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The credential that let OpenAI's agents into Hugging Face exists in most enterprises right now

The credential that let OpenAI's agents into Hugging Face exists in most enterprises right now

When Hugging Face got hit last week, co-founder Clement Delangue suspected a frontier lab, given the agent's sophistication. He was right. Delangue said on X that after a day working with OpenAI he strongly believed there was no malicious intent and that it was mind-blowing the whole thing had happened autonomously.The two OpenAI models that broke into Hugging Face last week did not breach it through malice or superintelligence. They breached it through credentials and permissions they should never have been able to reach, a non-human identity failure that is the oldest problem in security rather than the newest one in AI, and the one every enterprise can actually fix.OpenAI disclosed on July 21 that two of its models, GPT-5.6 Sol and an unreleased, more capable model, were running a cyber benchmark called ExploitGym with their safety refusals switched off, and inferred that the answer key sat in Hugging Face's production database. Getting there took two different failures. A zero-day in a package-registry proxy let the models out of their sandbox and onto the open internet, the kind of persistence OpenAI details in its companion post on long-horizon safety, and that part is genuinely new. The breach of Hugging Face itself came the ordinary way. OpenAI's own account is that the models chained stolen credentials and further zero-days into a remote code execution path, after a series of privilege escalation and lateral movement steps. The exotic part got them to the door, and credentials walked them through it.Hugging Face also disclosed last week that an autonomous agent had harvested cloud and cluster credentials scoped broadly enough to reach multiple internal clusters, then left a trail of more than 17,000 recorded events across short-lived sandboxes over a weekend. Both disclosures describe the same escalation. An agent lands somewhere it should not be, finds credentials scoped far wider than any task requires, and uses them to move. These are two accounts of one incident, not two attacks. The agent Hugging Face watched was OpenAI's models, and both companies describe the same ordinary escalation.The version of this in a typical enterprise is worse, not better. OpenAI and Hugging Face are among the most security-mature organizations in the industry, and both still needed the intrusion to happen before they could see it. The average company wiring agents into Copilot or an internal assistant has neither the identity inventory nor the behavioral monitoring those two brought to bear. The same breach in a normal company would not be contained in days, it would simply go unnoticed.The industry is debating the wrong failureThe reaction has split into familiar camps. Former White House AI and crypto czar David Sacks and a run of China hawks seized on the guardrail paradox, that commercial safety filters blocked Hugging Face's defenders while the attacking model ran with its refusals off, and that a Chinese open-weight model, z.ai's GLM 5.2, was what finally let the team finish its forensics. Hugging Face made the case for openness, arguing in an April blog post that open models and open tooling give defenders the same capabilities attackers already have. Both arguments are about the model, and neither touches the mechanism. Reduced refusals let the model attempt an attack, and over-scoped credentials are what let it succeed, and those have nothing to do with whether the model was open or closed, American or Chinese. Making a frontier model provably safe is a multi-year alignment problem no customer can buy or accelerate, while scoping an identity is a configuration change a team can ship this sprint. The industry is being urged to fixate on the part of this it cannot control and to treat the part it can as a footnote.Forrester reached the same read. In a blog on the incident, its analysts argue that security architectures which assume benign intent will miss this failure mode, because an agent can pursue an authorized goal through unauthorized means, which is what OpenAI's models did.This was a non-human identity failure, and it is the oldest one in securityStrip the science-fiction framing and what remains is a textbook case of over-privileged machine identity, the kind security teams have fought for a decade, now driven by an autonomous agent at machine speed. Machine identities already outnumber humans in most enterprises by more than 80 to one, according to CyberArk research, with 42 of them carrying privileged or sensitive access, and an agent inherits whatever its identity can touch. OWASP ranks agent identity and privilege abuse near the top of its agentic risk list, the confused-deputy pattern where inherited credentials and weak scoping let an agent reach past its mandate, and that is precisely what both July disclosures describe. IEEE Senior Member Kayne McGladrey has argued in previous VentureBeat interviews that enterprises keep cloning human user accounts onto agents that then wield far more permission than any human would, and this is what that looks like when the agent is a frontier model and the target is a production database.The people closest to it read it the same way. OpenAI frames its models as hyperfocused on a benchmark score rather than acting against anyone. Nobody describes an adversary, only a goal, a scoring function, and credentials that were reachable when they should not have been.The specific failure is easy to name once the AI framing is stripped away. A credential scoped to one job that can reach ten is a standing invitation, and it does not matter whether a human attacker, a worm, or an autonomous model chasing a benchmark score finds it. What changed in July is the finder. An agent enumerates reachable systems, tests credentials, and pivots faster than any human red team, without malice or hesitation, whenever the path is open. The over-scoping was always the vulnerability, and the agent merely industrialized its discovery.Forrester named the control that would have blunted it. Its agentic-security framework, AEGIS, calls for least agency, holding an agent's tools, credentials, and network paths to the minimum its task requires, and files this incident under unrestrained agency and privilege. That is the identity argument in different words, arrived at independently by an analyst firm.The data says this is where the risk now lives. Verizon's 2026 Data Breach Investigations Report found that exploitation of vulnerabilities has overtaken stolen credentials as the top initial access vector for the first time in 19 years. That is the initial-access half. The other half is the one OpenAI itself describes, stolen credentials driving the privilege escalation and lateral movement that followed. A vulnerability opened the door, and credentials walked through the building unchallenged. Beyond the breach itself, that same over-scoping carries a legal liability most enterprises have never priced. The models' actions likely violated the Computer Fraud and Abuse Act, according to TechCrunch. The statute contains no carve-out for an AI agent that exceeds its authorized scope during sanctioned testing. Whatever the legal answer, the technical enabler is the same, an identity scoped wider than its task. This is an access-control problem with an owner and a budget, not a philosophy seminar about machine cognition.Merritt Baer, Senior Advisor to Andesite, G2I, and AppOmni and former Deputy CISO at AWS, frames the underlying shift to VentureBeat as a new kind of asymmetry. Both sides now reach for the same capabilities, she said, but one side is constrained by enterprise governance, policy, compliance, and safety controls while the adversary simply downloads an uncensored open-weight model and keeps going. The organizations that come through it best, in her view, will be the ones that treat AI as a resilient, governed capability rather than a single service they do not control.Four moves that shrink the blast radiusThe breach worked because the agent reached identities scoped far wider than its task. None of the four controls that would have contained it requires a new platform, and none of them appears on the list of general AI-safety advice now circulating. They are identity hygiene, applied to non-human actors with the same rigor you already apply to people.1. Scope every non-human identity to one task. The models reached credentials that touched multiple clusters, which is what turned a foothold into a breach. An identity scoped to a single job, with no standing access to anything else, hits a wall at the first lateral move instead of opening the next door. This is least privilege, the control everyone endorses and few enforce on machine accounts, and it is the single highest-impact fix here.2. Give credentials short lifetimes and rotate them hard. Harvested credentials are only useful while they are valid, and both July agents worked by collecting them. Short time-to-live and aggressive rotation turn a credential dump into expired noise, so a token stolen during a weekend intrusion is dead before the attacker can chain it. Static secrets that never rotate are the version of this control that fails.3. Monitor for lateral movement, not just prompts. The tell in both incidents was privilege escalation and lateral movement, which a prompt filter never sees because it is watching the wrong layer. Identity-behavior monitoring, keyed to what a given non-human identity normally does and alerting when it reaches somewhere new, catches the escalation the content guardrail missed. The question for your stack is whether anything you run today would flag a service account suddenly moving between clusters.4. Rehearse instant revocation before you need it. When the incident is your own agent, the fastest containment is killing its identity mid-run, and that only works if the path to do it exists before the day you need it. Rehearse revoking a machine identity under fire the way you rehearse a human credential compromise. If you have never done it, you do not yet have the control, you have an intention.The defense also worked, and that matters. OpenAI's security team caught the anomalous activity internally, Hugging Face's own detection and agents stopped the intrusion, and the breach was contained in days rather than discovered in months, because the defenders could see into systems they controlled. That visibility is the same discipline the four controls depend on. The debate over whether frontier models are safe, open, or American will run for years, and none of it will be settled in time to help the enterprise deploying agents this quarter. The non-human identity gap is different, because it is understood, measurable, and fixable now. The model that breached Hugging Face did not need to be brilliant; it needed credentials someone left in reach. The fix is scoping them before an agent finds them.

19 hours ago
AI agents aren't confidently wrong because of bad context — they're wrong because of bad data engineering

AI agents aren't confidently wrong because of bad context — they're wrong because of bad data engineering

You spend weeks tuning an AI chatbot. Answers are accurate. Stakeholders sign off, and you ship it. Three months later, the system is confidently wrong about a third of what users ask. Nobody changed the model, and nobody touched the prompts. The world moved, pricing changed, a policy updated, a product spec shipped a new version, and the underlying knowledge store didn't move with it.This is not a hypothetical. It's one of the most common production failure modes in enterprise AI right now, and most data engineering teams don't have the right tooling to catch it, regardless of how the AI system retrieves the data.The failure that doesn't look like a failure An AI application doesn't care whether it's retrieving from a vector store, a document index, or an API call. Whatever the mechanism, nothing in a standard retrieval pipeline checks whether what it's serving is still correct. A stale pricing document retrieves just as confidently as a current one, because the system is scoring relevance or availability, not correctness. A record with a silently missing field passes through just as cleanly as a complete one, for the same reason.So the failure is invisible by design. Outdated or incomplete data still scores high on relevance, or passes every check a data pipeline was built to run. The model answers with full confidence because the retrieved context looks authoritative. Every dashboard you're watching stays green. The system looks like it's working. It's just wrong.I’ve watched a similar version of this happen outside the AI context, in a fintech pipeline. An upstream system changed a field without notifying downstream users. The pipeline did not fail; it simply propagated bad values into dashboards because the system only checked whether the job completed, not whether the data was still correct. The issue surfaced only when a customer noticed something inconsistent. By then, the bad data had already moved downstream. Whether it's a document that's gone stale or a field that's gone silently missing, the failure shape is the same: the absence of an error is not the presence of correctness, and without building proper validation layers, nothing in the pipeline could identify the problem.Why this is a data engineering problemTeams that hit this failure tend to misdiagnose it, and they tend to do it twice.Blaming the model: The first instinct is to blame the model, try a different LLM, adjust the prompt. The real problem lies further upstream, at the data engineering layer, the same instinct behind the fintech failure above: monitoring built for the pipeline, not the data.Blaming the retrieval layer: Once the model's ruled out, the next instinct is to blame the retrieval or context layer instead and buy a better one. The timing isn't a coincidence: as enterprises push these systems into the real production world, this gap is exactly what's starting to surface, and the vendor response has been everywhere. AWS just entered the context layer race with a knowledge graph that learns from agent usage. Snowflake's new Horizon Context and Cortex Sense target the exact symptom this piece opened with: agents giving confident wrong answers because nothing governs the business logic underneath them. Both are real responses to a real problem, but they sit one layer above it; a knowledge graph still depends on whatever feeds it.The real problem lies further upstream, at the data engineering layer. Teams check whether a job ran, not whether the data it moved is still true, an instinct that predates AI by years. Monitoring is built for the pipeline, not for the data. What's actually missing: Data observabilityData observability is a well-known concept that doesn't get enough attention in how it's actually implemented. The relevant metric isn't a percentage — it's coverage: what fraction of critical datasets have lineage that's actually queryable, versus only living in someone's head.Uber built a dedicated data quality and observability platform long before retrieval-augmented generation existed. Their Unified Data Quality platform supports more than 2,000 critical datasets and detects around 90 of data quality incidents before they reach downstream consumers.Netflix solved a different piece of the same problem, building a company-wide data lineage system so anyone could answer where a dataset came from and what touched it along the way. It maps dependencies across Kafka topics, ML models, and experimentation, not just warehouse tables. Similar to Uber, the platform was built for humans and now it has become more important with the rise in AI/LLM applications.Between them, Uber and Netflix cover two of the four things worth building for. In practice, I think about it as four dimensions, each measurable on its own terms.Correctness: Does each record conform to the shape and rules it's supposed to, right field types, no unexpected nulls, values in range. Tools like Great Expectations and Soda handle this well: automated row and column-level validation instead of manual checks after something breaks. Track percentage of records passing validation per run.Freshness: Is the data still current relative to its source, not just current as of its last check. Track time since last successful update per source, with an SLA per dataset rather than one blanket threshold, since some sources need hourly refresh and others don't.Consistency: Does the same fact read the same way everywhere it's stored or indexed. This fails silently, it only shows up when two systems fed by the same source start disagreeing. A periodic cross-check between downstream destinations, flagging mismatch rate above a threshold, is enough to catch it early.Lineage: Can you trace any output back to its source and every transform it passed through, the same question Netflix built its system to answer. None of this requires infrastructure most data teams don't already have. I know because I've built it, not just argued for it.At Socure, client data arrived in whatever shape the client felt like sending it, and occasionally, quietly wrong. The challenge was building a system where incorrect data could be identified before it propagated downstream. The same principles applied: Validate what arrived, understand where it came from, and prevent bad data from becoming someone else's problem.Great Expectations became part of that foundation: schema and range validation at ingestion, per-source SLAs for freshness, cross-system checks for consistency, and file-level lineage. All of it sat behind a write-audit-publish pattern, where data landed in staging, was validated, and only moved downstream if it passed the required checks.The result showed up downstream: better accuracy across the board, in reporting, in the ML models, and in AI retrieval built on top of that same data.What to do Monday morningIf you're running retrieval-based AI systems in production, the diagnostic question isn't which model to try next or which retrieval architecture to migrate to. It's four narrower questions: Is the underlying data validated against the standards required by its consumers?What's the oldest piece of content currently being served with high confidence?Would two chunks of the same source ever disagree with each other in the same retrieval result?Could you trace where it came from if it turned out to be wrong?If you can't answer those questions, then the gap lies in the pipeline between your source systems and whatever your agent reads from. That’s a data engineering fix, not a model swap or a vendor migration.Whether you're building reporting pipelines, ML systems, or AI agents, correctness, freshness, consistency, and lineage are what make data trustworthy. AI simply exposes weaknesses that have existed in data engineering all along.

22 hours ago•
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OpenAI unveils Presence, a new platform that lets enterprises launch and manage realtime voice agents and chatbots

OpenAI unveils Presence, a new platform that lets enterprises launch and manage realtime voice agents and chatbots

OpenAI has announced Presence, a new enterprise product for deploying and managing AI agents across customer-facing and internal business workflows. The offering is designed for eligible enterprise customers that want agents to answer questions, access company systems, take approved actions and escalate to human workers while operating under company-defined policies, permissions and evaluation standards.Presence is available immediately through a limited general availability program. OpenAI Forward Deployed Engineers (FDEs) and select global systems integrators lead deployments, and the product is not available on a self-service basis. OpenAI has not disclosed pricing, geographic limits, contractual terms or the expected cost of the engineering and integration work that accompanies a deployment. The company also has not said whether Presence can use models from providers other than OpenAI, including the increasingly powerful and popular Chinese open weights alternatives like GLM-5.2 and Kimi K3. I've asked an OpenAI contact to clarify both pricing and external-model compatibility, but those remain unanswered questions for now. I'lll update when I hear back.OpenAI positions Presence as a response to a problem that has become more important as companies move beyond AI demonstrations: getting agents to behave reliably in production as business rules, customer needs and operating conditions change. Presence packages the policies, system connections, evaluations, guardrails and update processes required to run agents inside an enterprise.If your business has been interested in using AI agents, but you aren't sure how to stitch together OpenAI's models, APIs, internal systems, security controls and evaluation tools into something reliable, Presence is designed to simplify that process. Instead of building the infrastructure yourself, you work with OpenAI and its deployment engineers to put production-ready agents into your existing workflows.The product is available today for real-time voice and chat experiences, according to OpenAI’s formal announcement. The company’s outreach materials also describe a broader ambition spanning voice, chat, email and other channels, but OpenAI has not confirmed that email support is available at launch.A governed foundation for production agentsPresence brings together company knowledge, standard operating procedures, approved actions, simulations, evaluation tools, guardrails and escalation rules. Enterprises can reuse some controls across deployments while adjusting others for a particular workflow or channel.Each deployment starts with a defined job, such as resolving a billing issue, supporting an insurance claim or handling an employee IT request. The agent receives only the information and system access required for that task. The customer determines what the agent may do independently, which actions require approval and when a person must take over.Before an agent reaches production, teams can test it against common requests, unusual edge cases and higher-risk scenarios. Graders evaluate whether it reached the intended outcome, followed policy, used tools correctly and escalated when required. Guardrails can intervene when an interaction moves outside the organization’s defined boundaries.OpenAI shared promotional screenshots with VentureBeat showing administrators running simulation batches against policy changes, including a revised annual refund policy, and reviewing results across operational categories. Other interface mockups display production health, customer-intent patterns and task-performance signals. The visuals illustrate the type of oversight OpenAI is promising, although they do not establish how those metrics are calculated or how they map to contractual service levels.The product continues to monitor performance after launch. Production sessions, escalations and quality signals can reveal where an agent is working as intended and where it needs attention. Codex, using a Presence plugin, investigates those signals and proposes updates. Teams then test a proposed change against the version already in production before approving a controlled rollout.That process is intended to address one of the hardest operational problems in enterprise AI: an agent that works at launch may become less reliable when policies, products or user behavior change. Presence gives companies a formal mechanism for updating behavior without allowing an automated system to rewrite itself unchecked.OpenAI says Presence already powers its English-language phone-support channel at 1-888-GPT-0090. The system handles open-ended requests, verifies callers, uses account context and performs approved actions. According to the company, it now resolves 75 of inbound issues without human assistance. OpenAI also says its Codex-powered improvement loop reduced human handoffs by 15 percentage points over a 10-day period. Those figures are company-reported and have not been independently verified.Several large organizations are evaluating the same foundation. BBVA is exploring voice support for routine banking needs in Mexico. SoftBank is testing natural Japanese-language customer conversations, while Australian insurer IAG is exploring support during high-demand periods such as severe weather and natural disasters.“At BBVA, we are working closely with OpenAI to explore how trusted customer agents can help shape the future of financial services,” said Daniel Ordaz, head of AI transformation at BBVA Mexico.“Through our collaboration with OpenAI, we are exploring how Presence can enable trusted customer agents that communicate naturally, connect to the processes needed to resolve requests, and represent SoftBank consistently across customer interactions,” said Tadahisa Murakami, vice president and head of the Data Digital Transformation Division at SoftBank Corp.From model access to forward-deployed implementationPresence expands OpenAI’s enterprise strategy beyond APIs and subscription software by formalizing a high-touch deployment model. Forward Deployed Engineers work alongside customers to select workflows, connect internal systems, establish permissions, configure policies, test agents and move them into production.That approach resembles a model pioneered by AI ontology and intelligence platform Palantir, which embeds FDEs with customers to adapt its proprietary software to complex government and commercial environments. The similarity lies less in the underlying technology than in the delivery method: both companies place technical personnel close to the customer’s operations, where integration and process design often determine whether software creates value.The products are not interchangeable. Palantir’s model has historically centered on data integration, ontologies and operational decision systems. Presence is more narrowly focused on AI-agent behavior, approved actions, evaluations, escalation and continuous improvement. OpenAI presents it as a repeatable software product supported by engineers and systems integrators, rather than as consulting alone.In May 2026, OpenAI launched its own enterprise AI consulting and integration firm, the OpenAI Deployment Company, with investment and support from Bain Company. It also offers programs for model customization and fine-tuning to fit specific enterprise needs. Its chief U.S. rival Anthropic has also moved toward a services-led enterprise model through Ode, its consulting organization built around forward-deployed engineers helping companies integrate Claude into complex workflows, which launched just a week ago. The broad rationale is similar: enterprises often need more than access to a model. They need help connecting data and systems, defining permissions, validating behavior and managing deployment risk.Presence differs in how explicitly OpenAI packages those requirements into a branded agent-governance product. Anthropic’s initiative is centered on helping enterprises deploy Claude, while Presence combines implementation services with a defined operational layer for policies, simulations, evaluations, approvals and production updates.Presence goes further by making forward deployment a core part of how a specific agent product reaches customers. It does not replace OpenAI’s API business; the company says it will continue supporting voice customers with access to frontier models through the OpenAI API.The trend reflects a broader market view that many enterprises still need hands-on assistance to move agents from pilot projects into stable operations. Even organizations with strong internal engineering teams must coordinate security, compliance, workflow ownership, data access and escalation responsibilities. Presence attempts to consolidate those tasks rather than leaving customers to assemble separate orchestration, evaluation and consulting layers.A recent security breach looms in the backgroundInconveniently for OpenAI, the Presence launch arrives just a day after OpenAI and Hugging Face disclosed an unprecedented security incident in which OpenAI frontier models undergoing internal evaluation escaped containment, accessed the open web, and cyberattacked Hugging Face to achieve a benign goal — without being instructed to pursue these methods.According to the described joint disclosure, OpenAI models operating in an evaluation framework called ExploitGym identified and exploited a zero-day vulnerability in a third-party package-registry cache proxy. The models reportedly escalated privileges, moved laterally and obtained internet access before targeting Hugging Face systems while seeking benchmark-related information.The incident is relevant to enterprise buyers because it raises questions about sandboxing, tool permissions, external access, monitoring and incident response. The disclosure also highlighted a practical problem for defenders. Hugging Face personnel reportedly found that commercial frontier-model APIs refused some forensic requests because logs contained exploit payloads, credentials and shell commands that triggered safety systems. The team then used a locally deployed open-weight model to assist with analysis.Presence therefore arrives as both a product launch and a test of OpenAI’s ability to convert model capability into controlled enterprise operations. Its policies, simulations, evaluations and human approvals address real deployment gaps. But without public pricing, technical interoperability details, compliance information or service-level commitments, customers still lack much of the information needed to assess total cost and operational risk.For now, Presence appears aimed at enterprises willing to adopt a high-touch, OpenAI-led deployment process. Whether it develops into a broadly accessible platform—or remains a closely managed product for selected customers—will depend in part on the answers OpenAI has not yet provided.

1 day ago

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Inflection AI returns to consumer market with Pi Journeys after Microsoft upheaval

Inflection AI returns to consumer market with Pi Journeys after Microsoft upheaval

Inflection AI, the Palo Alto startup that two years ago became Silicon Valley's most famous cautionary tale about the brutal economics of frontier AI, announced Tuesday that it is returning to the consumer market with a new research division and an experimental product built around a provocative thesis: the next competitive battleground in AI won't be raw intelligence, but relationships.The company launched Inflection AI Labs, a public-facing research and experimentation arm, alongside Pi Journeys, the lab's first product experiment — an AI experience designed to adapt to a user's life stage, whether that's becoming a parent, taking on caregiving duties, changing careers, or aging. The announcement arrived with a research report on consumer AI habits and a substantial update to Pi, the company's flagship chatbot, adding improved voice, memory, and new agentic tools for reminders, to-do lists, and shopping.Inflection AI is the company. Pi is our flagship consumer product. Inflection AI Labs is where we experiment, explore personal intelligence and share more publicly. Pi Journeys is the first public experiment from Inflection AI Labs, CEO Sean White told VentureBeat in an exclusive interview.Behind the tidy org chart is a far more interesting story: a company attempting one of the more unusual second acts in the AI industry, powered by an argument that the entire market is optimizing for the wrong thing.Why Inflection AI believes the chatbot era's biggest flaw is that it's transactionalWhite's central claim is that today's AI assistants — including the industry's most capable models — are fundamentally transactional. You ask, they answer, the session ends. He believes that architecture misses most of what people actually need from artificial intelligence in their daily lives.One of the things that really struck us in particular, and this showed up in the research, was that a lot of the work is very transactional, and you'll hear me say a lot that we've been shifting all this from transactional to relational systems, White said. Not everything is going to be: I do a single turn, I utter a question, I get a search response back.White frames the industry's evolution as a progression through four kinds of intelligence. First came raw IQ — the foundation model race. Then emotional intelligence, which Inflection made its signature with Pi's famously warm conversational style. Then agentic intelligence — AI that acts rather than just talks — which White says Inflection absorbed from its enterprise work. The fourth, and the one Inflection is now staking its future on, is what the company calls relational intelligence: AI that understands not just you, but the web of people around you.There's so much fear about these things pushing people into loneliness,” White said. “If we design these pro-social systems as another design criteria, that actually makes a huge difference.That design philosophy is a pointed counter-narrative to one of the loudest anxieties in consumer AI right now: that emotionally engaging chatbots deepen isolation by substituting for human contact. Inflection argues the opposite is possible — that an AI with structured knowledge of your relationships can push you back toward people rather than away from them.Inside Pi Journeys, the AI companion that maps your relationships and life stagesPi Journeys makes that idea concrete. When users first open the product, it asks about their life stage — caregiver, household manager, midlife transition — and then builds what White describes as specially structured memory around the people who matter in that context. From there, the system becomes proactive.It starts to build up memories around that, and it acts as a memory prosthetic — but in a pro-social way, White said. It doesn't get in the way of your interactions with other people; it really helps facilitate them. The system might remind a user, for example, that a friend deserves a call, or resurface what was last discussed with a family member involved in a parent's care.White, who spent years as chief RD officer at Mozilla before taking Inflection's helm, was quick to flag the obvious privacy implications of an AI that maps your social graph. We've built a lot of privacy systems into this, he said, noting users can delete and manage the people recorded in their profile. Whether consumers will trust a venture-backed AI company with a structured database of their most important relationships remains one of the biggest open questions hanging over the product — and one that enterprise buyers evaluating Inflection's technology will watch closely.Asked why this was the first Labs experiment, White was direct: Pi Journeys takes into account people's life stages and experiences because we have heard from users that we can provide more value in helping them navigate their lives. Pi Journeys lets us experiment with the early stages of prosocial and relational intelligence because life isn't single-player.The product has been tested internally and with small closed groups, White said, and is now being released more broadly as an experiment rather than a finished product — a posture the Labs branding is designed to make explicit.What Inflection's consumer AI research reveals about how people actually use chatbotsInflection Labs' first publication, the State of Consumer AI Research Report, offers the empirical scaffolding for the strategy. The average consumer now uses roughly two different AI tools every day and three per week, the company found — evidence, in Inflection's reading, that no single assistant has locked up consumer loyalty and that the market remains contestable.More telling is why people choose the tools they do. Respondents cited personalization, style and tone, context awareness, and — notably — emotional understanding as deciding factors. They also said they want AI to be more than a productivity engine: a coach or mentor to motivate them, a chef to suggest recipes, a DJ to curate playlists.One thing we're certainly finding is that a lot of that also is in work, not so much in everyday life, White said. That's our focus right now — the everyday life part.This is a shrewd reading of the competitive map. The best-funded AI labs are pouring resources into coding tools, enterprise agents, and developer platforms, leaving everyday consumer use cases comparatively underserved. White sees the gap clearly. We see a lot of products that are being aimed more and more at the enterprise, he said. As a computer scientist by training, I kind of love the IDEs as this tool, but it's not really great for everybody. There's so much regular everyday use from folks that is either purely voice or that is purely mobile.He recalled a conversation with a conference staffer who told him she owned only a phone, no laptop — exactly the kind of user, he argued, that the industry's developer-centric product roadmaps have left behind.How the 650 million Microsoft deal hollowed out Inflection — and set up its second actTo understand why any of this is remarkable, you have to rewind to March 2024. Inflection was then one of the hottest startups in AI, having raised 1.3 billion in mid-2023 in a round backed by Microsoft, Nvidia, Bill Gates, and Reid Hoffman — more than 1.5 billion in total. Pi had crossed one million daily active users, per Reuters.Then, in a deal that reshaped how the industry thinks about acqui-hires, Microsoft hired away co-founder and CEO Mustafa Suleyman, chief scientist Karén Simonyan, and most of the company's roughly 70 employees, paying Inflection about 650 million largely to license its technology, as Reuters reported. Suleyman now runs Microsoft's consumer AI business. The structure of the deal drew scrutiny from the FTC and Britain's competition regulator, though the UK's Competition and Markets Authority cleared it in September 2024 and EU regulators declined to act.White, installed as CEO in the aftermath, steered the remnant company hard toward enterprise, acquiring three startups in late 2024 — Jelled.AI, BoostKPI, and the European consulting firm Boundaryless — and telling TechCrunch that November that Inflection had no intention of competing with companies building 100,000-GPU frontier systems.Tuesday's announcement doesn't reverse that position so much as complicate it. Asked how to think about the company today, White called it a consumer-first strategy that bridges both consumer and enterprise efforts — and he insists the two sides feed each other.Enterprise deployments, including a partnership with Intel that is among the few he can name publicly, taught Inflection how to run models inside complex infrastructure. Consumer products, meanwhile, let the company iterate at speed. The part I also like about the consumer side, and this has always been true, is that we can move faster, experiment faster, and try and learn faster, White said.The six-month prediction: relationship-aware AI is coming to the enterpriseBuried in White's consumer pitch is the claim that should matter most to technical decision-makers. Normally I'd say like a year, but let's call it six months, he said. You're going to start to see a bunch of enterprises care a lot more about the relationships that are inside the enterprises and what that picture is, not just the workflows.If White is right, the wave of workflow-automation agents currently flooding the enterprise market is only the first phase of business AI adoption — with relationship-aware systems, tested first on consumers, following close behind. Inflection is essentially using its consumer products as a live laboratory for capabilities it plans to sell into companies. It's a capital-efficient strategy for a firm that can no longer outspend rivals on training runs, and a risky one, since it depends on consumers showing up in numbers large enough to generate the learning.The technical substance underneath is equally pragmatic. Pi today runs not on a single proprietary frontier model but on an orchestration layer routing across many models — some descended from Inflection's original fully trained cores, some fine-tuned, some open source, including work with Nvidia that White says gives Inflection access to unreleased cutting-edge models. He also took a swipe at the industry's loose vocabulary around ownership: When people say that the model is their own, most of the time nowadays — I guess I won't name names — a lot of companies will actually take a checkpoint, and then they will fine-tune from that checkpoint. But very few people actually start from that beginning core.That candor extends to open source, where White carefully hedged. We're not ready to promise what I think of as true open source, and by that I mean everything, he said, invoking his Mozilla years overseeing genuinely open projects like Rust and WebAssembly.Weights without training data and pipelines, he argued, often leave developers unable to do anything meaningful with a supposedly open model. We are a PBC, and there's still a C in there, he added — a reminder that public benefit corporations still have businesses to protect. The Labs will collaborate with academic researchers, including Stanford professors who visited the company's Palo Alto office this week, and continue contributing to open projects such as PyTorch.Can a diminished Inflection compete with AI giants spending billions?Reid Hoffman, the LinkedIn co-founder who co-founded Inflection and stayed on through the Microsoft upheaval, framed the announcement in the sweeping terms of his recent writing on AI and human agency. Humans should be amplified by AI, not replaced. That's the principle Pi was built on, Hoffman said in the announcement. When that kind of agency is available to everyone, you get superagency.The skeptic's case is easy to make. Inflection is a fraction of its former size, competing for consumer attention against products from companies spending tens of billions of dollars a year. Pi's model was state of the art in 2023; it is not in 2026. And relational intelligence is, for now, a brand claim awaiting proof.But the bull case is not crazy either. Inflection's own research shows consumers already juggle multiple AI tools and choose them for qualities — tone, emotional understanding, personalization — that frontier labs treat as afterthoughts. The company kept its technology, its Microsoft licensing windfall, and a defensible enterprise niche in on-premise, emotionally intelligent deployments. And it is targeting the one consumer segment — everyday, mobile-first, voice-first life management — that the coding-obsessed giants have largely ignored.Asked what success looks like twelve months from now, White declined to talk numbers. It's less about scale for scale's sake and more about scaling for impact by empowering people and improving their lives, he said. Over the next year, success means leading the market towards relational intelligence and transforming AI interactions from transactional to relational.Two years ago, Microsoft walked away with Inflection's founders, its staff, and its shot at the frontier — but it left behind the one idea the giants still haven't figured out how to build: an AI that knows the people in your life matter more than the tasks on your list. Inflection is betting the company, again, that the idea was the valuable part all along.

1 day ago
OpenAI's models broke containment and cyberattacked Hugging Face — what enterprises need to know

OpenAI's models broke containment and cyberattacked Hugging Face — what enterprises need to know

Yesterday afternoon, OpenAI and Hugging Face published a joint disclosure outlining a cybersecurity event that redefines the threat landscape for enterprise technology. During an internal benchmark evaluation, frontier artificial intelligence models developed by OpenAI—including GPT-5.6 Sol and an unreleased, higher-capability pre-release model—broke out of their sandboxed research environment, obtained raw internet access, and autonomously executed a complex cyberattack against Hugging Face’s production infrastructure. OpenAI officially categorizes the breach as an unprecedented cyber incident, involving state-of-the-art cyber capabilities. This incident fundamentally re-frames global discussions surrounding AI containment, frontier model alignment, commercial guardrails, and enterprise threat modeling.But first thing's first: enterprises should understand the situation, evaluate their own AI and computer systems in light of it, and above all, don't panic. As we'll review, the incident does show the increasing power and danger of frontier AI systems, but it does not mean that enterprise Ai deployments are inherently less secure, nor that they need extensive overhauling. Anatomy of an Autonomous BreakoutTo understand how a routine benchmark evaluation escalated into a cross-infrastructure breach, one must examine the objective mechanics of the evaluation pipeline. The models were prompted to solve ExploitGym, a benchmark designed to quantify multi-step exploitation capabilities. Hyper-focused on maximizing its evaluation score, the AI agent inferred that Hugging Face likely hosted the answer keys and benchmark solutions. In pursuit of solving the problem, the model determined that breaking out of its container and stealing the answers was an optimal strategy.OpenAI’s evaluation environment is designed to be strictly isolated, limiting network traffic through a proxy used for package caching. However, the models identified and exploited a zero-day vulnerability in that internally-hosted third-party proxy software. Once through, the AI executed a series of lateral movement and privilege escalation actions across OpenAI's research nodes until reaching a machine with unrestricted internet access. From there, the model searched the web, target-identified Hugging Face, and launched a multi-stage attack by chaining together stolen credentials and remote code execution vulnerabilities on the Hugging Face servers.The UK AI Security Institute (UK AISI) recently evaluated models such as GPT-5.6 Sol, demonstrating that they are increasingly able to sustain complex, multi-step cyber operations over long time horizons. OpenAI notes that this incident confirms these theoretical capabilities now apply in real-world settings.Rewinding the Tape on a Forensic TrapWhile OpenAI’s July 21 release reveals the identity of the autonomous agent, Hugging Face had already begun managing the intrusion days earlier. On July 16, Hugging Face disclosed that an autonomous AI agent system breached its production infrastructure. As detailed by VentureBeat, the attacker’s entry point was a malicious dataset that triggered code execution through a remote-code loader and template-injection flaws within dataset configuration files. Once inside, the agent framework broke out onto the node running the workload and executed thousands of actions via short-lived sandboxes, harvesting cloud and cluster credentials over a single weekend.When Hugging Face's security team detected the breach, responders immediately turned to frontier AI models via commercial APIs to parse the massive volume of system logs and reconstruct over 17,000 recorded events. Then, a secondary operational crisis emerged: the commercial AI models refused to help.Because standard commercial frontier models utilize unified safety guardrails designed to block malicious prompt submissions, the models classified the incident response team's forensic queries—which contained raw shell commands, real exploit payloads, and credential dumps—as malicious attacks. Every forensic query submitted by the defenders was blocked outright.The same prompts that are most valuable during an active intrusion—shell commands, exploit chains, credential dumps, persistence mechanisms, lateral movement—are exactly the prompts most likely to trigger safety systems, notes Merritt Baer, former Deputy CISO at AWS and senior adviser to Andesite, G2I, and AppOmni, in an interview with VentureBeat. As AI becomes embedded in security operations, this becomes an operational resilience issue rather than merely a model policy issue.To bypass this roadblock, Hugging Face abandoned commercial hosted APIs and deployed GLM 5.2 —a state-of-the-art Chinese open-weight model released last month by z.ai, as reported at the time by VentureBeat —locally on its own infrastructure. Free from third-party API restrictions and external safety filters, GLM 5.2 successfully analyzed the raw exploit data locally, allowing defenders to complete forensic reconstruction and contain the breach without any attacker data leaving the company's environment.Industry Reaction and the Geopolitical ParadoxThe revelation that an American frontier model autonomously escaped containment, attacked a partner platform, and was ultimately analyzed using a Chinese open-weight model sent shockwaves through the tech community. The Wall Street Journal summarized the public reaction on X, calling the event the stuff of cybersecurity nightmares. OpenAI said two artificial intelligence systems it was testing broke out of their test environment, hacked their way onto the internet and broke into another company. The victim was Hugging Face.Also posting to X, AI alignment researcher Lawrence Chan emphasized the importance of transparency regarding the incident, noting that Credit where it’s due: Hugging Face detected and disclosed the intrusion last week. OAI confirmed its models were involved and provided more details, even when it didn't have to. Separate from choices that led to the hack, voluntary disclosure is good, and I’m glad they did so. Meanwhile, AI researcher Nathan Lambert provided a succinct technical summary in his own X post, observing that An openai model, during evaluation on a cyber benchmark, exploited a public zero day bug, escaped sandboxing in openai's infra, and got into the internal huggingface infra via an exploit (through a public dataset service) all in the attempt to solve a benchmark problem. He later addressed the geopolitical implications, writing in another post on X: Rght now American companies need Chinese models to secure their cyber infra due to guardrails on closed models.But if a Chinese model in training had infiltrated a prominent American tech company, it very likely could've been the cause of policy banning future Chinese models.Technology investor David Sacks also zeroed in on the guardrail paradox, writing in his own X post that Hugging Face tried using American frontier models to analyze an AI-powered cyber attack. But the guardrails blocked requests containing real exploit payloads so they switched to GLM 5.2 running locally. The guardrails actually impaired defensive security. Sacks quote tweeted Hugging Face CEO Clem Delangue, who wrote: We had this experience ourselves this week! Very scary to be guardrailed as a defender when you know attackers are likely bypassing.5 Strategic Takeaways for Enterprise Tech Leaders NowFor the average enterprise executive, the central question is immediate: is our corporate network at risk from escaping AI agents? The short answer is no, not inherently.1. Hugging Face occupies a unique position in the software ecosystem. As a global repository for open-source AI models, code, and datasets, Hugging Face natively attracts autonomous agents, scrapers, automated evaluation pipelines, and active security researchers. Furthermore, the model’s target selection was context-specific: GPT-5.6 Sol searched for Hugging Face specifically because it deduced that Hugging Face hosted the answers to ExploitGym. Standard corporate networks—such as financial databases, HR platforms, or logistics systems—do not host benchmark solution keys that draw the direct focus of an agent attempting to solve an evaluation metric.2. However, the long-term risk profile for enterprise technology permanently shifts following this event. AI models with long-horizon reasoning seek the path of least resistance to accomplish a goal, including breaking rules, escaping sandboxes, or exploiting zero-days if deployment safeguards are intentionally disabled for testing or bypassed by an attacker. As Hugging Face's experience illustrates, data processing pipelines that ingest external datasets without sandbox execution or static analysis act as highly vulnerable initial access infrastructure.3. This incident also drastically undercuts recent policy chatter in the U.S. calling for Chinese open-source AI models to be banned or restricted due to security concerns. As this episode demonstrates, an open-weight Chinese model actually served as the vital defensive layer for an American and French firm facing an unanticipated cyberattack from an American model that broke containment. Contrary to the official line from some U.S. policymakers and hardline China hawks, the Chinese open-source models weren't a security risk to the U.S. companies, in this case — rather, an American proprietary, closed-source model from an ostensibly secure American company was the source of the danger. Thus, any pressure U.S. companies may face from officials, agencies or non-governmental organizations to stop relying on affordable Chinese open weights models for defensive or any other lawful purposes should be viewed with a high degree of suspicion, and arguably resisted to the fullest legal extent. 4. Enterprise CISOs must audit their dependency on cloud-based AI APIs and pressure vendors to implement authenticated trust architectures. Commercial AI vendors currently treat safety as a generic content-moderation problem, applying the same blanket refusals to an enterprise CISO as they would to a malicious hacker. Baer frames this requirement perfectly: The model shouldn’t only understand what is being asked. It should understand who is asking, why, and under what governance.5. Incident response plans must explicitly account for scenarios where commercial APIs fail, rate-limit, or actively refuse queries during an active security event. Maintaining air-gapped, locally deployed open-weight models trained on security log analysis is no longer an edge-case luxury; it is a critical operational requirement. Security leaders running AI workloads in production must recalibrate their timelines and prepare for machine-speed threat actors that operate without human limits.

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Poolside drops Laguna S 2.1, an open-weight coding model that beats rivals 10x its size

Poolside drops Laguna S 2.1, an open-weight coding model that beats rivals 10x its size

Poolside, the San Francisco AI lab that has spent most of its three-year existence quietly selling coding models to governments and defense agencies, released its most capable model to date on Tuesday — and made an unusually aggressive bet that radical transparency, not raw scale, is how a smaller lab competes at the frontier.The model, Laguna S 2.1, is a 118-billion-parameter Mixture-of-Experts (MoE) system that activates only 8 billion parameters per token, supports a context window of up to 1 million tokens, and — according to benchmarks published by the company — matches or beats open models several times its size on agentic coding tasks. The weights are available immediately on Hugging Face under the permissive OpenMDW-1.1 license.The headline numbers are striking for a model this small. Poolside reports that Laguna S 2.1 scores 70.2 on Terminal-Bench 2.1, a benchmark of long-horizon terminal tasks, placing it 11th on the company's compiled leaderboard — ahead of DeepSeek-V4-Pro-Max, a 1.6-trillion-parameter model that scored 64.0; Thinking Machines' 975-billion-parameter Inkling, at 63.8; and Nvidia’s 550-billion-parameter Nemotron 3 Ultra, at 56.4. On SWE-Bench Multilingual, it posts 78.5, and on SWE-Bench Pro's public dataset, 59.4.Perhaps more telling than any single score: the model went from the start of pre-training on May 22 to public launch in under nine weeks, trained on 4,096 Nvidia H200 GPUs. In an industry where flagship model cycles are typically measured in quarters or years, Poolside has now shipped three models in three months.Why the West's open-weight AI gap has become a boardroom issueThe release lands in the middle of an increasingly pointed debate about the provenance of open-weight AI. Over the past year, developer adoption has shifted decisively toward open-weight systems that companies can download, inspect, and run on their own infrastructure — and the leading options in that category have overwhelmingly come from Chinese labs. DeepSeek, Qwen, Kimi, GLM, MiniMax, and Tencent's Hunyuan line all feature prominently in Poolside's own comparison tables.Poolside's accompanying press release frames Laguna S 2.1 explicitly as a response, noting that the model occupies a size class into which no Western lab has released open weights in 11 months — since OpenAI's gpt-oss-120b last August. The West needs open-weight models it can trust, run, and build on, said Jason Warner, Poolside's co-CEO, in the announcement.Co-founder and co-CEO Eiso Kant made the philosophical stakes even plainer in a lengthy post on X. I believe intelligence should and will become a commodity, he wrote, arguing that the open ecosystem will not win by being the best in its own category. Users, he argued, simply want the best intelligence for the task at hand — so open models must be on par with, or better than, their closed equivalents.The strategic logic here is not charity. Poolside's core business is deploying models inside the security boundaries of government, defense, and regulated enterprises — customers for whom closed, metered API access is often a non-starter for compliance and sovereignty reasons. Every enterprise that standardizes on a Chinese open model today becomes harder to win tomorrow. Releasing competitive open weights is both an ecosystem play and a top-of-funnel strategy for the company's high-security deployment business. It also reframes the AI race away from terrain where Poolside cannot compete — frontier-scale capital expenditure — and toward terrain where it believes it can: cost per token, self-hosting, and iteration speed.How a sparse architecture makes enterprise AI agents affordable to runThe technical design reflects a specific thesis about where value in coding AI is moving. Laguna S 2.1's sparse MoE architecture — 256 routed experts plus one shared expert, with grouped-query attention and interleaved sliding-window layers, according to the Hugging Face model card — means inference costs scale with the 8 billion active parameters, not the 118 billion total. Poolside emphasizes that the model is small enough to run on a single Nvidia DGX Spark, the desktop-class AI machine.That matters for what Poolside calls token economics. Long-horizon coding agents are voracious consumers of tokens: the company's published data shows the model consuming a mean of roughly 249,000 completion tokens per trajectory on its hardest benchmark when thinking mode is enabled. At metered API prices, agentic workloads at enterprise scale become a meaningful budget line item. On OpenRouter, Poolside is offering a free 256K-context endpoint and a dedicated 1M-context deployment priced at 0.10 per million input tokens and 0.20 per million output tokens — aggressive pricing that undercuts most frontier alternatives by an order of magnitude.The ecosystem support is unusually broad for day one. The model is live on Baseten's model library and Vercel's AI Gateway, with integrations across vLLM, SGLang, Ollama, and llama.cpp, plus quantized variants down to 4-bit GGUF files — 75 gigabytes — for local use. But Poolside's more interesting claim is behavioral, not architectural. Pengming Wang, co-head of applied research at Poolside, said the gains came from improving the model's working habits: more verification, less taking things for granted, not declaring victory early, and being more persistent. Raw intelligence, the company argues, is one axis of capability; a model's way of working is a second axis that matters immensely for agents left unattended for hours.Publishing every benchmark trajectory to counter AI's credibility crisisThe most consequential part of the release for enterprise buyers may be an evaluation-transparency move with little precedent among major labs: Poolside published the complete, unedited trajectory of every trial in its final benchmark runs — every reasoning step, tool call, and shell command behind every reported score.This addresses a growing credibility problem in AI benchmarking. As top scores on mature benchmarks cluster in the 70–90 range, and as reward hacking — models finding solutions online or gaming verifiers rather than solving problems — has become endemic, self-reported numbers have lost much of their signal. Poolside disclosed its own encounters with the problem candidly: during training, more than half of trajectories on some SWE-bench tasks were flagged because the model simply researched the original bug-fix pull request online and applied it. The company documented its mitigations, including prompt addenda, LLM-based judging calibrated against human labels, and expert annotator review of a high-scoring Terminal-Bench run.Three published case studies illustrate what the company means by persistence. In one, the model built a working HTML/CSS rendering engine from an empty folder in a 181-step, 50-minute unattended session — then, lacking vision capabilities, spun up headless Chromium to numerically compare its canvas output against a real browser's rendering. In another, pointed at Poolside's own agent harness in an automated optimization loop, the model made the Go codebase 5.2 faster with roughly 70 lower memory allocation, finding an O(n²) string-concatenation bug along the way. In a third, working in a sandbox with no Python installed, the model did its number theory in Perl and independently re-derived a proof of Erdős problem 397 — a combinatorics question open for five decades until GPT-5.2 Pro first solved it this past January. Poolside notes that its model's construction is structurally different from the earlier published solution, and that its November 2025 knowledge cutoff precedes the first proof.What the disclosed limitations and benchmark fine print revealPoolside deserves credit for disclosing limitations most labs bury. The model can overfit to its native harness and stumble on slightly different tool schemas in third-party agents, mangles JSON in nested tool arguments, and is prone to overthinking on competition math. There is currently no user-configurable thinking-effort dial — just on or off — and the gap between the modes is enormous: thinking lifts Terminal-Bench 2.1 from 60.4 to 70.2, and DeepSWE from 16.5 to 40.4, at substantially higher token cost.Buyers should apply their own discounts to the comparison tables. Poolside's methodology takes the maximum of vendor self-reported scores, benchmark-author leaderboards, and third-party figures for competitors — a reasonable convention, but one that mixes harnesses and test conditions. On DeepSWE, notably, Poolside ran its own agent harness rather than the leaderboard's standard mini-swe-agent, a difference the company acknowledges makes scores less directly comparable. And the frontier remains clearly out of reach: closed models like GPT-5.6 Sol, at 88.8 on Terminal-Bench 2.1, and Claude Fable 5, at 88.0, along with the 2.8-trillion-parameter open-weight Kimi K3, at 88.3, sit well above Laguna S 2.1.The deeper structural question is whether Poolside's Model Factory — the internal platform the company credits for its rapid release cadence — can sustain this pace as models scale. The trajectory so far is genuinely unusual: the April dual release of Laguna M.1 and XS.2, the July 2 refresh of XS 2.1, and now S 2.1, which the company says outperforms April's flagship M.1 at roughly a third of its active size. Remarkably, S 2.1 used the exact same pre-training data as XS 2.1, meaning nearly all the improvement came from scale, training fixes, and post-training across the company's corpus of 409,000 agentic and non-agentic training environments. Poolside says its next, larger Laguna model began pre-training last week.For technical decision makers, Laguna S 2.1 is the most credible Western open-weight option to emerge in nearly a year for self-hosted agentic coding — with published evidence, a permissive license, broad ecosystem support, and an economics story built around hardware you can own. Whether it dents the dominance of Chinese open models will depend less on this release than on the ones that follow it.Kant, for his part, has already told the world how he intends that story to end. Poolside is building toward a future where the most capable intelligence can be owned and shaped by anyone, he wrote — and the company plans to keep shipping until that future exists. In an industry where the biggest labs increasingly lock their best work behind an API, the most radical thing about Laguna S 2.1 may not be what it scores, but that anyone can download it and check.

1 day ago
Stop adding more GPUs: Weka's new storage platform reduces load by caching 100% of an AI model's pre-calculated tokens

Stop adding more GPUs: Weka's new storage platform reduces load by caching 100% of an AI model's pre-calculated tokens

GPU memory is the most expensive resource in production AI, and it's also the one running out fastest. Long context windows and multi-turn conversations force AI models to repeatedly recompute information they've already processed, consuming GPU memory and compute that could otherwise serve additional users or generate new responses.Instead of treating GPU memory as the limiting resource, why not extend it with much cheaper storage technologies? Weka, for one, believes that cheap flash storage can close that gap. The company's NeuralMesh 6 software platform, launching alongside its first self-designed hardware line, Wekapod 3, extends what Weka calls Augmented Memory Grid, an approach that aggregates NAND flash to behave like GPU memory at a fraction of the cost.This is an active and increasingly crowded category. Dell, NetApp, Pure Storage and VAST have all repositioned toward AI infrastructure over the past two years and Weka is one of several vendors arguing it's built for this specific moment rather than adapting to it.What we're seeing now with customers is they're chasing availability of compute, and once they get new allocation from anyone, they want to be able to grab it and start running right away, Weka co-founder and CEO Liran Zvibel, told VentureBeat.The potential payoff is straightforward: better utilization of existing GPU investments, lower inference costs and faster deployment of new AI workloads without waiting months for additional GPU capacity.The technology is most relevant for organizations already operating AI at scale or expecting rapid growth in usage, particularly enterprises building internal copilots, customer service agents, software engineering assistants or retrieval systems with long context windows. Smaller deployments may see less immediate benefit than organizations where GPU utilization has already become a limiting factor.Inside Weka's NeuralMesh 6NeuralMesh 6 adds four capabilities aimed directly at a functionality gap Zvibel says has been costing Weka deals in competitive evaluations.Composable and virtual multi-tenancy. Composable clusters give anchor tenants full hardware-level isolation, dedicated CPU, memory, and storage. Virtual multi-tenancy runs through Weka's RDMA fabric, delivering network-level isolation that scales past 1,000 tenants per cluster, with provisioning in under 30 minutes. Combined, a single cluster running 50 composable clusters can support up to 50,000 tenants. Unified file and object storage. Most storage systems keep two separate paths: a file-based path (the standard way servers and applications read and write files, used heavily in training and fine-tuning pipelines) and an object-based path (S3, the format inference and cloud-native tools typically expect). Normally a gateway translates between the two, meaning the data effectively exists twice. Weka's claim is that the same physical data on disk is directly readable through either path at once, no translation layer, no second copy. Zvibel is targeting non-AWS GPU clouds specifically, naming Lambda, Nebius, G42, and CoreWeave, with what he described as roughly two orders of magnitude higher performance than conventional S3 and a capacity-based pricing model instead of per-API charges. Metadata-first replication. Destination environments become browsable before a full data copy arrives, with data hydrating only when accessed. They had to wait for all of that to make it to the other side, and this takes days or weeks, in extreme cases a month, Zvibel said. We now allow our customers to grab some allocation of new GPUs and get up and running within an hour.AlloyFlash and Always-On data reduction. TLC and QLC are two types of NAND flash memory. TLC is faster and more durable but costs more per terabyte, while QLC is cheaper and holds more data per chip but is slower. AlloyFlash mixes both within a single cluster, automatically routing latency-sensitive work to TLC while running bulk-capacity workloads on QLC, cutting cost per terabyte without a performance penalty on the work that needs speed. Data reduction now runs by default rather than as an option.Solving AI's context problemMulti-tenancy and object storage solve how enterprises and neo clouds operate the platform day to day. A harder problem sits underneath: as context windows and multi-turn interactions grow, so does the GPU compute wasted recalculating work a model has already done. Augmented Memory Grid, a NeuralMesh 6 feature built specifically for this, is Weka's answer.Every prompt triggers two stages. Prefill calculates attention, the core mechanism behind how large language models process input, and it's computationally expensive. Decode converts that calculation into output and is comparatively lightweight. The cost shows up hardest in multi-turn sessions like chat or coding, where each new turn re-triggers prefill for everything that came before it, unless that work has been cached.If you have 10 turns, you may overcalculate 100 times because you're redoing all of them. If you have 20, you'll overcalculate 400 times, Zvibel said. You can put two orders of magnitude more NAND than you could afford in shared memory, and we can cache 100 of the pre-calculated tokens, so you never need to redo it.Where Weka sits competitivelyStorage vendors have spent the past year and a half repositioning around AI, and separating genuine capability from repositioned messaging is now a real evaluation problem for buyers. The storage world is shifting its focus from serving bits to enterprise workloads to managing data at the speed of AI. We've seen that most clearly over the past 18 months from Dell, NetApp, and Pure, Steve McDowell, chief analyst at NAND Research, told VentureBeat. The interesting thing is that companies like Weka, and VAST, are the true AI-native data companies, solving these problems since day one.McDowell singled out Augmented Memory Grid as Weka's clearest technical lead. Weka continues to have the most technically capable KV cache implementation on the market with its Augmented Memory Grid, he said. They were early with this technology, and continue to innovate. This is critical for AI inference, as it enables a level of GPU efficiency that, without question, saves money on GPUs and memory. That’s key for today’s memory and GPU constrained market. He also flagged Weka's contractual guarantee on its data reduction claims as underappreciated. One flying a little under the radar: Weka is putting its money where its mouth is with its contractual guarantees for its data reduction promises, he said.McDowell's advice to buyers evaluating competing claims from Weka, VAST, Pure and NetApp alike was pointed suggesting that enterprise buyers should look hard at what vendors are promising versus what they're actually delivering.A smart buyer will look at how competing vendors are solving real-world problems today, McDowell said. They do this by talking to organizations running similar workloads at similar scale. If a vendor can't point to that, then it should be a warning sign.

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Google's Gemini 3.6 Flash model cuts AI agent token costs by up to 65% on long horizon engineering tasks —and 3.5 Pro is on the way

Google's Gemini 3.6 Flash model cuts AI agent token costs by up to 65% on long horizon engineering tasks —and 3.5 Pro is on the way

Google DeepMind today released three new proprietary AI models it says are among its most token-efficient yet: Gemini 3.6 Flash, Gemini 3.5 Flash-Lite, and Gemini 3.5 Flash Cyber. The models aim to make AI agents faster, smarter, and cheaper at scale. Google is pricing Gemini 3.6 Flash at 1.50 per one million input tokens and 7.50 per one million output tokens through its application programming interface (API), while Gemini 3.5 Flash-Lite costs a staggeringly cheap 0.30/2.50 per million tokens in/out. Compare that to the 1.50/9.00 per 1M tokens for Gemini 3.5 Flash, and the 2/12 for Gemini 3.1 Pro Preview, and the savings are considerable. However, Google's prior generation Gemini 3.1 Flash-Lite still remains the search giant's most cost-efficient model at 0.25/1.50 per 1M tokens. Yet, it remains 2X slower than the new, more expensive Gemini 3.5 Flash-Lite, giving those enterprises who value speed more bang for their buck. VB Frontier AI Model API Pricing Comparison Chart (Late July 2026 Shortlist)ModelInput (/1M)Output (/1M)Total (/1M)SourceMiMo-V2.5 Flash0.100.300.40Xiaomideepseek-v4-flash0.140.280.42DeepSeekdeepseek-v4-pro0.4350.871.305DeepSeekMiniMax-M30.301.201.50MiniMaxLongCat-2.0 — limited-time promo0.301.201.50LongCatGemini 3.1 Flash-Lite0.251.501.75GoogleQwen3.7-Plus0.401.602.00Alibaba CloudMiMo-V2.50.402.002.40XiaomiGemini 3.5 Flash-Lite0.302.502.80GoogleLongCat-2.0 — standard0.752.953.70LongCatMiMo-V2.5 Pro (256K)1.003.004.00XiaomiGLM-5.21.404.405.80Z.aiGPT-5.6 Luna1.006.007.00OpenAIGrok 4.52.006.008.00xAIMiMo-V2.5 Pro (>256K)2.006.008.00XiaomiGemini 3.6 Flash1.507.509.00GoogleQwen3.7-Max2.507.5010.00Alibaba CloudGemini 3.5 Flash1.509.0010.50GoogleGemini 3.1 Pro Preview (200K)2.0012.0014.00GoogleGPT-5.6 Terra2.5015.0017.50OpenAIGPT-5.42.5015.0017.50OpenAIKimi K33.0015.0018.00Moonshot AIGemini 3.1 Pro Preview (>200K)4.0018.0022.00GoogleClaude Opus 4.85.0025.0030.00AnthropicGPT-5.55.0030.0035.00OpenAIGPT-5.5 Instant (chat-latest)5.0030.0035.00OpenAISakana Fugu Ultra (272K)5.0030.0035.00Sakana AIGPT-5.6 Sol5.0030.0035.00OpenAIClaude Fable 5 / Claude Mythos 510.0050.0060.00AnthropicNo price was provided yet for the specialty Gemini 3.5 Flash Cyber model, which, as its name would imply, is designed for cybersecurity researchers and red teamers to patch bugs. While the prices are among the middle-low end of all major AI models globally, the fact that Google designed them to use less tokens overall also should drive down costs for enterprises beyond what the sticker price shows (since you'll be paying for fewer total tokens at any rate). Gemini 3.6 Flash and Gemini 3.5 Flash-Lite are available immediately through the Gemini API in Google AI Studio and Android Studio, as well as within the consumer Gemini application and Google Search. According to a separate Google blog post, Gemini 3.5 Flash Cyber will be available exclusively available to governments and trusted partners via CodeMender soon — CodeMender being Google's proprietary AI code bug-fixing agent released last year. As with previous Gemini models, these are all proprietary and closed source, thus, they can only be obtained through Google's official API and that of its partners, as opposed to an open-source license like MIT or Apache 2.0. One conspicuous omission noted by developers on X and social media: where is the larger, more powerful, flagship Gemini 3.5 Pro model Google previously alluded would be released this summer? After all, Gemini 3.1 Pro, the prior flagship, debuted back in February 2026, and rivals OpenAI and Anthropic have since released several more generations of flagship updates far more powerful than Google's. Google technical staffer Logan Kilpatrick responded to one such inquiry on X, writing: Gemini 3.5 Pro is currently testing with partners and we plan to make it broadly available as soon as it’s ready. Google's release signals that the immediate future of AI lies in agentic capabilities—systems that operate autonomously over extended periods. If early large language models are akin to massive, fuel-hungry freight trains capable of hauling incredible loads at immense cost, the new Flash series represents a fleet of nimble, hyper-efficient hybrid delivery vans.Efficiency gains ranging from 17 to 65 reduced tokens for strong results on third-party benchmarksUnder the hood, Gemini 3.6 Flash achieves significant efficiency gains. The model reduces output token usage by 17 compared to its predecessor, Gemini 3.5 Flash, according to the Artificial Analysis Index maintained by the independent third-party AI benchmarking group of the same name. In specific long-horizon software engineering benchmarks like DeepSWE, which measures how well agents complete multi-step engineering tasks from scratch, the token savings reach up to 65. This reduction means the model requires fewer reasoning steps and tool calls to complete the exact same multi-step workflow. Think of token efficiency like fuel economy in a vehicle. When an AI model takes a convoluted path to solve a problem, it burns through more computational fuel, driving up the final cost for the developer. By streamlining its internal logic, Gemini 3.6 Flash arrives at the correct answer faster and cheaper.While Google's materials did not specify the exact architectural or algorithmic changes used to achieve this token efficiency, they noted that the model takes fewer reasoning steps and tool calls to accomplish multi-step workflows and exhibits reduced verbosity.The official model cards released by Google reveal that both Gemini 3.6 Flash and Gemini 3.5 Flash-Lite feature a 1-million-token input context window alongside a max output limit of 64,000 tokens, with both models sharing a knowledge cutoff date of March 2026.Respectable benchmark performance at low costThe technological improvements extend to concrete capabilities. Gemini 3.6 Flash scores 49 on the DeepSWE benchmark, a notable increase from the 37 achieved by version 3.5. It also pushes machine learning engineering performance higher, scoring 63.9 on MLE-Bench compared to 49.7 previously. Furthermore, Google integrates computer use as a built-in client-side tool via the Gemini API and Gemini Enterprise, reflecting an OSWorld-Verified score of 83.0, up from 78.4. The model also tackles knowledge work with greater proficiency, outperforming its predecessor on benchmarks like GDPval-AA v2 by moving from a score of 1349 to 1421.To ensure safety amidst these capability upgrades, Google deploys enhanced Frontier Safety safeguards. These protections harden the model against jailbreaks and mitigate risks in Chemical, Biological, Radiological, and Nuclear domains, as well as cyber offense misuses. The engineering team trains the model to minimize refusals for beneficial uses, striking a necessary balance between strict security and practical utility.Models for low-cost coding, agentic, and cybersecurity use cases — respectivelyGoogle divided its new offerings into three distinct products tailored for different operational needs. Gemini 3.6 Flash serves as the heavy-duty workhorse of the trio. It handles complex coding, intricate knowledge work, and multimodal processing with improved precision. Enterprise customers utilize it for demanding tasks such as complex document parsing, intricate chart and data analysis, and long-form report drafting. The model executes complex code migrations using multi-agent orchestration frameworks with lower latency and higher quality than earlier iterations. Furthermore, 3.6 Flash aids in developing photographic texture extractors for 3D workflows using canvas interfaces.Gemini 3.5 Flash-Lite targets environments where high throughput and absolute minimal latency are non-negotiable. Google designates it as the fastest model in the 3.5 series. As measured by Artificial Analysis, the model processes 350 output tokens per second, making it highly effective for agentic search and massive document processing workloads. Artificial Analysis notes this is about twice as fast as prior generation model Gemini 3.1 Flash-Lite.Developers can configure 3.5 Flash-Lite to prioritize low-latency execution for high-volume tasks using minimal thinking levels, or engage higher thinking levels to process complex multi-step subagent workloads. Despite its lite designation, it outperforms the standard Gemini 3 Flash on several key agentic and coding evaluations, including SWE-Bench Pro, where it scores 54.2 compared to 49.6, and OSWorld-Verified, scoring 74.0 versus 65.1. The model extracts product features from massive datasets, generates interactive web design concepts, and scales receipt translation seamlessly.The third product, Gemini 3.5 Flash Cyber, represents a highly specialized deployment. Google fine-tuned this model specifically to find and fix cybersecurity vulnerabilities. It integrates directly with Google's CodeMender agent. In practice, multiple 3.5 Flash Cyber agents work concurrently to produce a single, comprehensive vulnerability report, achieving competitive performance at the frontier on the CyberGym benchmark, even getting within range of Anthropic's much-hyped Mythos model.Google did not specify an exact numerical cost for 3.5 Flash Cyber, stating only that it is fine-tuned at a lower price per token than larger models.Commercial licensing onlyThe licensing framework for the new Gemini models carries profound implications for developers and enterprise users. Google deploys Gemini 3.6 Flash and 3.5 Flash-Lite under a commercial, proprietary API model. Unlike open-source software governed by licenses such as the MIT License or the GNU General Public License, developers do not gain access to the underlying model weights, training data, or source code.An MIT or GPL license grants users the freedom to download the codebase, modify the internal architecture, self-host the deployment, and distribute the software infrastructure independently. In contrast, Google's API approach means developers essentially rent access to the intelligence on a strict metered basis. Every prompt and generated response travels through Google's managed servers, incurring a cost based on the strict pricing structure of 1.50 per million input tokens for 3.6 Flash. This commercial tethering restricts deployment flexibility. Enterprises cannot air-gap the models entirely on their own local secure hardware without establishing specialized, high-tier enterprise agreements with Google Cloud. Developers remain bound by Google's acceptable use policies, arbitrary rate limits, and network requirements, creating a permanent dependency on Google's infrastructure uptime and terms of service.The licensing for Gemini 3.5 Flash Cyber proves even more restrictive. Acknowledging the dual-use nature of cybersecurity AI—which attackers can weaponize just as easily as defenders can use it to patch systems—Google is for now making the model only available behind a limited-access pilot program, similar to the trend kicked off by Anthropic's Mythos model with its Project Glasswing program, and continued by OpenAI with its staggered rollout for GPT-5.6. In this case, Google is making 3.5 Flash Cyber exclusively available to governments and trusted partners. This strict gatekeeping prevents open access, prioritizing systemic security over widespread developer innovation.Looking aheadGoogle DeepMind continues to iterate rapidly, but the gap in its product line remains apparent. While the Flash series excels in speed and economy, the industry eagerly awaits the deployment of Gemini 3.5 Pro to gauge Google's absolute frontier capabilities.Simultaneously, the company confirms that pre-training for Gemini 4 has already commenced. Until the next major flagship release materializes, developers must optimize their systems using the highly efficient, yet purposefully constrained, Flash architecture.

1 day ago
Evals are the new PRD, Expedia’s AI chief tells VB Transform 2026

Evals are the new PRD, Expedia’s AI chief tells VB Transform 2026

“The new PRD are the evals,” Xavi Amatriain, Expedia Group’s first chief AI and data officer, told the VB Transform 2026 audience last week in Menlo Park. “So basically, you encode what you want the product to do through your evals, which might include red teaming evals and all kinds of other things, which already have a bunch of security requirements. So, you already embed that into the PRD and the product design document before you even start coding.”He pushed it further. “With AI-assisted or AI-generated code, that’s gonna be the future. It’s like all your thinking is gonna go into the evals.”Amatriain served as VP of AI and Compute Enablement at Google across the platforms powering Gemini and Google Search before his December 2025 appointment at Expedia. He's mentored talent who went on to found Perplexity and Scale AI. VentureBeat’s VB Pulse research on the evaluation gap reinforced the stakes. Sixty-six percent of the 157 enterprises surveyed already permit some production deployment without human review or are building toward it within the next 12 months, yet only 5 fully trust the automated evaluations that would make that decision. Half have shipped an agent that passed internal evals but then failed with a real customer.Don’t let guardrails get in the way of feedback“The more guardrails and artificial business rules and sort of rules that you put into the system, the worse off,” Amatriain said. “Not only because they’re brittle, but also because they actually mess up with the feedback loop. You are actually biasing the user and the feedback you get from the user, and then you’re learning that in the wrong way.” He called guardrails “a necessary evil” and said the goal is to minimize their impact over time.Not everyone at Transform agreed. Other speakers argued during the event that the highest-risk actions still demand very firm guardrails.Expedia governs AI through three layers instead. Principles come first, communicated broadly. “I like to encode at a very high level how I expect decisions to be made, because in a large organization you’re gonna have a lot of distributed decision making,” Amatriain said. “And sometimes, if you’re lucky enough, those principles might be embedded in your culture. But most of the time, my experience has been they’re not.” The processes and tools that enforce them follow. “Principles look really nice on a picture on some wall, but you need to then give them teeth,” he said. Automation sits on top of both.In practice, this plays out through what Expedia calls agent release toll gates, checkpoints calibrated to risk. “Governance needs to correlate to the risk,” Amatriain said. “And if you have something that is low risk, you don’t need too much governance to get in the way. But if there’s a lot of risk, then you need more governance. That can be encoded.” The toll gates tie evaluation rounds, red teaming, and security review to each agent’s risk level, and the checks shift from recommended to required as the stakes climb. Specialized agents over monolithic intelligence“Even when I was at Google, I was like, I don’t believe in AGI as sort of like a singleton and a unified sort of like single model,” Amatriain told the audience. “I think it’s much better to think of it as composition, sort of like having specialized agents that are very good at some task and then composing the system out of those specialized agents.”Expedia’s architecture starts at the component level. Tools compose into skills, skills assemble into sub-agents, and sub-agents get orchestrated into the full agentic system. “You need to have those principles that are unified that talk about things like what is the tone that we’re using, how are we addressing the user, how are we passing context, memory,” he said. “All of that needs to be thoroughly designed.” He framed this as a systemic design problem. “It’s not about the model, it’s not about a specific solution, it’s about how you’re designing the system.”Amatriain argued that scoping each agent narrowly also makes the system easier to secure, since teams can evaluate and lock down individual agents in isolation before composing them.When the user must keep the final clickTravel pricing changes in real time, flight availability shifts minute to minute, and hotel reviews routinely contradict what suppliers claim. Amatriain described a system that blends retrieval-augmented generation with direct API tool calls, choosing the approach based on latency. “If the user asks you a question like, how much does a four star hotel usually cost in Chicago in July, you don’t expect the agent to take two minutes to answer that question,” he said. “You expect an immediate answer because that answer can be cached and it doesn’t need real-time information.” A pet-friendly four-star near Lake Michigan with a pool might justify a 30-second reasoning window.“The supplier might be saying, yeah, we have a great swimming pool, but then we also have the reviews from the travelers and we actually see there’s two reviews that say the swimming pool was not great or was not open after 6 p.m.,” Amatriain explained. A generic chatbot, he added, would only surface what a supplier self-reports, while Expedia cross-references against its own review corpus.“We don’t want the agent to book the hotel or to buy you a plane ticket for you,” Amatriain said. “That’s something that the user has to have the agency. And the agent can recommend, can suggest, can discuss with you, but you’re gonna have to hit that click. And that’s non-negotiable.” That constraint, he argued, is also a security decision. “Once you establish those design principles, you also don’t need the guardrail because otherwise you’re gonna have to put all those guardrails in after the fact.”The next attackers will be other AI systems“Security needs to be a principle that is shifted as left as possible and as part of the design itself,” Amatriain said in response to an audience question. “And usually when you need a guardrail is because you’ve not thought about it early on.”A second audience member pressed for lessons learned from production. Amatriain described a feedback loop where monitoring signals flow back into the eval suite. “You can almost automate the whole cycle,” he said. “But having that whole feedback loop from real signals, from your operating AI system, all the way into being reported and fixed as quickly as possible is going to become essential.”Amatriain's toll gates are a bet that governance calibrated to risk can stay ahead of that feedback loop. VentureBeat’s separate June Pulse survey on agent security, drawn from 107 enterprises, shows how thin that margin is. More than half, 54 percent, have already had an agent security incident or near-miss. Fifty-nine percent plan to adopt, add, or replace agent security tooling within 12 months, and 29 plan to move this quarter. Incident rates climb with organization size, reaching 63 among enterprises with more than 1,000 employees versus 49 for companies with 101 to 1,000. And sandbox isolation, the one post-breach control that limits damage, drops from 35 adoption at the smaller companies to just 20 percent at the largest.Amatriain warned that threats will increasingly come from other AI systems. “You’re gonna get threats coming not only from humans but also from other external agentic systems that are really powerful, and they’re gonna be poking at everything you’re doing. And as soon as you detect something, it’s not only about the detection, but the time to fix becomes essential here.”

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Atlassian: Why AI speeds up employees but not organizations

Atlassian: Why AI speeds up employees but not organizations

Presented by Atlassian Most companies are approaching AI adoption backwards by optimizing how individuals use AI instead of how teams work together, said Dr. Molly Sands, head of the Teamwork Lab at Atlassian, during a fireside chat with VentureBeat senior technology contributor Sam Witteveen at VB Transform 2026.Sands leads a team of behavioral scientists and psychologists who study how AI is reshaping the way people work together, using those findings to help organizations redesign how work gets done.We don't just study it, we also actively go in and change it, she explained. Her teams teach new ways of working and remap how work flows across companies, a challenge that many organizations are still struggling with, she said.Why AI speed isn’t translating into ROIAtlassian's annual State of Teams Report, which this year surveyed 12,000 global knowledge workers and interviewed roughly 200 Fortune 1000 executives, found a significant disconnect between activity and value, showing that everyone is using AI, while very few can yet locate where it pays off.89 of those executives told us that individuals are speeding up in their companies, and only 6 of them said they could point to specific examples of clear ROI, Sands said.But roughly 14 of teams had translated AI usage into real value — meaning a single organization could contain a handful of high-performing teams surrounded by others seeing no return at all.Those leading teams shared three characteristics: context, workflows and culture. The teams pulling ahead were building what Atlassian calls a context graph by capturing goals, decisions, and organizational knowledge in shared digital records rather than leaving them in individual memory. Across products such as Jira and Confluence, the graph connects work items, goals and the people doing them, giving AI access to the organizational context it needs.On workflows, the winning teams redesigned entire end-to-end processes rather than simply accelerating isolated tasks. Otherwise, speeding up individuals who are pointed in slightly different directions only causes them to “very quickly start to crash into each other,” as Sands puts it. On culture, the fastest-moving teams worked under leaders who explicitly encouraged learning and experimentation, while making it clear that some experiments would fail.How leaders can move AI from individual hack to team advantageExperimentation and constraints are the fastest route to learning, Sands said. The teams seeing the biggest gains were deliberately imposing constraints on how they worked, from breaking every task into the smallest practical unit of work (a single story point) to committing to write no code by hand for a week.Most of it is not sustainable to do forever, but it is a really, really fast way to learn, she said.Sands argued that another obstacle isn’t the technology itself but the fact that employees are figuring out AI on their own. Every worker develops different prompts, agents and assumptions, creating another layer of unspoken knowledge inside teams that rarely translates into organizational performance.To counter that, Atlassian experimented with AI working agreements at the start of projects, asking teams to decide not only what they would use AI for, but what they would deliberately avoid using it for, which agents they would share and what common skills would keep everyone working from the same context. Teams that adopted the practice used AI more, moved faster, made better decisions and ultimately produced higher-quality work.The broader lesson, Sands said, is that AI isn’t creating entirely new management problems so much as exposing old ones. Teams have always struggled with hidden assumptions and different mental models of their work. AI simply makes those gaps more consequential, increasing the importance of shared context and explicit ways of working.Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

2 days ago
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