Stanford 423-Page AI Report: US-China Gap Only 2.7%, Tsinghua DeepSeek Breaks into Global Top Ten

marsbitPublicado a 2026-04-15Actualizado a 2026-04-15

Resumen

The 2026 AI Index Report from Stanford HAI reveals a rapidly closing gap between the U.S. and China in AI model performance, now at just 2.7%. Chinese models like DeepSeek and Tsinghua have entered the global top ten. Over 90% of cutting-edge AI models now come from industry, not academia. AI capabilities are advancing unprecedentedly—models now outperform humans in tasks like coding (SWE-bench), mathematics (IMO), and multimodal reasoning. However, "jagged frontiers" persist, with models excelling in complex tasks but struggling with basics like reading analog clocks (50.1% accuracy). Global corporate AI investment reached $581.7 billion in 2025, doubling year-over-year, with the U.S. leading. Yet, AI researcher immigration to the U.S. has plummeted 89% since 2017. AI adoption is high globally (58% workplace usage), especially in China (over 80%). Concerns include rising AI-related incidents (362 in 2025) and significant job displacement for young developers (20% decline in employment among 22-25-year-olds). The report highlights a disconnect between rapid AI progress and slower adaptation in regulation, education, and public trust.

Author: Xinzhiyuan

Editors: Haokun, Taozi

[Xinzhiyuan Insight] Stanford's "2026 AI Index Report" is out! This 432-page report is extremely valuable: the US-China AI showdown has nearly leveled, with the gap shrinking to just 2.7%. The world's top AI models, 95 in total, are mostly concentrated in big tech. Most critically, employment for developers aged 22-25 has been cut by 20%.

Today, Stanford HAI重磅 released the "2026 AI Index Report"!

This 423-page annual report comprehensively reveals the latest power dynamics of the global AI industry.

It presents a core conclusion: AI's capabilities are growing rapidly; but humanity's ability to measure and manage it hasn't kept pace.

Among the most shocking conclusions—

The performance gap between US and Chinese AI models has essentially disappeared, with the lead frequently changing hands in this peak showdown; currently, Anthropic's leading advantage is only 2.7%.

The US invests more money in AI than anyone else, but it's increasingly struggling to attract top talent.

The report also points out that AI evolution has not hit a so-called "bottleneck"; instead, it's advancing at an unprecedented pace.

Over the past year, over 90% of the world's top models have matched or surpassed human performance on doctoral-level scientific questions, multimodal reasoning, and competition mathematics.

Especially in coding ability, SWE-bench scores surged from 60% to nearly 100% in one year.

However, AI's "uneven proficiency" is extremely severe, presenting a distorted reality:

LLMs can win IMO gold medals but can't read analog clocks correctly, with an accuracy rate of only 50.1%.

Meanwhile, AI taking jobs has moved from prediction to reality, and the first to suffer are today's young "workers".

Here are the highlights: the 12 most hardcore trends from the "2026 AI Index Report".

Other quick highlights:

  • Global AI computing power increased 30-fold in 3 years, NVIDIA独占 60%, almost all chips come from one company, TSMC

  • Global corporate AI investment in 2025 was $581.7 billion, doubling year-over-year, the US alone accounted for nearly half

  • AI researchers entering the US fell 89% over 7 years, dropping 80% in the past year alone

  • Employment for software developers aged 22-25 has fallen 20% since 2024, entry-level positions precisely cut

  • China has cumulatively built 85 public AI supercomputers, more than double North America's, ranking first globally

  • AI usage rate in Chinese workplaces exceeds 80%, far surpassing the global average of 58%

  • The strongest models are becoming black boxes, 80 out of 95 representative models did not公开 training code

US-China Face-to-Face Gap Only 2.7% Left

Stanford plotted the US #1 and China #1 from the Arena leaderboard since May 2023 on the same coordinate system.

In May 2023, gpt-4-0314 led with 1320 points, China was still chatglm-6b, a gap of over 300 points.

In February 2025, DeepSeek-R1 briefly tied with the US top model for the first time.

In March 2026, the US's Claude Opus 4.6 scored 1503 points, China's dola-seed-2.0-preview scored 1464 points.

The gap between US and Chinese AI is now only 39 points. Converted to a percentage, 2.7%.

More noteworthy is the frequency of lead changes over the past year. Since early 2025, the top models of the two countries have swapped positions on the Arena several times.

The numbers are also close to fifty-fifty.

In 2025, the US released 50 "significant models", China closely followed with 30 top-tier large models.

In the first tier, OpenAI, Google, Alibaba, Anthropic, xAI stand together, a fifty-fifty split of the global TOP 5.

Looking further down to TOP 10, Chinese institutions and companies occupy four spots: Alibaba, DeepSeek, Tsinghua, ByteDance.

The重心 of the open-source ecosystem has also明显东移 this year.

DeepSeek, Qwen, GLM, MiniMax, Kimi have been pushing the capability curve of open-source weights forward.

Add in论文发表量, citation counts, patent output, industrial robot installations, China ranks first globally in all.

<极速发展的AI:能力飞升,其他一切都在脱节" alt="">

Pricing is another battlefront.

Overseas developers calculated on X that the output price of Seed 2.0 Pro is about one-tenth that of Claude Opus 4.6.

Performance is face-to-face, price is one-tenth. The ripple effects of this are just beginning.

90% of Frontier Models Come from Industry, Deification Speed Unprecedented

Of the 95 most representative models released last year, over ninety percent came from industry, not academic institutions or government labs.

The release speed is also变态 accelerating.

In February 2026 alone, eight or nine flagship models entered the arena同月: Gemini 3.1 Pro, Claude Opus 4.6, GPT-5.3 Codex, Grok 4.20, Qwen 3.5, Seed 2.0 Pro, MiniMax M2.5, GLM-5.

The deification cycle has changed from "years" to "months".

The most猛 curve is programming.

SWE-bench Verified, a benchmark for real bug fixing, went from 60% to nearly 100% in one year.

Not a few points increase, but basically capped.

Terminal-Bench tests Agent's ability to handle real terminal tasks, rising from 20% last year to 77.3%.

The success rate of cybersecurity Agents solving problems increased from 15% to 93%.

Gemini Deep Think won a gold medal at the International Mathematical Olympiad.

PhD-level scientific问答(GPQA Diamond), competition mathematics (AIME), multimodal reasoning (MMMU)—these were once considered "insurmountable by humans"—have all been conquered by frontier models.

最能说明问题的是Humanity's Last Exam.

This is a test specifically designed to "stump AI, favor human experts", with questions provided by top experts in various fields.

Last year OpenAI's o1 scored 8.8%; frontier models pushed the score up another 30 percentage points in a year; currently Claude Opus 4.6 and Gemini 3.1 Pro have both passed 50%.

Jagged Frontier: Can Win IMO Gold But Can't Read a Clock

But the same index presents another set of numbers.

The strongest model's accuracy rate on the task of "reading an analog clock" is 50.1%.

The success rate of robots operating in lab simulation environments (RLBench) has reached 89.4%. But when moved to real household scenarios to complete chores like washing dishes or folding clothes, the success rate immediately drops to 12%.

Between the lab and the kitchen, there's a gap of 77 percentage points.

Researchers have named this phenomenon the "jagged frontier". The distribution of AI capabilities is uneven; it can win a math olympiad gold medal but can't reliably tell you what time it is.

AI can win math olympiad gold medals, but only has a fifty percent chance of reading an analog clock. AI is accelerating, but not in the same direction.

Also, in agent tasks, in the OSWorld test, frontier AI strength (66.3%) is approaching the human baseline.

However, in the PaperArena test专门评估科研逻辑, the strongest AI-powered Agent scored only 39%, half the capability of a PhD student.

But this unevenness doesn't stop companies from integrating AI into production lines.

Another number from the AI Index is that the global enterprise AI adoption rate has reached 88%. Ninety percent of companies have integrated AI into some workflow.

The cost is rising simultaneously. Recorded AI-related incidents increased from 233 in 2024 to 362.

Money is Accelerating: $581.7 Billion Poured into AI

Global corporate AI investment in 2025 reached $581.7 billion, a year-on-year increase of 130%.其中, private investment was $344.7 billion, up 127.5% year-on-year.

Both curves almost doubled.

By country, the US is in a league of its own. US private AI investment in 2025 was $285.9 billion. And it added 1,953 AI startups in one year, also more than 10 times the number of the second-ranked country.

Money is accelerating into the US. But another core US resource is moving in the opposite direction.

People are Flowing Out: AI Researchers Entering the US Fell 89%

There's a set of numbers that makes one pause.

From 2017 to now, the number of AI researchers and developers entering the US has fallen by 89%.

More critically, this decline is accelerating. In the past year alone, the drop was 80%.

The US still has the highest density of AI researchers globally, but the inflow tap is tightening.

The curves of money and people are starting to反向. This is a situation not seen in the past decade.

Computing Power Rose 30-Fold in 3 Years, Lifelines in One Company's Hands

The AI capability curve is accelerating, but the computing power curve behind it is running even faster.

From 2021 to now, global AI computing power has increased 30-fold. Over the past three years, it has tripled every year.

This curve is supported by a few companies.

NVIDIA's GPUs alone account for over 60% of the world's AI computing power. Amazon and Google rank second and third with their own chips, but combined they are far behind NVIDIA.

And almost all these chips come from one foundry, TSMC. The steeper the computing power curve, the narrower the lifeline.

Meanwhile, the cost is also increasing.

The total power of global AI data centers has reached 29.6 GW, equivalent to New York State's entire peak electricity demand. The estimated carbon emission for one training run of xAI Grok 4 is 72,816 tons of CO2 equivalent, equal to the tailpipe emissions of 17,000 cars driving for a year.

Where data centers are built, where electricity comes from, where chips are produced—these three questions have become the most headache-inducing issues on every AI company CEO's desk this year.

Generative AI Penetrated 53% in Three Years, Chinese Workplace Usage Exceeds 80%

Generative AI reached a global population penetration rate of 53% within three years.

This speed is faster than personal computers, faster than the internet.

But penetration speed is highly correlated with country. Singapore 61%, UAE 54%, both ahead of the US. The US ranks only 24th among the surveyed countries, with a penetration rate of 28.3%.

If we change the dimension from consumers to the workplace, the contrast is greater.

Another set of data in the report shows that in 2025, 58% of employees globally had already started using AI regularly at work. But in five countries—China, India, Nigeria, UAE, Saudi Arabia—this proportion exceeded 80%.

China's workplace AI penetration rate is already more than 20 percentage points higher than the global average.

Even more interesting is consumer value.

AI Index estimates that by early 2026, generative AI tools create $172 billion in value annually for US consumers. From 2025 to 2026, the median value per user tripled.

The vast majority of users are still using the free version.

Entry-Level Positions Sharply Reduced, 22-25 Year-Old Dev Jobs Slashed 20%

The part of the entire AI Index that might be most沉默 for Chinese readers is probably the section on youth employment.

The number of employed software developers aged 22 to 25 has fallen by about 20% since 2024.

During the same period, older peer groups actually grew.

Not just development roles. Other high-AI-exposure industries like customer service are also showing the same pattern.

More worrying are the results of corporate surveys. Respondent executives generally expect future layoffs to be larger than in the past few months.

This isn't about the macro unemployment rate; it's about entry-level positions being precisely cut off.

If the first job is gone, the entire career ladder loses a rung. The long-term impact of this is something no one can calculate yet.

AI is Rewriting the Way Science is Done

If the employment section is cold, the science section is hot.

AI-related papers in natural sciences, physical sciences, and life sciences grew by 26% to 28% year-on-year in 2025.

Specifically in application, this year for the first time an AI completely ran an end-to-end weather forecasting process. From raw meteorological observation data directly outputting final forecasts for temperature, wind speed, humidity, with no traditional numerical models介入.

AI is moving from "helping you write papers" "helping you calculate numbers" to "making discoveries itself".

It's the same in hospitals. In 2025, many hospitals began deploying AI tools that can automatically generate clinical records from consultation dialogues. Doctors in multiple hospital systems reported that time spent writing medical records was reduced by up to 83%, with significant decreases in burnout.

But the same index pours cold water on medical AI. A review of over 500 clinical AI studies found that nearly half relied on exam-style datasets, and only 5% used real clinical data.

AI can reduce doctors' typing time, that's certain. AI's clinical value on real patients currently has many question marks.

Self-Learning Wave Explodes Globally, Formal Education Has Fallen Behind

Formal education can't keep up with AI.

4/5 of US high school and college students now use AI to complete school assignments. But only half of secondary schools have AI usage policies, and only 6% of teachers think these policies are clear.

Students are running ahead, teachers are still in place, rules haven't appeared yet.

While formal education falls behind, the self-learning wave is exploding globally. It says the three countries with the fastest growth in learning AI engineering skills are the UAE, Chile, and South Africa.

Not the US, not Europe.

The steepest part of the skill curve is growing in places no one is looking.

Strongest Models Become the Most Opaque, Experts and Public are分裂

The strongest models are becoming the most opaque models.

The Foundation Model Transparency Index's average score fell from 58 last year to 40 this year. The AI Index directly点名, Google, Anthropic, OpenAI have all stopped公开 the training data scale and training duration of their latest models.

Of the 95 most representative models released last year, 80 did not公开 training code.

Public sentiment has also become more complex.

Globally, the proportion believing AI's benefits outweigh the risks rose from 52% to 59%. But during the same period, the proportion feeling nervous about AI rose from 50% to 52%.

Both directions are growing simultaneously.

The most分裂 is the US. Only 33% of Americans think AI will make their jobs better, the global average is 40%. Americans' trust in their own government to regulate AI is the lowest among surveyed countries, 31%.

Singaporeans' trust in their government to regulate AI is 81%.

After the recent incident at Sam Altman's house was袭击, Silicon Valley insiders were "surprised to find" that ordinary people in the Instagram comments were not sympathetic, some even felt "it should be more intense".

They didn't realize things had gotten this bad.

The Pew and Ipsos data cited in the report show that the perception gap between experts and the public on the impact of AI on employment, healthcare, economy, etc.,普遍 exceeds 30 percentage points, with the largest gap reaching 50 percentage points.

On one side, the curves in the lab are soaring; on the other, ordinary people's unease is accumulating.

There is no bridge in between.

In Conclusion

The 423-page report has hundreds of charts, but it really only draws one picture.

The horizontal axis is time, the vertical axis is capability.

The model capability curve is flying, the computing power curve is flying, the investment curve is flying, the adoption rate curve is flying. Everything else is stagnating or moving downward.

This is the entire content of the 2026 AI Index.

AI is accelerating. Everything else is decoupling.

If you are in this industry, the question to ask now is not "what will the future be like", but "which curve are you standing on".

Preguntas relacionadas

QWhat is the performance gap between the top AI models of the US and China according to the Stanford AI Index Report 2026?

AThe performance gap between the top AI models of the US and China has narrowed to just 2.7%.

QWhich Chinese institutions or companies are ranked in the global top 10 for AI models?

AAlibaba, DeepSeek, Tsinghua University, and ByteDance are the Chinese institutions and companies ranked in the global top 10.

QWhat percentage of the world's top AI models in the past year came from industry rather than academia?

AOver 90% of the world's top AI models in the past year came from industry, not academia or government labs.

QWhat significant negative impact on employment is highlighted in the report, particularly for a specific age group?

AEmployment for software developers aged 22-25 has decreased by approximately 20% since 2024, as entry-level positions are being disproportionately affected.

QWhat is the term used in the report to describe the uneven and inconsistent development of AI capabilities?

AThe term used to describe the uneven development of AI capabilities is 'jagged frontier' (锯齿前沿).

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Agent S: El Futuro de la Interacción Autónoma en Web3 Introducción En el paisaje en constante evolución de Web3 y las criptomonedas, las innovaciones están redefiniendo constantemente cómo los individuos interactúan con las plataformas digitales. Uno de estos proyectos pioneros, Agent S, promete revolucionar la interacción humano-computadora a través de su marco agente abierto. Al allanar el camino para interacciones autónomas, Agent S busca simplificar tareas complejas, ofreciendo aplicaciones transformadoras en inteligencia artificial (IA). Esta exploración detallada profundizará en las complejidades del proyecto, sus características únicas y las implicaciones para el dominio de las criptomonedas. ¿Qué es Agent S? Agent S se presenta como un marco agente abierto innovador, diseñado específicamente para abordar tres desafíos fundamentales en la automatización de tareas informáticas: Adquisición de Conocimiento Específico del Dominio: El marco aprende inteligentemente de diversas fuentes de conocimiento externas y experiencias internas. Este enfoque dual le permite construir un rico repositorio de conocimiento específico del dominio, mejorando su rendimiento en la ejecución de tareas. Planificación a Largo Plazo de Tareas: Agent S emplea planificación jerárquica aumentada por la experiencia, un enfoque estratégico que facilita la descomposición y ejecución eficiente de tareas complejas. Esta característica mejora significativamente su capacidad para gestionar múltiples subtareas de manera eficiente y efectiva. Manejo de Interfaces Dinámicas y No Uniformes: El proyecto introduce la Interfaz Agente-Computadora (ACI), una solución innovadora que mejora la interacción entre agentes y usuarios. Utilizando Modelos de Lenguaje Multimodal de Gran Escala (MLLMs), Agent S puede navegar y manipular diversas interfaces gráficas de usuario sin problemas. A través de estas características pioneras, Agent S proporciona un marco robusto que aborda las complejidades involucradas en la automatización de la interacción humana con las máquinas, preparando el terreno para una multitud de aplicaciones en IA y más allá. ¿Quién es el Creador de Agent S? Si bien el concepto de Agent S es fundamentalmente innovador, la información específica sobre su creador sigue siendo elusiva. El creador es actualmente desconocido, lo que resalta ya sea la etapa incipiente del proyecto o la elección estratégica de mantener a los miembros fundadores en el anonimato. Independientemente de la anonimidad, el enfoque sigue siendo en las capacidades y el potencial del marco. ¿Quiénes son los Inversores de Agent S? Dado que Agent S es relativamente nuevo en el ecosistema criptográfico, la información detallada sobre sus inversores y patrocinadores financieros no está documentada explícitamente. La falta de información disponible públicamente sobre las bases de inversión u organizaciones que apoyan el proyecto plantea preguntas sobre su estructura de financiamiento y hoja de ruta de desarrollo. Comprender el respaldo es crucial para evaluar la sostenibilidad del proyecto y su posible impacto en el mercado. ¿Cómo Funciona Agent S? En el núcleo de Agent S se encuentra una tecnología de vanguardia que le permite funcionar de manera efectiva en diversos entornos. Su modelo operativo se basa en varias características clave: Interacción Humano-Computadora Similar a la Humana: El marco ofrece planificación avanzada de IA, esforzándose por hacer que las interacciones con las computadoras sean más intuitivas. Al imitar el comportamiento humano en la ejecución de tareas, promete elevar las experiencias de los usuarios. Memoria Narrativa: Empleada para aprovechar experiencias de alto nivel, Agent S utiliza memoria narrativa para hacer un seguimiento de las historias de tareas, mejorando así sus procesos de toma de decisiones. Memoria Episódica: Esta característica proporciona a los usuarios una guía paso a paso, permitiendo que el marco ofrezca apoyo contextual a medida que se desarrollan las tareas. Soporte para OpenACI: Con la capacidad de ejecutarse localmente, Agent S permite a los usuarios mantener el control sobre sus interacciones y flujos de trabajo, alineándose con la ética descentralizada de Web3. Fácil Integración con APIs Externas: Su versatilidad y compatibilidad con varias plataformas de IA aseguran que Agent S pueda encajar sin problemas en ecosistemas tecnológicos existentes, convirtiéndolo en una opción atractiva para desarrolladores y organizaciones. Estas funcionalidades contribuyen colectivamente a la posición única de Agent S dentro del espacio cripto, ya que automatiza tareas complejas y de múltiples pasos con una intervención humana mínima. A medida que el proyecto evoluciona, sus posibles aplicaciones en Web3 podrían redefinir cómo se desarrollan las interacciones digitales. Cronología de Agent S El desarrollo y los hitos de Agent S pueden encapsularse en una cronología que resalta sus eventos significativos: 27 de septiembre de 2024: El concepto de Agent S fue lanzado en un documento de investigación integral titulado “Un Marco Agente Abierto que Usa Computadoras Como un Humano”, mostrando las bases del proyecto. 10 de octubre de 2024: El documento de investigación fue puesto a disposición del público en arXiv, ofreciendo una exploración profunda del marco y su evaluación de rendimiento basada en el benchmark OSWorld. 12 de octubre de 2024: Se lanzó una presentación en video, proporcionando una visión visual de las capacidades y características de Agent S, involucrando aún más a posibles usuarios e inversores. Estos marcadores en la cronología no solo ilustran el progreso de Agent S, sino que también indican su compromiso con la transparencia y la participación comunitaria. Puntos Clave Sobre Agent S A medida que el marco Agent S continúa evolucionando, varios atributos clave destacan, subrayando su naturaleza innovadora y potencial: Marco Innovador: Diseñado para proporcionar un uso intuitivo de las computadoras similar a la interacción humana, Agent S aporta un enfoque novedoso a la automatización de tareas. Interacción Autónoma: La capacidad de interactuar de manera autónoma con las computadoras a través de GUI significa un salto hacia soluciones informáticas más inteligentes y eficientes. Automatización de Tareas Complejas: Con su metodología robusta, puede automatizar tareas complejas y de múltiples pasos, haciendo que los procesos sean más rápidos y menos propensos a errores. Mejora Continua: Los mecanismos de aprendizaje permiten a Agent S mejorar a partir de experiencias pasadas, mejorando continuamente su rendimiento y eficacia. Versatilidad: Su adaptabilidad en diferentes entornos operativos como OSWorld y WindowsAgentArena asegura que pueda servir a una amplia gama de aplicaciones. A medida que Agent S se posiciona en el paisaje de Web3 y criptomonedas, su potencial para mejorar las capacidades de interacción y automatizar procesos significa un avance significativo en las tecnologías de IA. A través de su marco innovador, Agent S ejemplifica el futuro de las interacciones digitales, prometiendo una experiencia más fluida y eficiente para los usuarios en diversas industrias. Conclusión Agent S representa un audaz avance en la unión de la IA y Web3, con la capacidad de redefinir cómo interactuamos con la tecnología. Aunque aún se encuentra en sus primeras etapas, las posibilidades para su aplicación son vastas y atractivas. A través de su marco integral que aborda desafíos críticos, Agent S busca llevar las interacciones autónomas al primer plano de la experiencia digital. A medida que nos adentramos más en los reinos de las criptomonedas y la descentralización, proyectos como Agent S sin duda desempeñarán un papel crucial en la configuración del futuro de la tecnología y la colaboración humano-computadora.

429 Vistas totalesPublicado en 2025.01.14Actualizado en 2025.01.14

Qué es AGENT S

Cómo comprar S

¡Bienvenido a HTX.com! Hemos hecho que comprar Sonic (S) sea simple y conveniente. Sigue nuestra guía paso a paso para iniciar tu viaje de criptos.Paso 1: crea tu cuenta HTXUtiliza tu correo electrónico o número de teléfono para registrarte y obtener una cuenta gratuita en HTX. Experimenta un proceso de registro sin complicaciones y desbloquea todas las funciones.Obtener mi cuentaPaso 2: ve a Comprar cripto y elige tu método de pagoTarjeta de crédito/débito: usa tu Visa o Mastercard para comprar Sonic (S) al instante.Saldo: utiliza fondos del saldo de tu cuenta HTX para tradear sin problemas.Terceros: hemos agregado métodos de pago populares como Google Pay y Apple Pay para mejorar la comodidad.P2P: tradear directamente con otros usuarios en HTX.Over-the-Counter (OTC): ofrecemos servicios personalizados y tipos de cambio competitivos para los traders.Paso 3: guarda tu Sonic (S)Después de comprar tu Sonic (S), guárdalo en tu cuenta HTX. Alternativamente, puedes enviarlo a otro lugar mediante transferencia blockchain o utilizarlo para tradear otras criptomonedas.Paso 4: tradear Sonic (S)Tradear fácilmente con Sonic (S) en HTX's mercado spot. Simplemente accede a tu cuenta, selecciona tu par de trading, ejecuta tus trades y monitorea en tiempo real. Ofrecemos una experiencia fácil de usar tanto para principiantes como para traders experimentados.

847 Vistas totalesPublicado en 2025.01.15Actualizado en 2025.03.21

Cómo comprar S

Discusiones

Bienvenido a la comunidad de HTX. Aquí puedes mantenerte informado sobre los últimos desarrollos de la plataforma y acceder a análisis profesionales del mercado. A continuación se presentan las opiniones de los usuarios sobre el precio de S (S).

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