. “Now, a new benchmark is introduced for competition-level mathematics and the AI starts at 30 percent, and then in a year it gets to 90 percent.” While there are still complex cognitive tasks where humans outperform AI systems, let’s check in next year to see how that’s going.

13. Developing norms of AI responsibility

When an AI company is preparing to release a big model, it’s standard practice to test it against popular benchmarks in the field, thus giving the AI community a sense of how models stack up against each other in terms of technical performance. However, it has been less common to test models against responsible AI benchmarks that assess such things as toxic language output (RealToxicityPrompts and ToxiGen), harmful bias in responses (BOLD and BBQ), and a model’s degree of truthfulness (TruthfulQA). That’s starting to change, as there’s a growing sense that checking one’s model against theses benchmarks is, well, the responsible thing to do. However, another chart in the report shows that consistency is lacking: Developers are testing their models against different benchmarks, making comparisons harder.

14. Laws both boost and constrain AI

Between 2016 and 2023, the AI Index found that 33 countries had passed at least one law related to AI, with most of the action occurring in the United States and Europe; in total, 148 AI-related bills have been passed in that timeframe. The Index researchers also classified bills as either expansive laws that aim to enhance a country’s AI capabilities or restrictive laws that place limits on AI applications and usage. While many bills continue to boost AI, the researchers found a global trend toward restrictive legislation.

15. AI makes people nervous

The Index’s public opinion data comes from a global survey on attitudes toward AI, with responses from 22,816 adults (ages 16 to 74) in 31 countries. More than half of respondents said that AI makes them nervous, up from 39 percent the year before. And two-thirds of people now expect AI to profoundly change their daily lives in the next few years.

Maslej notes that other charts in the index show significant differences in opinion among different demographics, with young people being more inclined toward an optimistic view of how AI will change their lives. Interestingly, “a lot of this kind of AI pessimism comes from Western, well-developed nations,” he says, while respondents in places like Indonesia and Thailand said they expect AI’s benefits to outweigh its harms.

The Conversation (3)
Vaibhav Sunder
Vaibhav Sunder01 May, 2024

I have to put my cents on this surely - While I agree Gemini works best, how many of us understand the pivotal role of Android in pushing Chinese goods? And how many non-US people agree that Android ecosystem is best for AI? ARM and Skywater are an equal requirement.

Secondly, the last graph - it is all numbers to the AI! this fact is little known, and little spread out. The regulation bill in US due to patent laws. I wrote about 13 books self-published; the AI could illustrate marvelously before the bill (Leonardo Stable Diffusion) now it cannot.

Mathew Lodge
Mathew Lodge17 Apr, 2024

Chart 12 is particularly ridiculous: the largest models are trained on the right answers to the AI Benchmarks. So it's hardly surprising that they are getting better at taking those tests -- because they have memorized the answers.But it gets worse, because Chart 12 also falsely equates non-LLM and LLM accuracy measures. Non-LLM model accuracy (e.g. ImageNet) can only be measured using inputs the model has not seen before, because its accuracy will always be perfect for things in the training set.

Anjan Saha
Anjan Saha16 Apr, 2024

The Commercial Status of AI is explained very well by 15 Charts in this Article. However we miss many things

Like Technology and R&D Projects going on regarding development of AI which need to be explained. Of Course Technological matters

barred to be discussed in free publication and remains confidential by Software companies and marketing agencies because of financial

aspects. Other things like Quality, safety and comparative studies of AI technology available in the market need Qualitative studies. We need graphical studies of AI status developed by Microsoft, Google, IBM for brevity. Both

Qualitative & Quantitative studies is better