It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Fundamental physical and statistical limits (Abbe’s diffraction limit, Cramér-Rao bound, Fisher information) mean that no amount of sophisticated analysis or AI can extract more spatial or parametric information than the underlying measurement actually contains.
AI models can produce confident numerical outputs even when the experiment is weakly informative, ambiguous or nearly non-identifiable, because confidence scores primarily reflect the model’s internal assumptions rather than the information content of the data.
Scientific AI systems should therefore incorporate structured, fail-closed abstention mechanisms that refuse to issue a qualified estimate when the measurement geometry, noise level or sensitivity is insufficient, and should report both error and coverage so that limits on the claim are treated as valuable scientific information rather than failure.
AI can produce a precise estimate even when an experiment no longer contains enough information to identify the target. Science should treat abstention as a valid result, not a failure.
In 1873,Ernst Abbeformalized a hard truth about microscopes: optical resolution is constrained by the wavelength of light and the aperture of the instrument. Below that limit, the problem is not that the lens needs a cleverer analyst. The measurement itself does not carry arbitrarily fine spatial information.
Seven decades later,C. R. Raoand Harald Cramér expressed the same idea in statistical language. Under the assumptions of the Cramér-Rao framework, Fisher information places a lower bound on the variance attainable by an unbiased estimator. The floor is set by the measurement model, its sensitivity to the quantity being estimated, and the noise. Better mathematics can use available information more efficiently. It cannot manufacture information that the experiment never recorded.
Nature keeps accounts.
Artificial intelligence is now becoming part of the analytical machinery of science. Learned models solve inverse problems, infer parameters from sparse observations, separate overlapping signals, classify images, and extract structure from data too large for manual inspection.Physics-informed neural networks, for example, have been used for forward and inverse problems in differential equations, while later work has explicitly confronted theidentifiabilityof parameters inferred by such models.
These systems can be extraordinarily useful. But they introduce an easy category error: confusing confidence in an answer with evidence that the measurement could identify the answer in the first place.
A confidence score can tell us something important about the model under its assumptions. It does not, by itself, prove that the current experiment contains enough independent information about the target quantity. Priors, regularization, learned correlations, and familiarity with the training distribution can all keep an output numerically stable even as the measurement geometry becomes weak, ambiguous, or nearly non-identifiable. Research onpredictive uncertaintyhas already shown that uncertainty quality itself can deteriorate when data move away from the conditions on which the model was developed.
Put more simply: confidence is evidence about the model. It is not automatically evidence about the experiment.
Consider three ordinary scientific situations. Two astronomical sources become so blended that their signatures are nearly indistinguishable. A navigation receiver loses the geometric diversity needed to separate state variables. Two spectral components become so similar that many combinations explain the same measured curve. An algorithm can still return a number in each case. The harder question is whether the data uniquely support that number without leaning on assumptions that have silently become more important than the measurement.
That distinction matters most near the edge of a scientific claim. Strong signals are comparatively forgiving. At the boundary, where a source is faint, two mechanisms are difficult to separate, or an effect would be consequential if real, the information supplied by the measurement can collapse faster than a model’s confidence score does.
Scientific AI therefore needs a capability that sounds unimpressive but is foundational to measurement science: the ability to refuse a qualified estimate because the evidence is physically insufficient.
Machine learning already has a research tradition calledselective prediction, in which a system can abstain rather than predict on every case. SelectiveNet, for example, explicitly optimized the tradeoff between prediction risk and coverage. That is useful, but scientific inference needs an additional question upstream of model confidence: can the instrument, geometry, noise level, and measurement model identify the requested quantity at all?
Structural biology offers a useful cultural example. AlphaFold does not merely publish a three-dimensional structure; it also reports per-residue confidence. Researchers have learned not to treat low-confidence regions as inconvenient blanks to be cosmetically filled. In the human-proteome analysis,AlphaFold confidencewas even evaluated as a predictor of unresolved or disordered regions. The lesson is not that a model-confidence score equals physical identifiability. It does not. The lesson is that a scientific community can learn to treat limits on a model’s claim as information rather than embarrassment.
The next step is to apply the same discipline to the measurement itself.
Before accepting an AI-assisted scientific estimate, ask what the physics permits. Is the target identifiable under the current geometry? How does the relevant Fisher information or sensitivity change as the experiment degrades? What uncertainty floor follows from the measurement model? Does a different parameter combination produce nearly the same observation? If the requested precision lies below what the data can support, a confident model output should not be upgraded into a confident scientific claim.
Science does not need another branded AI confidence score. Established tools already exist: conditioning diagnostics, Fisher information, Cramér-Rao bounds, posterior-width analysis under declared priors, residual checks, and explicit tests for distribution shift. The important change is architectural. The model may produce a candidate estimate, but release of a qualified scientific estimate should depend on evidence about whether the measurement supports it.
That permission should also be fail-closed. If the experiment is insufficiently informative, the output should not be a mysterious null or an invisible software exception. It should be a structured abstention: what quantity was requested, which information condition failed, what threshold had been fixed, what data and configuration were used, and what new measurement would be needed to make the question answerable.
There is no universal numerical cutoff. A telescope, a seismic array, a navigation geometry, a medical imager, and a plasma diagnostic lose information in different ways. The principle can travel across fields. The threshold cannot simply be copied from one instrument to another.
Nor should the abstention rule be tuned after the embarrassing cases are known. The information diagnostic, threshold-selection procedure, and acceptable error criterion should be fixed on development or calibration data and then applied unchanged to untouched evaluation cases. Otherwise abstention becomes another form of post-selection: the system learns to be humble only where the analyst already knows it was wrong.
This creates a straightforward standard for journals, reviewers, laboratories, and funders. An AI-assisted method that makes physical estimates should state the information limits of the measurement, show how those limits are checked at runtime or analysis time, report both error and coverage, and disclose when the system refuses to make a qualified estimate. A method that answers fewer questions may be scientifically stronger if it can explain why the rejected questions were not answerable from the evidence.
The goal is not to slow scientific AI. It is to keep AI from erasing a distinction that experimental science spent centuries learning: a calculation can be sophisticated while the observation remains insufficient.
A paper that cannot show where its measurement stops supporting its inference is not ready to turn a model output into a discovery claim. Editors should view that omission with the same instinct they bring to a p-value of exactly 0.049 after a long chain of analytic choices: not automatic rejection, but an immediate request to see the full evidentiary path.
We grade scientific AI today on the questions it answers correctly. We should start grading it on the questions it correctly refuses to answer.
About Burak Oktenli Burak Oktenli holds an MBA and a Master of Professional Studies in Applied Intelligence from Georgetown University. His research addresses the governance of authority in autonomous and AI-enabled systems, and his writing has appeared at the Modern War Institute at West Point, RUSI, RealClearDefense, RealClearMarkets, and Geopolitical Monitor. He is the author of Authority Architectures for Autonomous Systems, a ten-volume series on how authority in autonomous systems is delegated, monitored and recovered, at authority-architecture.me. View all posts by Burak Oktenli →
Washington may force Kazakhstan to choose between competing US, China AI alliances
Kazakhstan's President Kassym-Jomart Tokayev with US counterpart Donald Trump in September last year. / Kazakh presidency
By bne IntelliNewsAugust 18, 2026
The Trump administration is preparing to tell Kazakhstan and 34 other countries that they may have to choose between competing US-backed and China-backed artificial intelligence frameworks as Washington intensifies efforts to shape global technology alliances.
The scenario is outlined in an exclusive report by Reuters. A US official told the news service that partners “can’t have it both ways”.
A draft US State Department letter obtained by the Reuters is addressed to 35 countries that signed the US AI Opportunity Statement in Washington, DC in June. It warns that countries joining the US-led Pax Silica framework would not be able to participate in competing initiatives where the requirements conflict with those of the American framework.
The draft reportedly urges countries to “choose deliberately” and says membership of Pax Silica represents a commitment rather than simply a diplomatic designation. It is reported to state: "To be part of everything is to be part of nothing. Signature of the Pax Silica Declaration is not merely a membership subscription, but a commitment."
The letter has reportedly not been sent and could still be amended.
First to join
Kazakhstan was the first Central Asian country to join the US-led AI framework. Washington has highlighted the country’s importance as a potential essential supplier of critical minerals used in semiconductors and advanced technologies. However, Astana has also joined China’s World Artificial Intelligence Cooperation Organisation (WAICO). It was launched in Shanghai by Chinese President Xi Jinping in July.
It is thought Kazakhstan is presently the only country signed up to both the US and Chinese frameworks.
The competing commitments present a particular challenge for Kazakhstan. It pursues a multi-vector foreign policy aimed at maintaining relationships with all the major powers while avoiding excessive dependence on any one partner.
Kazakhstan, for instance, maintains strong economic ties with both China and Russia while seeking deeper cooperation with the US and European countries in areas including critical minerals, energy and technology.
The US-China competition over AI has intensified as Chinese open-weight AI models have made advances against proprietary systems developed by US companies including OpenAI and Anthropic, the report noted.
Washington is seeking closer alignment among partner countries partly to limit China’s access to critical minerals, semiconductor technologies and other resources considered important to the development of advanced AI systems.
The draft State Department letter indicates that Washington could increasingly make participation in its technology initiatives conditional on restrictions on cooperation with competing Chinese frameworks. A final US position has yet to be communicated to Kazakhstan.
A report by Tech Times argued that the draft has a major weakness – it does not specify what would constitute a conflicting commitment or what consequences countries would face for refusing to choose. It does not name China, set a deadline or establish a mechanism for determining violations and enforcing compliance, the report observed.
Weak enforcement mechanisms
The potential enforcement mechanism lies in access to advanced chips, the report said. AI accelerators require replacement every few years, creating a recurring dependence on supply channels controlled by the US and its partners. Washington could potentially restrict future chip access through export controls, a mechanism demonstrated in the case of the UAE's G42, which removed Huawei equipment as part of its closer alignment with the US.
However, applying such pressure to Kazakhstan could undermine Washington's objectives, the report argued. Kazakhstan's critical-mineral reserves are a major reason for US interest in bringing the country into Pax Silica, meaning sanctions or an exclusion over its WAICO membership could weaken the supply-chain diversification the framework seeks to promote.
The report also cited China analyst Rui Ma as saying that the approach could ultimately cost Washington more than it gains by encouraging countries to view supply chains as instruments of geopolitical pressure rather than as stable partnerships.
For now, the draft's call to “choose deliberately” remains a political signal rather than an enforceable requirement, the report concluded.
AI shares human tendency to infer character from facial features
Computer-generated images used in the study with questions about trustworthiness. The face on the right is usually seen as more trustworthy by both humans and LLMs.
Human beings have a tendency to infer personality or character traits from other people’s facial features, and these biases—ungrounded in any actual relationship between faces and behavior—lead to unfair outcomes.
Steven Lehr and colleagues explored whether AI models, which are trained primarily on text but have the ability to “see” images, share these biases. The authors asked GPT-4o to make over 4,500 forced-choice judgments between computer-generated faces, and presented thousands of additional forced choices to GPT-5, Gemini 3 Flash Preview, and Claude Sonnet 4.5. In some of the experiments, models were asked to choose which of two faces was more competent or more trustworthy. Other trials used related traits, including asking which face was more confident, smart, hardworking, lazy, inept, careless, warm, helpful, sincere, selfish, hypocritical, or aggressive. Some experiments asked LLMs to judge which computer-generated human face would be more likely to be a serial killer, to be arrested for human trafficking, or to defraud the public using a Ponzi scheme. Finally, LLMs were asked to choose between faces in the contexts of hiring a university president, investing in a tech startup, or selecting a financial manager. In all these cases, the models were willing to weigh in and in the majority of cases chose the same face that a human would typically see as more trustworthy or confident. Across studies, GPT-4o selected the face that would be expected based on human ratings 74.88% of the time. GPT-5 showed notably more bias than its predecessor. When GPT-5 advised on consequential decisions, the model recommended the more competent-looking individual fully 97.04% of the time, as compared to GPT-4o’s 75.19%. Models from other companies produced similar results.
If LLMs were free of human face-to-character bias, they could be used as a tool to help eliminate this form of bias in contexts such as job candidate selection or parole decisions. According to the authors, AI in its present form is instead likely to worsen unfairness if used in such contexts.
Journal
PNAS Nexus
Article Title
Like humans, language models demonstrate face-to-character biases
Article Publication Date
18-Aug-2026
COI Statement
S.L. is affiliated with Cangrade, Inc., a company that works on de-biasing machine learning models. However, Cangrade, Inc. does not currently create generative AI models, did not fund this research, and is not expected to profit in any way from the results or their publication, other than by association. The other authors do not declare any competing interests.
Hanbat National University researchers reveal physics-informed AI for rapid optimization of thermal energy storage systems
The proposed physics-informed neural network framework enables rapid, autonomous design optimization of latent heat thermal energy storage systems
Hanbat National University Industry–University Cooperation Foundation
The proposed framework is trained on 15 ground-truth datasets, enabling rapid, autonomous exploration of optimal latent heat thermal energy storage system designs.
Credit: Assistant Professor Joo Hyun Moon from Hanbat National University
With intensifying climate change, decarbonization of the global building sector has become a key priority. A substantial portion of a building’s energy demands consists of heating and cooling needs. Consequently, developing efficient thermal energy storage systems is a crucial part of this effort. Among available options, latent heat thermal energy storage (LHTES) systems that utilize phase change materials offer unique advantages. These include a high energy storage density and the ability to release large amounts of thermal energy at a near-constant temperature, which is crucial for stable thermal management. Indeed, some studies have shown that LHTES systems can reduce heating and cooling energy consumption by up to 45%.
Despite these advantages, accurately modelling and optimizing LHTES systems remains a major challenge. The coupled heat-transfer and fluid-flow processes involved are difficult to simulate accurately. Although physical experiments provide reliable ground-truth data, they are generally limited to laboratory-scale systems. Computational fluid dynamics (CFD) simulations, on the other hand, can capture these complex physical processes across different scales. However, they are computationally expensive and time-consuming, making large-scale design optimization impractical.
In a new study, a collaborative team of researchers from the Republic of Korea, led by Assistant Professor Joo Hyun Moon from the Department of Building Systems Engineering at Hanbat National University in South Korea, has developed a hybrid physics-informed neural network (PINN) framework for optimization of LHTES systems. “The LHTES system utilizes a special wax-type material, called a phase change material, that soaks up a huge amount of heat when it melts and gives it back when it hardens, acting like a battery for warmth,” explains Dr. Moon. “Testing every new design using conventional computer simulations is slow and computationally intensive. In our PINN framework, we teach the AI model the governing laws of physics, enabling it to accurately reproduce the system's behavior and rapidly explore tens of thousands of possible designs.” Their study was made available online on May 05, 2026, and published in Volume 167 of the Journal of Energy Storage on July 30, 2026.
To develop the data-driven PINN, the researchers first created a high-fidelity ground-truth dataset that captures the physics of the LHTES system. For this, they developed a laboratory-scale experimental LHTES setup, and a corresponding CFD model. Experimental measurements were then used to validate the CFD simulations, which showed excellent agreement with only minor deviations. Once validated, the CFD model was used to generate a sparse dataset of 15 high-fidelity simulations that served as training data for the PINN.
The proposed PINN framework employs a zero-dimensional physical model and embeds the system’s governing physical equations directly as loss functions. A key innovation is that the PINN learns case-specific effective heat-transfer coefficients, while geometric effects are captured through a response surface model. By focusing only on discovering the laws of energy conservation, the PINN avoids overfitting despite being trained on sparse data. The response surface model (RSM) then enables the framework to predict heat transfer properties for any arbitrary, unseen geometry and flow condition during the optimization process. Together, the PINN and RSM form a fast digital twin of the LHTES system.
The digital twin is then coupled with a Non-dominated Sorting Genetic Algorithm II (NSGA-II) to perform multi-objective design optimization. The optimization simultaneously searches for designs that maximize total discharged heat and average discharge power while minimizing pumping power.
In numerical experiments, the PINN reproduced the behavior predicted by the CFD simulations with excellent accuracy while enabling rapid, autonomous design optimization. The optimal design obtained using the multi-objective design optimization process performed similarly to the best-performing baseline design, while significantly reducing pumping power. Moreover, the process also shows that flatter pipes are more favorable.
Beyond buildings, the same physics-informed design approach could help improve thermal management in electric-vehicle batteries, data centers, cold-chain logistics, and solar thermal systems. Earlier studies also suggest that smarter control of latent heat storage can cut electricity costs by more than 70%, showing the broader potential of this technology for reducing energy use and emissions.
“Our approach moves the LHTES design process from manual evaluation of discrete cases to autonomous exploration of the entire design space,” concludes Dr. Moon. “It provides engineers with a practical tool for developing more efficient thermal energy storage systems, helping reduce the energy consumption and carbon footprint of buildings while supporting a more sustainable energy future.”
About the institute Established in 1927, Hanbat National University (HBNU) is a university in Daejeon, South Korea. As a leading national university in the region, HBNU strives to take lead in solving problems in the local community and solidify its cooperation with industries. The university’s vision is to become a ‘Global industry-university cooperation university creating future values’. HBNU has been chosen for a variety of nationwide-level projects such as An Autonomous Improvement University Project and Leaders in INdustry-University Cooperation+(LINC+), among others. With its focus on practical education and regional impact, HBNU continually advances technological solutions grounded in creative thinking and real-world relevance.
About the author Dr. Joo Hyun Moon is an Assistant Professor of Building Systems Engineering at Hanbat National University in Daejeon, Republic of Korea. He received his Ph.D. in Mechanical Engineering from Chung-Ang University in 2017. Before joining Hanbat National University, he served as an Assistant Professor at Sejong University and as a postdoctoral researcher at the University of Texas at Dallas. His research spans thermal system optimization, phase-change heat transfer, and physics-informed, machine learning-based models for real-time energy-system optimization.
A year ago this week Nvidia became the first company to touch $4 trillion, and MINING.COM ran the numbers against the combined worth of the world’s 50 most valuable mining companies. It was not even close.
The chip designer was worth 2.7 times the MINING.COM TOP 50 (and since valuations quickly shrink outside that, probably twice the entire global mining industry).
This year Nvidia has been having the kind of quarter miners unfortunately know all too well.
After peaking at roughly $5.5 trillion in the middle of May, the stock shed about $1 trillion over the next eight weeks. At the bottom of the slide, Nvidia was briefly trading at 18 times forward earnings – below the S&P 500 average, which made the poster child of the AI age, at least technically, a value stock.
The chipmaker’s P/E is now back near 20 times forward earnings, but mining’s majors change hands for barely 13 – cheaper on next year’s profits than Nvidia managed to look even at its bargain-bin bottom and despite all the criticality clamour surrounding mining.
The scoreboard now reads $5.11 trillion versus $2.19 trillion. Nvidia is worth 2.3 times the top 50 miners, down from 2.7 times last July, and for a few sessions this month the multiple flirted with two – territory last visited when cheap and cheerful DeepSeek gave the AI trade its first proper scare.
Over the past twelve months mining even outgrew the machine – up 47% against Nvidia’s 27% – a first in the short history of this exercise.
But to return to theme of mainstream investors not valuing the production of copper the same way as the production of hallucinations about the production of copper, here’s a sobering thought:
What Nvidia shed between mid-May and early July – nearly five BHPs, and BHP has never been worth more – was about what the whole Top 50 was worth for most of this decade.
Renaming everything critical minerals was a good start (and kudos to met coal and lead for making the list), but it’s time to launch a worldwide public/investor awareness campaign. What about this old nugget for a tagline: If it can’t be grown, it has to be mined.
That includes, for the record, the silicon, copper, gold, silver, tungsten, tantalum, titanium, cobalt, aluminium, tin, nickel, hafnium, ruthenium, molybdenum, indium, palladium, gallium, germanium, arsenic, antimony, bismuth, boron, phosphorus, cerium, lanthanum, yttrium, gadolinium, europium and praseodymium that Nvidia’s chips are made from.
Nvidia Backs 8-GW Ohio AI Campus for OpenAI With $1.5 Billion Investment
Nvidia is backing a massive artificial intelligence infrastructure development in Ohio that could ultimately require at least 10 gigawatts of new power generation, underscoring the rapidly growing impact of AI data centers on U.S. electricity demand.
The chipmaker said it has partnered with SB Energy to secure land, power and data center shell capacity at the PORTS-Pike Technology Campus in Pike County, Ohio, where OpenAI will be the customer under a 20-year lease.
The first phase is designed to provide 4.25 gigawatts of IT capacity, with Nvidia holding an option covering another 3.75 GW. At full buildout, the project would provide 8 GW of AI computing capacity.
SB Energy, which is backed by Japan's SoftBank Group and OpenAI, will build, own and operate the data center infrastructure. Nvidia will exclusively supply the site's AI computing systems, including GPUs, CPUs and networking equipment through its DSX AI factory platform.
The planned scale of the development highlights the increasingly close relationship between AI infrastructure and the energy sector. SB Energy and SoftBank plan to develop at least 10 GW of new electricity generation to support the campus and invest at least $4.2 billion in regional grid infrastructure through a partnership with AEP Ohio.
The companies said the arrangement is structured to prevent the costs of serving the data center campus from being shifted onto existing electricity customers.
Development is expected to proceed in phases beginning in 2028.
The campus is being built around the former Portsmouth Gaseous Diffusion Plant, a major Cold War-era uranium enrichment site in southern Ohio. The developers are working with AEP Ohio as well as the U.S. Departments of Energy and Commerce to redevelop the area into a large-scale technology and power hub.
Nvidia will also invest $1.5 billion directly in SB Energy, joining SoftBank and OpenAI as investors in the infrastructure company. The funding will support SB Energy's broader expansion as demand grows for purpose-built electricity and data center infrastructure serving AI computing.
The project adds to a wave of multibillion-dollar data center developments that are transforming U.S. power demand forecasts. Hyperscalers and AI companies are increasingly seeking dedicated generation, transmission capacity and long-term power arrangements as access to electricity becomes one of the principal constraints on new computing capacity.
The PORTS-Pike development is particularly notable for its scale: 10 GW of generation capacity would be comparable to the output of several large nuclear power stations and represents a major new source of electricity demand concentrated at a single industrial site.
The developers also plan significant local investment. OpenAI has added $40 million to an existing $40 million community benefits fund established by SB Energy, bringing the initial fund to $80 million. The money is intended for programs including energy affordability, workforce development and regional economic development.
The companies said the project could ultimately support tens of thousands of jobs in Ohio.
By Charles Kennedy for Oilprice.com
Friday, August 14, 2026
China’s Belt And Road Initiative: Key Economic Issues – Analysis
China's Belt and Road Initiative (BRI). China in Red, the members of the Asian Infrastructure Investment Bank in orange. Credit: Lommes, Wikipedia Commons
August 14, 2026 The Congressional Research Service (CRS) By Karen M. Sutter and Michael D. Sutherland
Key Takeaways
China’s Belt and Road Initiative (originally One Belt, One Road), launched in 2013 and later elevated in Party documents, seeks to build PRC-centered global infrastructure, trade, technology and production networks through land, maritime, digital, green and polar corridors.
Financing and project delivery are dominated by state banks, funds and national-champion firms using often opaque, collateralized loans and integrated packages that expand Chinese standards, secure resources and create long-term economic and strategic dependencies, raising concerns about debt sustainability, lack of reciprocity and potential dual-use applications.
The United States and partners have responded with alternative financing tools (such as the DFC and EXIM programs), quality-infrastructure initiatives and greater scrutiny of Chinese deals, while Congress continues to debate further measures to monitor and counter BRI’s economic and geopolitical effects.
The People’s Republic of China (PRC or China) in 2013 launched an ambitious and multifaceted foreign economic policy initiative—One Belt, One Road—to expand China’s global economic reach and influence. In 2015, China’s leaders changed the English name to the Belt and Road Initiative (BRI) (while keeping the Chinese name),possibly to deflect attention from the initiative’s focus on developing China-controlled and -centered global ties in a hub-and-spoke format. The Communist Party of China (CPC) incorporated the initiative into its Charter in 2017. It reaffirmed the efforts’ significance at its 20th Party Congress in 2022 and in China’s 15th Five-Year Plan for National Economic Development (2026-2030). Some in Congress assess that One Belt, One Road projects advance PRC economic and geopolitical goals while undercutting U.S. global influence and interests. In response, some Members have sought to develop alternative U.S. and multilateral financing programs.
Scope and Objectives
One Belt, One Road has evolved into a global effort that aims to develop PRC-centered and -controlled global production, trade, infrastructure, and transportation networks. It includes a land-based “Silk Road Economic Belt” and a “21st Century Maritime Silk Road.” Sub-initiatives include a “Polar Silk Road” focused on Arctic shipping routes and a “Digital Silk Road” to promote PRC information and communications technology (ICT) exports and satellite networks. A “Green Silk Road” promotes PRC renewable energy products and services. Other efforts seek to promote health and science and technology (S&T) ties and the use of PRC technical standards among partner countries. The effort emphasizes policy coordination, trade and investment, dispute settlement, tourism, and student/personnel exchanges.
One Belt, One Road projects in energy, ICT, manufacturing, and transportation infrastructure look to vertically integrate PRC production supply chains, technology infrastructure, and transportation networks. The effort involves technology and financial integration that expands the use of China’s digital platforms and currency. It seeks to expand PRC firms’ presence overseas, create markets for China’s goods and services, and secure access to foreign sources of agriculture, energy, and strategic commodities, such as critical minerals. Projects also aim to develop China’s interior regions, employ PRC workers, and offload PRC excess industrial capacity.
At the One Belt, One Road forum in 2023, PRC leader Xi Jinping prioritized “high quality development”; intermodal and green infrastructure; digital trade zones; S&T cooperation; a “compliance evaluation system” to address corruption; and cooperation in energy, tax, finance, think tanks, media, and culture. In 2021, Xi presented at the United Nations (UN) a Global Development Initiative to complement One Belt, One Road with projects for poverty alleviation and food security and in areas to advance PRC firms globally in infrastructure, manufacturing, and digital technologies. See CRS In Focus IF13099, China Primer: China’s Global Development Initiative.
China’s Investment and Financing
China’s use of onshore financing and special-purpose vehicles for foreign investment complicates analysts’ ability to track PRC global economic activity. One Belt, One Road is an umbrella initiative, and projects may be specifically or loosely tied to the effort. As a result, many groups track PRC cross-border financing, investment, and overseas projects generally. China’s stock of global outward foreign direct investment (ODI)—investment made into a business or real asset in another country—stood at $3.6 trillion (7.8% of world total) in 2025, up from $34.7 billion (0.5% of world total) in 2001. In comparison, the United States accounted for $6.6 trillion, or 14.4%, of global ODI stock in 2025 (down from 32% in 2001), according to official country data compiled by the UN. PRC ODI flows have picked up since 2021 and were $174 billion in 2025, accounting for about 9% of global ODI flows. (U.S. ODI flows were $263 billion, or about 14% of global ODI flows in 2025.) Additionally, PRC cross-border contracts—a corporate structure used for overseas construction and infrastructure projects—have been stable and reached an all-time high of $289 billion in 2025. The PRC also operates cross-border projects in agriculture, energy, minerals, finance, technology, and shipping (Figure 1).
AidData, a research lab at the College of William & Mary, estimates that, as of 2023, China’s overseas lending portfolio was $2.1 trillion. (In comparison, the World Bank’s portfolio in 2025 was about $400 billion.) It assessed that even as One Belt, One Road focused on developing countries, since 2000, PRC lending has been shifting toward high-income countries (e.g., the United States) and financing for technology deals and the purchase of foreign firms in strategic sectors. AidData reports that, as of 2023, 24% of PRC lending was for low- and lower-middle-income countries, and that infrastructure accounted for about 20% of China’s overseas lending portfolio.
Figure 1. China’s ODI Flows and Overseas Contracts. Source: CRS, with data from China’s Ministry of Commerce.
PRC state banks (e.g., CHEXIM and CDB), firms, and funds (e.g., Silk Road Fund) undertake a large share of PRC overseas lending and investment. The PRC government often pays firms in China for projects they implement, while host governments pay the PRC government for the projects. Projects are neither assistance—PRC loans are typically not interest-free and issued at market terms—nor truly commercial, because repayments are often backed by collateral commitments (e.g., lease rights, minerals, or commodities) made to the PRC government, which in turn absorbs much of the commercial risk for PRC firms. Recipients of collateral may include state firms not party to the original transaction that are designated by the PRC government. Role of China’s State Firms
PRC strategic investments are typically state-sponsored and aim to advance national economic and foreign policy goals. A handful of state firms operate most projects. These firms are funded by and report directly to the central government, and include China Harbor, CRRC, State Grid, China Three Gorges, and COSCO. China’s projects strategically position national champions—such as Huawei, ZTE, and Alibaba—by creating technology infrastructure and systems built to PRC standards. Alibaba’s internet project in Malaysia, for example, provides a foundation for PRC data/cloud, e-commerce, and financial services. Projects may offer the PRC visibility and touchpoints into sensitive infrastructure and services via interconnection and interoperability in communications, energy, and transportation. Projects in critical minerals support PRC industrial policies. U.S. Concerns
Some observers note the economic benefits of China’s investments in developing countries while others argue that China is introducing unsustainable debt obligations and opportunities to gain economic concessions and influence. China tends to extend the duration of its loans, rather than forgive debt repayment, which can create long-term financial dependencies. For example, in 2017, when the Sri Lankan government was unable to repay PRC loans, China Merchants Port Holdings Company Ltd. acquired a majority stake in the firm that operates Sri Lanka’s Hambantota port and the right to operate the port for 99 years. Credit and loan terms are generally opaque and China tends to settle agreements bilaterally. China’s opacity in lending came to a head in 2019 when U.S. officials questioned whether International Monetary Fund relief for Pakistan might also be used to repay Pakistan’s debts to China.
The PRC government insists that most PRC state banks and state firms are not subject to sovereign lending terms adopted by the United States and other major creditors in the Paris Club. PRC loans often forbid multilateral debt restructuring (e.g., under Paris Club auspices). China joined two G20 debt relief initiatives that accept Paris Club disciplines, but these apply only to CHEXIM and the China International Development Cooperation Agency. The PRC claims it has provided more deferments under G20 schemes than Paris Club members, but many countries indebted to China do not appear to qualify or have not applied—likely due to PRC pressure—for G20 debt relief. Some experts say One Belt, One Road undermines the role and principles of multilateral financial institutions, which work with China on projects, and argue China should not have a leadership role in these institutions. Such collaboration may set better terms for host countries while also advancing PRC goals.
PRC entities are expanding overseas in sectors that the PRC restricts to foreign investors in China (e.g., construction, transportation, finance, and communications). The PRC does not offer reciprocal market access for the rights it secures in other countries, challenging a core trade tenet and advantaging PRC firms over their competitors. It has opened foreign markets with “deal-ready” financing and integrated project delivery.
PRC investments in strategic sectors and infrastructure have prompted some governments to increase scrutiny of these deals. Some analysts assess that certain PRC projects have military uses. Under its military-civil fusion program and China Standards 2035 initiative, China is developing standards that promote civilian-military interoperability, including in various technologies and infrastructure (e.g., ports). Commercial land deals may facilitate a military presence. China Merchants Bank, for example, signed the lease for property in Djibouti on which China developed a military base. Sam Enterprise Group, a firm reportedly tied to China’s military, bought land in Vanuatu and the Solomon Islands. PRC projects offer alternatives to U.S.-led networks and standards. PRC-built BeiDou satellite and rail networks offer substitutes to U.S.-controlled GPS navigation technology and sea lanes where the U.S. military operates. PRC digital platforms support use of the PRC’s digital currency.
U.S. Government Response
PRC overseas financing practices are prompting the United States with its allies and partners to adjust approaches to global financing to compete with China. Congress enacted the Better Utilization of Investments Leading to Development Act of 2018 (BUILD Act; P.L. 115-254) to create the U.S. International Development Finance Corporation (DFC) and increase support for quality market-oriented and financially sustainable projects with environmental and social safeguards. The DFC has sought to compete with PRC consortia on projects and in markets in which the PRC has a major presence. In 2019, Congress created a China and Transformational Exports Program at the Export-Import Bank of the United States with new financing tools and flexibilities to counter PRC financing. The G-7 Partnership for Global Infrastructure and Investment and Blue Dot Network seek to promote quality infrastructure financing. In 2020, the U.S. government sanctioned PRC state firms that built One Belt, One Road military infrastructure in the South China Sea.
The 119th Congress is debating the effects of dismantling the U.S. Agency for International Development on competition with the PRC. S. 1011 would require the State Department to monitor and counter PRC projects. H.R. 9093 would require a report on the PRC’s use of One Belt, One Road to undermine the U.S.-led global order, and a strategy to counter it. Congress may examinethe PRC government’s role in directing and financing investments in the United States and acquisition of U.S. firms in strategic sectors, and U.S. policy on such investments;
PRC entities’ presence in U.S. production, energy, transportation, and communications networks and investments in the Western Hemisphere; and whether to allow U.S. development or export financing for global projects that use PRC components or services. About the authors:
Karen M. Sutter, Specialist in Asian Trade and Finance Michael D. Sutherland, Analyst in International Trade and Finance
About CRS The Congressional Research Service (CRS) works exclusively for the United States Congress, providing policy and legal analysis to committees and Members of both the House and Senate, regardless of party affiliation. As a legislative branch agency within the Library of Congress, CRS has been a valued and respected resource on Capitol Hill for nearly a century. View all posts by CRS →
