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  • The math behind the OpenAI Jalapeño chip

    OpenAI’s financial trajectory hinges heavily on infrastructure costs, a reality that drove the development of the new custom OpenAI Jalapeño chip. Developed in collaboration with Broadcom, the application-specific integrated circuit (ASIC) represents a direct attempt to mitigate the heavy capital expenditure associated with third-party hardware. 

    While Nvidia currently commands an estimated 75% profit margin on its high-end processors, OpenAI operates on tighter margins, keeping roughly 33 cents of profit on each dollar generated after accounting for its massive operational expenses. The financial burden of running large language models at scale is severe. 

    Last year, keeping ChatGPT servers responsive had cost OpenAI a staggering US$8.4 billion. With the platform now attracting 900 million weekly users, that operational cost is projected to reach approximately US$14 billion this year. Over the next eight years, OpenAI has committed roughly US$1.4 trillion to computing power, a massive bet for a company currently generating US$25 billion in annual revenue.

    Designing Hardware for LLM Inference

    The OpenAI Jalapeño chip, dubbed as the company’s first “Intelligence Processor”, is built specifically for large language model (LLM) inference rather than general-purpose AI workloads. OpenAI provided the core architectural design based on its specific model roadmaps and serving systems, while Broadcom managed the silicon engineering and high-performance networking integration. 

    TSMC handles the physical manufacturing in Taiwan, and Celestica is tasked with building the board and rack systems. According to OpenAI, early lab samples are already running frontier workloads, including an unreleased GPT-5.3-Codex-Spark model, at target production frequency and power. 

    Richard Ho, head of OpenAI’s hardware program, noted that the architecture minimizes data movement to push realized utilization closer to its theoretical peak performance. Unlike general-purpose accelerators adapted from legacy AI workloads, this architecture specifically balances compute, memory, and networking resources to solve the data-movement bottlenecks native to interactive LLM serving.

    To achieve this at scale, the platform integrates Broadcom’s Tomahawk networking silicon directly into the design, allowing the custom processors to communicate across massive, clustered data center environments.

    The vertical integration flywheel

    By moving into custom silicon, OpenAI shifts from being a mere software layer to a vertically integrated infrastructure company. This full-stack strategy spans the entire pipeline: chip architecture, software kernels, memory systems, network scheduling, and the final application layer. Much like Apple’s tight coupling of proprietary hardware and iOS, OpenAI can now optimize its infrastructure around its exact internal model roadmaps.

    This integration feeds a continuous operational flywheel. Enhanced infrastructure efficiency lowers the cost of both training and serving models. More affordable serving leads to better, more responsive products, which drives user volume and revenue to be reinvested back into the next generation of custom infrastructure.

    Overcoming the late-mover advantage

    By introducing its own silicon, OpenAI enters a landscape where its primary competitors have spent nearly a decade developing proprietary hardware. Google began deploying its Tensor Processing Units (TPUs) in 2015 and now controls roughly a quarter of global AI computing capacity outside of Nvidia’s supply chain. 

    Amazon has shipped over one million of its custom chips, while Meta and Microsoft continue to scale their own infrastructure.

    “Jalapeño is part of our long-term full-stack infrastructure strategy to make compute more abundant,” said Greg Brockman, president and co-founder of OpenAI. “By designing more of the stack ourselves, we can serve more intelligence with greater efficiency.”

    To close this timeline gap, OpenAI accelerated the development phase. The OpenAI Jalapeño chip transitioned from a blank-slate design to manufacturing tape-out—the final step before physical production—in just nine months. The engineering teams achieved this timeline by utilizing OpenAI’s own language models to automate and optimize portions of the hardware design process.

    This creates a unique feedback loop where the models served to users are actively being leveraged to build the physical infrastructure that will run future iterations. Initial deployment of the hardware into data centres is scheduled to begin by the end of 2026.

    Broadcom CEO Hock Tan confirmed that the rollout will scale alongside infrastructure partners, including Microsoft, to prepare for gigawatt-scale data centre integration.

    (Photo by OpenAI)

    See also: Omio scales travel product development using OpenAI models

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  • Samsung opens ChatGPT Enterprise and Codex access after AI restrictions

    Samsung Electronics is expanding employee access to ChatGPT Enterprise and Codex, giving staff wider use of AI tools for technical and non-technical work.

    According to OpenAI, the deployment covers all Samsung Electronics employees in Korea and all Device eXperience employees worldwide. The DX division includes smartphones, consumer electronics, and home appliances.

    Samsung plans to use the tools in software development, marketing, product development, manufacturing, and other business functions. The tools will support tasks such as information search, document drafting, idea development, data interpretation, and code-related work.

    Samsung revisits employee AI use

    The rollout comes three years after Samsung restricted employee use of generative AI tools over data-security concerns. In 2023, the company limited the use of ChatGPT and similar tools after concerns that sensitive internal information had been uploaded to an external AI platform.

    The new deployment gives employees access to ChatGPT Enterprise, which includes controls for data protection, user access, and security management. OpenAI said the enterprise version allows organisations to manage users, apply access controls, and use AI tools within internal security requirements.

    Samsung’s earlier restrictions applied to employee use of ChatGPT and similar generative AI tools. The new rollout gives employees access through an enterprise product with data protection and access controls.

    Samsung has not limited the deployment to a single business unit or technical group. OpenAI said the tools will be used across a broad range of functions, including technical and non-technical teams.

    OpenAI said ChatGPT can support knowledge-based tasks such as searching for information, analysing material, drafting documents, developing ideas, and interpreting data.

    Codex for technical and non-technical work

    Codex will be used for software-related tasks such as writing, reviewing, and debugging code. OpenAI said the tool is also being used for internal tools, websites, software prototypes, and automated workflows.

    OpenAI said Codex can also support non-technical teams in day-to-day work, including by helping employees create internal tools and automated workflows.

    OpenAI said Codex now has more than five million weekly users across technical and non-technical workflows. In Korea, weekly active users of Codex have grown nearly 800% since February 1, 2026, according to the company.

    Harrison Kim, general manager of OpenAI Korea, said the agreement is one of OpenAI’s largest enterprise deployments. He said Samsung is using AI across teams and functions rather than limiting it to specific departments.

    In October 2025, Samsung said it would work with OpenAI as a strategic memory partner for the Stargate AI infrastructure initiative, with OpenAI’s memory demand projected to reach up to 900,000 DRAM wafers per month.

    Samsung SDS also entered a potential partnership with OpenAI to jointly develop AI data centres and provide enterprise AI services. Samsung said the agreement would allow Samsung SDS to provide consulting, deployment, and management services for businesses integrating OpenAI models into internal systems.

    Samsung SDS also signed a reseller partnership to offer OpenAI services in Korea. Under that arrangement, Samsung SDS said it would support Korean companies adopting ChatGPT Enterprise and other OpenAI services.

    Reuters reported that Samsung Electronics and SK Hynix had signed letters of intent to supply memory chips for OpenAI’s Stargate project. The report said the two South Korean chipmakers together account for about 70% of the global DRAM market and nearly 80% of the high-bandwidth memory market.

    High-bandwidth memory supports fast data movement between memory and processors in AI systems. Reuters reported that OpenAI’s chip demand for Stargate may reach 900,000 wafers per month, citing South Korea’s presidential office.

    Samsung said its semiconductor businesses would support OpenAI’s demand with advanced memory solutions. The company also said its affiliates were exploring broader work with OpenAI in areas including data centres, enterprise services, and AI infrastructure.

    AI adoption and productivity

    Deloitte’s 2026 State of AI in the Enterprise report found that 66% of organisations reported productivity or efficiency gains from enterprise AI adoption. The same report found that 53% reported improved insights and decision-making.

    A Bpifrance survey reported by Reuters found that 77% of 534 French mid-sized company heads said their firms used generative AI, but only 17% of those using it reported time savings.

    Samsung has identified use cases across document work, information analysis, coding, product development, marketing, and manufacturing. The deployment gives employees access to ChatGPT Enterprise and Codex for those tasks under a company-wide agreement.

    OpenAI’s Korea partnerships

    OpenAI has also announced other partnerships in Korea. Seoul National University recently began providing ChatGPT Edu to 47,000 students, faculty, and staff.

    OpenAI has also worked with Kakao to bring ChatGPT responses into KakaoTalk group chats. The company said Korean organisations including LG Electronics, LG Uplus, LG CNS, GS E&C, Samsung SDS, TVING, Krafton, Toss, MUSINSA, Korea Zinc, Nexen Tire, and HanaTour are using ChatGPT Enterprise, OpenAI APIs, or Codex.

    (Photo by Zulfugar Karimov)

    See also: Omio scales travel product development using OpenAI models

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    The post Samsung opens ChatGPT Enterprise and Codex access after AI restrictions appeared first on AI News.

  • Anthropic drops ‘workplace AI agents’ directly inside Slack

    Anthropic launched a beta version of its Claude Tag feature for Enterprise and Team tiers, shifting its chat model into shared Slack channels. Moving away from traditional isolated chat boxes, users pull the artificial intelligence model into active group threads by typing @Claude. 

    The integration allows any team member in the channel to delegate a task, review the model’s outputs, and pick up the discussion thread from a previous point. This structural shift follows a US$65 billion Series H funding round that brought Anthropic’s post-money valuation to US$965 billion, positioned above rival OpenAI’s US$852 billion mark. 

    Following a confidential S-1 filing for an initial public offering, market competition for business software placement remains tight. Data from corporate expense platform Ramp’s May 2026 AI Index indicates Anthropic’s enterprise adoption rate reached 34.4%, passing OpenAI’s 32.3% footprint.

    Modifying the channel workstream

    Standard generative software requires enterprise employees to move data between team chats and separate browser instances. Anthropic aims to reduce this back-and-forth movement by restructuring workplace AI agents to work in multiplayer environments.

    “Instead of a private back-and-forth, Claude Tag shows up in the open,” stated Rob Seaman, general manager of Slack, regarding the operational mechanics of the application. This shared visibility alters how context is tracked inside an organisation. Because Claude Tag logs its task status directly inside the communication window, multiple employees can monitor the live execution steps. 

    The system tracks ongoing information from its active channels to build a contextual background. This automated history tracking limits the need for team members to continuously retype foundational company data or project scopes.

    Functional mechanics and asynchronous tasks

    The technical foundation for this channel integration relies on Anthropic’s Opus 4.8 engine. When assigned a request, the model divides the operation into sequential execution phases and utilises connected corporate databases, tools, and code repositories to complete the work.

    The primary operational difference for these workplace AI agents is their capability to function asynchronously without real-time human prompting. If a network administrator activates the tool’s “ambient” configuration, Claude Tag monitors threads and tracks tasks autonomously. The agent checks inactive text threads, signals priority notifications from integrated software extensions, and tracks unresolved assignments across multi-day intervals.

    Cat Wu, head of product for Claude Code, noted that the change centres on user configuration rather than completely new logic. “The form factor of being able to tag it the same way that you would a coworker is really powerful,” Wu told Reuters. Wu explained that connecting her personal Claude Tag agent to her email archive allows the system to analyse incoming communications, categorise urgent entries, and send immediate alerts inside Slack.

    Metrics and administrative controls

    Internal reporting from Anthropic shows that automated code generation has altered engineering activities, with the firm’sinternal product group creating 65% of its code through its private version of Claude Tag.

    Beyond software development, the vendor targets non-technical office workforces. Early customer implementations focus on querying database metrics, parsing analytics data, and processing internal IT support tickets.

    This expansion of background agent operations requires a distinct security infrastructure to protect proprietary information. To restrict data access to approved departments, system administrators must establish scoped Claude identities. All localised memories and tool integrations are confined strictly to specific channels authorised by the IT department. 

    Additionally, management portals offer full tracking logs of user queries alongside specific organisational caps to regulate monthly token costs. 

    The enterprise calculation: Autonomy vs. governance

    Frankly, moving generative tools from individual sandboxes into persistent corporate communication channels presents distinct operational trade-offs. The clear upside is the optimisation of routine knowledge work. By centralising information logs directly inside active threads, companies can lower task friction, capture context across changing project teams, and reduce the time spent on manual codebase tracking or database updates.

    However, delegating cross-app workflows to background agents introduces significant structural risks for IT departments. Permitting automated systems to read chat histories, connect to email accounts, and modify central code repositories expands an organisation’s internal data-exposure risks.

    If access boundaries are misconfigured, sensitive proprietary context could cross into unapproved channels. Furthermore, autonomous asynchronous execution removes direct human verification from intermediate workflow stages, leaving teams vulnerable to systemic errors if the underlying model misinterprets instructions mid-task. 

    Corporate decision-makers must ultimately evaluate whether the productivity gains of channel-based automation outweigh the rigorous auditing, compliance overhead, and channel-by-channel security configurations required to safely govern an always-on agent.

    See also: Anthropic releases Claude Opus 4.8

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  • Omio scales travel product development using OpenAI models

    Omio integrates OpenAI models across its engineering operations to accelerate travel product development and launch booking interfaces.

    The multimodal travel platform coordinates operations with over 3,000 transportation providers across 47 countries. Omio explicitly rejects the superficial addition of technology to outdated internal processes. The company’s CTO, Tomas Vocetka, requires all internal functions to completely redesign their operational execution frameworks from the ground up to operate as a native AI enterprise.

    OpenAI Codex integration

    Vocetka initiated the internal deployment by providing base ChatGPT access to the workforce, establishing a baseline familiarity with generative models before executing the primary technical integration.

    Omio subsequently embedded OpenAI Codex directly into its engineering operations, mandating its application across the entire software development lifecycle. Engineers currently apply Codex to preliminary research, architectural planning, active coding, automated testing, code reviews, and ongoing system maintenance.

    The engineering division constructs custom internal connectors to link proprietary data environments directly with these tools. This setup allows developers to bypass basic information retrieval and proceed directly to active task execution within their integrated development environments.

    Vocetka categorises the initial ChatGPT rollout as a preliminary introduction, emphasising that Codex handles the actual production workload. The deployment execution matured beyond the technical divisions. Management actively expands the use of Codex into non-technical corporate functions across the wider organisation. This expansion ensures standard operational procedures adapt to the new capabilities introduced by the engineering team.

    Internal analysis indicates the technical effort required to build specific products now sits at approximately 20 percent of previous levels. Delivery timelines show corresponding compression. Projects demanding the attention of multiple developers over an entire fiscal quarter now require a single engineer operating for roughly one month.

    Faster cycle times allow the engineering teams to test experimental concepts and validate consumer demand with minimal resource expenditure. Management allocates capital and engineering hours with greater precision, relying on prototyping to eliminate unviable features before committing to full-scale production.

    Lowering the time and cost barrier for software creation enables quicker internal decision-making. The technical teams iterate on existing products at a much higher velocity, pushing updates and new interface elements to the live environment at accelerated pace.

    Conversational commerce built on real-time transport data

    Omio launched one of the earliest conversational travel booking interfaces in 2023 by connecting OpenAI models to its proprietary transportation inventory.

    The system processes natural language queries regarding complex multimodal routes. Travelers input natural language requests asking for the fastest route from Rome to Florence, or comparing flights and trains between Paris and Barcelona.

    Omio aggregates services spanning trains, buses, ferries, and flights. Legacy travel booking required users to navigate multiple websites, manually compare modes of transport, and independently aggregate itineraries across multiple providers. Omio replaces this fractured process with a unified interface capable of parsing consumer intent.

    The generative models analyse text inputs and ping the booking systems to construct viable travel paths. The application functions by grounding the model responses in live pricing and availability data. The architecture prevents the generation of travel options based on static or outdated training data. The resulting output provides consumers with directly bookable itineraries.

    Omio expanded its initial integration into a dedicated ChatGPT experience. This dedicated application directly accesses the global transportation network maintained by the company. By grounding the user interaction in verified data, the technical team ensures high-fidelity responses. Consumers receive highly-personalised journey options rather than generic travel advice.

    Omio defines this structural setup as a new category of conversational commerce. The AI operates as the primary interface layer mediating the interaction between the consumer and the underlying global transportation network. The company views this as a broader departure from legacy search-based interfaces toward native generative customer experiences.

    The deployment points to a future where travel planning relies entirely on interacting with intelligent systems connected directly to live transportation networks.

    Omio’s corporate policy explicitly mandates that human personnel retain full accountability for all deployed code and final business outcomes. Generative tools function strictly as acceleration engines for development, analysis, and decision-making.

    “The responsibility and accountability stay with people. AI helps us develop faster, analyse faster, and make decisions faster, but people stay in charge,” explains Vocetka.

    This governance structure prevents automated systems from independently executing irreversible changes to the booking infrastructure or the core multimodal routing algorithms. The combination of broad employee access to OpenAI tools and rigorous oversight models creates an environment prioritising both speed and systemic stability.

    See also: Mitigating vendor lock-in with Sakana AI Fugu multi-agent models

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    AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

    The post Omio scales travel product development using OpenAI models appeared first on AI News.

  • Top spy agencies say AI cyber threats will impact you within months. Here’s why

    The global surge in AI cyber threats is no longer a distant problem for corporate data centres, according to an urgent public warning from the world’s most powerful intelligence alliance. On June 22, 2026, the cybersecurity chiefs of the Five Eyes nations—comprising the US, UK, Canada, Australia, and New Zealand—issued a rare joint intelligence briefing stating that upcoming artificial intelligence models will supercharge offensive hacking capabilities on a timeline measured in months, not years. 

    While the advisory specifically tells corporate executives to overhaul their network defences, the rapid evolution of these tools means everyday internet users are about to face a much shiftier digital landscape. 

    The massive shift in AI cyber threats

    The intelligence brief highlights an immediate danger: advanced, upcoming models like OpenAI’s “GPT-5.5-Cyber” and Anthropic’s “Mythos” are actively lowering the technical barriers for digital crime. Rogue actors no longer need elite coding skills to build complex, devastating software exploits.

    Instead, automated digital agents can scan internet-connected infrastructure around the clock to find software vulnerabilities before human engineers can patch them. This drastically shrinks the safety window that technology companies rely on to keep user applications secure.

    How does this hit home for regular users?

    When criminal networks use automated tools to breach large databases, the immediate consequence is the theft of regular consumer data. Your personal information, saved passwords, and cloud backups are the ultimate targets in these accelerated corporate intrusions. 

    Furthermore, bad actors are leveraging conversational models to generate hyper-personalised phishing scams at an industrial scale. This trend is hitting the Asia-Pacific (APAC) region particularly hard, with countries like India recording a staggering 165% spike in ransomware incidents in early 2026 due to AI-assisted targeting.

    Rather than relying on easily spotted, poorly written spam emails, automated systems can scan your public social media profiles to write flawless, highly convincing messages designed to steal your credentials. 

    Fighting back with the same tech

    The primary challenge facing cyber defenders is that machine-paced offence naturally moves faster than human-led detection. According to the World Economic Forum’s Global Cybersecurity Outlook, a massive 94% of corporate executives identify AI as their top threat vector, yet two out of three organisations report moderate to critical cybersecurity talent shortages.

    Network administrators are finding it impossible to review and deploy traditional security patches manually when rogue AI agents can discover and exploit a software vulnerability within minutes. 

    The Five Eyes alliance emphasises that the most effective way to withstand these accelerating AI cyber threats is to deploy automated defences. Security teams are actively integrating defensive artificial intelligence models to monitor unusual behaviour and isolate network breaches.

    For individual users, the basic rules of internet safety are becoming mandatory. Turning on multi-factor authentication and deleting old, unused online accounts remain the most effective ways to break the automated chain of an AI-driven attack.

    See also: AI web search risks: Mitigating business data accuracy threats

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    AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

    The post Top spy agencies say AI cyber threats will impact you within months. Here’s why appeared first on AI News.

  • Mitigating vendor lock-in with Sakana AI Fugu multi-agent models

    Sakana AI launched Fugu to orchestrate multi-agent operations and mitigate single-vendor dependency risks in enterprise deployments.

    Enterprises face operational vulnerabilities when relying entirely on monolithic AI APIs. Japanese AI firm Sakana AI designed Fugu as a response to these concentration risks by creating an orchestration language model that calls upon a pool of varied models to complete multi-step tasks.

    Users access this ecosystem through a single OpenAI-compatible endpoint. Fugu routes queries internally, deciding whether to resolve a prompt directly or to assemble a coordinated team of expert models for deeper analysis. The system handles model selection, delegation, verification, and synthesis internally. Engineering teams interact with what appears to be one model while a background system of specialists executes the actual computation.

    Sakana AI targets the geopolitical and regulatory risks associated with AI sourcing. Recent export controls affecting Anthropic models like Fable and Mythos demonstrated that access to specific foundational architectures can vanish based on foreign policy decisions.

    Fugu functions as a hedge against these sudden supply chain disruptions. The platform relies on a completely swappable agent pool. Fugu dynamically routes traffic around any restricted or degraded provider to maintain service continuity. Sakana AI states this capability provides the resilient architecture required for AI sovereignty.

    Fugu deployment tiers

    Two tiers are available to accommodate different operational latency requirements.

    The standard Fugu model prioritises low latency for daily tasks, integrating into standard developer tools like Codex for live coding and code review. Organisations subject to strict data governance or privacy mandates can manually opt specific underlying models out of the standard Fugu routing pool.

    Fugu Ultra targets complex, multi-step analytical problems that demand maximum accuracy. The Ultra variant coordinates a deeper pool of expert agents for intensive tasks such as academic paper reproduction, literature investigations, and patent analysis.

    Sakana AI reports that Fugu Ultra performs competitively against leading closed models like Fable 5 and Mythos Preview across scientific, engineering, and reasoning benchmarks:

    Benchmarks of Sakana AI Fugu standard and Ultra compared to rival frontier models.

    The orchestration method ensures companies can access top-tier computing capabilities without carrying the vendor concentration risk or export control exposure inherent to those closed models.

    Implementation in cybersecurity

    Almost 500 early users tested the system during an extended beta program focused on lengthy, multi-step computational workflows. With cybersecurity such a focus for models like Claude Mythos, engineering teams deployed Fugu Ultra to automate complete security assessment cycles.

    Human operators issued one scoped instruction, and the orchestration engine executed the entire reconnaissance phase. The model successfully conducted cross-site scripting and SQL injection checks alongside thorough authentication reviews.

    A participating cybersecurity engineer confirmed the model stayed strictly within its operational parameters and avoided initiating destructive actions against the target infrastructure. Fugu concluded the automated engagement by generating a clean vulnerability report complete with verifying evidence and exact retest steps for human remediation teams.

    The implementation demonstrated that multi-agent routing maintains strict compliance boundaries while executing complex penetration testing sequences.

    Software development teams also integrated Fugu Ultra into their primary code review pipelines to compare defect detection rates against established monolithic tools. The orchestration engine consistently outperformed baseline models in identifying logic flaws and security vulnerabilities within complex enterprise codebases.

    “For code review, Fugu Ultra is significantly better than GPT-5.5. It gives comprehensive answers and finds the bugs others miss,” reported a software engineer involved in the beta deployment. “Where other tools flag about three issues, Fugu surfaced more than twenty. It’s become the model I run all my reviews through.”

    Automated research and persona stability

    Data science units deployed the system in an almost fully-automated research mode. Fugu Ultra successfully explored mathematical hypotheses, executed experimental code runs, interpreted failure states, and revised its own approaches to sustain progress over extended periods with minimal human intervention. This capability directly addresses the operational limitations of single-call models that require constant human prompting to recover from logic errors.

    Leadership at an unnamed enterprise platform company identified long-term persona stability as a primary advantage during these extended sessions. Conventional monolithic architectures often suffer from context degradation and identity drift when processing extensive conversational histories.

    “Raw output quality is on par with top frontier models, but Fugu showed unusually strong persona stability across long sessions, holding its identity where other models drift,” the executive stated. “For agent products, that may matter more than raw benchmark scores.”

    Extended benchmark validation

    Sakana AI built the internal routing logic upon extensive research into learned model orchestration. The technical foundation for the product stems from findings published in the company’s ICLR 2026 papers, specifically the Trinity and Conductor frameworks.

    These academic foundations allow Fugu to process requests by understanding precisely when a task requires delegation versus direct resolution. The internal language model dictates communication protocols between the individual agents and structures the final synthesis of their separate computational outputs.

    Validation testing against frontier AI competitors covered complex, open-ended disciplines ranging from financial time series prediction to mechanical design. Fugu also demonstrated high proficiency in niche physical logic tests and visual interpretation tasks, including solving the Rubik’s Cube and performing Japanese handwriting analysis. The capacity to excel in both quantitative financial modelling and qualitative image processing confirms the efficacy of the multi-agent orchestration approach.

    Sakana AI designed the system to scale organically as the broader AI hardware and software market matures. Because the product relies entirely on learned orchestration logic rather than fixed operational rulesets, it automatically benefits from third-party innovations. Sakana AI plans to continuously expand the available pool of expert agents.

    The engineering team will fold newly-released open-source tools and proprietary Sakana AI models into the routing pool as they become available. Both the standard Fugu and Fugu Ultra models are available to enterprise clients today.

    See also: SAP and Google Cloud deploy agentic commerce architecture

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    AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

    The post Mitigating vendor lock-in with Sakana AI Fugu multi-agent models appeared first on AI News.

  • L’Oréal brings Maybelline virtual try-on to ChatGPT

    L’Oréal has announced a collaboration with OpenAI that will bring Maybelline New York’s virtual makeup try-on feature into ChatGPT.

    The announcement was made at VivaTech 2026. The partnership covers consumer-facing shopping tools, product discovery, advertising pilots, research, and internal content production. The collaboration also covers L’Oréal’s internal use of AI in research, formulation, content production, and employee tools.

    OpenAI said in 2026 that ChatGPT had more than 900 million weekly active users and more than 50 million subscribers.

    Maybelline try-on comes to ChatGPT

    Maybelline’s Makeup Virtual Try-On will be available directly within ChatGPT. The feature will use L’Oréal’s ModiFace technology, which allows users to test makeup looks digitally through a conversational interface.

    ModiFace is L’Oréal’s augmented reality and AI beauty technology business. L’Oréal acquired the Canadian company in 2018 to expand its digital beauty services across areas such as virtual makeup try-on, hair colour try-on, and augmented reality shopping.

    L’Oréal’s 2025 Annual Report said its Beauty Tech services had more than 120 million uses across 66 countries and 31 brands by the end of 2025.

    Product discovery and advertising

    L’Oréal will also work with OpenAI to improve how its products are surfaced in ChatGPT in the United States. The company said the work will cover brands including Lancôme and Kérastase.

    L’Oréal said the ChatGPT work also includes product discovery. The company said e-commerce grew by double digits in 2025 and passed 30% of sales. Several L’Oréal brands are also involved in OpenAI’s global ChatGPT advertising pilot. They include SkinCeuticals, CeraVe, and Garnier. The programme focuses on ads within AI-assisted consumer interactions.

    L’Oréal described the pilot as focused on AI-native advertising at moments of consumer intent and commerce. The company has not provided further operational details on how the ad placements will appear inside ChatGPT.

    AI use in research and formulation

    The partnership also extends to L’Oréal’s research work. The company said it is using GPT-Rosalind, OpenAI’s life sciences reasoning model, to map the skin microbiome.

    OpenAI launched GPT-Rosalind as a model for life sciences research tasks, including evidence synthesis and experimental planning. L’Oréal said it is applying the model to skin microbiome research, starting with La Roche-Posay. The skin microbiome refers to the community of microbes that live on the skin. L’Oréal said the work is aimed at identifying beneficial bacteria that can support the development of new skincare products.

    L’Oréal’s 2025 Annual Report also cited AI work in formulation science. L’Oréal Research & Innovation and IBM are developing a Formulation Foundation Model for beauty formulation.

    L’Oréal has also worked with NVIDIA on AI development and deployment. The company has said the partnership covers areas including 3D product rendering and predictive formulation science.

    Internal AI tools

    OpenAI’s latest model will also be used in CreAItech, L’Oréal’s internal generative AI content platform. The platform is designed to create images and videos while reflecting the visual identity and history of L’Oréal’s brands.

    CreAItech is used by L’Oréal teams for beauty content creation. The OpenAI model support will apply to image and video generation.

    Asmita Dubey, L’Oréal’s chief digital and marketing officer, said the company wants to use AI to support consumers and employees. She also cited its use across marketing and research.

    Emmanuel Marill, OpenAI’s managing director for EMEA, said the work with L’Oréal covers research and employee tools, as well as consumer-facing services.

    The collaboration forms part of L’Oréal’s wider AI programme. The company said the programme covers consumer tools and internal work across marketing and research. L’Oréal said 73,000 employees have already been trained in generative AI. The company has also introduced internal tools including L’OréalGPT and personal AI companions.

    The announcement coincides with L’Oréal’s 10th year at VivaTech.

    (Photo by Helio E. López Vega)

    See also: Microsoft sells OpenAI models in China. OpenAI and Anthropic won’t.

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  • SAP and Google Cloud deploy agentic commerce architecture

    SAP and Google Cloud are deploying agentic commerce architecture to automate multi-agent marketing and retail operations at enterprise scale.

    SAP research indicates 78 percent of businesses consider AI essential for retaining customers in 2026. However, the same data reveals fewer than two in five companies share customer data across customer experience (37%) or CRM (39%) platforms. 

    Addressing this structural data failure requires direct infrastructure intervention. SAP and Google Cloud expanded their partnership to build an agentic customer experience architecture, connecting data, AI, engagement, and commerce operations.

    The deployment relies on restructuring how AI interacts with backend commercial platforms. Most digital commerce infrastructures rely on fragmented APIs. SAP Commerce Cloud adopts the Universal Commerce Protocol to standardise data exchange among retailers, payment gateways, and autonomous agents. This framework allows software to independently execute the full retail sequence, spanning initial search, transaction processing, and post-sale resolution.

    Deploying the Universal Commerce Protocol

    Engineering teams integrating the Universal Commerce Protocol facilitate direct interactions between intelligent agents and commerce platforms. The standardisation lowers integration costs and accelerates onboarding into AI-driven channels.

    SAP plans to collaborate with Google to ensure merchant products surface organically across the Gemini application and Google Search, specifically incorporating AI Mode functionalities. Consumers interact with these interfaces while the backend architecture processes inventory checks, cart management, and payment processing without requiring retailers to rebuild existing infrastructure.

    SAP Commerce Cloud integrates Google Gemini capabilities to power a designated Shopping Assistant. Brands deploy the assistant directly to their consumers to facilitate chat, voice, and text engagements. State retention remains active throughout the complete shopping cycle. The deployment ingests live behavioural inputs, current warehouse capacities, and active marketing data to assemble distinct merchandise pairings, including full event configurations. By continuously refining recommendations, the application ensures high relevance and strict physical fulfilment capability.

    Enterprise systems often fail when promotional campaigns trigger demand that physical inventory cannot satisfy. Frontend interfaces failing to synchronise with backend warehouse systems frequently halt digital purchases. Users regularly click promotional emails, load the associated mobile application, and face sudden out-of-stock notices during checkout. Fulfilment updates experience severe delays, leaving support agents without a complete operational picture. SAP and Google Cloud engineered their joint solution to correct these specific systemic customer experience failures.

    Instead of managing disconnected points of contact, the architecture unifies the entire sequence. Traditional commercial setups require consumers to repeatedly input previously shared information. Support staff frequently lack access to unified records, preventing them from resolving issues efficiently. The integration targets these operational breakdowns, ensuring the system recognises the user and their precise context instantly across all digital properties.

    Bidirectional data flows

    Marketing execution demands highly accurate data pipelines. SAP Engagement Cloud partners with Google Cloud to formulate an autonomous multi-agent framework. The technical foundation relies on SAP Business Data Cloud Connect for Google BigQuery. The deployment relies on bidirectional, zero-copy data linking secured by strict administrative controls. Leaving vast data stores in place rather than duplicating them drops storage expenses and network latency.

    BigQuery ingests live variables like weather conditions, precise locations, and active advertising interaction rates. SAP Customer Experience solutions supply the internal behavioural context, tracking customer profiles, exact transaction histories, specific service interactions, and consented engagement records. SAP Engagement Cloud activates the combined intelligence, deploying autonomous agents to orchestrate personalised interactions throughout the customer lifecycle.

    Routing information through the Business Data Cloud while BigQuery handles the logic forces immediate inventory synchronisation. The Shopping Assistant actively queries live warehouse records before displaying any product. Software checks physical supply against consumer requests, verifying availability prior to making the suggestion.

    Generative execution in production environments

    Advanced generative models dictate the localised output of the marketing campaigns. Google Gemini models, specifically including the Nano Banana 2 iteration, provide specialised agentic skills. The models dynamically generate localised messaging, customised imagery, and campaign variations based on the exact specifications provided by the bidirectional data flow.

    The deployment upgrades standard text messages into immersive and interactive interfaces via Google Rich Communication Services. Advertising creatives evolve continuously based on incoming engagement data. The system processes the interaction, evaluates the response against the user profile, and instructs the Nano Banana 2 model to adjust the subsequent communication.

    Marketing departments achieve high efficiency by abandoning manual execution. Instead of configuring rigid campaign parameters, teams establish business goals and provide enterprise data access to the SAP Engagement Cloud. The autonomous agents coordinate the necessary steps, segmenting audiences based on Google BigQuery analytics and generating specific content variations through Google Gemini models.

    Evaluating the infrastructure impact

    Deploying the architecture restructures standard commerce operations. Consumers dictate their purchasing intent to search engines and conversational interfaces. The embedded AI agents process the intent, navigate the Universal Commerce Protocol connections, and complete the purchase directly against the enterprise backend.

    Retailers retain full ownership of the customer relationship despite the transaction occurring within a third-party environment. The architecture captures the consented engagement data, feeding the transaction history back into the SAP Customer Experience solutions. The system updates the localised customer profile, providing the Google Gemini models with fresh context prior to the next engagement cycle.

    The system continuously improves campaign performance without requiring direct human intervention. The multi-agent framework evaluates the success of a generated Rich Communication Services text message, adjusting the variables prior to the next automated dispatch.

    See also: Computer vision deployments drive retail productivity gains

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  • Computer vision deployments drive retail productivity gains

    Computer vision deployments are driving retail productivity gains as operators automate physical shelf tracking to protect eroding margins.

    This hardware deployment directly addresses the persistent in-store execution failures currently costing the industry billions. A study authored by Coresight Research – in partnership with technology providers Simbe and RELEX Solutions – calculates the exact cost of these operational shortfalls.

    Inefficiencies consume 6.4 percent of gross sales across the sector. Hardware, mass merchandise, and grocery categories will surrender $196.4 billion to these operational failures in 2026. The monetary value of these losses is jumping 21 percent over the previous year. This deficit vastly outpaces the three percent projected sales growth for the entire sector.

    Nine in ten retailers report active difficulties managing their shop floors. Empty shelves and inaccurate pricing structures directly suppress operating margins. Margin erosion exceeds five percent for 89 percent of operating businesses.

    Full-scale deployments of store intelligence platforms operate across 60 percent of enterprise footprints. This adoption rate represents an 18-percentage-point jump year-over-year.

    Experimental pilot programmes account for a mere 18 percent of current market activity. The adoption curve skews heavily toward top-tier enterprises. 73 percent of retail companies generating over $5 billion in annual revenue maintain fully scaled deployments.

    Mid-market operators lag behind, with only 42 percent of sub-$1 billion companies achieving similar deployment maturity. Treating physical stores as separate entities from digital channels degrades customer lifetime value. Capital expenditure directly targets out-of-stock tracking, automated pricing, planogram verification, and assortment planning.

    Production deployments in hardware and grocery

    BJ’s Wholesale Club provides a documented case study of applied shelf digitisation. The operator deployed Simbe robotics platforms to monitor inventory and price accuracy across its locations.

    Management used this hardware foundation to generate digital twins of individual warehouse clubs. This application established real-time visibility systems previously absent from their physical operations.

    BJ’s applied these digital models to route planning for online orders and curbside fulfillment. The engineering team recorded a 40 percent year-over-year improvement in picking efficiency through this data application. CEO Bob Eddy reported the technology enabled the company to elevate quality standards within fresh merchandise categories.

    Grocery operator Albertsons applies AI to automate complex retail operations. The grocer targets $1.5 billion in productivity gains spanning three fiscal years. CEO Susan Morris explained: “We will be equipping our merchants with AI-driven insights and automated execution to optimise pricing, promotions, and assortment decisions, transforming category management and driving margin improvement.

    “Our vision is the future where intelligent automation guides these decisions, freeing our people to focus on strategy and innovation.”

    Flaws in deployment sequencing

    Many organisations prioritise the installation of pricing software while ignoring foundational sensor infrastructure. 43 percent of surveyed technology leaders direct their capital toward pricing optimisation software.

    Supplier collaboration platforms rank second in priority, attracting investment from 36 percent of operators. Only 33 percent of these organisations invest in the shelf digitisation hardware required to feed accurate data into those pricing models.

    This hardware includes the sensors and cameras needed to verify physical stock availability. Store intelligence deployments require strict sequencing to function properly. Retailers must first digitise the shelf, deploy data analytics, install inventory tracking software, and finally execute pricing automation.

    This inversion of the technology stack creates downstream data failures. Markdown algorithms process outdated inventory counts when physical tracking sensors are absent. Mispricing rates hit 13 percent in 2026, marking a four-point increase since 2024.

    Pricing and promotional execution dominates the priority list, presenting an active difficulty for 92 percent of operators. Kim Anderson, VP of Store Operations at Schnucks Markets, states that shelf data must precede all other implementations. Without accurate physical inventory monitoring, downstream applications fail to meet their performance targets.

    Out-of-stock events remain severely disruptive, with 52 percent of operators ranking inventory availability as highly demanding. Operators attempt to fix multiple problems simultaneously, with 40 percent directing capital toward three or more operational inefficiencies at once.

    Labour reallocation and efficiency metrics

    Lowe’s demonstrates the financial impact of automating the associate workflow through its ‘Perpetual Productivity Improvement’ initiative. Executive VP of Stores Joseph McFarland directed the deployment of workforce management tools and inventory solutions to eliminate redundant associate tasks.

    The engineering rollout saved 80 non-productive labour hours per store on a weekly basis. Lowe’s advanced the initiative by deploying full shelf replenishment technologies powered by AI to track stock depletion in real-time.

    Management distributed financial bonuses to the workforce based on documented productivity enhancements. The company issued $5,000 to associate store managers and varied payouts to hourly staff.

    Broad industry data validates the performance metrics recorded by Lowe’s. The deployment of intelligence applications drives a 14 percent average reduction in time spent on manual store tasks. 86 percent of organisations record defined decreases in manual assignment hours.

    Retailers report distinct performance disparities based on total revenue. 56 percent of operators generating over $5 billion report advanced reductions in task completion times, compared to only 36 percent of mid-market companies.

    Organisations cite operational efficiency as their primary investment objective, followed closely by the unification of store data. Retailers expect these tools to generate new capital, with 40 percent of leaders seeking to establish alternative revenue streams like retail media networks.

    Securing market competitiveness

    Store intelligence technologies function as an interconnected ecosystem rather than standalone fixes for isolated problems. Deploying these systems without a coherent sequencing plan forces operators to build upon an unstable foundation.

    Establishing real-time, shelf-level visibility proves strictly necessary before attempting to scale downstream software. Pricing automation, supplier collaboration platforms, and inventory forecasting applications require verified physical data to generate accurate outputs.

    Customer behaviour responds directly to correct operational upgrades. Proper deployments increase customer lifetime value by 11 percent across the sector, while conversion rates improve for 50 percent of the operators executing physical automation frameworks.

    48 percent of companies record increased enrollment in their loyalty programmes following system integration. Accurate pricing and consistent stock availability elevate online review metrics for 47 percent of surveyed operators.

    Retailers compounding value through integrated, properly sequenced hardware and software capabilities possess a distinct market advantage over competitors accumulating disconnected applications.

    See also: HSBC expands AI banking partnership with Google Cloud

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  • HSBC expands AI banking partnership with Google Cloud

    HSBC has entered a multi-year partnership with Google Cloud to develop and deploy artificial intelligence tools across its global operations.

    Announced at Google Cloud Summit London 2026, the agreement covers work in wealth management, financial crime risk management, and internal decision support. HSBC will work with Google Cloud and Google DeepMind engineering teams on AI tools and programmes using Gemini models and the Gemini Enterprise Agent Platform.

    AI rollout across HSBC

    HSBC expects the partnership to support more than 200 AI use cases over the next two years. Selected initiatives could each return more than US$100 million through direct revenue gains or efficiency improvements, according to the bank.

    HSBC had existing AI deployments before the Google Cloud agreement. In its 2025 Strategic Report, the bank said it had more than 100 active generative AI use cases and was increasing AI partnerships.

    HSBC says it has more than 600 AI use cases across the group. These include fraud detection, cyber security, transaction monitoring, customer service, and risk assessment. More than 600 HSBC applications already run on Google Cloud.

    A 2026 Cambridge Centre for Alternative Finance report found that 71% of surveyed industry respondents were adopting generative AI, while 52% were adopting agentic AI.

    Existing AI work

    HSBC announced a separate multi-year partnership with Mistral AI in December 2025. The agreement gives the bank access to Mistral AI’s commercial models. HSBC said the models would support internal tools, financial analysis, multilingual reasoning, translation, and prototyping.

    HSBC has listed other generative AI uses in credit analysis, customer support, document analysis, and text assistance. CIO Dive reported in February that 85% of HSBC employees had access to generative AI tools.

    The report also said the bank was assessing the technology across 50 processes, including fraud detection and credit applications.

    Financial crime detection

    The Google Cloud agreement follows earlier AI work between HSBC and Google in financial crime detection. HSBC has previously said it partnered with Google to co-develop Dynamic Risk Assessment, an AI system used to check for financial crime.

    HSBC said the system was piloted in 2021 and found two to four times more financial crime than previous methods. Google Cloud has said HSBC screens more than 1.2 billion transactions each month for signs of financial crime.

    Under the new partnership, HSBC will use generative AI and agentic AI in financial crime risk management. The bank expects the tools to help it intervene twice as fast when risk is detected across the nearly one billion transactions it monitors each month.

    Wealth and staff tools

    In wealth management, HSBC plans to combine AI-generated insights with the work of relationship managers. The bank said the tools are intended to support financial advice and client service.

    HSBC said it will expand an AI-powered decision assistant already used by thousands of employees. The tool has reduced administrative work and client meeting preparation from hours to minutes, according to the bank.

    HSBC has applied generative AI in software development. More than 20,000 developers are using coding assistants, with a 15% efficiency gain in time spent coding, according to the bank.

    HSBC plans to use AI to organise regulatory procedures into a structured format. The bank said this would provide employees with options and analysis for decision-making while keeping human judgement involved.

    AI leadership

    In March, HSBC announced that David Rice would become its first Chief AI Officer, effective 1 April. HSBC said the role was created to oversee AI adoption across the group.

    Georges Elhedery, Group CEO of HSBC, said the bank is using AI to create more personalised customer experiences while retaining human judgement and accountability.

    Thomas Kurian, CEO of Google Cloud, said the partnership would support HSBC’s AI work through Gemini, the Gemini Enterprise Agent Platform, and Google DeepMind’s research expertise.

    See also: Visa ChatGPT integration enables AI agent retail purchasing

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