Peskov Says Russia Is Among Top Five Leaders in AI Race. What Do Global Rankings Say?

cryptonews.ruОпубліковано о 2026-07-28Востаннє оновлено о 2026-07-28

Анотація

On July 27, 2026, Kremlin spokesman Dmitry Peskov stated that Russia remains among the top five countries in the global AI development race. He acknowledged Russian models still lag behind leading U.S. counterparts but claimed they have reached a "very high level," aiming to close the gap with "superhuman efforts." He highlighted the differing approaches of Russia's GigaChat, built from scratch, and Yandex, which initially used foreign technology. However, this claim is not supported by major international AI rankings. Stanford University's Global AI Vibrancy Tool (2024-25) ranks Russia 28th out of 36 countries. The top five are the U.S., China, India, South Korea, and the UK. The Stanford AI Index Report 2026 does not mention Russia's position, focusing instead on U.S. and Chinese leadership across various metrics like investments and model performance. In benchmarks, GigaChat ranks 25th on the Russian-language LM Arena. While it passed a financial analyst exam in December 2025, its business usage costs are reportedly tens to hundreds of times higher than China's DeepSeek. In related developments, President Putin signed a law on July 26, 2026, establishing a legal framework for sovereign AI models and granting developers access to state data. Previously, Russia joined 28 other nations, including China, to establish the World AI Cooperation Organization (WAICO) in Shanghai. The article notes that rankings vary due to different criteria, such as research, investment, infra...

Russia remains among the top five countries participating in the race for artificial intelligence development — this statement was made on July 27, 2026, by Russian Presidential Press Secretary Dmitry Peskov at the All-Russian Youth Educational Forum "Territory of Meanings" in Solnechnogorsk, near Moscow. "We remain among the five countries participating in the race," Peskov stated.

According to the Kremlin representative, domestic developments cannot yet compete with leading American language models but have already reached a "very high level of development." He added that the plan is to close the gap with "superhuman efforts."

Peskov described two different approaches taken by Russian companies to create their own models: "GigaChat started from scratch. From the very beginning, it began building a model that is now developing. Yandex took a different path: it decided to take the first stone from abroad and then build a sovereign thing on it — internal, its own. They are competing within the country," he noted.

What Independent Rankings Show

International rankings of countries by AI development do not confirm Russia's entry into the top five. According to Stanford University's Global AI Vibrancy Tool (data for 2024–2025, covering 36 countries), Russia ranks only 28th with a score of 10.67. The tool shows the following top five:

  • USA — 78.6 points

  • China — 36.95

  • India — 21.59

  • South Korea — 17.24

  • United Kingdom — 16.64

Data from the Stanford AI Index Report 2026

The Stanford AI Index Report 2026 does not form a single country ranking but compares countries across individual areas. The USA and China remain the main leaders in model development, with the performance gap between them nearly closed by March 2026 — the USA leads its competitor by only 2.7 percent. The United States also leads in the number of notable models, volume of private investment — $285.9 billion in 2025 — and the number of data centers. China ranks first in the volume of publications, citations, patents, and adoption of industrial robots, while South Korea leads in AI patents per capita. Russia's position is not mentioned in this report.

Other Indicators

On the Russian-language version of LM Arena, the best domestic model — GigaChat — ranked only 25th, losing out even to earlier versions of ChatGPT and Gemini.

However, in December 2025, GigaChat passed the professional exam for financial analysts (CFA) with a score above the passing threshold.

As a comparison showed, the cost of using GigaChat Pro in typical business scenarios is tens to hundreds of times higher than the cost of China's DeepSeek.

Law on AI Support and International Cooperation

On July 26, 2026, Russian President Vladimir Putin signed a law introducing the concepts of sovereign and national large foundational artificial intelligence models and granting developers access to state data. The document creates a legal basis for the development, implementation, and application of such systems.

Earlier, on July 16, 2026, in Shanghai, representatives of 29 states, including Russia and China, signed an agreement to establish the World Artificial Intelligence Cooperation Organization (WAICO) with its headquarters in Shanghai.

Why Rankings Differ

Different evaluation systems use different criteria, which explains the discrepancies in country positions:

  • Stanford University's Global AI Vibrancy Tool is based on seven "pillars" — from research and development to infrastructure and public opinion.

  • The Stanford AI Index Report 2026 does not consolidate indicators into a single ranking but compares countries across individual areas — publications, patents, investments, model performance.

  • The Global AI Index by Tortoise Media uses 122 indicators grouped into three main and seven additional "pillars."

  • The Global Index on Responsible AI assesses 135 countries not by technological capabilities but by the responsibility of regulation — inclusivity, ethics, trust, and safety.

Peskov's statement about Russia's place among the top five AI leaders diverges from the data of major international rankings, where the country holds positions closer to the end of the third ten. At the same time, the authorities are cementing the technology's development legislatively and expanding participation in international structures like WAICO.

Domestic developments — GigaChat and Yandex models — continue to evolve within the country, competing with each other, but still lag behind foreign counterparts in independent benchmarks and cost of use.

AI Opinion

From the perspective of machine data analysis, the AI race is measured not only by ranking position but also by physical resource — the computing power behind the models. An industry analysis of the market shows that the conversion of mining data centers into AI infrastructure in Russia is currently impossible due to the technological incompatibility of equipment, lack of domestic demand, and sanctions restrictions on GPU imports. Even the surplus energy capacity accumulated over years of mining does not convert directly into resources for training models.

Legislative support and participation in WAICO create a legal and diplomatic framework but do not directly solve the hardware shortage problem. A question for reflection: Can a model's sovereign legal status compensate for the lack of physical hardware on which that model is trained?

end-content

Пов'язані питання

QAccording to the article, which statement did Kremlin press secretary Dmitry Peskov make about Russia's position in the AI race?

ADmitry Peskov stated on July 27, 2026, that Russia remains among the top five countries participating in the race for artificial intelligence development.

QHow does Russia rank in Stanford University's Global AI Vibrancy Tool, and which countries lead this ranking?

AAccording to Stanford University's Global AI Vibrancy Tool (data for 2024-2025), Russia ranks 28th with a score of 10.67. The top five leaders are: United States (78.6), China (36.95), India (21.59), South Korea (17.24), and the United Kingdom (16.64).

QWhat two approaches to developing AI models did Peskov describe for Russian companies?

APeskov described two different approaches: GigaChat started building its model from scratch, while Yandex initially took a foundational component from abroad and is now building its own sovereign, internal model upon it.

QWhat recent legislative and international cooperation steps regarding AI did Russia take according to the article?

AOn July 26, 2026, President Vladimir Putin signed a law introducing the concepts of sovereign and national large fundamental AI models and granting developers access to state data. Earlier, on July 16, 2026, representatives from 29 states, including Russia and China, signed an agreement in Shanghai to establish the World Artificial Intelligence Cooperation Organization (WAICO).

QAccording to the article, what key technical and resource-related challenge does the machine analysis of data (AI opinion) highlight for Russia's AI development?

AThe analysis highlights that converting cryptocurrency mining data centers into infrastructure for AI in Russia is currently impossible due to technological incompatibility of equipment, lack of domestic demand, and sanctions restrictions on GPU imports. It questions whether a sovereign legal status for a model can compensate for the lack of the physical hardware on which it is trained.

Пов'язані матеріали

Cardano Price Forecast: Can Hoskinson's $1B RealFi Bet Stop ADA's Fall to $0.14?

Cardano Price Forecast: Can Hoskinson's $1B RealFi Bet Halt ADA's Drop to $0.14? Cardano (ADA) gained 1% on July 28, testing a key Fibonacci support level at $0.1618 after breaking below a consolidating triangle. Technical analysis shows a descending trend line from May's peak maintaining resistance, with the 20-day and 50-day EMAs also capping upside moves. A daily close below $0.1618 could see ADA fall towards the June low of $0.1386. During a recent AMA, founder Charles Hoskinson identified RealFi as Cardano's most likely product to achieve $1 billion in Total Value Locked (TVL) within a year, citing the operational independence of the Real5 Foundation and its team's microfinance experience. Other development updates included the beta launch of 'Midnight City,' the public testnet for Ouroboros Leios, and commentary on the unlikely passage of the CLARITY Act in the US. Following a June exploit that drained 16.1 million ADA, SecondFi launched a recovery roadmap featuring a ZK-proof-based fund return portal, allowing users to claim compensation without exposing private keys. Derivatives data shows a 159% surge in trading volume as ADA tested support, accompanied by a drop in open interest. The liquidation ratio was 58:1 in favor of longs, indicating leveraged long positions were being washed out. Retail traders are net short, creating potential for a short squeeze if a price rebound occurs. The bullish scenario targets a rebound to the 20-day EMA at $0.1650 and the 0.382 Fibonacci level at $0.1762. The bearish case envisions a breakdown below $0.1618, leading to a test of the June low at $0.1386.

cryptonews.ru6 хв тому

Cardano Price Forecast: Can Hoskinson's $1B RealFi Bet Stop ADA's Fall to $0.14?

cryptonews.ru6 хв тому

Stablecoin Market Sees Significant Contraction for First Time in Four Years

For the first time in four years, the stablecoin market's total capitalization has contracted significantly, dropping by over $10 billion from its May peak to around $310 billion in late July. This represents the largest monthly outflow since the collapse of Terra in May 2022. Paradoxically, while the supply shrank, adjusted transaction volume in June 2026 surged to a record $1.79 trillion, a 63% monthly increase. A key driver of this divergence is the **$GENIUS Act**, passed in July 2025, which prohibited stablecoin issuers from paying interest on payment-focused stablecoins. This did not eliminate the demand for yield but redirected capital towards alternatives like tokenized U.S. Treasury funds, DeFi lending protocols, and offshore stablecoin issuers. The shift is evidenced by the rapid growth of the Real-World Asset (RWA) sector, where tokenized Treasury funds grew from $11 billion to $16 billion in five months. The changing dynamics have reshaped the competitive landscape. **$USDC** has become the dominant instrument for institutional transactions, accounting for approximately 70% of transaction volume in the first half of 2026 and $1.21 trillion in adjusted transfer volume for June. Meanwhile, **$USDT** retains its lead in overall market capitalization, serving as a "savings account" in developing economies. The industry's fundamental economics are transforming: the old issuer model reliant on yield from reserves is diminishing; revenue is shifting towards infrastructure providers like payment networks and blockchains that charge transaction fees; and corporate (B2B) payments, while still a small fraction of total volume, are experiencing explosive growth.

cryptonews.ru7 хв тому

Stablecoin Market Sees Significant Contraction for First Time in Four Years

cryptonews.ru7 хв тому

LeCun Continuously Endorses, New Work VISReg Tackles the Core Challenge of 'Representation Collapse' in JEPA World Models

"VISReg: A New Self-Supervised Learning Method Tackles Representation Collapse in JEPA World Models" Yann LeCun has highlighted the new self-supervised learning (SSL) work VISReg (Variance-Invariance-Sketching Regularization), which addresses the core challenge of "representation collapse" in JEPA-based world models. SSL often collapses, mapping different inputs to similar vectors, losing discriminative power. While methods like VICReg and SIGReg attempted to regularize the representation distribution, they suffered from issues like vanishing gradients during collapse or coupled scale and shape optimization. VISReg overcomes these by decoupling the regularization into independent "scale" and "shape" objectives. It uses a variance term to prevent amplitude collapse (providing constant gradient even during collapse) and a sliced Wasserstein distance (SWD) based sketching target with stop-gradient to align the distribution shape to an isotropic Gaussian, without interfering with scale. This approach, requiring only ~15 lines of PyTorch core code, offers linear computational complexity and scales efficiently across multiple GPUs. Evaluated across 15 datasets (in-domain, out-of-distribution/OOD, dense prediction), VISReg outperforms 7 mainstream SSL methods (MoCoV3, DINO, iBOT, I-JEPA, MAE, data2vec) without relying on heuristic tricks like EMA or stop-gradient. Key results include: superior average OOD accuracy; matching DINOv2's OOD performance using only ~1/10 of the data (ImageNet-22K vs. LVD-142M); better transfer learning fine-tuning results across multiple datasets; and competitive performance in semantic segmentation and generative guidance. VISReg also demonstrates robustness on low-quality data like long-tailed or low-rank datasets where other methods fail. The work provides a more stable, efficient, and generalizable regularization solution for SSL and JEPA models.

marsbit16 хв тому

LeCun Continuously Endorses, New Work VISReg Tackles the Core Challenge of 'Representation Collapse' in JEPA World Models

marsbit16 хв тому

Торгівля

Спот
活动图片