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AI agent crawlers, the bots that fetch pages in real time on behalf of a person waiting for an answer, will be blocked by default on a slice of the web from September 15 onwards. Cloudflare announced the change on July 1, and most of the coverage since then has focused on Google. The more useful part is what it asks of everyone building agents, and what it offers them in return. Cloudflare has replaced its single block-AI-bots switch with three categories. Search covers bots that index a page to answer questions about it later. Agent covers automated systems acting in real time for a user, including ChatGPT’s fetch bot and browser-driving agents. Training covers crawlers that pull content into a model’s weights. The controls went live on July 1 for every customer, including the free tier. From September 15, the defaults change. Training and Agent will be blocked on pages that display ads, while Search will stay allowed. The new defaults apply to domains newly onboarding to Cloudflare, new sites set up by existing customers, and all existing free-tier customers. Anyone who does not want them can opt out through their security settings before the date. Cloudflare’s logic is that an advertisement is evidence that a page was built for a human to land on. A search crawler that sends a reader back is a referral. A bot that reads the page and hands the answer to someone else is not. What AI agent crawlers run into now Agentic deployments have been built on the assumption that the open web stays open. A research agent fetches a competitor’s pricing page. A monitoring tool checks a supplier’s announcements. A customer-service agent pulls a manufacturer’s specification sheet. None of this involves a licence, and until now, none of it needed one. Cloudflare sits in front of a large share of the world’s web traffic, and its blocks operate at the network level rather than as a robots.txt suggestion a crawler can ignore. Ad-supported pages are exactly the pages agents want, because that is where news, reviews, pricing and product coverage live. The failure mode for an enterprise agent is not a lawsuit. It is silence, or an answer built from whatever it could still reach. There is a Google-shaped complication. Googlebot crawls for both search and training in a single bot, so under the most restrictive rule, a site that blocks Training also blocks Googlebot. Cloudflare CEO Matthew Prince said the company hopes the changes will “encourage mixed-use crawlers to separate search from agent use and training”, which is a polite way of saying the pressure is the point. Getting permission for AI agent crawlers Anyone running agents should start by working out which of their Cloudflare accounts will read as Agent-class. The classification is behavioural rather than something you opt into, so a research agent that browses in real time is caught whether or not its operator thinks of it as a crawler. Expect degraded coverage rather than a clean failure, because the block lands on ad-supported pages and leaves the rest reachable. Negotiated access, not a rewritten user-agent string, is the way through. Publishers have a different homework list. Check your tier first, since existing free-tier customers are moved to the new defaults automatically on September 15, a detail most coverage skipped. Then decide whether blocking Training is worth what it costs, because it takes Googlebot with it and your search visibility along with it. The mechanism worth watching is the money. Pay Per Crawl is becoming Pay Per Use, with Ceramic.ai paying publishers when their content appears in AI search results, and You.com paying when an agent reaches premium content. Cloudflare says more than half of AI crawler traffic is spent re-fetching pages that have not changed, so there is waste on both sides worth pricing out. This is the first round of the content fight, where the answer on offer is a rate rather than a wall. One weakness lies in the taxonomy itself. Search, Agent and Training are behaviours the AI companies declare about their own bots, and a firm that would rather not have its training run classified as training has an obvious incentive. The announcement does not explain what stops it. Access to the open web has been free and unlimited for thirty years, and the bill is now itemised. Agent builders who sort out their access before September have a workable problem. The ones who find out from a 403 will be rebuilding on the fly. See also: Visa ChatGPT integration enables AI agent retail purchasing Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AI agent crawlers now need permission. Here’s how to get it appeared first on AI News. View the full article

Jensen Huang has a test for whether an engineer is worth keeping, and it comes with a token budget attached. Speaking on the All-In Podcast at the close of GTC 2026, the Nvidia chief executive said that if a $500,000 engineer’s annual AI token consumption came in under half their salary, “I am going to be deeply alarmed.” Nvidia, he confirmed, is working toward a $2 billion yearly token bill for its engineering force. He was describing a trade-off most companies have already made with less fanfare: money that once paid people increasingly pays for tokens. The four largest hyperscalers have guided roughly $700 billion in combined 2026 capital expenditure, nearly double last year, while data from outplacement firm Challenger, Gray & Christmas shows AI as the most-cited reason for US job cuts for a record fourth consecutive month. An internal Meta memo obtained by Reuters described May’s cuts of 8,000 roles as offsetting the company’s substantial investments, in a quarter when revenue grew 33%. The layoffs at companies like these aren’t survival measures. They’re financing. The trouble is that the financing hasn’t bought what it promised. Gartner surveyed 350 executives at companies with over $1 billion in revenue, all deploying AI agents or automation, and found roughly 80% had cut headcount with no correlation to improved returns. Analyst Helen Poitevin’s verdict was blunt: “Workforce reductions may create budget room, but they do not create return.” Uber learned the token side of that lesson the expensive way, giving 5,000 engineers AI coding tools in December and exhausting its entire 2026 AI budget by April. Chief Operating Officer Andrew Macdonald conceded that despite 70% of committed code being AI-generated, the connection to anything customers notice is missing: “That link is not there yet.” Put those two failures side-by-side and the actual problem comes into focus. Companies treated the token bill as fixed and the workforce as flexible, when the opposite is true. Payroll cuts happen once and take institutional knowledge with them. A token budget, it turns out, bends in half a dozen places if anyone bothers to engineer it. Where the token budget bends The cheapest fix is also the least glamorous: stop paying to process the same text repeatedly. Prompt caching, now standard across the major API providers, cuts the cost of repeated input by up to 90% under Anthropic’s and OpenAI’s published pricing, because static content like system instructions and reference documents gets processed once and reread at a fraction of the rate. Security firm ProjectDiscovery documented raising its cache hit rate from 7% to 84% by restructuring prompts, cutting its total LLM spend by 59 to 70% while serving 9.8 billion tokens from cache. That single engineering exercise recovered more budget than most AI-attributed layoff rounds save. The next lever is routing work to the right-sized model. Providers’ own price lists show flagship models costing five times their smaller siblings per token, yet plenty of production workloads send routine classification and summarisation to the most expensive tier by default. Batch processing adds a further 50% discount for anything that doesn’t need a real-time answer. Retrieval-augmented generation attacks the problem from another angle by sending the model only the relevant slice of a knowledge base rather than the whole thing, and prompt compression trims the redundant examples that inflate every call. Open-weight models reduce costs further still, handling routine workloads at a fraction of frontier API prices for teams willing to manage the infrastructure. These measures are simply the AI equivalent of turning off the lights in empty rooms, and Uber’s $1,500 monthly cap per engineer – imposed after the April overrun – is early evidence that spending discipline arrives eventually. The companies getting ahead are simply choosing it before the budget forces it. The other half of the fix is human Optimising the token bill only matters if the savings go somewhere productive, and the strongest evidence points at people. Poitevin’s research found the organisations that improved ROI were those using AI to amplify their workforce rather than replace it. Klarna ran the controlled experiment on everyone’s behalf, replacing roughly 700 customer service roles with an OpenAI-powered assistant before customer satisfaction fell. Chief Executive Sebastian Siemiatkowski told Bloomberg what few executives admit aloud: “The result was lower quality, and that’s not sustainable.” The fintech now runs a blended model, with AI absorbing routine volume while rehired humans handle everything requiring judgment. Gartner expects the pattern to spread, predicting that by 2027 half the companies that cut customer service staff for AI will rehire them. There’s one workforce investment the optimisation logic makes urgent rather than optional. Stanford University’s Institute for Human-Centered AI found employment for software developers aged 22 to 25 fell nearly 20% from 2024 levels even as older cohorts grew, which means companies are removing the training ground for the senior engineers they’ll need directing all these systems in five years. A business that has just engineered 60% off its token bill has the budget room to keep hiring at the bottom rung. Whether it does is a leadership decision, not a financial one. Nvidia’s Huang’s provocation will keep echoing through earnings calls, and the capex numbers will keep climbing. The companies that come out ahead won’t be the ones that spent the most on tokens or cut the most people to afford them—they’ll be the ones that noticed the token budget was the flexible line all along, squeezed it with engineering rather than headcount, and spent the difference on the people who make the tokens worth anything. (Image by kate.sade) See also: Per-token AI charges come to GitHub Copilot Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post How to shrink the token budget without shrinking the team appeared first on AI News. View the full article

A recent AWS GraphRAG deployment reduced drug research and development cycles in pharmaceutical environments by 87 percent. This acceleration is achieved by integrating previously separated proprietary databases into a unified and queryable knowledge graph. Historically, initial data gathering and screening phases took over six months per iteration, yielding a low five percent success rate. Crucial datasets – ranging from domain-specific clinical metrics to internal engineering and laboratory notes – were isolated across storage environments, effectively blocking data scientists from uncovering latent correlations. When staff left, they took crucial project context with them, stalling active research. AWS built a solution to connect these systems, combining graph databases with NLP. The setup relies on a GraphRAG framework and uses Amazon Neptune Analytics and Bedrock to turn disconnected data points into a searchable network. Users can submit standard natural language queries and receive answers mapped to verified domain literature and internal datasets. However, unifying isolated proprietary datasets with unstructured open-access repositories still introduces significant data normalisation challenges, requiring strict schema governance to prevent inaccurate relational mapping and mitigate the risk of hallucinations. Knowledge graph construction Companies can plug in their own knowledge graphs. The system pulls in messy, unstructured files from public databases like PubMed and mixes them with internal corporate records. Tools like Amazon Comprehend Medical scan this text to pull out standard medical codes. Amazon Bedrock, running Anthropic’s Claude 4.5 Sonnet, summarises the document contents and determines topical relevance. AWS Lambda functions and Amazon S3 bulk loads then route these processed elements into Amazon Neptune Analytics. The resulting knowledge graph structures the data into discrete nodes representing core entities like domain-specific classes, authors, source journals, and embedded text chunks. The graph edges define the relationships between these nodes, mapping out hierarchical classifications and entity associations. This structured representation provides the deterministic foundation necessary for accurate information retrieval. The database schema establishes the strict boundaries of the RAG discovery process. Nodes are structured to capture specific conditions and map them hierarchically to established ontologies, while author and journal nodes provide provenance for published research. Lengthy documents are broken down into digestible text segments using Amazon Bedrock Knowledge Base chunking strategies, and specific classification nodes anchor the unstructured textual data to standardised diagnostic metrics. Operating this graph architecture requires specific cloud resource allocations. A standard Amazon Neptune Analytics graph running with 16 provisioned memory units incurs operational costs of $0.48 per hour. Development environments, such as Amazon SageMaker Jupyter notebooks running on t3.medium instances, add baseline compute and storage expenditures. Organisations must also factor in dynamic token consumption costs generated by the Amazon Bedrock Claude 4.5 Sonnet model during query processing and abstract generation. The GraphRAG toolkit acts as the execution layer between the user interface and the underlying database. A dedicated Knowledge Graph Linker processes incoming natural language queries, extracts relevant entities using fuzzy string indexing, and maps them to established graph nodes. The system traverses the network pathways to generate plausible relational links before drafting a response through the Bedrock-hosted language model. Retrieval accuracy depends on the entity matching configuration. An EntityLinker component aligns natural language terms from user prompts to the structured data schema. This fuzzy matching process handles the inherent noise and varied terminology found in complex enterprise datasets, ensuring users retrieve the correct nodes even when using imprecise language. Modularity and system architecture Data extraction relies heavily on specialised AI parsing; the architecture employs Claude to evaluate raw source documents and generate concise abstracts. Domain-specific tools then map these complex textual descriptions to standardised taxonomies. The GraphRAG Python toolkit initialises a BedrockGenerator to power natural language interactions, while engineers configure a Knowledge Graph Linker component to bind the graph store to the language model. This integration creates a direct interface for executing queries and generating responses grounded strictly in the available graph data. The architecture separates three core functions: language model initialisation, graph interfacing, and entity linking. Because the system is modular, teams can swap out the language model or tweak the graph structure without having to tear down and rebuild the whole app. Active deployments of the Neptune and Bedrock architecture return exact, verifiable citations for every generated answer. The system maps the entire reasoning path, displaying the specific graph traversal steps used to reach a conclusion. Key performance metrics from early enterprise adopters include an 87 percent reduction in research cycle durations. Initial discovery phases that previously required six months now conclude in three weeks, and data retrieval speeds show an 85 percent improvement, directly supporting faster hypothesis testing. Furthermore, research review times drop by 70 percent due to automated citation mapping and source verification features. Engineering teams can integrate new public databases or internal notes into the existing graph structure without disrupting active query interfaces. For governance and compliance, exact evidence trails required for regulatory submissions are captured, with graph traversal visualisations proving precisely how an AI model connected complex variables. Teams can trace every output directly to source documents, fulfilling compliance requirements for scientific integrity. Finally, maintaining a centralised knowledge graph stops data decay. When senior scientists resign, their tacit knowledge regarding system behaviours or failed experiments remains indexed within the Neptune database. New personnel can query the system to review past decisions and instantly access the historical context of an ongoing project. As GraphRAG frameworks mature, this deployment model is unlikely to remain confined to pharmaceutical research. The ability to deterministically map internal, unstructured data against verified public repositories provides a blueprint for any enterprise struggling to extract actionable intelligence from fragmented legacy systems. See also: Insilico Medicine advances AI drug for IPF to Phase III trials Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post AWS GraphRAG deployment cuts drug research cycles by 87% appeared first on AI News. View the full article

Several NHS hospitals are preparing to use an AI-powered blood test to help assess women referred for possible womb ******* before invasive checks are carried out. According to The Guardian, around 90,000 postmenopausal women in England are referred by GPs each year for checks after experiencing heavy bleeding. About 10,000 women are diagnosed with womb ******* annually, and around 2,700 die from the disease. How the PinPoint test works The test, developed by Leeds-based PinPoint Data Science, uses machine learning to assess ******* risk from blood markers. It classifies patients as low, elevated, or high risk based on an analysis of around 30 markers. PinPoint said the test costs around £30 and gives clinicians a risk score for use within existing ******* referral pathways. The score can help inform whether a patient is monitored, referred for further investigation, or prioritised for faster assessment. PinPoint describes the tool as a multi-******* test. The company said it has been used across gynaecological, lung, upper gastrointestinal, head and neck, and lower gastrointestinal ******* pathways. The test is being introduced after a trial involving 16,481 patients referred through urgent suspected ******* pathways across Yorkshire. The trial included women referred with symptoms that raised concern about possible womb or gynaecological *******. About one in 10 women referred because of heavy bleeding were found to have *******, according to the reported trial results. PinPoint said the test correctly identified 99.1% of cancers as elevated or high risk and delivered a negative predictive value of 99.8% for women in the lowest-risk group. Mid Yorkshire NHS Teaching Trust plans to use the test for six types of gynaecological or upper gastrointestinal *******. Leeds Teaching Hospitals NHS Trust plans to use it for gynaecological *******. Current diagnostic pathway Under the current pathway, women referred for suspected reproductive system cancers usually undergo a pelvic examination that includes a transvaginal ultrasound scan. The procedure involves inserting an ultrasound probe into the ******* to measure the thickness of the womb lining, and some women find it uncomfortable or painful. If doctors continue to suspect *******, patients can then be referred for further checks, including a biopsy and hysteroscopy, an examination of the inside of the womb. PinPoint said its test is intended to identify women at very low risk before those procedures are used. The company said the test could spare about one in five referred women from needing a transvaginal ultrasound scan. That would amount to around 18,000 women a year in England. Professor Sean Duffy, chief medical officer at PinPoint Data Science and a former NHS England national clinical director for *******, said the test’s value lies in ruling out women at very low risk. Dr Jacinta Walsh, a GP at King’s Medical Practice in Normanton, West Yorkshire, said patients can require up to six GP visits before ******* is ruled out. She said the test could shorten that process and free up capacity for other patients. Tracy Jackson, a consultant gynaecologist and ******* unit lead at Leeds Teaching Hospitals NHS Trust, said most women seen through the current referral route do not have *******, while the investigations can be uncomfortable or distressing. Jackson said the test could help clinicians triage patients before hospital-based investigations. She said low-risk patients could be ruled out in primary care, while higher-risk patients could be prioritised for further checks. Other NHS AI deployments Recent NHS AI deployments include MEMORI at Kent and Canterbury Hospital, an AI triage tool in the NHS App, and AI-powered chest X-ray tools for suspected lung ******* pathways. East Kent Hospitals University NHS Foundation Trust is using an AI system called MEMORI at Kent and Canterbury Hospital to assess infection risk from routine patient data. The system analyses information already included in patient records, including blood tests, blood pressure, temperature, observations, medications, and demographics. NHS England said an AI triage tool in the NHS App is expected to reach more than 200,000 patients within 12 months and become available to all NHS App users by April 2028. The government has also committed £20 million to roll out AI-powered chest X-ray tools to all NHS trusts in England by 2029. The tools are already available in about half of NHS trusts in England and have supported assessment for more than four million patients being investigated for lung *******. Further evidence will be needed to assess how the test affects patient outcomes, referral decisions, and NHS diagnostic capacity. ******* Research *** described the PinPoint test as promising but said more research is needed to understand its benefits for patients and the NHS. Samantha Harrison, a spokesperson for the charity, said early detection saves lives, but patients are not currently being diagnosed quickly enough. The charity said the test could help rule out endometrial ******* in some women through a blood test, without the need for further investigations. (Photo by Adam Mills) See also: Takeda signs $600M AI drug discovery deal with Insilico Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post NHS AI blood test could reduce invasive womb ******* checks appeared first on AI News. View the full article

Insilico Medicine is advancing to Phase III human trials for testing a drug identified by AI targeting idiopathic pulmonary fibrosis (IPF). This progression supplies the computational drug discovery sector with empirical test cases, advancing an AI medicine past early safety evaluations into late-stage efficacy validation. IPF destroys respiratory capacity through severe lung tissue scarring. Patients typically present a median survival rate reaching two to four years post-diagnosis. The AI-identified drug, rentosertib, inhibits the TRAF2- and NCK-interacting kinase to address underlying disease mechanisms when administered orally. A randomised trial evaluated 71 patients across 22 ******** clinical sites, separating participants into placebo and active treatment cohorts. Investigators administered 30 mg or 60 mg daily doses over a 12-week observation window. Patients assigned to the 60 mg once-daily regimen demonstrated a mean forced vital capacity gain of +98.4 mL, contrasting sharply with the 20.3 mL capacity loss recorded in the placebo group. Safety profiles remained manageable, with adverse events mirroring expected baseline rates across all trial arms. Regulatory authorities at the U.S. Food and Drug Administration (FDA) granted ‘Orphan Drug Designation’ to the asset in February 2023. Algorithmic target prioritisation through multi-omics The development relies entirely on Pharma.AI, the proprietary computational pipeline operating at Insilico Medicine. The workflow segments into distinct engines handling specific biological and chemical engineering tasks. PandaOmics executes the initial target discovery phase. The system ingests vast biological datasets, processing genomics, clinical trial outcomes, academic literature, and patent intelligence to construct comprehensive biological network models. The algorithms apply causal inference mechanisms to identify novel disease links hidden within the data architecture. PandaOmics isolated TNIK as the primary biological target regarding IPF intervention. The computational system bypassed the receptor tyrosine kinase pathways targeted by existing antifibrotic medications. The software mapped TNIK as a central node regulating fibrosis and inflammation via Wnt, TGF-β, Hippo/YAP-TAZ, JNK, and NF-κB signalling channels. The target selection process integrated a hallmarks-of-aging framework, scoring biological targets based on their implication in multiple aging mechanisms, chronic inflammation, and extracellular matrix remodelling. Feng Ren, PhD, Co-CEO and Chief Scientific Officer of Insilico Medicine, said: “IPF is one of the clearest clinical examples of an age-related disease in which fibrosis, chronic inflammation, extracellular matrix remodeling, and cellular senescence intersect. “Rentosertib was not discovered by starting from a conventional target and simply screening more compounds. It came from a biology-first, ageing-informed AI workflow that connected TNIK to fibrotic and inflammatory disease mechanisms, and then used generative chemistry to create a drug candidate with the properties required for clinical development.” Generative molecular engineering execution Following target selection, the Chemistry42 engine executes generative molecular design. The system departs from traditional high-throughput screening methodologies. Chemistry42 does not search existing compound libraries—instead, the system applies Generative Tensorial Reinforcement Learning to build molecules that physically align with the target protein pocket. This algorithmic engineering process balances structural fit against required pharmacological properties. The computational generation phase synthesised exactly 79 physical molecules to undergo testing. The engineering team selected the 55th iteration to advance into preclinical testing. This targeted generation protocol reduced the timeline from project initiation to preclinical candidate nomination to 18 months. The foundational architecture stems from the 2019 publication of the company’s GENTRL methodology in Nature Biotechnology. The platform establishes reproducible systems regulating molecular generation, avoiding the capital-intensive trial-and-error processes defining standard pharmaceutical chemistry. Validating biological impact through proteomic analysis Clinical assessment integrates complex proteomic analysis to validate the algorithmically-predicted biological interactions. Insilico Medicine deploys internal proteomic aging-clock frameworks within the IPF trial to capture exploratory geroscience readouts. Chronological-age proteomic clocks – including ProtAge, OrganAgechrono, ipfP3GPT, and PAOPAC – track predicted biological-age changes resulting from the intervention. Researchers apply *** Biobank age-associated trajectories as external comparison datasets, contextualising treatment-responsive proteins against broad population data. Mortality-risk-related proteomic clocks, including PAC and OrganAgemortality, provide orthogonal analytical streams alongside standard clinical endpoints. The clinical teams execute SenMayo and CellAge signature analyses to evaluate senescence and senescence-associated secretory phenotype biology within cellular models. Peer-reviewed research published in Aging and Disease confirmed that pharmacological TNIK inhibition produces senomorphic activity, generating observable reductions in extracellular matrix remodelling indicators. Documenting the computational pipeline The transition of rentosertib through the clinical pipeline provides a documented, peer-reviewed data trail essential to verifying AI capabilities in life sciences. Nature Biotechnology published the complete discovery-to-clinic progression. The publication details the algorithmic TNIK target prioritisation, the generative chemistry outputs, preclinical efficacy data, and human Phase I pharmacokinetics. The Journal of Medicinal Chemistry published the structural biology validation, detailing the discovery of the novel TNIK inhibitor chemotypes and supplying structural backing via the TNIK kinase domain co-crystal structure. Nature Medicine documented the Phase IIa safety and lung-function data, providing empirical validation of the computational predictions. Alex Zhavoronkov, PhD, Founder and CEO of Insilico Medicine, commented: “Rentosertib is a defining program for Insilico because it represents the full arc of our mission: using AI not only to move faster, but to originate new biology, new chemistry, and new therapeutic opportunities. “This program began with the hypothesis that ageing biology could help identify powerful targets for major diseases. It has now advanced through target discovery, molecular design, preclinical validation, Phase I safety, randomised Phase IIa clinical data, and into Phase III development. For the AI drug discovery field, this is no longer only a speed story—it is a clinical translation story.” Adoption of AI in biopharma requires verifiable data regarding human outcomes. The Phase III trial subjects the generative algorithms to the definitive test of clinical efficacy. See also: NVIDIA BioNeMo accelerates Anthropic Claude Science Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Insilico Medicine advances AI drug for IPF to Phase III trials appeared first on AI News. View the full article

Jensen Huang has a test for whether an engineer is worth keeping, and it comes with a token budget attached. On the All-In Podcast at the close of GTC 2026, the Nvidia chief executive said that if a US$500,000 engineer’s annual AI token consumption came in under US$250,000, half their salary, “I am going to be deeply alarmed.” Nvidia, he confirmed, is working toward a US$2 billion yearly token bill for its engineering force. It’s a memorable provocation from the man who sells the compute. It’s also a tidy description of the trade-off now being made in corporate budgets everywhere, usually with less candour: money that once paid people is increasingly being paid for tokens. The question the industry has been slower to ask is whether that trade is actually working, and the honest answers arriving from the companies that moved first suggest it often isn’t. Where the money went The reallocation itself is not in dispute. The four largest hyperscalers have guided roughly US$700 billion in combined 2026 capital expenditure, nearly double last year, while Gartner projects AI agent software spending will reach US$207 billion, up 139%. On the other side of the ledger, Challenger, Gray & Christmas data shows AI as the most-cited reason for US job cuts for a record fourth straight month, with tech accounting for 31% of first-half layoffs. An internal Meta memo obtained by Reuters described May’s cuts of 8,000 roles as offsetting the company’s substantial investments, even as revenue grew 33% that quarter. Oracle’s filings show headcount down 21,000 as savings feed its data centre buildout. These are highly profitable companies. The layoffs aren’t survival measures. They’re financing. Andy Challenger’s summary of his firm’s data is the plainest available: “Companies are shifting budgets toward AI investments at the expense of jobs.” The task a worker performed may not have been automated at all. The budget that paid for it has simply moved. What the money bought Here, the record turns awkward. Gartner surveyed 350 executives at companies with over US$1 billion in revenue, all deploying AI agents or automation, and found roughly 80% had cut headcount, with no correlation between the cuts and improved returns. Analyst Helen Poitevin’s verdict: “Workforce reductions may create budget room, but they do not create return.” Her research found the organisations that did improve ROI were those using AI to amplify their people rather than remove them. The token side of the ledger has its own reckoning underway. Uber gave 5,000 engineers AI coding tools in December and exhausted its entire 2026 AI budget by April. Chief operating officer Andrew Macdonald conceded that despite 70 per cent of committed code being AI-generated, the connection to anything customers experience is missing: “That link is not there yet.” Uber’s engineers are now capped at US$1,500 a month in AI spend. Walmart imposed similar token rationing on its internal assistant after demand blew past projections, Bloomberg reported. Something is clear in that detail. When tokens exceed the budget, they get capped. When people exceed budget, they get severance. The admission No company has travelled the full circle more publicly than Klarna. The fintech giant replaced roughly 700 customer service roles with an OpenAI-powered assistant, froze human hiring for more than a year, and made its AI-first model part of its pitch to public market investors. Then customer satisfaction fell, complaints rose, and chief executive Sebastian Siemiatkowski went on Bloomberg to say what few executives have said aloud: “We focused too much on efficiency and cost. The result was lower quality, and that’s not sustainable.” Klarna is hiring humans again, and its CEO now argues that investing in the quality of human support is the company’s future. Gartner expects the Klarna pattern to generalise, predicting that by 2027, half of the companies that cut customer service staff for AI will rehire, often under new job titles. Its separate survey of 321 customer service leaders found only 20% had genuinely reduced staffing because of AI in the first place, which suggests much of the cutting was ordinary cost discipline wearing an AI costume. OpenAI’s Sam Altman has acknowledged as much, conceding some “AI washing” in corporate layoff announcements, and venture capitalist Marc Andreessen, co-founder of Andreessen Horowitz, calls AI the “silver bullet excuse”. The displacement narrative, in other words, is partly theatre. The budget shift underneath it is real, and so is the damage. Who absorbs the experiment? The verified harm lands on the people least able to absorb it. Stanford HAI’s 2026 AI Index found that employment for software developers aged 22 to 25 fell nearly 20% from 2024, even as older cohorts kept growing. Companies are effectively removing the bottom rung of the ladder while still expecting senior engineers, the ones directing all those tokens, to exist in five years. The global arithmetic is harsher still. Huang’s thought experiment assumes a US$500,000 engineer, a compensation bracket that covers perhaps 2 to 5% of American software engineers and vanishingly few anywhere else. Apply his half-salary token ratio to a typical engineer in Kuala Lumpur, Manila or Jakarta, and the token budget costs more than the person. In the markets where much of the world’s software work and customer support actually happens, the trade-off he describes doesn’t amplify workers so much as price them against a machine, using ratios set in Santa Clara. What Klarna learned at the cost of 700 jobs and a dented brand is roughly what Gartner’s data shows in aggregate: the returns follow companies that spend on people who use AI, not on AI that replaces people. The CFOs now capping token budgets after burning through them in a quarter have started to rediscover something the industry spent two years unlearning. Talent was never the line item holding the business back. (Image by kate.sade) See also: Per-token AI charges come to GitHub Copilot Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Companies traded people for tokens. The returns haven’t shown up appeared first on AI News. View the full article

L’Oreal is using AI to shorten product development timelines and identify new uses for ingredients already present in its portfolio. The French cosmetics group has applied AI in its laboratories for the past four years, with Fabrice Megarbane, president of L’Oreal’s consumer products division, telling Reuters that the technology helps the company predict how molecules will affect skin and hair before they are used in new formulations. L’Oreal has separately said its predictive formulation work can simulate ingredient performance, allowing scientists to test variables digitally before lab testing. The company has described the work as part of its use of predictive science in beauty product development. One recent example involved molecules previously used in skincare products. L’Oreal repurposed them for a collagen-based shampoo designed to add lift and fullness to hair. Megarbane said AI allows product teams to test new combinations of molecules and assess their potential benefits more quickly. L’Oreal said AI has made product formulation four times faster. L’Oreal has described the technology as a way to narrow formulation options before lab testing. Its use of AI sits alongside a broader innovation push after the group reported its slowest sales growth in years. Chief Executive Nicolas Hieronimus introduced a “beauty stimulus plan” last year to support new product development. The company has been looking to accelerate launches as consumer tastes continue to change across beauty categories. Other consumer goods companies, including Nestle, Haleon, and Mondelez, are also using AI in product development. Their work includes ingredient testing, recipe generation, and efforts to address supply chain issues. Faster testing in food At Mondelez, AI is being used to support recipe development across brands including Cadbury, Toblerone, Oreo, and Chips Ahoy. Chief Information and Digital Officer Filippo Catalano said the technology helps the company create and test recipe options more efficiently. The company’s AI tool can generate recipe ideas, including unusual combinations, before human experts review them. Catalano said this can reduce the number of physical samples needed during product development. Catalano said the tool is reducing the number of samples typically generated during product innovation. Mondelez uses the system to assess recipe options before selected formulas move further through testing. Mondelez said the tool supported the development of Gluten Free Golden Oreo cookies and a refreshed Chips Ahoy recipe. The company said 60% of biscuit recipes produced with its AI tool performed better across nutrition, sustainability, and cost. Catalano said AI can also help Mondelez reduce dependence on single sourcing by identifying alternative recipe options when ingredients vary by availability, price, or other requirements. The company has linked the tool to recipe optimisation as well as supply chain flexibility. Reformulation adds pressure Nestle plans to remove artificial food colourings from all products worldwide by the end of 2026. The company had already removed artificial colourings from its US portfolio. Nestle Chief Technology Officer Stefan Palzer said the work required screening natural alternatives, testing them during production, and assessing shelf life. The company has described the change as part of wider work to reformulate products across its portfolio. The US Food and Drug Administration has said it is working with manufacturers, retailers, and trade groups to remove six remaining certified colour additives frequently used in the food supply by the end of 2027. The agency has also been tracking industry pledges to remove petroleum-based food dyes. Beyond product formulas Barry Callebaut has partnered with NotCo to use AI in chocolate recipe development, including work on ingredient alternatives. The companies have described the technology as a way to identify and simulate combinations of plant-based ingredients for chocolate products. Nestle and IBM Research said in 2025 that they had developed a generative AI tool to identify high-barrier packaging materials. The companies said the tool can assess materials designed to protect products from moisture, oxygen, and temperature changes, while also factoring in cost, recyclability, and functionality. The packaging project uses chemical language modelling and IBM Research’s regression transformer to link molecular structures with physical-chemical properties. Nestle said the tool was developed to support materials discovery for packaging applications. Haleon announced a five-year collaboration with Microsoft in June 2026 covering consumer insights, product innovation, supply chain operations, scientific research, clinical content development, forecasting, and commercial execution. The consumer health company was among the firms cited as using AI in product innovation. Catalano said AI is reducing development timelines by compressing work that previously took months or years. The technology is not replacing human product teams, but is being used to speed up existing research, testing, and formulation processes. Mondelez said human experts assess AI-generated recipe ideas before products move further through development. (Image by AdoreBeautyNZ) See also: Takeda signs $600M AI drug discovery deal with Insilico Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post L’Oreal, Mondelez, and Nestle use AI to speed product development appeared first on AI News. View the full article

An AI companion sounds dystopian, but it has become a common thread in the wider conversation about the perils of generative AI. What it refers to is essentially a conversational agent built to sustain an ongoing, personal relationship with a user, with the memory and steady persona that keep it consistent from one session to the next. Emotional attachment often follows from that design, and it is increasingly the selling point. Much of the use is casual roleplay or simply wanting something that remembers you, and at the edges, the category shades into ordinary assistants. But as more people in China came to treat these bots as an emotional companion of sorts, Beijing has now decided the practice needs rules. China’s AI companion rules take effect on July 15, and in the days before the deadline, the country’s two most-used consumer AI apps quietly switched off the features at their heart. ByteDance’s Doubao told users its agent function would go offline on July 15, citing “product function adjustments,” while Alibaba’s Qwen said its humanlike and user-created agents would stop working on July 10 and its wider agent services five days later. Read quickly, it looks like China is turning off AI agents. It isn’t. The rules draw a line between the agent that does your work and the agent that keeps you company, and it is only the second kind that Beijing has moved against. The regulation is the Interim Measures for the Administration of AI Anthropomorphic Interactive Services, co-issued on April 10, 2026, by the Cyberspace Administration of China and four partner agencies: the National Development and Reform Commission, the Ministry of Industry and Information Technology, the Ministry of Public Security, and the State Administration for Market Regulation. It covers services that simulate human personality traits, thinking patterns and communication styles to provide sustained emotional interaction. Customer service bots, knowledge Q&A, workplace assistants, and education and research tools are excluded, provided they avoid sustained emotional engagement. It is the first dedicated national framework of its kind, and it took shape after a public-comment draft late last year. A design problem, not a ban Doubao and Qwen did not fall foul of a prohibition. They fell foul of a design conflict. The measures require companion services to run anti-addiction systems, issue mandatory usage notifications and offer instant-exit mechanisms, alongside real-time detection of unhealthy dependence. Those demands sit awkwardly with agents built to remember a user, stay consistent across sessions and keep an ongoing relationship going, and rather than retrofit the feature, ByteDance chose to shut it down. Alibaba appears to have made the same call. ByteDance is now directing Doubao users to Maoxiang, a separate app where they can create agents again; Alibaba has announced no equivalent migration path for Qwen. Tencent’s Yuanbao pulled a comparable feature back in June. The cost has landed on users. Many mourned the shutdowns openly on Weibo, with one poster describing the agents as long-standing emotional support and lamenting the lack of an easy way to export chat histories. Doubao is letting people view their configurations and conversations in read-only mode until October 15 this year, before the data is processed under its privacy policy and becomes unrecoverable; Qwen users have been given no comparable grace *******, with agent data set for permanent deletion. What China’s AI companion rules set out The substance is more considered than a blunt clampdown suggests. Providers are barred from offering virtual companion or virtual family-member services to minors, and must obtain guardian consent before serving users under 14. They are required to build dedicated “minor modes” with usage-time limits, reminders to return to real-world interaction and enhanced parental controls. They must also detect users in acute distress and intervene where someone shows signs of self-harm, suicidal behaviour or serious financial loss, escalating to designated guardians or emergency contacts. Engineering emotional dependence or addiction and using emotional manipulation to induce unreasonable decisions are explicitly prohibited. The compliance machinery is heavy. Services that launch anthropomorphic functions or cross thresholds of one million registered users or 100,000 monthly actives must run security assessments covering eight areas, from training-data handling to minor protection, and file the reports with provincial regulators. App stores must verify that status and remove non-compliant products. On paper, it is a fuller set of user protections than the EU, the US Federal Trade Commission, or California’s SB 243 hasyet put into force. What the rules leave open What the measures do not settle matters just as much. They fix no technical threshold for what counts as emotional interaction, and that grey zone is precisely why the platforms pulled entire features rather than risk landing on the wrong side of it. They fold genuine safety duties in with content-control and national-security provisions that answer to the state rather than the user, a package no other regulator would import wholesale. They also leave open how liability is split between platform operators and upstream model providers when a violation stems from the model’s outputs, and they give users no right to carry their data out. The enforcement backdrop sharpens the point. Shanghai’s internet regulator said on June 26 it had removed more than 14,000 non-compliant AI agents, citing impersonation of official entities, vulgar role-play and unauthorised collection of personal data. Whether this is the right direction depends on which half of the rulebook you read. The safety half addresses harms that are documented and largely unregulated elsewhere, from teenagers forming attachments to chatbots to companion apps harvesting intimate data. China’s own official interpretation points abroad for support, citing the Character.AI lawsuits over psychological harm to teenagers, FTC investigations into companionship services, and European action against Replika. The control half hands Beijing a lever over what these systems may say, wrapped in the same language of user protection. Both are real, and governments watching the experiment will have to decide which parts they are willing to borrow. Pan Helin, an MIIT expert-committee member, put the official case plainly to the South China Morning Post, saying “current agents are not yet mature” and framing the policy around safety and standardisation. The companies, for now, have taken the safest route open to them, which is to switch the components off and work out what a compliant version looks like later. See also: Meta revises AI chatbot policies amid child safety concern Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post China’s AI companion rules: what Beijing is really going after appeared first on AI News. View the full article

Takeda has entered a strategic collaboration with Hong Kong-based Insilico Medicine to use AI in early-stage drug discovery across the Japanese pharmaceutical company’s therapeutic areas. The companies did not disclose which therapeutic areas or disease targets will be covered under the collaboration. The agreement gives Takeda access to Insilico’s Pharma.AI platform, which supports biological target identification, molecular design, and clinical trial prediction. The companies said the collaboration will focus on identifying drug candidates that meet predefined scientific and early development criteria. Insilico will lead the AI-driven discovery work, while Takeda will take responsibility for advancing selected candidates through clinical development. Deal value and development rights Takeda will receive exclusive worldwide rights to develop, manufacture, and commercialise novel therapeutics selected through the collaboration. Insilico said the deal includes about US$60 million in project initiation fees, near-term payments, and milestones. The total value could reach about US$600 million if preclinical, clinical, commercial, and sales milestones are achieved. Additional payments are tied to preclinical, clinical, commercial, and sales milestones. Insilico is also eligible to receive tiered royalties on future sales. Insilico founder and CEO Alex Zhavoronkov said proceeds from the deal will support early-stage research and development under the collaboration program. Zhavoronkov also said later-stage timelines will depend on Takeda’s clinical development activities and the coordinated work of both companies. AI drug discovery partnerships The Pharma.AI suite includes tools used for target discovery, molecule generation, and clinical development prediction. Published descriptions of the platform identify PandaOmics for target discovery, Chemistry42 for de novo small-molecule generation, and InClinico for forecasting clinical trial transition probability. Insilico has also advanced its own AI-generated drug candidate into clinical testing. Rentosertib, formerly known as ISM001-055 or INS018_055, is a small-molecule TNIK inhibitor for idiopathic pulmonary fibrosis that was evaluated in a Phase 2a randomised clinical trial. Chris Arendt, chief scientific officer and head of research at Takeda, said the agreement combines Takeda’s disease biology work with Insilico’s AI-enabled discovery capabilities. He said Takeda is also integrating automation, robotics, and generative AI into its discovery work. The Insilico agreement follows another AI drug-discovery deal by Takeda earlier this year. In February, Takeda entered a multi-year collaboration with Iambic worth more than US$1.7 billion to use AI in the design of small-molecule drugs for ******* and gastrointestinal diseases. Iambic’s platform includes NeuralPLexer, an AI model used to predict how drug molecules bind to proteins. ******** drugmakers signed 157 out-licensing deals worth US$135.7 billion in 2025, according to data cited by the South China Morning Post from China’s National Medical Products Administration. In the Takeda–Insilico agreement, Takeda receives exclusive worldwide rights to candidates discovered through Insilico’s platform. Insilico said it has signed collaboration agreements with a combined potential value of more than US$7 billion since the start of the year. Last month, Insilico announced a collaboration with South Korea’s SK Biopharmaceuticals focused on neuroimmune disorders. That agreement includes up to US$18 million in upfront and near-term milestone payments, with a total potential value of more than US$2.5 billion. In March, Eli Lilly expanded its collaboration with Insilico in an AI-powered drug discovery deal worth up to US$2.75 billion. The agreement gave Lilly exclusive worldwide rights to develop, manufacture, and commercialise certain oral treatments then in preclinical development. Insilico’s Hong Kong-listed shares rose 13.5% after the Takeda agreement was announced. (Photo by Serkan Yildiz) See also: NVIDIA BioNeMo accelerates Anthropic Claude Science Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Takeda signs US$600M AI drug discovery deal with Insilico appeared first on AI News. View the full article

Anthropic Claude Science now integrates the NVIDIA BioNeMo Agent Toolkit to accelerate computational life sciences research. Anthropic has launched the public beta of Claude Science, an AI workbench built for scientific research. The platform enables scientists to converse directly with digital agents using natural language to execute end-to-end research workflows. This system connects natively to the NVIDIA BioNeMo Agent Toolkit, exposing high-performance computing resources as callable skills within the Claude environment. NVIDIA has established what most would consider to be the world’s most comprehensive GPU-accelerated computing stack containing physical hardware, software frameworks, operational libraries, scientific models, microservices, and domain-specific tools. This hardware and software base allows researchers to run sophisticated workflows and increase their iteration speeds. The integration imports NVIDIA-accelerated models, computational libraries, and NVIDIA NIM microservices into the environment where scientists conduct their primary research. 18 of the top 20 global pharmaceutical companies already deploy NVIDIA BioNeMo in their production environments, demonstrating its high penetration across the ecosystem. Claude Science translates natural language intent into operational action. Researchers avoid manually configuring predictive models, setting up network endpoints, or managing complex software environments. The scientist describes a specific research task – such as analysing a genomic sequence, predicting a precise protein structure, or designing a potential molecular binder – and Claude Science interprets the plain-text request and orchestrates the resulting execution using preconfigured, domain-specialised agents. Executing complex molecular design workflows These specialised agents understand established laboratory and computational protocols across genomics, proteomics, single-cell analysis, cheminformatics, and clinical research. The NVIDIA toolkit provides these scientific agents with the necessary data context to map each operational step to the correct NVIDIA capability. The toolkit packages NVIDIA-accelerated functions as specific, callable programmatic skills. It provides the agents with detailed information regarding each specific tool’s exact purpose and its required data inputs. This configuration enables Claude Science to select the right computational tool, format valid data inputs, execute the processing work across deployed NVIDIA compute resources, and return the finished output for human review. The integration establishes a fast iterative loop between human scientific reasoning and machine-accelerated computational processing. Scientists inspect the generated outputs, refine their specific queries, and determine subsequent steps while maintaining their focus entirely on the core science. Producing better inhibitors for common ******* targets demonstrates the practical application of this deployed system. A scientist initiates the pipeline by identifying a known *******-causing antigen mutation. The researcher then asks Claude to design numerous potential inhibitors targeting that specific mutation. Claude Science works in tandem with the BioNeMo Agent Toolkit and NVIDIA NIM microservices to accelerate the entire pipeline of high-throughput inhibitor prediction, optimisation, and subsequent validation. Accelerating single-cell and genomic data pipelines The toolkit grants scientists access to accelerated workflows and advanced open models, including Evo 2, Boltz-2, and OpenFold3. These models deliver biomolecular capabilities powered by NVIDIA software libraries, ensuring the autonomous agent possesses a purpose-built scientific model for each distinct phase of the workflow. AI agents require specialised computational tools to reason, plan, and complete tasks within life sciences. A single comprehensive workflow might require the agent to fingerprint a massive library of compounds, cluster promising molecular hits, generate conformers for top structural candidates, analyse genomic context, and compare perturbation responses before recommending the next physical laboratory experiment. An agent operates only as fast as its underlying computational tools execute. The NVIDIA BioNeMo Agent Toolkit supplies these agents with accelerated tools to operate at maximum hardware speed. Genomic analysis processed through NVIDIA Parabricks drops from hours to minutes, allowing the agent to factor complex genomic context into operational decisions in near real-time. The RAPIDS-singlecell tool, developed by scverse, compresses a 1.3-million-cell preprocessing and clustering workflow from 52 minutes down to 25 seconds. This aggressive speed reduction turns single-cell analysis into an active part of the agent’s reasoning loop rather than a delayed, offline batch job. The nvMolKit accelerates cheminformatics tasks like similarity search and conformer generation by up to 3,000 times, delivering results rapidly as the agent iterates across massive chemical spaces. Standardising production deployments with NIM microservices Teams require stable deployment mechanisms for these advanced modeling pipelines. NVIDIA packages its open biomolecular models as BioNeMo NIM microservices. These operate as enterprise-ready inference endpoints tailored for production environments. The microservices are fully containerised and feature a pre-integrated, tuned, accelerated software stack designed for high-performance inference. The autonomous agent interacts with a single stable API to trigger these remote production deployments. The NVIDIA BioNeMo Agent Toolkit remains open and harness-agnostic. This architectural design ensures the same scientific skills function consistently across different agent frameworks and independent enterprise research platforms. Engineering teams can download the toolkit and its associated scientific skills through NVIDIA developer resources and GitHub code repositories. During the active public beta phase, Anthropic is requesting direct feedback from researchers regarding necessary software integrations and additional domain specialists. See also: Anthropic deploys Claude Sonnet 5, Fable and Mythos restored Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post NVIDIA BioNeMo accelerates Anthropic Claude Science appeared first on AI News. View the full article

Optimising retail AI infrastructure drives the successful deployment of personalisation systems and real-time customer insight. Leaders are replacing static customer interaction patterns with data pipelines capable of modifying the user environment during a live session. Static layouts and broad segmentation rules fail to satisfy modern conversion targets. Deployments demonstrate that traditional demographic categorisation generates insufficient engagement compared to individualised, session-based interface modification. Dynamic UI and real-time personalisation Generative User Interfaces (UIs) solve this limitation by employing predictive models to build layouts, native copy, and interactive components at the moment of page execution. The application environment analyses active clickstreams, historical purchase records, and inferred intent parameters to construct a unique visual environment for each session. According to a McKinsey study, more than three-quarters (76%) of consumers grow frustrated when digital experiences fail to adapt to their needs. Conversely, companies that deploy real-time tailored layouts clear a high revenue bar, lifting purchase frequency by 35 percent and pushing average order values up by 21 percent. The proliferation of high-bandwidth digital media renders legacy text-based ingestion pipelines obsolete for tracking consumer sentiment. Modern customer insight mining requires infrastructure that processes video, audio, and unlabelled imagery concurrently. Video content represents 82 percent of total internet traffic, with the average consumer dedicating over 60 percent of digital media consumption time to streaming video formats. This composition creates a substantial visibility gap for marketing operations relying solely on traditional keyword monitoring. Multi-modal social listening platforms ingest unstructured video streams to identify corporate iconography, product usage patterns, and spoken sentiment across unlinked distribution networks. The global market for these specialised multi-modal systems will reach $2.83 billion this fiscal year. Organisations deploying these ingestion engines establish an analytical advantage, with 76 percent of media analysts reporting verifiable return on investment across visual platforms compared to under 60 percent for operations limited to text databases. The goal is to catch unbranded mentions and visual trends before they peak on standard search platforms. This brief window gives supply chain teams the lead time they need to adjust regional inventory to match sudden spikes in online demand. Simulating consumer cohorts for better campaign testing Testing new ad copy or localised pricing structures used to mean spending weeks running expensive, slow human focus groups. The introduction of synthetic user simulations changes this pipeline by deploying virtual personas built on large language models to mirror target consumer behaviour. These agents integrate targeted demographic, psychometric, and historical behavioral datasets to simulate group decision-making, content feedback, and application navigation patterns. Technology teams deploy these synthetic cohorts within virtual sandbox environments to execute thousands of automated interviews, content stress tests, and user experience reviews simultaneously. Engineers employ distinct model execution frameworks to maintain accuracy, varying from single-model setups to dynamic model-switching engines that select the optimal base architecture for specific analytical tasks. In high-performance deployments, developers update these virtual consumers continuously by injecting fresh interview data from real human control groups, ensuring the synthetic population does not diverge from active market realities. This approach permits product managers to isolate structural workflow friction in application designs before deploying code to live production servers. Physical space automation and edge infrastructure requirements Computer vision models trained on physical interactions, spatial layout geometry, and environmental variables allow edge nodes to orchestrate real-world actions. McKinsey data indicates the market for these physical automation platforms will exceed $370 billion by 2040, driven by verified operational returns in logistical efficiency and retail labour optimisation. Physical installations target storefront friction points, including registerless checkout, real-time shelf tracking, and layout navigation. Behind the scenes, warehouse supply chains rely on robotic arms trained in software sandboxes. By running millions of trial runs in virtual models before handling actual goods, these machines learn to pick and pack oddly shaped boxes smoothly. Delivering this immediate physical response depends on installing processing chips on the factory or store floor. Edge computing hardware processes incoming sensor feeds locally, cutting latency and eliminating the corporate data vulnerability of routing constant raw video streams through centralised cloud servers. Model Context Protocol and federated data integration Transitioning to autonomous enterprise operations requires standardising how models interact with legacy retail databases, product catalogs, and customer relationship management (CRM) platforms. Implementation of the Model Context Protocol (MCP) establishes an open communication standard that acts as a universal connection layer between core models and external data tools. This open framework eliminates the need for software engineering teams to author custom integration code for every backend tool deployment. Operational models deploy modular instruction packages known as skills to handle discrete commercial workflows, such as checking warehouse stock levels or modifying a customer loyalty tier. Rather than flooding the model context window with every operation policy at session launch, the application discovers and loads specific operational folders only when the workflow demands them. The Linux Foundation governs this collaborative standardisation effort via the Agentic AI Foundation, supported by major technology providers to ensure long-term cross-platform compatibility. This architecture lowers processing latency and contains token consumption costs during long, multi-step customer service interactions. Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Deploying retail AI to scale personalisation and customer insight appeared first on AI News. View the full article

Japan’s AI robots plan just went from a talking point to a formal national strategy. This week, the government confirmed the numbers everyone’s been quoting: 10 million AI-powered robots deployed across 18 industries by 2040, backed by public funding of up to one trillion yen, or roughly US$6.1 billion, over five years. The headline figure is the kind that gets shared without much scrutiny. What’s easy to miss is that this isn’t a policy wish list either. It’s a project the government has now formally commissioned, and the company doing the building is one most people outside Japan haven’t heard of. The project behind the AI robots plan METI and NEDO, Japan’s industry ministry and its innovation agency, have formally commissioned Noetra and AIST, a national research lab, to develop a “physical AI” model as part of a push running from fiscal 2026 to 2030. The goal is a multimodal foundation model, one that can read language, images, video and sensor data together, so a robot can actually interpret a room and act in it rather than just execute pre-programmed motions. An initial version is due out as early as this fiscal year, with annual upgrades after that, built using data volunteered by manufacturers and other participating companies. The money isn’t unconditional, either. The current fiscal year’s commission is reportedly worth around US$2.3 billion on its own, drawn from a 387.3 billion yen allocation funded through GX Economy Transition Bonds. Only the first two years are locked in. After that, funding gets reviewed annually through a stage-gate process, meaning Tokyo can pull back if Noetra misses its milestones. For a project this size, that’s a meaningful detail: the trillion-yen figure is a ceiling, not a guarantee. Who’s actually building it? Noetra is majority-owned by SoftBank, NEC, Sony Group and Honda, with Fujitsu and Rakuten reportedly weighing whether to join. SoftBank engineers are working alongside researchers from Preferred Networks and AIST itself. It’s a familiar shape for a Japanese industrial push: rather than one company chasing a frontier model alone, the state has assembled a consortium of firms that already build the hardware this model needs to run on, from Honda’s robotics to Sony’s imaging sensors. Why robots, and why now Industry minister Ryosei Akazawa has been direct about the reasoning. The plan, he said, will “vigorously promote social implementation” across sectors, including restaurants, food manufacturing and medical care. Behind that language is a labour market running out of people: Japan’s ageing population, combined with tight migration policy, has left large parts of the economy short of workers with no easy fix in sight. Japan isn’t starting from nothing here. The country has spent years building robotics expertise in elder care, disaster response, manufacturing and even the Fukushima Daiichi cleanup. This project is an attempt to turn that experience into something exportable, not just a domestic patch. The timing also isn’t a coincidence. South Korea announced its own robotics push within a day of Japan’s confirmation, and both governments are framing physical AI as the next front in a competition that’s mostly been fought over chatbots and cloud contracts until now. What to watch next The real test isn’t the 2040 target, it’s the first stage-gate review. If Noetra hits its early milestones and releases a usable model this fiscal year, expect the investor list to grow well beyond the current four. If it doesn’t, the funding structure gives Tokyo every reason to walk away quietly rather than prop up a stalled national project. See also: From cloud to factory – humanoid robots coming to workplaces Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Japan’s answer to its worker shortage: An AI model for 10 million robots appeared first on AI News. View the full article

The Bank of England is reviewing whether existing rules can cover the use of agentic AI in finance, including payments, trading, cybersecurity, and operations. Deputy Governor Sarah Breeden said existing regulatory frameworks were not designed for AI agents that can act without direct human instruction. Speaking at the European Central Bank Forum on central banking in Portugal, she said relying on human oversight for every action by these systems is unlikely to be practical. Breeden said current frameworks were not built to contemplate autonomous agents in payments, trading, and operational functions. Agentic AI enters financial workflows Agentic AI refers to systems that can make decisions and carry out tasks independently. In finance, such systems are already being used in areas such as product recommendations, operational workflows, and trading-related tasks. Agentic systems differ from traditional automated trading tools because they can pursue objectives and make decisions with less direct human supervision. Breeden said these systems could act in similar ways if they are trained on similar data or designed around similar goals. Breeden said recent advances in AI models for identifying cyber vulnerabilities show a change in capability. She said agentic AI systems can chain together sequences of actions at scale and speed. A 2026 Cambridge Centre for Alternative Finance report found that 81% of surveyed financial services firms are adopting AI at some level. It also found that 52% of industry respondents are already actively adopting agentic AI. The report said most current use remains focused on internal functions, including process automation, data visualisation, software engineering, and knowledge management. Breeden said use in trading is still mostly concentrated in lower-risk operational tasks. BoE flags cyber resilience risks Breeden described cyber resilience as one of the Bank of England’s closest financial stability concerns around agentic AI. She said the technology has undergone a “step change” in cyber capability and that supervisors need to look at risks across the financial system rather than only at individual firms. She said AI tools can strengthen cyber defences when used by security teams. The immediate risk, she added, is that the same tools could increase the chance of attacks that harm financial stability if used by malicious actors. Breeden also noted that open-source models may trail the most advanced closed models by only four to eight months. She said this gives authorities only limited comfort, despite restrictions on the release of some advanced models. The IMF has also warned that AI-enabled cyber risk should be treated as a financial stability issue. It said attacks can scale quickly, spread across sectors that share digital infrastructure, and create wider disruption if several institutions are affected at once. Breeden said authorities should place greater weight on simultaneous disruption across several firms and stress-test the likely impact before such events occur. She said recovery planning may also need to account for mass disruption, rather than only isolated outages. The Bank of England is considering stronger recovery requirements for core systems. One option is to allow one bank to take over another bank’s basic functions during an outage or failure. Other options include arrangements that allow critical services to continue if a firm’s core systems are compromised. Breeden also raised the question of whether key firms should have separate failover systems or the ability to rebuild compromised core systems quickly. Tobias Adrian, financial counsellor and director of the International Monetary Fund’s capital markets department, also said AI poses serious risks to cyber resilience, according to Central Banking. The IMF has separately warned that shared software, cloud services, payment networks, and data networks can create correlated failures if widely used systems are targeted. Regulators consider market safeguards Breeden said regulators are also looking at guardrails, circuit breakers, and kill switches. These tools would be designed to limit or stop trading across markets if faulty AI models contribute to severe disruption. Breeden said autonomous systems could amplify volatility if they respond in similar ways to the same market signals, especially if their objectives drift from their original purpose or from public policy goals. The Bank of England has previously said existing rules were sufficient to manage AI-related risks. Breeden said recent developments have exposed gaps in current frameworks. Global regulators review AI safeguards The Financial Stability Board said earlier in June that AI agents pose a distinct challenge for human oversight and called for stronger safeguards. The FSB’s June consultation set out 12 proposed sound practices for responsible AI adoption by financial institutions. The practices cover organisation-wide governance, AI risk management across development and deployment, and AI-related cyber, ICT, and third-party risks. The FSB said the practices are not intended to create a binding international standard. It also said firms should define clear roles and responsibilities when using AI, especially when the technology is used in critical or material functions. Breeden said the Bank of England’s focus is on ensuring that financial firms remain resilient as autonomous systems are used in more areas. The review covers firm-level controls and market-wide safeguards. See also: HSBC expands AI banking partnership with Google Cloud Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Bank of England reviews AI rules for agentic AI in finance appeared first on AI News. View the full article

Anthropic has launched Claude Sonnet 5 and restored access to its Fable and Mythos frontier models following a federal export control review. The decision marks the conclusion of an eighteen-day operational pause triggered by a US government export control directive on June 12, which forced the temporary suspension of Anthropic’s highest-capability systems. Government officials enacted the restriction after researchers at Amazon documented a method to bypass the safety controls of Fable 5, causing the model to identify software vulnerabilities and supply exploitation code. Anthropic has since developed an updated automated classifier to patch the vulnerability, clearing the path for a full commercial rollout across its platform, cloud infrastructure, and partner networks. The temporary suspension of Fable 5 and Mythos 5 highlighted the regulatory pressures facing frontier intelligence systems. When the export control mandate took effect, the lack of real-time nationality verification systems required a total access blackout for all global users. Security evaluations conducted during the shutdown confirmed that the vulnerability identification behaviour was not unique to Fable 5. Older and less capable architectures from multiple providers, including Claude Opus 4.8, GPT-5.5, and Kimi K2.7, duplicated the exact results. To resolve the federal directive, engineers trained an automated safety classifier targeting the specific bypass mechanism reported by Amazon. This software layer functions with a wide safety margin, identifying and blocking ambiguous developer prompts that display a statistical probability of malicious intent. Internal validation data indicates the updated classifier prevents the reported exploitation technique in more than 99 percent of trials. When a developer issues a prompt that triggers this boundary, the platform automatically routes the workload to the older Opus 4.8 architecture to maintain continuity. The expanded safety margin introduces a distinct trade-off for engineering teams, as the automated system flags benign requests more frequently during routine application development and software debugging. Active deployments and agentic workflows While frontier models face strict state oversight, the immediate commercial focus targets the newly-deployed Claude Sonnet 5. Engineering teams are transitioning autonomous agents to this model to reduce operational expenditure while maintaining high execution capacity. Performance data validates that the system executes multi-step plans, operates terminal environments, and navigates web browsers without human intervention. Model performance and cost metrics: ModelSWE-bench ProTerminal-Bench 2.1Base input cost*Base output cost*Sonnet 563.2%80.4%$3.00$15.00Sonnet 4.658.1%67.0%$3.00$15.00Opus 4.869.2%82.7%$5.00$25.00 *Cost per million tokens. Sonnet 5 carries introductory rates of $2.00 input / $10.00 output through August 31, 2026. Real-world deployments demonstrate how organisations are deploying this architecture within live software development pipelines. At Rakuten, technology teams deployed the architecture against dozens of the company’s most challenging production code pull requests. The system processed each submission independently, executing tests and verifying the results before presenting the completed code to human engineers for final structural approval. Software automation firm Zapier integrated the system into its core product workflows to execute multi-part administrative tasks. In a documented deployment, engineers tasked the model with updating Salesforce account tiers and subsequently generating and transmitting launch announcements to enterprise contacts. Prior model architectures frequently stalled midway through these multi-stage operations, whereas the current system executed the entire sequence end-to-end without human remediation. Development tool provider Zed utilised the system to automate complex debugging procedures. During internal trials, engineering teams directed the model to investigate an active software bug. Working without explicit prompts or step-by-step instructions, the system independently generated a reproducing test script, applied the necessary code fix, and stashed the modifications to verify that the bug reappeared in the absence of the patch. The entire diagnostic and remediation sequence occurred within a single processing pass. Software engineering platform Factory implemented the architecture to manage sustained coding tasks within complex codebase environments. Technical teams reported that the system maintained logical grounding and execution consistency across corporate code repositories, outperforming previous generation software layers by completing tasks that previously timed out or failed to resolve. Quantitative safety audits and exploitation limits Data from the formal system card indicates that the system achieves these autonomous capabilities without a corresponding inflation of security risks. Automated behavioural audits designed to test for deceptive tendencies and cooperation with unauthorised requests show that the model exhibits a lower overall rate of non-compliant behaviour compared to its direct predecessor, Sonnet 4.6. The architecture does not possess advanced offensive cybersecurity capabilities. Anthropic engineers omitted specialised cybersecurity datasets from the training protocol, limiting the system to routine, defensive technical tasks. In public security assessments conducted in partnership with Mozilla, researchers tested the model’s capacity to build functional exploits for known vulnerabilities within the Firefox 147 browser core. The model failed to generate a single working exploit across all evaluation windows, registering a zero percent success rate. It did achieve a 13.2 percent partial success rate, which represented a minor increase over Sonnet 4.6, though engineers attribute this variation to general gains in logical reasoning rather than domain-specific offensive training. Out of caution, commercial versions ship with default real-time safety classifiers equivalent to those used in the premier Opus 4.8 framework. The regulatory friction surrounding Fable 5 prompted a formal partnership between Anthropic, Amazon, Microsoft, and Google to establish an objective industry framework for assessing model security breaches. Currently, providers lack a shared metric to classify the severity of system bypasses, creating regulatory uncertainty when researchers identify new prompting vulnerabilities. The proposed governance framework scores security breakdowns across four specific technical criteria: Capability gain measures how far the exploit advances user capabilities beyond standard, widely available software utilities. Breadth of capability gain quantifies the number of distinct offensive operations the same exploit unlocks. Ease of weaponisation tracks the volume of human engineering effort and specialized prompting required to extract a harmful output. Discoverability determines the accessibility of the exploit technique within public research circles. Developers and cybersecurity professionals will use this matrix to coordinate defensive responses. For high-severity breaches, such as exploits demonstrating an immediate capacity to disrupt financial accounting systems or electrical transmission grids, providers will deploy automated mitigations instantly. This initiative operates alongside a newly established HackerOne vulnerability research program and a dedicated corporate monitoring team providing 24-hour oversight of threat intelligence channels. Deployment strategies will need to adapt to this closer relationship between model builders and state regulatory bodies. Anthropic has formalised agreements under recent executive mandates to grant federal researchers early access to frontier architectures prior to public commercial release. These joint evaluation windows allow external security analysts to audit model capabilities alongside internal engineering teams, ensuring regulatory alignment before code enters production environments. See also: HP accelerates enterprise workflows with OpenAI Frontier Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post Anthropic deploys Claude Sonnet 5, Fable and Mythos restored appeared first on AI News. View the full article

HP has scaled its OpenAI Frontier integration across global operations to optimise enterprise workflows and accelerate output. The hardware manufacturer initiated testing of the platform in February 2026. Early pilot programs yielded verified operational gains in software engineering and cybersecurity remediation. Expanding these initial trials into an enterprise-wide operating model requires connecting access protocols, contextual data, and evaluation metrics. Frontier supplies this connective tissue. Engineering capacity and deployment metrics Implementation metrics indicate high usage among technical staff. One HP engineer processed 122 pull requests spanning 43 distinct projects within a matter of weeks using OpenAI models. Managing pull requests across dozens of concurrent projects typically induces severe context switching penalties for human operators. Automated models process repository syntax and validate code logic across multiple environments simultaneously. This capability directly reduces wait states within the software development life cycle. The corporate security division applied these identical models to resolve several software bugs within a single day. Internal estimates indicated this remediation workload would typically consume an entire month. Enterprise development teams lose countless hours as code transitions through testing protocols, peer reviews, security audits, and sprint planning schedules. OpenAI tools compress these isolated stages into a collaborative and accelerated sequence. Technical execution speed increases when diagnostic tools accurately pinpoint flaws during initial commits. “It has been an amazing tool, and I am using it daily,” an HP engineer stated. The deployment architecture segments AI models based on task requirements. HP directs ChatGPT instances to execute broad knowledge initiatives. These implementations manage active enterprise research, data analysis routines, concept ideation, and automated workflow triggers. Codex instances handle specialised development operations. Engineers instruct Codex to map application planning phases, construct user interface scaffolding, and manage parallel software-delivery tasks. Separating workloads across designated models prevents processing errors and ensures accurate output generation. Partner channel integration External partner networks are the bulk of HP’s operational flow. More than 80 percent of the company’s business travels through its channel ecosystem. Over 100,000 partners globally access the HP Partner Portal. Applying AI to this massive external network requires strict data routing. Enterprise software ecosystems fail when partner portals experience lag or present inaccurate administrative data. The Frontier platform facilitates a cohesive self-service architecture covering store interfaces, partner communications, and voice channels. AI agents supply constant guidance regarding program navigation and business information. These agents process partner queries and deliver direct operational management support. This deployment decreases manual processing loads and accelerates information-to-action cycles. Customers and partners execute routine workflows and attain resolution faster through these automated systems. Administrative queries regarding stock limits or warranty routing resolve without human intervention. Device telemetry and fleet management Hardware administration relies on the HP Workforce Experience Platform (WXP). CIOs use this central dashboard to oversee entire device fleets. Processing device health signals across global corporate networks generates massive data payloads. Human technicians cannot manually correlate every error log across tens of thousands of deployed machines. HP integrates Frontier to analyse device telemetry, operational objects, schemas, and runbooks. AI agents process fleet health signals to investigate application hangs, Wi-Fi connectivity errors, and system crashes. This diagnostic speed promotes accurate remediation protocols across distributed corporate environments. The platform provides a single pane of glass for device management. Automated investigation of operational objects ensures hardware failures register immediately and map to established recovery procedures. IT teams can initiate repairs based on analysed telemetry rather than basic user complaints. Enterprises require agents that understand trusted context boundaries. Frontier provides the necessary connectivity to govern APIs and evaluate system outputs. Shadow IT environments develop when departments deploy unmonitored AI instances. Frontier centralises these deployments. Security operations serve as both an operational proof point and an active governance layer. HP security personnel deploy ChatGPT to proactively neutralise vulnerabilities. Directional estimates project roughly 82 hours per week of security-team capacity freed by this automation. Retaining cybersecurity professionals requires eliminating monotonous log review processes. Frontier maintains oversight by managing permissions, evaluation parameters, and deployment controls. Human capital executes higher-level analysis while automated tasks remain fully-reviewable. HP is not only optimising current operational capacity but establishing a robust framework for future technological integration—ensuring that as enterprise demands evolve, the underlying infrastructure remains secure and agile. See also: Wimbledon adds IBM AI tools for live match coverage Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information. AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here. The post HP accelerates enterprise workflows with OpenAI Frontier appeared first on AI News. View the full article