Researchers report that neural networks are nearly matching human performance on computer tasks.

As of March, artificial intelligence successfully completed 66% of computer tasks, compared to 72% for humans, according to Stanford University's annual AI Index report.

In 2024, neural networks could only perform 12% of tasks in the digital environment.

In a key programming test for AI, the SWE-bench Verified, performance surged from 60% to nearly 100% in just one year. Significant progress is also noted in other key technical benchmarks.

Researchers noted that large language models could win a gold medal at the International Mathematical Olympiad, yet they struggle with accurately telling time.

The top model, Gemini Deep Think, correctly reads analog clock faces only 50.1% of the time. This issue is referred to as the "jagged boundary" by scientists.

Meanwhile, the performance gap between AI models from the U.S. and China has virtually disappeared. Since early 2025, American and Chinese solutions have frequently swapped places in leadership.

Technology Adoption and Investment

The adoption of large language model-based solutions in organizations has reached 88%. At the same time, four out of five university students use chatbots.

The average adoption rate of generative AI among the general population has reached 53% over three years—faster than personal computers or the internet. However, the pace varies by country and strongly correlates with GDP per capita.

"The adoption of artificial intelligence is spreading at an unprecedented rate, and consumers are reaping significant benefits from tools that are often available for free," Stanford concluded.

In 2025, global corporate investments in the industry reached $581.7 billion—more than double the previous year's figure. The U.S. accounted for $285.9 billion, which is 23 times greater than China's private investment volume.

The United States hosts the largest number of AI data centers, with a significant share of chips produced by a single Taiwanese factory, experts noted.

Pressing Issues

According to the study's authors, all current systems for measuring, managing, and implementing AI lag significantly behind the technology itself. Industry safety standards are outdated, and the number of incidents has sharply increased.

"Almost all leading developers of advanced models report performance results, yet reports on responsible AI metrics remain incomplete. The number of documented AI-related incidents rose to 362 from 233 in 2024," the authors noted.

The situation is exacerbated by recent studies showing that improving one aspect of responsible AI, such as safety, can negatively impact another, like accuracy.

Stanford also pointed out another issue. Over 80% of American high school and college students now use AI for academic assignments. However, only half of secondary and higher education institutions have rules regarding the technology, and just 6% of teachers find them clear.

On the other hand, the number of new PhDs in AI in the U.S. and Canada increased by 22% from 2022 to 2024. Many specialists have taken positions in academia rather than the commercial sector.

In March, a Stanford study identified risks associated with excessive reliance on AI for advice.