VentureBeat Every one of the 63 stories VentureBeat has led with here, newest first. venturebeat.com cybersecurity, hacking China-linked hackers backdoored executives' laptops via USB, exploiting a fix companies had but weren't using Chinese state-linked hackers compromised executives' laptops via USB at a conference, exploiting unpatched vulnerabilities. VentureBeat · 1mo ago A Chinese state-linked hacking group compromised executive laptops at an agricultural industry conference on Hainan Island this spring — not through phishing or a network breach, but by breaking into hotel rooms and booting the machines from a USB stick while the executives were at dinner. CrowdStrike, which tracks the group as OVERCAST PANDA, disclosed the campaign in its 2026 Threat Hunting Report and detailed the operation's timeline in an interview with VentureBeat at Fal.Con 2026: an intruder entered one room at around 8 p.m. local time and a second room by 9:57 p.m., writing a backdoor called FlowCloud directly to each laptop's storage before rebooting the machines and leaving. There was no network intrusion, no phishing email, and no… Meta, Muse Spark Meta says Muse Spark 1.3 has frontier performance — but its best results come from a model developers can’t broadly use yet Meta's Muse Spark 1.3 shows frontier performance on benchmarks but most capable version unavailable for broad use. VentureBeat · 1mo ago Meta’s newest AI model Muse Spark 1.3, unveiled yesterday, is faster and more performant on third-party benchmarks than its predecessor — with a caveat. "Muse Spark 1.3 is rolling out today with frontier performance almost too cheap to meter," Meta co-founder and CEO Mark Zuckerberg wrote on X, calling it Meta’s “biggest jump” yet in coding and agentic work. There is substance behind both parts of that claim. Muse Spark 1.3 makes significant gains over last month’s 1.2 release, particularly on long-running agent tasks. The version developers can access now is also one of the strongest price-performance offerings near the top of independent model rankings. Meta’s strongest Muse Spark 1.3 benchmark results come from its max reasoning configuration. Meta says… Microsoft, speech recognition Microsoft AI’s MAI-Transcribe-2 undercuts OpenAI, Google and ElevenLabs on price and speed Microsoft releases speech-recognition model cheaper and faster than competitors' offerings at ten cents per audio hour. VentureBeat · 1mo ago Microsoft AI on Thursday released MAI-Transcribe-2, a speech-recognition model the company says is faster, more accurate, and cheaper than anything OpenAI, Google, or ElevenLabs currently sells. Then it priced the thing at 10 cents per hour of audio. That figure deserves a pause. When Microsoft AI shipped the first model in this line just five months ago, it charged $0.36 an hour. Thursday's early-bird price cuts that by roughly 72%. For an enterprise processing 100,000 hours of call-center audio a year — a modest volume for a large bank or telecom — the bill drops from $36,000 to $10,000. At that level, transcription stops being a line item anyone argues about. The release arrives as Microsoft executes a strategy that… AI search, marketing The AI visibility gap: Why great brands disappear from AI answers Marketing teams face visibility gap as AI search synthesizes answers without ranking traditional sites. VentureBeat · 1mo ago Presented by Contentful Most marketing teams still measure visibility the same way they always have: rankings, click-through rates, and organic traffic. But buyers have moved on. Search tools and AI engines now synthesize answers directly on the screen, creating a world of zero-click searches where your website is entirely bypassed. The “old days” are not coming back. The question for marketers is no longer, How do we rank first? It's How do we become part of the answer? The answer isn't publishing more content; it’s making your knowledge impossible for AI to ignore. Brand visibility has a new dimension Showing up is only half the battle. Where you appear inside an AI-generated response matters just as much. Think about the… Google Gemini Google’s Gemini 3.8 Flash is built for agents, while its Cyber twin hunts vulnerabilities Google releases Gemini 3.8 Flash variants designed for agent tasks, software development and multi-step reasoning. VentureBeat · 1mo ago Google keeps cranking out Flash models: the company on Wednesday announced two versions of a new 3.8 Flash. The variants include a standard Flash, a “workhorse” model for agentic tasks, software development, and multi-step reasoning, and Flash Cyber optimized for vulnerability detection and mitigation. Google CEO Sundar Pichai said in an X post that 3.8 Flash delivers “significant leaps” from 3.7 Flash across software engineering, agentic tasks, and multi-step reasoning. For instance, it outperformed many large frontier models on the DeepSWE coding benchmark, at far lower cost. Meanwhile, Flash Cyber is the company’s “most capable” cybersecurity model, Pichai said; it also matches frontier-level performance when it comes to discovering vulnerabilities and patching them at scale. The model achieved 86.2% on… speech recognition Meta prices Muse Voice Transcribe at $0.18 an hour, with real-time diarization for 20+ speakers: a steal for enterprises? Meta launches Muse Voice Transcribe for real-time speech-to-text with speaker diarization at $0.18 per hour. VentureBeat · 1mo ago Meta is entering the increasingly competitive real-time speech-to-text market with Muse Voice Transcribe, a new audio perception model that combines streaming transcription, endpoint detection and speaker diarization for more than 20 speakers — at a public API price of just $0.18 per hour of processed audio. Developed by Meta Superintelligence Labs, Muse is designed to process speech while it happens rather than waiting for a recording to finish. Meta’s launch post for Muse Voice Transcribe says the model supports long audio exceeding an hour, seamless multilingual code-switching, language and keyword biasing, and diarization without a separate post-processing pipeline. The model was trained across more than 70 languages, with 25 extensively validated for the initial release. The 20-plus-speaker figure is substantial,… AI accelerators Enterprises put non-Nvidia chips 14 points ahead of Nvidia's next-gen GPUs on their evaluation lists Enterprise AI buyers rank non-Nvidia chips ahead of Nvidia's next-generation GPUs on evaluation lists. VentureBeat · 1mo ago When enterprise buyers build out their next AI accelerator evaluation list this cycle, they're more likely to put a non-Nvidia chip on it than Nvidia's own next-generation GPU. According to VentureBeat's July VB Pulse survey of 170 AI infrastructure respondents, 39.4% said they're likely to evaluate non-Nvidia accelerators — AWS Trainium, Google TPU, AMD Instinct, Intel Gaudi or in-house ASICs — over the next 12 months, compared with 25.3% for Nvidia Blackwell (GB300) or other next-generation Nvidia GPUs, a 14-point gap. Nvidia remains the default in most production environments. But organizations are building real optionality into their accelerator strategy rather than treating Nvidia as the only evaluation worth doing. The finding sits inside a broader pattern: enterprises are expanding and… AI security vulnerability Stolen Claude session cookies can reach corporate Gmail through grants no IT admin can revoke Stolen Claude session cookies allow attackers to access corporate accounts without triggering two-factor authentication defences. VentureBeat · 1mo ago Infostealers replayed stolen Claude session cookies into paid accounts without ever touching the login page two-factor authentication guards. The accounts Anthropic flagged were card-billed, self-serve accounts, which is the population no corporate identity provider governs, and no admin console can sign out. Session-cookie replay bypasses SSO as thoroughly as it bypasses 2FA. What SSO provides here is revocation and visibility, not prevention. The company disclosed the campaign in notification emails to affected users, named six stealer families, signed the accounts out, stripped the saved payment methods, and refunded the charges it found. The burned usage is the small loss. What those sessions could reach is the exposure, and none of it sat behind an identity controlled by an enterprise. Anthropic… enterprise AI, deployment Forward-deployed engineering is how enterprise AI learns Forward-deployed engineers embed with enterprises to implement AI workflows on customer data. VentureBeat · 1mo ago Presented by Zeta Every forward-deployed engineering (FDE) pitch sounds identical for the first ten minutes: an engineer embedded on-site, a workflow encoded within weeks, a demo that finally works on the customer's real data. What differs is what happens in the following months, and most vendors will not tell you until you ask directly. FDE has become one of enterprise AI’s most consequential operating models. Vendors are building entire go-to-market motions around engineers who embed with customers, wire products into operating environments, and make the demo real. Investors often read FDE headcount as a growth signal and buyers read it as a promise of speed. Neither tells you whether the work is becoming a product advantage or simply accumulating as… LLM reasoning, hallucination Frontier models can recover up to 65% of facts they can't directly recall — just by thinking longer Research shows large language models can recover forgotten facts by reasoning longer. VentureBeat · 1mo ago When large language models (LLMs) hallucinate, developers typically assume the model lacks the required facts. Engineering teams diagnose the error as missing knowledge. The standard response is to increase model size, expand training data, or build complex retrieval architectures. A new study by researchers at Google Research and Technion demonstrates that the knowledge is often not missing. The model has the information encoded parametrically but fails to surface it during generation. Their experiments show that frontier models like GPT-5 and Gemini-3 encode 95-98% of tested facts. This indicates that in many cases, recall, rather than encoding, is the primary bottleneck for factual accuracy. By understanding how to unlock existing knowledge through inference-time computation, engineering teams can build more reliable applications… Azure OpenAI agents Closing an Azure OpenAI assistant's retrieval gap didn't take a new identity platform. It took one filter and a narrower assistant. Engineer solves Azure OpenAI retrieval problem with simpler configuration and filtering. VentureBeat · 1mo ago Egiziago Cioffi is the IT and Enterprise Architect and CEO of SynSphere Italia, a Microsoft partner based in Milan. He built an agent himself. He wrote the indexing job, configured the Azure OpenAI retrieval pipeline, connected it to SharePoint, and watched it pass every evaluation his team ran. His Azure OpenAI email assistant auto-resolves about 60% of inbound customer email, Cioffi told VentureBeat in written responses to our interview questions. The evaluation scores were clean, and the unit tests passed. None of them asked the question that mattered. Cioffi ran a low-privilege account against the same questions a high-privilege account had already put to the assistant. The outputs did not match. The assistant returned SharePoint content the requesting user could… Claude models Anthropic's Claude Fable 5.1 and Mythos 5.1 arrive with a 75% cost reduction for Fable cache reads Anthropic releases Claude Fable 5.1 and Mythos 5.1 with 75 percent cost reduction. VentureBeat · 1mo ago · also at Hacker News It's only the first day of September 2026, but the month and fall season are already off to the races in AI land, as Anthropic has just released its latest and most powerful large language models yet — Claude Fable 5.1 and Claude Mythos 5.1. The two names refer to the same underlying model. Fable 5.1 is the generally available version, with Anthropic’s production safeguards in place. Mythos 5.1 is available through restricted-access programs for vetted cybersecurity and life-sciences organizations that need capabilities normally constrained by those safeguards. For enterprise buyers, however, the release is about more than another round of benchmark gains. Anthropic is simultaneously changing the economics of running persistent agents, reducing the cost of cached context by… Perplexity, hybrid AI Your files stay put: Perplexity’s hybrid AI keeps confidential data off the cloud Perplexity launches hybrid compute letting its AI agent split work between cloud and local models on Apple devices. VentureBeat · 1mo ago Perplexity today launched hybrid compute for its agentic platform, Computer, a system that lets a single AI agent split its work between frontier models running in the cloud and smaller open-weight models running locally on Apple silicon Macs — routing sensitive data to the local machine so it never leaves the device. The company says it is the first time an AI agent can begin a task in the cloud and dynamically hand off the confidential portions of that same task to a model running on the user's own hardware, without restarting the job or losing context. The feature becomes available today through Perplexity's desktop app for enterprise customers that opt in, as well as Pro and Max subscribers, on… AI workforce impact AI is redefining the workforce — and most planning models aren’t ready Companies struggle to plan workforce changes as AI redefines jobs and skills across departments and business units. VentureBeat · 1mo ago Presented by SAP HR tracks employees and skills. Finance owns headcount targets and cost. Procurement manages contractors and services spend. Together, they leave executives unable to answer basic questions about how workforce decisions actually translate into business outcomes. Fragmented planning creates workforce blind spots Each function has its own systems, its own planning cadence, and its own assumptions about how work gets done. Recent SAP research found that 62% of C-suite executives are dissatisfied with their current level of integration between people and business performance data. The same research found that while 50% of organizations are planning for AI’s impact on productivity and capacity, only 21% are planning for AI’s impact on job design and organizational structure. That gap matters… AI agents OpenClaw 2.0 is here, ushering in the era of 'multiplayer' AI coding: What it means for enterprises OpenClaw 2.0 enables language models to act as autonomous AI workers via messaging apps. VentureBeat · 1mo ago The viral fervor we saw earlier this year around OpenClaw, the open source AI harness that turns powerful language models into autonomous workers the user can message via their favorite channels (Telegram, iMessage, WhatsApp, Discord etc), has cooled off substantially from its peak in March 2026. But over the weekend, OpenClaw's creator Peter Steinberger and current team of co-developers gave the world — especially enterprises — a reason to look at it again, announcing OpenClaw 2.0, billed as the most significant update to the harness and surrounding platform yet. OpenClaw 2.0 seeks to transform what began largely as a personal agent harness into something increasingly designed for teams, shared infrastructure and enterprise workflows. OpenClaw 2.0 introduces a rebuilt browser interface… AI agents, engineering Software engineers' new job isn't writing code — it's designing the boundaries AI agents can't break Software engineers now focus on designing boundaries to prevent AI agent misuse. VentureBeat · 1mo ago If you look at the commit histories of modern data platforms, something profound has shifted over the last two years. The friction of writing syntax has collapsed. With Cursor, Claude Code, and agentic workflows now living inside our Docker containers and IDEs, generating the first implementation of a distributed streaming pipeline or a complex API integration is no longer the central bottleneck. Agents can navigate repositories, write test coverage, inspect stack traces, and propose refactors. Describe a Kafka-to-Iceberg sink mapping in plain English, and an agent can produce a credible starting point before the engineer has opened every relevant file. That changes the question for software engineers. If the agent is becoming the primary author of local system logic, what… AI agents Identity and permissions aren’t enough to govern AI agent behavior Enterprise AI agents require behavior governance beyond identity and permissions controls. VentureBeat · 1mo ago Presented by Box Identity and permissions are no longer enough to secure enterprise AI agents. They govern what an agent can reach, not how it behaves once it starts working on its own, and an autonomous agent can turn legitimate access of enterprise data into unintended action in seconds. That gap is pushing enterprise AI security from just governing access toward a layered approach that includes governing execution, says Heather Ceylan, chief information security officer at Box. "Access controls and permissions are the foundation, but the challenge is they were designed for humans," Ceylan says. "Permissions are still the foundation, but you have to think about how the agents get their permissions scoped as well." Access controls were built for… AI agents AI agents need their own identity before they need a gateway Autonomous AI agents now coordinate workflows and invoke tools with minimal human oversight in enterprise settings. VentureBeat · 1mo ago Enterprise AI has entered a new era. Organizations are rapidly moving beyond assistants that answer questions to autonomous agents capable of reasoning, invoking tools, accessing enterprise applications, coordinating with other agents, and completing multi-step business workflows with minimal human intervention. This shift represents a fundamental change in how software operates. Traditional applications execute predefined logic written by developers. AI agents, however, dynamically determine how to achieve an objective. They decide which tools to use, which APIs to call, what information to retrieve, and how to sequence actions based on context. That flexibility unlocks enormous business value, but it also introduces a new class of security risks. Much of today's AI security discussion focuses on prompt injection, model vulnerabilities, and data… AI agent security AI agents that pass authentication can still drift, expose data, or get memory-poisoned AI agents with authentication access can still suffer data exposure and memory poisoning attacks despite security measures. VentureBeat · 1mo ago There is a clear repeating trend in agent deployments: The gateway is the first control teams reach for, but it is the one they are least ready to run. This is because gateways sit on top of identity and attribution layers that are mostly not there. The first layer of risk is not hypothetical. In June, CISA added a LiteLLM flaw to its Known Exploited Vulnerabilities catalog after attackers were caught abusing it in the wild. The bug ran commands on the host through the gateway itself, and chained with a second flaw it required no credentials. It was one of seven common vulnerabilities and exposures (CVEs) disclosed in that single AI gateway in a month. This is the layer… agentic AI security The three layers of agentic AI security: A defense-in-depth architecture for autonomous agents Framework for multi-layered security architecture to manage risks from autonomous AI agents. VentureBeat · 1mo ago Presented by Nutanix Autonomous systems that can reason, make their own decisions, and execute actions across an environment introduce a category of risk that application-level controls were never built to contain. Treating that risk as a single problem produces incomplete architectures, says Oscar Wahlberg, senior director of product management at Nutanix. "The guardrails to catch a malicious prompt won't stop an agent from hallucinating and doing something it never should have done, like accidentally deleting databases or leaking sensitive data with a credential it was granted but then uses for something entirely different," Wahlberg says. "That's the central problem as enterprises move autonomous agents out of experimentation and into production." Once an agentic system is granted execution privileges across the… large language models Meta researchers taught an 8B AI model to match Claude Opus 4.5 — without the frontier price tag Meta's 8B AI model matches Claude Opus 4.5 performance at significantly lower cost. VentureBeat · 1mo ago Consider an AI agent tasked with a complex enterprise workflow like migrating massive batches of customer records from a legacy CRM to a cloud database. The agent cannot rely solely on its internal context window for a job spanning hours and depends on the runtime layer, aka the harness. This harness provides execution feedback, like server logs, to help the agent maintain an accurate understanding of dynamic API connections. It also provides state trackers and control-flow mechanisms to manage completed and pending subgoals, ensuring the agent doesn't skip or duplicate data batches. When unexpected errors occur, such as a database rejecting a batch due to strict API rate limits, the harness provides tools and instructions to help the agent recover.… document processing AI Cohere Parse 5 loses the benchmark on points. It wins on cost per page. Cohere releases Parse 5 document extraction tool with improved cost efficiency over competitors. VentureBeat · 1mo ago Enterprises trying to feed PDFs, slides and scanned documents into AI pipelines keep running into the same wall: the tools either miss the structure — tables, charts, layout — or cost too much to run at scale. Cohere released Parse 5 on Thursday, positioning it on price-to-performance, not raw accuracy — the right cost-capability mix for enterprise scale. Parse 5 is a 2.3-billion-parameter vision language model built to convert PDFs, slides and images into structured Markdown at enterprise scale. Cohere's own published benchmark comparison puts Parse 5 behind three larger, general-purpose frontier models on accuracy. GPT-5.5, Opus 4.8 and Gemini 3.5 Flash all score higher than Parse on the three ParseBench dimensions Cohere reports. Cohere is not claiming the top… AI agents Enterprise AI's real risk isn't autonomous agents. It's the complexity between them. Enterprise AI complexity between multiple deployed agents poses real risk to organizations. VentureBeat · 1mo ago Presented by Gravitee Agent complexity is the insidious shadow lurking inside enterprises right now that needs a light shone on it. That’s because enterprises don't deploy a single agent and watch it run, they deploy fleets, each one calling APIs, calling other agents, reaching into applications that were never built with a machine decision-maker in mind. That's the failure mode that should keep you up at night: a windy, complicated system nobody can see clearly enough to govern. But why do things get so opaque so quickly? Add a second agent to a system, and you've added one connection. Add a tenth, and you haven't added ten connections, you've potentially added dozens, because now any agent might call any other,… autonomous AI agents Visa ships a security AI that patches production code before any human reviews it Visa deploys AI that autonomously finds security vulnerabilities, writes fixes, and validates them without human review. VentureBeat · 1mo ago Visa's open-source security harness now finds the vulnerability, writes the fix, and turns an adversarial panel on its own patch before any human reviews it. The whole loop ships on by default. A plain scan of the Visa Vulnerability Agentic Harness runs all 11 stages and edits source files in the target repo unless the operator caps it at detection. The announcement Thursday pairs the release with an expansion of the Visa Consulting & Analytics advisory practice. Visa is shipping that default 18 days after Tenet Security demonstrated GhostJacking on the DEF CON 34 main stage, an attack chain in which an agent read an attacker's payload out of a log file and rewrote DNS with a valid credential. Two… Show 24 more Loading
cybersecurity, hacking China-linked hackers backdoored executives' laptops via USB, exploiting a fix companies had but weren't using Chinese state-linked hackers compromised executives' laptops via USB at a conference, exploiting unpatched vulnerabilities. VentureBeat · 1mo ago A Chinese state-linked hacking group compromised executive laptops at an agricultural industry conference on Hainan Island this spring — not through phishing or a network breach, but by breaking into hotel rooms and booting the machines from a USB stick while the executives were at dinner. CrowdStrike, which tracks the group as OVERCAST PANDA, disclosed the campaign in its 2026 Threat Hunting Report and detailed the operation's timeline in an interview with VentureBeat at Fal.Con 2026: an intruder entered one room at around 8 p.m. local time and a second room by 9:57 p.m., writing a backdoor called FlowCloud directly to each laptop's storage before rebooting the machines and leaving. There was no network intrusion, no phishing email, and no…
Meta, Muse Spark Meta says Muse Spark 1.3 has frontier performance — but its best results come from a model developers can’t broadly use yet Meta's Muse Spark 1.3 shows frontier performance on benchmarks but most capable version unavailable for broad use. VentureBeat · 1mo ago Meta’s newest AI model Muse Spark 1.3, unveiled yesterday, is faster and more performant on third-party benchmarks than its predecessor — with a caveat. "Muse Spark 1.3 is rolling out today with frontier performance almost too cheap to meter," Meta co-founder and CEO Mark Zuckerberg wrote on X, calling it Meta’s “biggest jump” yet in coding and agentic work. There is substance behind both parts of that claim. Muse Spark 1.3 makes significant gains over last month’s 1.2 release, particularly on long-running agent tasks. The version developers can access now is also one of the strongest price-performance offerings near the top of independent model rankings. Meta’s strongest Muse Spark 1.3 benchmark results come from its max reasoning configuration. Meta says…
Microsoft, speech recognition Microsoft AI’s MAI-Transcribe-2 undercuts OpenAI, Google and ElevenLabs on price and speed Microsoft releases speech-recognition model cheaper and faster than competitors' offerings at ten cents per audio hour. VentureBeat · 1mo ago Microsoft AI on Thursday released MAI-Transcribe-2, a speech-recognition model the company says is faster, more accurate, and cheaper than anything OpenAI, Google, or ElevenLabs currently sells. Then it priced the thing at 10 cents per hour of audio. That figure deserves a pause. When Microsoft AI shipped the first model in this line just five months ago, it charged $0.36 an hour. Thursday's early-bird price cuts that by roughly 72%. For an enterprise processing 100,000 hours of call-center audio a year — a modest volume for a large bank or telecom — the bill drops from $36,000 to $10,000. At that level, transcription stops being a line item anyone argues about. The release arrives as Microsoft executes a strategy that…
AI search, marketing The AI visibility gap: Why great brands disappear from AI answers Marketing teams face visibility gap as AI search synthesizes answers without ranking traditional sites. VentureBeat · 1mo ago Presented by Contentful Most marketing teams still measure visibility the same way they always have: rankings, click-through rates, and organic traffic. But buyers have moved on. Search tools and AI engines now synthesize answers directly on the screen, creating a world of zero-click searches where your website is entirely bypassed. The “old days” are not coming back. The question for marketers is no longer, How do we rank first? It's How do we become part of the answer? The answer isn't publishing more content; it’s making your knowledge impossible for AI to ignore. Brand visibility has a new dimension Showing up is only half the battle. Where you appear inside an AI-generated response matters just as much. Think about the…
Google Gemini Google’s Gemini 3.8 Flash is built for agents, while its Cyber twin hunts vulnerabilities Google releases Gemini 3.8 Flash variants designed for agent tasks, software development and multi-step reasoning. VentureBeat · 1mo ago Google keeps cranking out Flash models: the company on Wednesday announced two versions of a new 3.8 Flash. The variants include a standard Flash, a “workhorse” model for agentic tasks, software development, and multi-step reasoning, and Flash Cyber optimized for vulnerability detection and mitigation. Google CEO Sundar Pichai said in an X post that 3.8 Flash delivers “significant leaps” from 3.7 Flash across software engineering, agentic tasks, and multi-step reasoning. For instance, it outperformed many large frontier models on the DeepSWE coding benchmark, at far lower cost. Meanwhile, Flash Cyber is the company’s “most capable” cybersecurity model, Pichai said; it also matches frontier-level performance when it comes to discovering vulnerabilities and patching them at scale. The model achieved 86.2% on…
speech recognition Meta prices Muse Voice Transcribe at $0.18 an hour, with real-time diarization for 20+ speakers: a steal for enterprises? Meta launches Muse Voice Transcribe for real-time speech-to-text with speaker diarization at $0.18 per hour. VentureBeat · 1mo ago Meta is entering the increasingly competitive real-time speech-to-text market with Muse Voice Transcribe, a new audio perception model that combines streaming transcription, endpoint detection and speaker diarization for more than 20 speakers — at a public API price of just $0.18 per hour of processed audio. Developed by Meta Superintelligence Labs, Muse is designed to process speech while it happens rather than waiting for a recording to finish. Meta’s launch post for Muse Voice Transcribe says the model supports long audio exceeding an hour, seamless multilingual code-switching, language and keyword biasing, and diarization without a separate post-processing pipeline. The model was trained across more than 70 languages, with 25 extensively validated for the initial release. The 20-plus-speaker figure is substantial,…
AI accelerators Enterprises put non-Nvidia chips 14 points ahead of Nvidia's next-gen GPUs on their evaluation lists Enterprise AI buyers rank non-Nvidia chips ahead of Nvidia's next-generation GPUs on evaluation lists. VentureBeat · 1mo ago When enterprise buyers build out their next AI accelerator evaluation list this cycle, they're more likely to put a non-Nvidia chip on it than Nvidia's own next-generation GPU. According to VentureBeat's July VB Pulse survey of 170 AI infrastructure respondents, 39.4% said they're likely to evaluate non-Nvidia accelerators — AWS Trainium, Google TPU, AMD Instinct, Intel Gaudi or in-house ASICs — over the next 12 months, compared with 25.3% for Nvidia Blackwell (GB300) or other next-generation Nvidia GPUs, a 14-point gap. Nvidia remains the default in most production environments. But organizations are building real optionality into their accelerator strategy rather than treating Nvidia as the only evaluation worth doing. The finding sits inside a broader pattern: enterprises are expanding and…
AI security vulnerability Stolen Claude session cookies can reach corporate Gmail through grants no IT admin can revoke Stolen Claude session cookies allow attackers to access corporate accounts without triggering two-factor authentication defences. VentureBeat · 1mo ago Infostealers replayed stolen Claude session cookies into paid accounts without ever touching the login page two-factor authentication guards. The accounts Anthropic flagged were card-billed, self-serve accounts, which is the population no corporate identity provider governs, and no admin console can sign out. Session-cookie replay bypasses SSO as thoroughly as it bypasses 2FA. What SSO provides here is revocation and visibility, not prevention. The company disclosed the campaign in notification emails to affected users, named six stealer families, signed the accounts out, stripped the saved payment methods, and refunded the charges it found. The burned usage is the small loss. What those sessions could reach is the exposure, and none of it sat behind an identity controlled by an enterprise. Anthropic…
enterprise AI, deployment Forward-deployed engineering is how enterprise AI learns Forward-deployed engineers embed with enterprises to implement AI workflows on customer data. VentureBeat · 1mo ago Presented by Zeta Every forward-deployed engineering (FDE) pitch sounds identical for the first ten minutes: an engineer embedded on-site, a workflow encoded within weeks, a demo that finally works on the customer's real data. What differs is what happens in the following months, and most vendors will not tell you until you ask directly. FDE has become one of enterprise AI’s most consequential operating models. Vendors are building entire go-to-market motions around engineers who embed with customers, wire products into operating environments, and make the demo real. Investors often read FDE headcount as a growth signal and buyers read it as a promise of speed. Neither tells you whether the work is becoming a product advantage or simply accumulating as…
LLM reasoning, hallucination Frontier models can recover up to 65% of facts they can't directly recall — just by thinking longer Research shows large language models can recover forgotten facts by reasoning longer. VentureBeat · 1mo ago When large language models (LLMs) hallucinate, developers typically assume the model lacks the required facts. Engineering teams diagnose the error as missing knowledge. The standard response is to increase model size, expand training data, or build complex retrieval architectures. A new study by researchers at Google Research and Technion demonstrates that the knowledge is often not missing. The model has the information encoded parametrically but fails to surface it during generation. Their experiments show that frontier models like GPT-5 and Gemini-3 encode 95-98% of tested facts. This indicates that in many cases, recall, rather than encoding, is the primary bottleneck for factual accuracy. By understanding how to unlock existing knowledge through inference-time computation, engineering teams can build more reliable applications…
Azure OpenAI agents Closing an Azure OpenAI assistant's retrieval gap didn't take a new identity platform. It took one filter and a narrower assistant. Engineer solves Azure OpenAI retrieval problem with simpler configuration and filtering. VentureBeat · 1mo ago Egiziago Cioffi is the IT and Enterprise Architect and CEO of SynSphere Italia, a Microsoft partner based in Milan. He built an agent himself. He wrote the indexing job, configured the Azure OpenAI retrieval pipeline, connected it to SharePoint, and watched it pass every evaluation his team ran. His Azure OpenAI email assistant auto-resolves about 60% of inbound customer email, Cioffi told VentureBeat in written responses to our interview questions. The evaluation scores were clean, and the unit tests passed. None of them asked the question that mattered. Cioffi ran a low-privilege account against the same questions a high-privilege account had already put to the assistant. The outputs did not match. The assistant returned SharePoint content the requesting user could…
Claude models Anthropic's Claude Fable 5.1 and Mythos 5.1 arrive with a 75% cost reduction for Fable cache reads Anthropic releases Claude Fable 5.1 and Mythos 5.1 with 75 percent cost reduction. VentureBeat · 1mo ago · also at Hacker News It's only the first day of September 2026, but the month and fall season are already off to the races in AI land, as Anthropic has just released its latest and most powerful large language models yet — Claude Fable 5.1 and Claude Mythos 5.1. The two names refer to the same underlying model. Fable 5.1 is the generally available version, with Anthropic’s production safeguards in place. Mythos 5.1 is available through restricted-access programs for vetted cybersecurity and life-sciences organizations that need capabilities normally constrained by those safeguards. For enterprise buyers, however, the release is about more than another round of benchmark gains. Anthropic is simultaneously changing the economics of running persistent agents, reducing the cost of cached context by…
Perplexity, hybrid AI Your files stay put: Perplexity’s hybrid AI keeps confidential data off the cloud Perplexity launches hybrid compute letting its AI agent split work between cloud and local models on Apple devices. VentureBeat · 1mo ago Perplexity today launched hybrid compute for its agentic platform, Computer, a system that lets a single AI agent split its work between frontier models running in the cloud and smaller open-weight models running locally on Apple silicon Macs — routing sensitive data to the local machine so it never leaves the device. The company says it is the first time an AI agent can begin a task in the cloud and dynamically hand off the confidential portions of that same task to a model running on the user's own hardware, without restarting the job or losing context. The feature becomes available today through Perplexity's desktop app for enterprise customers that opt in, as well as Pro and Max subscribers, on…
AI workforce impact AI is redefining the workforce — and most planning models aren’t ready Companies struggle to plan workforce changes as AI redefines jobs and skills across departments and business units. VentureBeat · 1mo ago Presented by SAP HR tracks employees and skills. Finance owns headcount targets and cost. Procurement manages contractors and services spend. Together, they leave executives unable to answer basic questions about how workforce decisions actually translate into business outcomes. Fragmented planning creates workforce blind spots Each function has its own systems, its own planning cadence, and its own assumptions about how work gets done. Recent SAP research found that 62% of C-suite executives are dissatisfied with their current level of integration between people and business performance data. The same research found that while 50% of organizations are planning for AI’s impact on productivity and capacity, only 21% are planning for AI’s impact on job design and organizational structure. That gap matters…
AI agents OpenClaw 2.0 is here, ushering in the era of 'multiplayer' AI coding: What it means for enterprises OpenClaw 2.0 enables language models to act as autonomous AI workers via messaging apps. VentureBeat · 1mo ago The viral fervor we saw earlier this year around OpenClaw, the open source AI harness that turns powerful language models into autonomous workers the user can message via their favorite channels (Telegram, iMessage, WhatsApp, Discord etc), has cooled off substantially from its peak in March 2026. But over the weekend, OpenClaw's creator Peter Steinberger and current team of co-developers gave the world — especially enterprises — a reason to look at it again, announcing OpenClaw 2.0, billed as the most significant update to the harness and surrounding platform yet. OpenClaw 2.0 seeks to transform what began largely as a personal agent harness into something increasingly designed for teams, shared infrastructure and enterprise workflows. OpenClaw 2.0 introduces a rebuilt browser interface…
AI agents, engineering Software engineers' new job isn't writing code — it's designing the boundaries AI agents can't break Software engineers now focus on designing boundaries to prevent AI agent misuse. VentureBeat · 1mo ago If you look at the commit histories of modern data platforms, something profound has shifted over the last two years. The friction of writing syntax has collapsed. With Cursor, Claude Code, and agentic workflows now living inside our Docker containers and IDEs, generating the first implementation of a distributed streaming pipeline or a complex API integration is no longer the central bottleneck. Agents can navigate repositories, write test coverage, inspect stack traces, and propose refactors. Describe a Kafka-to-Iceberg sink mapping in plain English, and an agent can produce a credible starting point before the engineer has opened every relevant file. That changes the question for software engineers. If the agent is becoming the primary author of local system logic, what…
AI agents Identity and permissions aren’t enough to govern AI agent behavior Enterprise AI agents require behavior governance beyond identity and permissions controls. VentureBeat · 1mo ago Presented by Box Identity and permissions are no longer enough to secure enterprise AI agents. They govern what an agent can reach, not how it behaves once it starts working on its own, and an autonomous agent can turn legitimate access of enterprise data into unintended action in seconds. That gap is pushing enterprise AI security from just governing access toward a layered approach that includes governing execution, says Heather Ceylan, chief information security officer at Box. "Access controls and permissions are the foundation, but the challenge is they were designed for humans," Ceylan says. "Permissions are still the foundation, but you have to think about how the agents get their permissions scoped as well." Access controls were built for…
AI agents AI agents need their own identity before they need a gateway Autonomous AI agents now coordinate workflows and invoke tools with minimal human oversight in enterprise settings. VentureBeat · 1mo ago Enterprise AI has entered a new era. Organizations are rapidly moving beyond assistants that answer questions to autonomous agents capable of reasoning, invoking tools, accessing enterprise applications, coordinating with other agents, and completing multi-step business workflows with minimal human intervention. This shift represents a fundamental change in how software operates. Traditional applications execute predefined logic written by developers. AI agents, however, dynamically determine how to achieve an objective. They decide which tools to use, which APIs to call, what information to retrieve, and how to sequence actions based on context. That flexibility unlocks enormous business value, but it also introduces a new class of security risks. Much of today's AI security discussion focuses on prompt injection, model vulnerabilities, and data…
AI agent security AI agents that pass authentication can still drift, expose data, or get memory-poisoned AI agents with authentication access can still suffer data exposure and memory poisoning attacks despite security measures. VentureBeat · 1mo ago There is a clear repeating trend in agent deployments: The gateway is the first control teams reach for, but it is the one they are least ready to run. This is because gateways sit on top of identity and attribution layers that are mostly not there. The first layer of risk is not hypothetical. In June, CISA added a LiteLLM flaw to its Known Exploited Vulnerabilities catalog after attackers were caught abusing it in the wild. The bug ran commands on the host through the gateway itself, and chained with a second flaw it required no credentials. It was one of seven common vulnerabilities and exposures (CVEs) disclosed in that single AI gateway in a month. This is the layer…
agentic AI security The three layers of agentic AI security: A defense-in-depth architecture for autonomous agents Framework for multi-layered security architecture to manage risks from autonomous AI agents. VentureBeat · 1mo ago Presented by Nutanix Autonomous systems that can reason, make their own decisions, and execute actions across an environment introduce a category of risk that application-level controls were never built to contain. Treating that risk as a single problem produces incomplete architectures, says Oscar Wahlberg, senior director of product management at Nutanix. "The guardrails to catch a malicious prompt won't stop an agent from hallucinating and doing something it never should have done, like accidentally deleting databases or leaking sensitive data with a credential it was granted but then uses for something entirely different," Wahlberg says. "That's the central problem as enterprises move autonomous agents out of experimentation and into production." Once an agentic system is granted execution privileges across the…
large language models Meta researchers taught an 8B AI model to match Claude Opus 4.5 — without the frontier price tag Meta's 8B AI model matches Claude Opus 4.5 performance at significantly lower cost. VentureBeat · 1mo ago Consider an AI agent tasked with a complex enterprise workflow like migrating massive batches of customer records from a legacy CRM to a cloud database. The agent cannot rely solely on its internal context window for a job spanning hours and depends on the runtime layer, aka the harness. This harness provides execution feedback, like server logs, to help the agent maintain an accurate understanding of dynamic API connections. It also provides state trackers and control-flow mechanisms to manage completed and pending subgoals, ensuring the agent doesn't skip or duplicate data batches. When unexpected errors occur, such as a database rejecting a batch due to strict API rate limits, the harness provides tools and instructions to help the agent recover.…
document processing AI Cohere Parse 5 loses the benchmark on points. It wins on cost per page. Cohere releases Parse 5 document extraction tool with improved cost efficiency over competitors. VentureBeat · 1mo ago Enterprises trying to feed PDFs, slides and scanned documents into AI pipelines keep running into the same wall: the tools either miss the structure — tables, charts, layout — or cost too much to run at scale. Cohere released Parse 5 on Thursday, positioning it on price-to-performance, not raw accuracy — the right cost-capability mix for enterprise scale. Parse 5 is a 2.3-billion-parameter vision language model built to convert PDFs, slides and images into structured Markdown at enterprise scale. Cohere's own published benchmark comparison puts Parse 5 behind three larger, general-purpose frontier models on accuracy. GPT-5.5, Opus 4.8 and Gemini 3.5 Flash all score higher than Parse on the three ParseBench dimensions Cohere reports. Cohere is not claiming the top…
AI agents Enterprise AI's real risk isn't autonomous agents. It's the complexity between them. Enterprise AI complexity between multiple deployed agents poses real risk to organizations. VentureBeat · 1mo ago Presented by Gravitee Agent complexity is the insidious shadow lurking inside enterprises right now that needs a light shone on it. That’s because enterprises don't deploy a single agent and watch it run, they deploy fleets, each one calling APIs, calling other agents, reaching into applications that were never built with a machine decision-maker in mind. That's the failure mode that should keep you up at night: a windy, complicated system nobody can see clearly enough to govern. But why do things get so opaque so quickly? Add a second agent to a system, and you've added one connection. Add a tenth, and you haven't added ten connections, you've potentially added dozens, because now any agent might call any other,…
autonomous AI agents Visa ships a security AI that patches production code before any human reviews it Visa deploys AI that autonomously finds security vulnerabilities, writes fixes, and validates them without human review. VentureBeat · 1mo ago Visa's open-source security harness now finds the vulnerability, writes the fix, and turns an adversarial panel on its own patch before any human reviews it. The whole loop ships on by default. A plain scan of the Visa Vulnerability Agentic Harness runs all 11 stages and edits source files in the target repo unless the operator caps it at detection. The announcement Thursday pairs the release with an expansion of the Visa Consulting & Analytics advisory practice. Visa is shipping that default 18 days after Tenet Security demonstrated GhostJacking on the DEF CON 34 main stage, an attack chain in which an agent read an attacker's payload out of a log file and rewrote DNS with a valid credential. Two…