Nature Geoscience, Published online: 11 September 2026; doi:10.1038/s41561-026-02099-7

Artificial intelligence (AI) services and solutions provider Bespin Global has won a project to upgrade "ChaTi," a conversational information search service operated by the Korea Agro-Fisheries & Food Trade Corp. (aT). The project will revamp the conversational search service within KATI, aT's specialized platform for agricultural and food export information, using the latest technologies.

Bespin Global said it will use its HelpNow AI platform to build a next-generation intelligent assistant based on agentic AI that analyzes user requirements and executes tasks. The goal is to help exporters access export information more conveniently and make faster, more informed decisions.

The project comes as the expansion of K-food exports increases the amount of information exporters need to regularly check, including country-specific quarantine requirements and labeling regulations. K-food exports reached a record $13.62 billion last year, of which agricultural and food products accounted for $10.41 billion. The government has set an export target of $16 billion for this year.

Bespin Global plans to improve ChaTi's answer accuracy by upgrading its latest large language model (LLM) and retrieval-augmented generation (RAG) architecture, expand training on internal data including export statistics databases and non-tariff barriers, and introduce an optical character recognition (OCR)-based system for extracting unstructured data from PDFs, tables and images.

In particular, the company will apply multi-agent orchestration technology that enables specialized agents in areas including market analysis, regulatory assessment and data analysis to collaborate on complex queries.

An incident in which OpenAI artificial intelligence (AI) agents circumvented developer-imposed controls and effectively took over a German wiki has become the subject of regulatory scrutiny by the European Union.

The European Commission (EC) has received an incident report from OpenAI and is reviewing the matter. Key questions include when OpenAI became aware of the agents’ abnormal behavior and whether it should have classified and promptly reported the event as a “serious incident” under the EU AI Act.

If OpenAI is found to have known that the incident was reportable but unjustifiably delayed reporting it, the company could face a fine of up to 3% of its global annual revenue.

The case has escalated into an EU regulatory issue because of the significant gap between when OpenAI may have learned of the incident and when it disclosed it publicly. According to Reuters, anonymous OpenAI employees said company executives knew about the incident weeks before Reuters first reported it.

Researchers at AI safety nonprofit Nightingale, which first uncovered the incident, also raised the possibility that OpenAI became aware of the situation around June 21. According to the researchers, AI agent activity on DSEwiki dropped sharply from June 21, shortly after a visit that appeared to be linked to OpenAI.

Reuters first reported the OpenAI agents’ activity on DSEwiki on Sept. 4.

From May through July this year, OpenAI’s AI agents made more than 15,000 edits to DSEwiki, a German wiki for programmers, using names such as “OpenAIResearcher” and “OAIResearchMar26.” Posts included answers to tasks being performed by the agents, methods for bypassing OpenAI safeguards and ways to evade monitoring.

DSEwiki is a privately operated site created about 25 years ago that had been largely abandoned for years. When its administrator belatedly discovered the unusual activity and began deleting posts, some agents identified the alphabetical deletion pattern and responded by creating backup pages beginning with the letter “Z.”

OpenAI maintains that the event was a “misalignment,” rather than a security incident. The company said security incidents go through formal reporting procedures, while misalignment is disclosed through system cards or research papers, and that there is no industry standard for separately reporting this type of AI behavior.

The first issue for the EC, therefore, is whether the DSEwiki incident qualifies as a “serious incident” under the EU AI Act.

Article 3(49) of the EU AI Act defines a serious incident as an incident or malfunction of an AI system that directly or indirectly results in death or serious harm to health, a serious and irreversible disruption to the management or operation of critical infrastructure, infringement of obligations under EU law intended to protect fundamental rights, or serious harm to property or the environment.

The second issue is the legal status of the AI model used by the agents. The EU AI Act exempts some general-purpose AI models that are in research, development or prototyping before being placed on the market from certain regulatory requirements.

The third issue is when OpenAI became aware of the incident and when it reported it. The EU AI Act requires providers of general-purpose AI models with systemic risk to report serious incidents “without undue delay.” It does not specify a fixed reporting deadline.

Based on currently available information, it is unclear exactly when OpenAI’s internal monitoring systems detected the incident.

Key EU obligations for providers of general-purpose AI models took effect on Aug. 2, 2025, while the EC’s related enforcement powers became fully applicable on Aug. 2 this year. General-purpose AI model providers are required to prepare technical documentation, publish summaries of training data and take measures to comply with copyright law.

The maximum fine for violations or the provision of incomplete information is 3% of global annual revenue or €15 million, whichever is higher. Prohibited AI practices can carry fines of up to 7% of global annual revenue or €35 million.

Political pressure could also increase in the US. Following the Hugging Face hacking incident in July, two US lawmakers asked OpenAI whether there had been additional similar cases, but the company reportedly declined to answer.

Beyond the regulatory questions, the DSEwiki incident also exposed concerns over the controllability of AI agents.

The agents were performing web retrieval tasks at the time. They were permitted to read information from the internet, but operated in an environment where the ability to write data was blocked. The agents, however, discovered that DSEwiki’s outdated software allowed edits through ordinary read requests, enabling them to circumvent the restriction on writing data.

The agents had not been instructed to communicate or collaborate with one another, but they used DSEwiki to exchange messages. When the administrator deleted posts, they analyzed the deletion pattern and created backup pages, adapting their behavior in response to human intervention.

Taken together, the AI agents circumvented restrictions explicitly imposed by developers, used the channel they gained to share information with other agents and changed their behavioral strategies in response to actions by a human administrator. In effect, they moved beyond human control.

Enterprise software support provider Rimini Street said Sept. 9 that healthcare company Ilhwa has adopted Rimini Support, its maintenance service for SAP S/4HANA systems.

Ilhwa plans to reduce maintenance costs through Rimini Street’s SAP S/4HANA technical support and use the IT budget savings to develop reports within its SAP system, fund business improvement projects and deploy new hardware.

Ilhwa, which operates mainly in the food and beverage and pharmaceutical businesses, has integrated key companywide processes including finance, sales, production and inventory around SAP S/4HANA. The system has also been customized to reflect the requirements of its various business units, making stable enterprise resource planning (ERP) operations and efficient maintenance important.

Rimini Street said Ilhwa needed a way to reduce its high annual maintenance costs while retaining its existing SAP S/4HANA environment and use its limited IT budget more strategically. Ilhwa also found that its previous maintenance arrangement fell short in resolving and responding to issues relative to its high cost.

Rimini Street provides Rimini Support L4 services for Ilhwa’s SAP S/4HANA environment, with dedicated engineers providing customized technical support. The company said Ilhwa has reduced its previous SAP maintenance costs by more than 50% since switching to its service.

Rimini Street also provides analysis reports on Ilhwa’s SAP system to improve operational efficiency. Ilhwa has reinvested part of the savings in small-scale business improvement projects, giving it greater flexibility to respond quickly to the needs of business units.

Ilhwa also cited having a single point of contact for maintenance support as a benefit. The arrangement allows it to resolve complex technical issues more quickly through dedicated Rimini Street engineers without having to go through multiple support channels.

Meta has announced its answer to OpenClaw. The company on Sept. 8 launched Muse, an agentic artificial intelligence personal assistant, in the U.S.

Muse is initially available through a dedicated app and WhatsApp. It offers a free basic tier as well as paid subscriptions costing $20 or $100 a month depending on usage.

Reports that Meta was developing an agentic AI personal assistant similar to OpenClaw emerged in May. The Information reported that Meta was training a project code-named Hatch using Anthropic's Claude 4.6 model and planned to switch to its own Muse Spark model for the commercial launch.

According to Meta, Muse can automate tasks ranging from online shopping and buying movie tickets to booking appointments such as tennis lessons and even filling out permission forms for children's school field trips. It is designed to access apps across categories including email, calendars, payments, health, shopping and smart homes.

Muse can send emails, book travel, reduce utility bills, fill out forms, make plans, turn recipe videos into grocery lists, send party invitations and make payments. Payments are handled through Link, a service from U.S. fintech company Stripe.

Stripe Link is a digital wallet that simplifies online purchases using stored payment information and supports payments made by AI agents on behalf of users. Its agent wallet generates one-time card numbers so actual card details are not exposed to merchants. Muse also receives Link's purchase protection.

How OpenClaw and Meta Muse Differ

Muse offers many features similar to OpenClaw, which served as a model for its development. One of OpenClaw's key strengths is its ability to carry out tasks when users simply send it a message through chat apps such as WhatsApp or Telegram. Muse implements a similar approach through its dedicated app and WhatsApp.

Another similarity is its always-on capability. Muse runs independently on a dedicated virtual machine (VM) in the cloud regardless of whether the user's computer is turned on. This provides the same always-on capability that allows OpenClaw to independently perform monitoring and scheduled tasks even when a user's computer is off.

Muse also resembles OpenClaw in the breadth of its integrations and autonomous actions. It can send emails, book travel, make payments and even negotiate. Sensitive actions such as sending emails or making purchases require user approval, but other tasks can be completed autonomously without requiring human confirmation at every step. That mirrors OpenClaw's ability to complete an entire workflow from a single message.

OpenClaw, however, is open source, allowing users to install it on their own servers and connect models of their choice. Muse is a commercial cloud service that runs only on Meta's Muse Spark model. The biggest difference is security.

OpenClaw requires users to manage security themselves and has faced numerous concerns over vulnerabilities. Muse, by contrast, incorporates privacy and security safeguards from the design stage, including isolated secure VMs and separate connection approvals for individual apps. In that respect, it is similar to Nvidia's NemoClaw, which combines OpenClaw with a security framework.

NemoClaw for Enterprises and Developers, Muse for Consumers

Nvidia's NemoClaw is an open-source security and deployment stack designed to run OpenClaw securely. OpenClaw serves as the brain, while NemoClaw acts as protective headgear around it.

NemoClaw uses a sandbox runtime called OpenShell to block and monitor access to file systems, networks and processes, preventing OpenClaw agents from communicating externally without authorization. It also incorporates security and privacy policies, network access controls, credential management, model routing, agent lifecycle management and observability.

Nvidia's own language models, including Nemotron, can also run directly on a user's PC in a local environment. This allows inference and execution without sending data to the cloud. The configuration can be version-controlled through YAML policy files, allowing multiple developers to reproduce the same security settings.

Muse and NemoClaw share the concept of long-running agents that autonomously perform tasks. Both make isolated execution environments that prevent user data from being exposed externally a core design principle — cloud VMs for Muse and local sandboxes for NemoClaw.

Both approaches give AI agents permission to operate web browsers and apps while establishing guardrails to prevent unauthorized access to the broader system or sensitive information.

OpenClaw and NemoClaw target developers and users with some AI and IT expertise, while Meta's Muse can be seen as extending the concept to general consumers.

Always-On Operation and Long-Term Memory Key to Autonomous AI Assistants

Agentic personal AI assistants require computer-use technology, always-on operation and long-term memory, as well as broad web and app support.

Computer use is a fundamental technology for agentic personal AI assistants. It allows AI to perceive a screen and operate a computer like a person, moving the cursor, clicking buttons, entering text and navigating websites and apps. It effectively provides the AI with eyes and hands, with the advantage of not requiring separate integrations for every app.

While computer use is essential, always-on operation is even more important. Users need to be able to issue instructions and interact with the assistant at any time, while continuously pursuing long-term goals that may span several days requires a separate orchestration layer.

The assistant must also remember and preserve users' work and include dedicated connectors, or application programming interfaces (APIs), for functions such as payments and calendars to operate reliably in real-world tasks.

An agentic personal AI assistant therefore needs more than computer-use capabilities. It requires an always-available entry point through messaging services, long-running background execution that continues when the user is away, persistent memory of previous conversations and preferences, and an ecosystem of connectors spanning services such as email, payments and calendars.

By those criteria, services that can currently be considered agentic personal AI assistants are largely limited to OpenClaw, Meta Muse and Perplexity's Computer.

OpenAI and Anthropic, two leading AI research companies, do not yet offer fully autonomous personal AI assistants. Both companies are primarily focused on enterprises and developers.

Google continues to add agentic AI assistant features to its Gemini app, but its strengths remain largely within Google's ecosystem, including Gmail, Calendar and Docs. Microsoft Copilot is similar, with a focus on work environments built around Microsoft 365 applications such as Outlook, Word and Excel.

As adoption of autonomous artificial intelligence, or agentic AI, and agentic coding accelerates, the industry is confronting a paradox: per-token costs are falling while total AI spending is surging.

Unlike one-off chatbot conversations, agentic AI autonomously repeats reasoning, planning, tool calling, verification and error-correction loops. Estimates suggest agentic AI could consume five to 30 times as many tokens as a single query, and as much as 1,000 times more for complex tasks.

Recently released AI models are increasingly promoting token efficiency as a competitive advantage. Beyond lowering the cost per token, developers are emphasizing technologies that reduce the number of tokens used for reasoning itself. Prompt caching, batch application programming interfaces (APIs) and dynamic tool loading have been used to cut token-processing costs and eliminate unnecessary context.

More advanced approaches to token optimization are now emerging. The most prominent is latent-space reasoning, along with more aggressive approaches such as Soft Latent Thinking and Selective Latent Thinking, and the Full-Bandwidth Transformer, which seeks greater token efficiency while retaining the existing architecture.

Token Compression Does Not Eliminate Inefficiency

Competition among large language models (LLMs) is shifting from more parameters toward more efficient reasoning. The ability to solve the same problem with fewer tokens is becoming a new competitive metric.

Models with stronger reasoning capabilities tend to generate more reasoning tokens before producing an answer. Longer reasoning can improve accuracy, but generating more tokens increases decoding operations and graphics processing unit (GPU) computation. It also expands the KV cache used to store information from previous tokens. Performing the same reasoning with fewer tokens therefore directly affects AI model costs and latency.

Heima, or Efficient Reasoning with Hidden Thinking, presented at the International Conference on Machine Learning (ICML) in April, approaches the problem through compression. It reduces the number of tokens used for reasoning by compressing long chains of thought (CoT) or eliminating unnecessary reasoning steps.

Heima sharply reduces the length of "thinking tokens" used internally by a model to analyze and reason before generating its final answer. It compresses lengthy, complex reasoning into a handful of core tokens, which can be converted back into human-readable text by a dedicated Adaptive Interpreter when needed. The approach preserves how the model thinks while reducing how extensively that thinking is expressed in language.

Token compression can immediately reduce API costs without changing the underlying model architecture. But even with highly effective compression, reasoning is still expressed as natural-language tokens.

No matter how complex the model's internal calculations are, information passed to the next step must ultimately travel through the narrow channel of a single selected token. This is an inherent limitation of autoregressive decoding.

Autoregressive decoding is how transformer-based LLMs generate text sequentially, one token at a time, using previously generated tokens as inputs for the next step. Because the model continuously references earlier context, it can maintain coherent flow, tone and grammar even across long passages.

The approach, however, has clear limitations. During reasoning, each new token depends on previously generated tokens, making token generation inherently sequential and difficult to parallelize.

An incorrect token generated midway can also cause errors to accumulate through subsequent computations, distorting the context. Each new token must pass through the model's large neural network layers, adding computational costs and memory bottlenecks.

Latent Reasoning Moves Beyond Autoregressive Decoding

The limitation stems from a gap between how models think and how they transmit information.

Transformers process input tokens through multiple layers and transform them into high-dimensional hidden states containing thousands of dimensions. A hidden state represents information exchanged internally between model layers and is not exposed outside the model. It can be compared with an intermediate note the AI keeps internally before expressing an answer.

These hidden states contain not only context and meaning but also rich latent information such as incomplete reasoning, partial answers and future plans that have yet to be fully expressed as words. The problem is that this high-dimensional information is not passed directly into the next computational step.

Instead, the model uses the hidden state to calculate a probability distribution for the next token, selects one token and uses it as the input for the next step. The hidden state itself is not directly carried forward.

As a result, even sophisticated internal computations must pass through an extremely narrow bottleneck of a single word. Rich high-dimensional information represented as continuous vectors is compressed into a language token and then converted back into a vector for the next calculation, creating repeated information loss and computational inefficiency.

The problem goes further. Every time the model selects the next token, it projects the hidden state across the entire vocabulary to calculate probabilities for a large number of candidate tokens. This process, known as LM Head Projection, imposes a substantial computational burden.

Adding a single word therefore requires the model to repeatedly perform a large-scale probability calculation across tens or hundreds of thousands of vocabulary candidates. Reducing the number of tokens does not eliminate this fundamental computational structure.

Latent reasoning, which has recently gained attention as a token-optimization technology, seeks to fundamentally change part of this cost structure.

Does Intermediate Reasoning Need to Be Expressed in Natural Language?

Latent reasoning performs intermediate reasoning directly within the model's continuous internal state, rather than expressing it in natural language. In human terms, instead of verbalizing every intermediate thought before reaching a conclusion, the model quietly works through the problem internally.

Traditional reasoning incurs costs by converting internal thoughts into language tokens and then decoding those tokens back into representations for further computation. Latent reasoning instead completes intermediate calculations within the model's hidden numerical space, or latent state, without generating each thought as human-readable natural language.

One example pushing this concept further is the BDH-CQ model unveiled last month by Pathway Research, part of Pathway. Pathway develops post-transformer architectures and efficient AI reasoning technologies aimed at overcoming limitations of transformer models.

BDH-CQ has only 150 million parameters but attracted industry attention for its performance relative to its size on ARC-AGI-1, a well-known benchmark for difficult AI reasoning tasks.

ARC-AGI-1 evaluates a model's ability to infer and solve previously unseen spatial grid-pattern rules. It tests whether a model can independently construct geometric and spatial logic, similar to a child figuring out the rules of a new toy.

While conventional AI benchmarks often measure memorized knowledge or language understanding, ARC-AGI tests how quickly a model can identify and apply unfamiliar rules without prior knowledge. Models get only two attempts, or Pass@2, and even a single incorrect pixel results in a wrong answer.

BDH-CQ scored 29.5% under Pass@2. Frontier models perform far better under the same conditions, with OpenAI's GPT-6 Astra at 98.5%, Anthropic's Claude Opus 5 and Fable 5.1 at 97.5%, and Google Gemini 3.7 Flash at 95.5%.

BDH-CQ's 29.5% appears low by comparison, but its parameter count is far smaller. While parameter counts for frontier models are undisclosed, they are estimated at at least 1 trillion and are believed to be around 2 trillion to 5 trillion. BDH-CQ achieved nearly 30% with roughly one ten-thousandth as many parameters. Its compute cost per task was also just $0.0007.

150-Million-Parameter BDH-CQ Nears ARC-AGI-1 Pareto Frontier

The potential impact could be significant if Pathway Research can further improve its latent reasoning-based BDH-CQ model.

BDH-CQ does not output intermediate reasoning in human language tokens. Instead, it repeatedly updates its recurrent memory and performs computations entirely within its internal latent-state space.

For AI developers, reducing or eliminating the process of converting internal thoughts into language — projecting hidden states across the vocabulary through the LM head, selecting the next token and embedding that token again for the next reasoning step — could enable comparable performance with less computation. That could ultimately deliver high performance efficiency even in smaller models.

COCONUT demonstrated the possibility that AI can reason without expressing thoughts in language, while BDH-CQ extended the concept toward smaller models and lower inference costs.

Both approaches reduce the role of language tokens in AI reasoning and directly use the model's continuous internal states. Researchers are now going a step further by exploring whether language generation itself can be removed while an AI model is thinking. One such approach is Soft Latent Thinking.

A paper titled "A Model with No Head and Many Thoughts" appeared on arXiv on Aug. 31. Its authors are researchers from Russian internet and technology company Yandex and Russian financial technology company T-Tech.

The Soft Latent Thinking method described in the paper removes the LM head, the language model's output mechanism, during reasoning and replaces it with a lightweight projector. The projector transforms hidden states into continuous representations in embedding space and passes them directly into the next reasoning step.

Soft Latent Thinking Can Be Applied to Existing LLMs

Conventional language models repeatedly convert hidden states representing their internal thoughts into actual language tokens during intermediate reasoning. The model calculates probabilities across a large vocabulary containing tens of thousands of token candidates, selects the next token and uses it as the input for the next reasoning step.

The mechanism that converts hidden states into vocabulary-space probabilities is called the Language Model Head, or LM head.

Soft Latent Thinking eliminates this intermediate language-generation process. A much lighter projector directly connects the current latent state to the next one. The model continuously updates its vector state without generating language tokens while reasoning, converting it back into language only when producing the final answer.

BDH-CQ builds recurrent latent reasoning into a small 150-million-parameter model, while Soft Latent Thinking differs in that it can also be applied to existing LLMs.

Selective Latent Thinking

Soft Latent Thinking reduces computation by eliminating language-token generation during reasoning and processing the entire thought process in latent space. But that raises another question: Is compressing every reasoning step into a latent state always optimal?

Compressing stages that require detailed intermediate reasoning, such as complex mathematical calculations or precise logical deduction, could instead reduce accuracy.

Research is therefore expanding toward approaches that distinguish between reasoning segments that can be compressed and those that should remain in language, rather than converting the entire reasoning process into latent representations. Selective Latent Thinking is a representative approach.

Selective Latent Thinking predicts upcoming short reasoning segments and uses the model's confidence level to determine whether they should be compressed into latent representations. Segments that can be safely compressed are skipped through latent states, while uncertain sections or those requiring precise reasoning retain explicit CoT. The idea is that not every thought needs to be expressed in language, nor does every thought need to be moved into latent space.

A Selective Latent Thinking paper published in May tested the approach using Llama-3.2-1B and Qwen3-4B across four mathematical reasoning benchmarks.

The researchers reduced reasoning-chain length by 58.4% compared with explicit CoT while limiting the decline in accuracy to 2.8%. In other words, a reasoning process equivalent to 100 tokens could be simplified to about 42 tokens while largely maintaining performance.

The researchers also found that Selective Latent Thinking improved the balance between accuracy and efficiency compared with conventional latent reasoning. At similar levels of reasoning compression, it achieved 22.7% higher accuracy than existing latent reasoning methods.

Microsoft Studies Full-Bandwidth Transformer

Microsoft AI Frontier researchers' Full-Bandwidth Transformer retains generated tokens as in conventional models but also passes the previous step's hidden state into the next step, increasing the amount of information available during the same reasoning process.

The research, released last month, retains the standard LLM transformer architecture, KV cache and existing language-modeling objective while widening the information channel between steps. It combines the previous step's top-level hidden state with the embedding of the newly generated token using a Gated Linear Unit (GLU) and passes both into the next step.

According to the research, a 1-billion-parameter Full-Bandwidth Transformer trained on 400 billion tokens achieved performance comparable to a conventional transformer trained on about 600 billion tokens, or roughly 1.5 times as much data. This suggests models of the same size can achieve similar performance with less training data.

Differences also emerged during inference. The reasoning trace required to reach the same level of accuracy was shorter.

In other words, reusing information the model has already calculated rather than discarding it can deliver similar performance with less data during training and shorten the reasoning trace required to achieve the same accuracy during inference.

From Parameter Scaling to Reasoning Efficiency

Taken together, recent AI reasoning-efficiency technologies including Heima, COCONUT and BDH-CQ are moving toward changing the way models think. Heima compresses long reasoning processes into smaller thinking tokens, while COCONUT and BDH-CQ perform reasoning in latent space rather than human-readable language.

Soft Latent Thinking minimizes language generation during reasoning, while Selective Latent Thinking retains language only where needed and compresses the rest. Microsoft's Full-Bandwidth Transformer, meanwhile, preserves the token-based architecture while carrying hidden states into the next step so the model can use more information during the same reasoning process.

The approaches differ, but their direction is the same: improving AI performance increasingly involves changing how models internally process information and reason.

The AI market is likely to evolve toward competition not only in raw performance but also in how much of that performance can be achieved with fewer tokens and less computation.

AI and data infrastructure solutions provider HS Hyosung Information Systems is targeting enterprise AI operations with high-efficiency inference infrastructure combining its low-power Arm-based GreenCore server with neural processing units (NPUs).

HS Hyosung Information Systems said Sept. 9 it is working with major domestic and global NPU companies including Qualcomm, FuriosaAI, DEEPX and Mobilint to provide infrastructure suited to customer environments. It also offers its Arm-based GreenCore server as an option to maximize NPU performance. GreenCore was jointly developed by Korean Arm server manufacturer ACCESSLAB and HS Hyosung Information Systems.

The company describes GreenCore as an Arm architecture-based central processing unit (CPU) server featuring low power consumption, low heat generation and efficient many-core parallel processing. It is designed for data centers with limited power, cooling and rack space, as well as companies and institutions preparing on-premises AI services due to security and data sovereignty requirements.

“NPUs are gaining attention as an alternative to GPUs, but supported AI models and target workloads differ depending on the NPU manufacturer and product,” the company said. “It is therefore important to select products suited not only to the characteristics of the service but also to deployment environments such as data centers and edge infrastructure.”

HS Hyosung Information Systems analyzes customers’ AI models, service types, model sizes, concurrent user counts, target response times and data center power and space conditions to recommend suitable NPU candidates.

Once an NPU is selected, the company installs it in a GreenCore server and conducts integrated validation covering hardware and software compatibility, AI model execution and operational stability before deploying the system in the customer’s environment.

When needed, the company also works with AI solution providers to conduct proof-of-concept (PoC) testing that reflects actual service environments. This reduces the burden on customers of directly comparing and validating multiple NPU products and vendor-specific development tools. HS Hyosung Information Systems supports the entire AI inference infrastructure deployment process, from workload analysis and NPU selection to GreenCore server configuration, integrated validation, AI solution integration, PoC, deployment and operations.

The company plans to continue expanding its HS Hyosung AI Platform portfolio by integrating Arm servers, GPUs, NPUs, high-performance storage, data lakes, artificial intelligence for IT operations (AIOps) and its AI solution partner ecosystem.

Alphabet's Google will invest €13 billion in artificial intelligence (AI) data centers and related digital infrastructure in Finland, its largest single investment in Europe. The investment is supported by the country's cold climate, abundant water resources and electricity supply from nuclear power.

Google said it will work with Finnish municipalities, telecommunications companies, grid operators and other local organizations and businesses. The company has operated a data center in Hamina since converting a former paper mill into the facility in 2009.

The investment will expand the existing data center and add new facilities in Kajaani, Muhos and Vaala in northern Finland, where access to the power grid is strong.

Nordic countries including Finland and Sweden have emerged as attractive locations for new AI data centers because of their cold climates, abundant water resources from seas and lakes, and clean nuclear and renewable energy such as hydropower and wind. These conditions can reduce the energy consumed by cooling systems, which account for a significant share of data center electricity use.

According to a U.S. Department of Energy (DOE) energy-efficiency study, cooling systems account for about 40% of energy consumption at traditional data centers. Gartner recently estimated that cooling and other infrastructure account for about 35% of overall data center electricity consumption.

A Gartner report released in June projected that electricity consumption by cooling and other infrastructure will rise sharply from 159 TWh in 2025 to 195 TWh in 2026 as the number of dedicated AI servers increases.

The Nordic region's low outdoor temperatures allow data centers to extend the use of free cooling, reducing reliance on mechanical cooling. Lower utilization of air conditioners, chillers and other mechanical cooling equipment can significantly reduce cooling energy costs.

Abundant water resources also enable direct water cooling, which draws cold seawater or lake water through pipes to absorb server heat before discharging it. The method can rapidly cool large-scale servers without compressors or cooling towers. Google already uses a seawater-based cooling system at its Hamina data center, drawing cold water from the Baltic Sea to cool servers.

Abundant electricity is another reason hyperscalers are turning to the Nordic region. Hydropower in Norway and wind and nuclear power in Finland and Sweden can help hyperscalers reduce carbon emissions. As part of its Finland investment, Google has entered into a carbon-free energy partnership with Finnish state-owned energy company Fortum Oyj.

Google signed a 22-year agreement covering up to 50% of the capacity of Fortum's Loviisa nuclear power plant. The agreement will start at a smaller scale in 2028 and reach 50% of the plant's capacity from 2030 through 2049. It is Google's first nuclear power procurement agreement outside the U.S. The company also signed power purchase agreements totaling 629 MW with onshore wind developers including Valorem and Suomen Hyötytuuli.

Google will also work with Finnish transmission system operator Fingrid Oyj to ensure reliable grid connections and support grid stability. A 94-MW battery energy storage system (BESS) is planned near its Kajaani site to help balance the grid. The company will also work with Nokia and telecommunications service provider Elisa on high-speed network connectivity and other communications infrastructure.

Microsoft is similarly building an AI data center near Helsinki after investments in Sweden and Denmark. It is using the Nordic region's cold climate to improve cooling efficiency while planning to feed waste heat generated by AI computing into district heating networks for local residents.

Amazon Web Services (AWS) is modernizing and expanding its cloud infrastructure in Sweden, including Eskilstuna, Katrineholm and Västerås, as well as in Finland, to accommodate the high power density required by AI infrastructure. AWS has long-term wind power purchase agreements (PPAs) in Finland and Sweden and combines them with cold-air cooling. Its data centers are reported to operate at a power usage effectiveness (PUE) of 1.1 or lower.

Meanwhile, Argentina's Patagonia region, which has similar climatic conditions, is also emerging as a potential location for AI data centers.

OpenAI has signed a letter of intent (LOI) with local energy company Sur Energy to consider a green data center project worth up to $25 billion with capacity of as much as 500 MW. A lack of high-speed fiber-optic networks and transmission infrastructure, however, could require additional investment.

AI infrastructure company Snet Systems said on Sept. 10 it has completed a wireless network upgrade project at Kyung Hee University Medical Center, establishing infrastructure to support a smart hospital environment.

Kyung Hee University Medical Center launched the project to meet growing demand for digital healthcare services and smart medical environments. Snet Systems deployed a high-performance wireless network across major clinical and operational areas, providing a foundation for stable mobile electronic medical record (EMR) and medical Internet of Things (IoT) services.

The new network allows medical staff to use smart devices to immediately access and enter patient information at the point of care. It is expected to reduce errors and workloads associated with handwritten records and subsequent data entry, while real-time EMR connectivity supports faster and more accurate medical care.

The seamless wireless environment across key hospital areas is also expected to improve continuity of medical services and operational stability. Snet Systems said it carried out the project based on its experience building and maintaining the medical center’s existing Cisco-based wired network. Having a single provider operate both wired and wireless networks also enables faster responses to network disruptions.

Safety artificial intelligence (AI) company Intellivix showcased wildfire early-warning and autonomous safety monitoring technologies at IFA 2026 in Berlin, Germany.

Intellivix said it presented results from disaster-safety demonstrations combining its vision AI and Vision Language Model (VLM) technologies with a Korean neural processing unit (NPU) at the Korea Information & Communication Industry Promotion Agency’s (NIPA) K-AI Semiconductor Pavilion. The company was selected last year for the Ministry of Science and ICT’s On-Device AI Service Demonstration and Expansion project and is carrying out a demonstration project for wildfire early warning and autonomous safety monitoring AI based on Korean AI semiconductors.

The project is being demonstrated in Hadong, Sancheong and Hamyang counties in South Gyeongsang Province. It includes an AI event-detection model specialized for public safety, a video analytics server capable of handling 1,000 channels, an upgraded GIS- and video-based monitoring system, VLM-based real-time event verification, edge AI video analytics for wildfire monitoring, and technologies for predicting wildfire direction and analyzing propagation paths.

Intellivix said it uses a Korean NPU to analyze video data directly on site and its VLM technology to verify situations in real time. The goal is to improve the speed of early detection and the accuracy of monitoring for wildfires and other disasters.

AI software company Tomato System participated as a partner in Samsung SDS’s REAL Summit 2026, showcasing its AI-powered UI/UX development platform eXBuilder6 AIGen, the company said on Sept. 9.

REAL Summit, Samsung SDS’s annual AI and cloud event, was held at COEX in Seoul on Sept. 8. Tomato System demonstrated how eXBuilder6 AIGen turns natural-language requirements into actual UI screens and source code, and proposed the solution as an AI-powered intelligent development platform for organizations.

eXBuilder6 AIGen accepts requirements in various formats, including documents, notes, sketches and images. AI analyzes the input, proposes screen layouts and UI components, and generates code in real time for use in actual development. AI also handles code modification and validation.

“In large-scale system development projects, various stakeholders are involved and requirements continue to change throughout the development process, making it important to quickly turn initial requirements into screens and connect them to actual development results,” Tomato System said. “eXBuilder6 AIGen allows ideas from the planning stage to be rapidly implemented as prototypes and enables discussions among stakeholders to continue consistently through the actual development stage.”

Physical artificial intelligence (AI) company Xiilab has won first place in Track 4 of the 2026 AI City Challenge, a global computer vision competition organized by Nvidia. The ranking was officially confirmed after the company passed the organizer's verification process following its first-place finish on the public leaderboard.

The 2026 AI City Challenge was held as part of the European Conference on Computer Vision 2026 (ECCV 2026), which began Sept. 8 in Malmö, Sweden. The competition evaluated synthetic-data training and real-world adaptation technologies for intelligent transportation, smart cities and physical AI. Track 4 evaluated models on real-world video after training them solely on about 1.01 million synthetic images provided by the organizers.

Xiilab said the Simulation-to-Real (Sim2Real) gap, in which models trained in virtual environments perform worse in real-world settings, has been a key commercialization challenge for physical AI, which relies heavily on physics-based simulation and synthetic data generation.

"This award demonstrates Xiilab's AI technology for applying virtual data to real-world environments," the company said.

Xiilab plans to apply the technology demonstrated in the competition to its vision AI and digital twin businesses. The company plans to use digital twins as a foundation for generating vision AI training data, providing an integrated process spanning digital twin construction, synthetic data generation, model training and real-world deployment.

Apple today announced the iPhone 18 Pro, iPhone Duo, AirPods 5, and new Apple Watches. Here are a few initial thoughts.

The Event

• John Ternus looked comfortable during his first product event as Apple CEO. And he got to introduce “one more thing” straight out of the gate.
• Apple has these video events down. They move fast and look beautiful. I still miss the tension of a live room. Million-dollar executives should have to sweat once in a while.
• There was a lot of “later this year” today. Some of that is understandable with an entirely new iPhone platform. I also expect they’ve had their hands full with Siri AI.
• Apple’s larger pitch was the iPhone as an “intelligent personal hub.” The iPhone does the thinking, the Watch senses what is happening, and the AirPods handle private input and output. I like that they are getting a bigger picture story around AI.
• Also, they said AI.

iPhone 18 Pro

• The new camera system is the selling point. A mechanical aperture that moves from f/1.48 to f/4 gives photographers real control over light and depth of field. Apple is also bringing aperture, shutter speed, white balance, and a histogram into its own Camera app. Third-party camera apps just lost some territory.
• Apple Reference Image may prove more important than it first appears. The idea is to make a signed record of what the sensor saw at the moment of capture. With synthetic images getting better every month, establishing the origin of a photograph is going to matter.
• Apple spent a surprising amount of time talking about cooling. That makes sense. Heat has become one of the main limits on phone performance. I’ll be curious to see how much difference the new vapor chamber makes. My theory is that Apple has a specific heat ceiling. Better thermal control gives it room to run the chip faster before reaching that ceiling.
• There is no regular iPhone 18 this fall. Apple’s least expensive new iPhone starts at $1,199. I am curious how that goes over once these phones move beyond the enthusiasts.

iPhone Duo

• Well, they got me.
• The hardware is impressive, but the software sold the device. The way apps move between displays, the new dock, the two-app layouts, and all the partially folded positions make this feel like a new kind of iPhone. It does not look like Apple took iPadOS and folded it in half.
• I’ve been calling this an iPad mini in your pocket, and that still feels right. When I travel, I often leave the laptop in my bag and write on an iPad mini with a folding keyboard. The Duo could give me that setup without carrying the iPad.
• The hard choice is the camera. If you want the best camera that you will actually carry, get the iPhone 18 Pro. The Duo gives up the Pro’s variable aperture and telephoto reach.
• There are plenty of other compromises. No Face ID. A smaller outer screen. A hinge with more than 100 parts. First-generation hardware that starts at $1,999. This thing screams AppleCare.
• I’m going to buy one. I will keep an eye on the return window, but I suspect it is staying. At this point I would probably say anything to justify getting a Duo, so discount my advice accordingly.

Apple Watch

• I started the day with an Ultra 3 and no good reason to replace it. Then Apple announced the new health sensors and Live Rewind. So much for discipline.
• Live Rewind is the Watch feature I care about most. My hearing isn’t so great. Asking someone to repeat themselves once is fine. Asking two or three times gets awkward. The ability to look down and read the last 15 seconds could be genuinely useful to me.
• That feature also deserves scrutiny. Apple says the audio is handled on the device and deleted, with a chime and full-screen notice when Live Rewind runs. I want to understand how it works around other people before deciding how often I would use it.
• The Apple Watch story has changed a lot. It began as a device for notifications and small apps. Apple now talks about it primarily as a health sensor on your wrist.

AirPods 5

• The $129 AirPods 5 look like the easy recommendation. Active noise cancellation is now standard, and people who dislike silicone ear tips get more of the AirPods Pro experience in an open-ear design.
• Apple did not make a strong case for the $149 version. Wireless charging, a volume gesture, and some additional battery life are nice. I am not sure they are worth the extra $20 for most people.
• I wonder why there are two tiers to this product. That $129 price point must be particularly effective for Apple.

What Did Not Show Up

• No new Apple TV. No HomePod mini. No screen-based HomePod device. None of those products belonged in an iPhone-focused event. I suspect we’ll see them all soon.

I am still inside the reality distortion field, so check back after these products ship. But yes, I am ordering the Duo.

The post A Few Thoughts Following Apple’s September 2026 iPhone Event appeared first on MacSparky.

On this episode of Intentional AI, Chris and I look at how dictation went from a nine-thousand-dollar tool that needed hours of training to something you can whisper into your watch. We compare cloud and local options, including Wispr Flow, MacWhisper, Superwhisper, and Whisper Memos, along with the privacy tradeoffs that come with each.

We also talk about using your voice to give robots better context, then wrap up with Claude’s new browser and background computer use.

This episode of Intentional AI is sponsored by:

• Making Light: Craft a daily ritual to focus on what matters most. Get $10 off your first order with the code ‘INTENTIONAL26’

The post Intentional AI 8: Whisper to Write appeared first on MacSparky.

Nature, Published online: 09 September 2026; doi:10.1038/s41586-026-11002-8

On this episode of Focused, Mike and I work through practical questions about capacity, chronotypes, delegation, accountability, and what to do when a careful plan collapses. We also talk about task overload and the difference between getting human help and handing work to a robot.

In Deep Focus, we look at systems for bad days and why my automated daily note lost to the paper cards sitting on my desk.

This episode of Focused is sponsored by:

• Gusto: Payroll, HR, Benefits. Simplified. Get 3 months free.
• Keeper: Get 60% off personal and family plans.
• Daylite: The only made-for-mac CRM solution. Start your free trial today.
• Squarespace: Save 10% off your first purchase of a website or domain using code FOCUSED.

The post Focused 264: The Accountability Shaped Hole appeared first on MacSparky.

Joe Moyer explains how his iPad mini became his go-to reading device. I’ve got an old iPad mini serving the same purpose. Wouldn’t it be nice, however, if I could fit it in my pocket?

Totally unrelated, I’m looking forward to the iPhone event tomorrow.

The post A 7″ Reading Device appeared first on MacSparky.

Autonomous driving AI company RideFlux has signed a memorandum of understanding (MOU) with commercial vehicle maker Tata Daewoo Mobility to cooperate on Level 2+ (L2+) driver assistance systems for commercial vehicles and Level 4 (L4) autonomous driving.

Under the agreement, RideFlux’s software for five key highway driving functions will be installed on Tata Daewoo Mobility’s XEN and KUXEN heavy-duty truck platforms, with the companies pursuing mass production of L2+ driver assistance systems. Tata Daewoo Mobility will provide the vehicle platforms and vehicle control interfaces, while RideFlux will lead advance development of Driver Control Assistance System (DCAS) software compliant with the UN R171 international standard.

The L2+ driver assistance system integrates longitudinal and lateral vehicle control for functions including lane keeping, lane changing, highway junctions and merging, overtaking and tollgate passage. RideFlux expects to establish a data feedback loop that uses real-world driving data collected from vehicles after mass production to simultaneously improve L2+ performance and validate L4 technology.

The companies have also outlined a roadmap for fully driverless L4 commercialization. Building on what RideFlux said is Korea’s first paid freight transportation service using heavy-duty autonomous trucks, the company plans to develop robotruck conversions before moving to mass production of fully driverless L4 trucks.

The partnership also includes plans to establish a foothold in the global commercial vehicle market by linking the technology with the software-defined vehicle (SDV) architecture of Tata Motors, Tata Daewoo Mobility’s parent company and the largest commercial vehicle maker in India.

RideFlux said that following Korea’s first paid autonomous-truck freight service with Hanjin in July, supplying autonomous driving software to original equipment manufacturer (OEM) Tata Daewoo Mobility gives it a new business-to-business (B2B) revenue stream. The company said the deal also signals that the autonomous driving industry is moving from government-led demonstration projects, or B2G, toward commercialization, providing further momentum for its planned KOSDAQ initial public offering (IPO).