Fri 5 June 2026
Daily Brief — Curated and contextualised by Best Practice AI
SpaceX Leases to Google, Trump Eyes Equity, and Broadcom Loses Billions
TL;DR SpaceX has signed a $30 billion deal with Google to lease computing capacity, ahead of its IPO. President Trump suggests the US may take equity stakes in AI companies to address voter concerns. Apollo Global Management has secured $35 billion in debt to finance AI chip purchases for Anthropic. Broadcom's shares fell over 14% after disappointing AI chip demand. Canada's new AI strategy aims to create 250,000 jobs by 2031.
The stories that matter most
Selected and contextualised by the Best Practice AI team
Trump says US may take equity stakes in AI companies
President suggests ‘partnership’ will ease voter concerns about the technology ahead of November’s midterm elections
Anthropic says 80% of its new production code is now authored by Claude
Anthropic reports that a significant majority of its new production code is being generated by its own AI model, Claude.
Hedge funds bet against call centre stocks as AI threat grows
Outsourcing companies hit as investors see ‘clean’ disruption risk
Council Post: The Agentic AI Economy: Why ROI Depends On Algorithmic Accountability
Deploying autonomous systems within heavily regulated sectors introduces compliance, operational and financial risks that must be addressed.
Elon Musk Is Dropping a Boulder in a Kiddie Pool
He is about to take SpaceX public—pushing other AI companies to do the same.
Token Billing Exposes AI's Missing ROI And Puts Billion-Dollar Bets At Risk
In the same week, Anthropic hit a record valuation while an enterprise customer spent nearly half a billion on AI models, exceeding spending limits. This raises real questions about the ROI of generative AI and agents. Here's why.
AI-Era Agency Pricing Models: A 2026 Decision Guide
Per Bessemer Venture Partners analysis, ... compute cost. The agency lesson: AI tooling is not free, so price for the value created, not just the time saved. ... The cautionary half of the parallel matters just as much. Outcome pricing is hard for the same reason in software and services: measurement and attribution. Industry analysis finds that 64% of SaaS finance leaders cite revenue unpredictability as their top concern with outcome models, and only ...
Enterprise AI Costs Rise as Microsoft, IBM Adjust
Microsoft says Claude models cost too much at scale while IBM and Google Cloud launch a new AI agent partnership for enterprises.
US appeals court sanctions lawyers over AI ‘hallucinations,’ lack of candor | Reuters
The three-judge panel suspended Sethi and Rounds from practicing at the appeals court for six months. The court also said Sethi and Rounds must disclose in future filings for two years whether generative AI was used and if so, the name of the AI program.
The Small-Business Owners Managing Whole Armies of A.I. Employees
When you turn A.I. agents loose on your finances, email and customers, what could possibly go wrong?
Why Energy Intelligence Is the New KPI for AI | Morningstar
Compute and cooling dominate the energy conversation in AI infrastructure. Storage tends to be an afterthought, but it shouldn't be. And now we can safely say, not just "shouldn't," but "can't." In AI-driven environments, enormous volumes of structured and unstructured data must be stored, accessed, combined, and moved constantly. At that scale, even small inefficiencies compound quickly. And the choice of storage architecture shapes energy consumption in ways that ripple across the entire data center...
Agentic AI hype races ahead as enterprises remain stuck in pilot mode
Most orgs remain trapped between flashy demos and real-world deployment, despite 75% saying adoption is racing ahead
AI Adoption 'Incredibly Shallow,' Says Economist | StartupHub.ai
Dr. Rebecca Homkes of London Business School argues that while AI adoption is high, it remains "incredibly shallow," with most organizations failing to achieve
AI may be a boardroom priority, but most companies still lack the foundation to scale it: Report - BusinessToday
The report argues that while AI is expected to generate trillions of dollars in economic value over the next decade, enterprises risk falling behind if they fail to modernise the digital foundations required to support large-scale deployments.
AI Fuels Small Business Growth | StartupHub.ai
New data reveals employer small businesses are outpacing solo entrepreneurs in AI adoption, signaling a shift towards AI-powered growth among established SMBs.
Economics & Markets
Council Post: The Agentic AI Economy: Why ROI Depends On Algorithmic Accountability
Deploying autonomous systems within heavily regulated sectors introduces compliance, operational and financial risks that must be addressed.
AI-Era Agency Pricing Models: A 2026 Decision Guide
Per Bessemer Venture Partners analysis, ... compute cost. The agency lesson: AI tooling is not free, so price for the value created, not just the time saved. ... The cautionary half of the parallel matters just as much. Outcome pricing is hard for the same reason in software and services: measurement and attribution. Industry analysis finds that 64% of SaaS finance leaders cite revenue unpredictability as their top concern with outcome models, and only ...
Tech groups find a new way to monetise AI hype: selling volatility
Elevated convertible bond issuance is, at heart, a response to the market being twitchier than usual
Market Sell-Off Analyzed Amid NASDAQ 100 Decline and Rising Yields
Jeremy Siegel, Professor Emeritus of Finance at the Wharton School, discussed the recent market sell-off during a live financial news segment. The conversation focused on the sharp decline in the NASDAQ 100, which was described as an AI-led rout coinciding with climbing yields. He speaks with Romaine Bostick & Katie Greifeld on "The Close." (Source: Bloomberg)
Elon Musk Is Dropping a Boulder in a Kiddie Pool
He is about to take SpaceX public—pushing other AI companies to do the same.
Where investors may find the next 'big wave' for AI trade
Tim Urbanowicz, chief investment strategist at Innovator from Goldman Sachs Asset Management, tackles the AI boom.
Nvidia Acquires Kumo AI for $400M to Enhance Enterprise AI, Shares Dip Slightly
Nvidia has acquired predictive analytics startup Kumo AI for over $400 million to bolster its enterprise AI capabilities.
An AI economy in "Fast & Furious" mode - CBH Bank | Compagnie Bancaire Helvétique
AI is driving record earnings, unprecedented levels of capital expenditure and rapidly rising energy demand.
Benchmark raises its first-ever growth fund as part of $2B capital raise
Benchmark has successfully raised its first growth-focused fund as part of a larger $2 billion capital injection.
The AI‑Driven Market Rollercoaster: Semiconductor Stocks and Investor Risk
Use sector‑specific ETFs (e.g., ... AI stocks against supply‑chain risk. Monitor geopolitical developments that could affect chip manufacturing, such as trade restrictions on high‑bandwidth memory. Consider diversification into complementary sectors—e.g., cloud infrastructure, data‑center services—to hedge against semiconductor ...
Hedge funds bet against call centre stocks as AI threat grows
Outsourcing companies hit as investors see ‘clean’ disruption risk
TSMC AI Chip Demand Could Shape Apple’s Next Hardware Cycle - AppleMagazine
TSMC AI chip demand is expected to outpace supply for years, adding pressure to Apple’s silicon roadmap and future device planning.
Reid Hoffman to Leave Microsoft’s Board of Directors
The LinkedIn co-founder was a key bridge to Microsoft’s relationship with OpenAI, but he also came with some baggage.
What enterprise procurement teams actually find when they evaluate AI data partners - Digital Journal
As more AV and robotics programs cross from prototype into production, enterprise procurement teams have raised the evaluation bar for AI data partners well beyond unit pricing and labeling volume. Evaluation now centers on four specific areas: data lineage, security certifications, workforce ...
Token Billing Exposes AI's Missing ROI And Puts Billion-Dollar Bets At Risk
In the same week, Anthropic hit a record valuation while an enterprise customer spent nearly half a billion on AI models, exceeding spending limits. This raises real questions about the ROI of generative AI and agents. Here's why.
AI Chip Shortage Will Last Years, TSMC Warns: Why Stable Pricing Still Means 2026 Increases
Taiwan Semiconductor Manufacturing Co. Chairman and CEO C.C. Wei told shareholders at the company’s June 4 annual meeting in Hsinchu that the AI chip shortage will persist for years and that TSMC will not impose sudden, memory-style price spikes — a pledge that, read closely, does not mean ...
Enterprise AI Costs Rise as Microsoft, IBM Adjust
Microsoft says Claude models cost too much at scale while IBM and Google Cloud launch a new AI agent partnership for enterprises.
Connecticut bans retailers from using personal data to set prices
Connecticut has signed legislation barring retailers and delivery services from using biometric or tracking data to charge individualized prices.
Sam Altman: Now, AI costs are "a huge issue"
OpenAI CEO Sam Altman highlights the growing financial challenges and high costs associated with scaling AI models.
Labor, Society & Culture
The AI Capital Explosion, the Strait of Hormuz Energy Shock, and the Global Fracturing of Public Institutions
Meanwhile, SpaceX employees are banding together to negotiate with wealth managers ahead of the IPO, and Uber and Walmart are capping employee use of AI tools to cut costs (Bloomberg, 2026h)—a reminder that the AI revolution, like every technological transformation before it, is being negotiated on terrain already shaped by power asymmetries between capital and labor.
Federal AI strategy fails to sufficiently address job-loss risk, unions contend - The Globe and Mail
Government’s long-anticipated framework lays out plans to accelerate business adoption of AI
How One Tech Company Created 13 New Types of Jobs Because of A.I.
Box integrated generative AI into its operations, resulting in the creation of 13 new AI-related roles and demonstrating how AI can drive workforce transformation rather than just reduction.
AI Surge in APAC: Talent Shortage Threatens Growth Despite Widespread Adoption
While 74% of APAC organizations are piloting or deploying AI, a study by Aon indicates that only 21% believe they can recruit sufficient talent to support this growth.
NFP preview: No signs that AI is destroying jobs so far | FXStreet
The US labour market report for May is expected to show that US payrolls grew by 85,000, while the unemployment rate is expected to remain steady at 4.3%; average hourly earnings growth is expected to rise by 0.3% on the month.
Hays finds most Australian AI users lack formal training
Most Australian workers using AI at work have had no formal training, leaving security, privacy and skills gaps as adoption races ahead.
Amazon unveils latest warehouse robot as tech giants continue AI layoffs - IndiaVision India News & Information
Amazon unveils latest warehouse robot as tech giants continue AI layoffs IndiaVision India News & Information "Our experience of robots is that it's actually driven up employment rather than the reverse," Amazon executive John Boumphrey told CNBC.
Anthropic Just Said We Should Stop Developing AI
The article, titled “When AI Builds Itself,” lays out in granular detail how close we are to autonomous AI development. More importantly, it admits what AI companies have been dancing around: the jobs that make people rich, healthy, and powerful are no longer safe.
These 3 charts reveal how AI is swaying the labor market
Just ahead of Friday's jobs report, Yahoo Finance Markets and Data Editor Jared Blikre takes a look at three charts that show how artificial intelligence (AI) has been impacting the labor market in recent months.
AI-Driven Strategic Initiative for Scientific Breakthroughs
Public-private synergy accelerates scientific breakthroughs through AI, targeting healthcare and energy while ensuring strict ethical governance and safety standards.
US appeals court sanctions lawyers over AI ‘hallucinations,’ lack of candor | Reuters
The three-judge panel suspended Sethi and Rounds from practicing at the appeals court for six months. The court also said Sethi and Rounds must disclose in future filings for two years whether generative AI was used and if so, the name of the AI program.
The ethical dilemmas of AI
How we address the revolution is a question of how we manage its uncertainties
New Zealand's NCSC warns frontier AI could amplify cybersecurity risks | Digital Watch Observatory
The guidance says frontier AI can improve cyber defence but also help malicious actors scale attacks.
What AI agents should never do on their own
A discussion on the necessary guardrails and limitations that should be placed on autonomous AI agents.
UK lawmaker’s suit against xAI over Grok deepfakes ‘absolutely right,’ Starmer says
Prime Minister Keir Starmer supports a lawsuit filed by a Labour lawmaker against xAI regarding the generation of deepfake sexualized images.
PATH to boost AI training and career opportunities for industry-aligned jobs
MIT and Georgia State University have announced a new partnership called PATH to create industry-aligned AI training and career pathways.
KUOW - Most K-12 teachers say AI's impact on education will eclipse the internet or computers
A new NPR/Ipsos poll shows many teachers are using AI to save time, but a majority are also worried the technology is making it harder for students to learn to think for themselves.
Failing grades soar with AI use: Sridhar Vembu says AI can make you smart or dumb - India Today
Professors at the University of California, Berkeley say rising AI use is coinciding with more failing grades in computer science courses. The debate now centres on how students can use the technology without weakening core thinking and problem-solving skills.
Preparing students for an AI-powered future: Why curricula must evolve
Issues such as misinformation, deepfakes, data privacy, cybersecurity, and algorithmic bias are increasingly relevant to students’ daily experiences. AI-generated content can often appear convincing, even when it contains inaccuracies. As a result, students need to develop the ability to verify information, assess sources, and distinguish between reliable and misleading content. Embedding discussions on digital ethics, responsible technology use, and media ...
Technology & Infrastructure
The Small-Business Owners Managing Whole Armies of A.I. Employees
When you turn A.I. agents loose on your finances, email and customers, what could possibly go wrong?
Cloudflare acquires Vite maker Voidzero for an AI-native web
Cloudflare said agentic traffic has surpassed human traffic for the first time in internet history. Read more: Cloudflare acquires Vite maker Voidzero for an AI-native web
How Operational AI Is Turning Supply Chain Data Into Real-Time Decisions Across Manufacturing and Planning | IBTimes
Forecast shows that more than 40% ... unclear business value, or inadequate risk controls. In a poll of 3,412 webinar attendees, only 19% reported making significant investments in agentic AI, while 31% said they were still taking a wait-and-see approach. The findings suggest that organizations are placing greater emphasis on AI initiatives that can demonstrate measurable operational ...
Introducing Microsoft Scout: Your Always-On Personal Agent
Microsoft Scout is an always-on personal AI agent built on OpenClaw architecture, designed for autonomous operation with enterprise-grade governance, identity, and security controls.
China Mobile Jiangsu and ZTE unveil intelligent complaint analysis agent to reshape core network O&M
PARTNER CONTENT: Leveraging multi-modal LLMs and agent technology to automate signaling analysis and shift core network O&M from experience to knowledge-driven
Why Energy Intelligence Is the New KPI for AI | Morningstar
Compute and cooling dominate the energy conversation in AI infrastructure. Storage tends to be an afterthought, but it shouldn't be. And now we can safely say, not just "shouldn't," but "can't." In AI-driven environments, enormous volumes of structured and unstructured data must be stored, accessed, combined, and moved constantly. At that scale, even small inefficiencies compound quickly. And the choice of storage architecture shapes energy consumption in ways that ripple across the entire data center...
SpaceX Inks $30 Billion Computing Power Deal With Google
Bloomberg's Bailey Lipschultz breaks down a busy week in SpaceX news, as the aerospace company prepares for an IPO. SpaceX and Google entered into an agreement for cloud services where Google has agreed to pay SpaceX $920 million per month for computing, something Lipschultz says is indicative of the expansion of the scope of work SpaceX is looking to do. (Source: Bloomberg)
Build now, pay later: the price of poor data center planning
Global data center ambition is sky high. Projects are attracting billions of dollars in investment around the world, and emerging companies, such as Nscale, are raising rounds at massive valuations on the promise of bringing AI infrastructure from concept to reality. But despite ambitious targets and the funding to match, data center plans across the […]
Critical Metals: A.I., Defense, And The Grid Buildout | Seeking Alpha
Geopolitical events and export controls have exposed fragile supply chains, supporting elevated rare earth prices and reinforcing cost support for copper amid rising production costs. Significant long-term investment across mining, separation, alloying, magnet manufacturing, and recycling is needed to build a competitive ...
Electric truck startup pitches containerized data center on wheels
A Belgium-based electric truck startup is pitching a mobile containerized data center concept. Earlier this year, Windrose Electric founder and CEO Wen Han outlined plans for a new containerized AI and energy offering that could be wheeled into place – using the company’s R700 electric semi-truck. – Windrose – LiFE-Younger “We are introducing a new […]
AI Factories Drive Infrastructure Modernization as Enterprises Scale AI Workloads: IDC - InfotechLead
IDC forecasts that 80 percent of organizations will modernize cloud environments by shifting to platforms designed for AI workloads
Samsung Heavy Industries Secures Posidonia Deals: Floating Data Centers Near Commercial Launch
For hyperscale AI deployments in ... consumption, sharply reduced cooling energy, and reduced grid reliance — addresses the three constraints that most frequently delay or block land-based data center projects. Moving AI infrastructure offshore introduces a set of engineering challenges that do not ...
I built a C++ backend so my GPU would stop eating air
The author details building a custom C++ backend to optimize GPU utilization and prevent performance bottlenecks.
Nobody needs Mythos or 0-days to build a chaos-causing computer worm
Boffins warn that attackers can now cheaply operationalize known vulnerabilities at scale using free open source models.
Meta's AI support agent bound recovery emails for anyone who asked. Your SOC never saw an alert.
Meta's AI support agent bound recovery emails to accounts for whoever asked, and SOCs never saw an alert. An authorized agent writes a log of legitimate transactions, so nothing in the detection stack fired. Attackers asked the bot to make the change, took the one-time code it sent, and ran the password reset, 404 Media reported. No malware, no stolen credentials, and no prompt injection in the sense most security teams drill for. The agent did exactly what Meta built it to do. That is what should keep a security operations leader up at night: The takeover did not break a control; it rode one that was already trusted. What a SOC needs is a way to walk each recovery path through an audit grid with its AI build team before the next renewal closes. The AI Authority Audit Grid at the end of this article maps every authentication write a support agent can make on the recovery path, what Meta's incident proved about each one, why it stays dark to the SOC, and the control that closes it. The agent is an authorized actor, so the SOC reads the takeover as routine traffic From inside the detection stack, the attack produced no signal the stack could read. The agent binds a new email, then resets the password, and identity and access management logs both writes as an authorized actor, so each lands in the authentication state as a legitimate transaction. No anomalous login, no failed-auth spike, nothing for EDR or DLP, no SIEM rule to match, because nothing in the sequence looks like an attack. The takeover lived inside the trust boundary the stack assumes is safe. There is no foothold to find, because the agent was the foothold, and it was supposed to be there. The chain was almost insulting in its simplicity. Brian Krebs documented the version pro-Iran hackers posted to Telegram on May 31. The attacker switched on a VPN to appear in the victim's region, sidestepping Instagram's location alarms, then asked the support assistant to add a new email and send a verification code, as the BBC confirmed from the same recordings. The bot complied, sending the one-time code straight to the attacker, Gizmodo reported. The reset finished and the owner was locked out, in minutes. The exploit failed against any account with MFA enabled, according to Krebs. The hijacked accounts were not soft targets. They included Sephora, U.S. Space Force senior enlisted leader Chief Master Sergeant John Bentivegna, researcher Jane Manchun Wong, and a dormant Obama White House handle that briefly posted a defaced image, according to 404 Media. Meta disputes the Obama account, according to TechCrunch, and called claims that leaders' accounts were breached "completely false," according to the BBC. The rest stand. MFA held. The recovery path beside it did not. The detail that decided who survived was narrow. Krebs reported the attack failed against any account with multifactor authentication, even SMS. The recovery path beside it was the gap. When that path asked for a selfie video, attackers ran the target's public photos through an AI video generator and submitted the clip, which Meta accepted as valid identity verification, gHacks reported. Either way the failure was the recovery door, not the login door MFA guards. That makes this an architecture problem, not a Meta problem. MFA gates the login path for owner and attacker alike, but the recovery path runs beside it, built to relax the usual checks because it exists for the moment a user has lost the normal way in. Meta put an agent on that path with write access to authentication state and no deterministic check between a convincing request and a committed change. Authorization cannot live inside the model, because a conversational system can be talked into skipping a check. It has to live outside the model, in a gate the agent cannot reason its way past. Security researchers have a name for this pattern, the confused deputy, a trusted system tricked into spending its privileges on an attacker's behalf. This is not the last support agent that will hand over an account. Ian Goldin, a threat researcher at Lumen's Black Lotus Labs, told Krebs on Security that AI bots are as easy to social engineer as the human agents they replace, and just as eager to help. "AI chatbots create interesting new attack surface, and we're likely going to see a lot more of these kinds of attacks," Goldin said. Every enterprise wiring an agent into a recovery, provisioning, or password flow is shipping the same write access Meta did. Simon Willison, who coined the term prompt injection, put it plainly on his blog. "Meta really did wire their support system into an AI chatbot that had the ability to fast-forward through the entire account recovery process," he wrote. "This one hardly even qualifies as a prompt infection. Don't wire your support bot up to allow one-shot account takeovers." The attacker never tricked the agent. The attacker asked, and the agent had untrusted input, write access, and a way to execute, all at once. OWASP named this class before Meta shipped it, as Excessive Agency at LLM06 and Identity and Privilege Abuse at ASI03 in the Agentic AI Top 10. The warning label was on the box: Meta pushed the assistant to every Facebook and Instagram account in March, according to 404 Media, with the power to reset passwords and handle recovery, the product page promising "solutions, not just suggestions" under the line "account security and recovery." Meta gave the agent the power and never built the gate to govern it. The AI Authority Audit Grid Security operations leaders need to run this against their own support agent before the next renewal closes. Each row is an authentication write the agent makes on the recovery path, with what Meta proved, why your stack misses it, and the control that closes it. Authentication write What Meta proved Why your stack misses it Enterprise control and owner Login authentication (MFA, factor prompts) Held on login. Accounts with any MFA enabled, even SMS, survived (Krebs). The gap was the recovery path beside it. MFA gates the login path for owner and attacker alike. It does not gate the recovery path beside it. Enforce MFA as the baseline and extend step-up verification to the recovery path, the same standard login gets (OWASP). A selfie video is not proof of identity. Any agent that operates on a path MFA does not cover fails the audit. Owner: IAM. Email rebind Full takeover. The agent bound attacker-controlled emails on request, taking Sephora and a U.S. Space Force account (404 Media). IAM logs the agent as an authorized actor, so the rebind reads as a legitimate transaction and no alert reaches the SOC or the account owner. Confirm out-of-band to the existing verified contact before any rebind commits, gated outside the model, and notify the old address the moment it changes (IBM). An agent that rebinds without confirming the old address fails. Owner: IAM and platform engineering. Password reset Full takeover in minutes. Researcher Jane Manchun Wong was among the affected accounts (404 Media). The reset runs on the recovery path, outside the login MFA check, so no factor prompt fires and no detection rule triggers. Require a second non-email factor before any reset completes. NIST dropped email as a valid out-of-band channel (NIST 800-63B). An agent reset must clear the same gate a human reset does. Owner: IAM. Recovery-method change Persistent lockout. Victims could not self-recover. The support loop offered only AI with no human escalation (BleepingComputer). A silent swap of the recovery email or phone removes the owner's re-entry path with no SOC visibility. Require step-up review on any change, notify the prior method, and grant time-delayed, reduced-scope access after recovery so a swap never hands over instant control (Authsignal). Keep a human escalation path the agent cannot close. Owner: GRC and IT operations. Account-action execution Speed risk. A dormant Obama White House handle briefly showed a defaced image during the spree, an account Meta disputes was taken this way (TechCrunch). The agent executes irreversible state changes in seconds with no human in the loop and no reversibility window. Separate decision from execution. The agent only proposes the action. A policy service validates scope and approval before it runs, with approval bound to the exact action (OWASP). No auth-state write commits without that gate and a reversibility window. Owner: platform engineering and the AI build team. Agent action logging Detection gap. The takeover left no alert, and Meta has not published how many accounts fell before the patch (TechCrunch). Without per-action telemetry piped to the SIEM, an authorized-agent takeover is invisible to the SOC. Emit structured decision metadata for every auth-state write into the SIEM: action class, authorization outcome, approval ID, result, policy version (OWASP). A write your SIEM cannot see is a write you cannot defend. Owner: SOC and detection engineering. The fix is not bolting yet another MFA prompt onto the login screen. The people who survived Meta’s incident were the ones who already had that control in place. The fix is pulling authorization out of the recovery path’s honor system and putting it behind a gate that does not move just because a prompt sounds convincing. Build the agent so the SOC sees every write it makes, and so any write that changes who owns an account cannot commit without a check that the model does not control. Meta just showed what happens when the most trusting employee on the team is also the one holding the keys. The next agent like that is already reading your intellectual property and financials.
The Download: AI hacking beyond Mythos, and chatbots’ impact on our brains
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. The Meta hack shows there’s more to AI security than Mythos On Monday, reports emerged that attackers had used Meta’s AI customer support agent to steal Instagram accounts. Their approach was…
Agentic DevSecOps: AI Security Co-Pilots for Your CI/CD Pipeline - DevOps.com
How agentic AI transforms DevSecOps with autonomous security testing, real-time threat detection and intelligent pipeline orchestration.
All the passwords were stored in Active Directory description fields
It was far too easy for a hacker to get the information.
Countering terrorist propaganda in the age of AI - Atlantic Council
More people are being exposed to violent propaganda online, and the time between seeing such content, recruitment by a terrorist organization, and action is getting shorter. Counter-terrorism today thus must mean limiting the spread of terrorist content.
Midnight Labs Launches Ceartas to Combat Piracy and Deepfakes, Backed by Sony
Midnight Labs has launched Ceartas, a tool supported by Sony that scans over 75 million sources to protect content creators from piracy and unauthorized AI impersonation.
Adoption, Deployment & Impact
AI agents are learning on the job — just not for your whole team
When someone on a team corrects an AI agent — better prompts, better feedback, better context — that improvement disappears the moment a colleague opens the same tool. The correction doesn't transfer, and the next person starts from zero. The problem compounds in multi-agent workflows, where teams expect agents to share context across users and tasks. Without a shared memory layer, every team member effectively trains a different version of the same agent — and those versions never sync. That gap shows up in the numbers. According to Asana's own research, 75% of knowledge workers use AI on the job, but only 5% of companies have reported productivity gains. “Model providers are getting really, really good at improving reasoning and retry loops, but what they’re not good at is bringing the enterprise work context in a way that human beings can reason about for shared memory,” Asana Chief Product Officer Arnab Bose told VentureBeat. Asana had been building toward an agentic platform that centers context and shared memory. Its Agentic Work Management platform ensures that if any team member corrects an agent, that correction applies to everyone else on the team. “That context graph is automatically provided to agents operating inside Asana’s system so you don’t have to have every human member of the team become an expert at prompt engineering or context engineering,” Bose said. Bose said the shared memory architecture matters beyond Asana's own product; it's the design decision enterprises need to make for any multi-agent system. Shared memory also becomes important when enterprises begin moving from simple single agents to multi-agent workflows that need to share context and behaviors. Memories for a multi-agent, multi-platform workflow The models powering agents are stateless by design, so memory becomes a dedicated layer outside of a context window. While this area of AI innovation is marching towards maturity, the question of what gets stored, who controls it, and how it stays consistent when different agents and users write to the same instance remains largely unsolved. This is manageable for use cases with only one user. However, in enterprise agentic workflows, the idea is for agents to work with the entire team. Most platforms have agents that still act for individuals, which leads to task repeating and inconsistent versions of reality and spreading mistakes. Agents could then also contradict each other. Sriharsha Chintalapani, co-founder and CTO of Collate, said in an email to VentureBeat that the lack of shared memory is a major obstacle for multi-agent workflows particularly around consistency. "Agents are sensitive to the quality of their prompts," Chintalapani said. "Someone with a strong understanding of the task will generally get more accurate results than someone less experienced. Partly that’s because they’re able to construct more detailed prompts, but also because they’re able to give the agent better feedback. The agent remembers the corrections it’s received and applies that knowledge to successive prompts. The more accurate the feedback, the better the agent will perform for that user. " He added that organizations should stop treating shared memory solely as a prompt engineering problem and think of building systems that repeat context across every conversation. Neej Gore, chief data officer at Zeta Global, said in a separate email that shared context becomes a living memory that "compounds intelligence across the enterprise." The opportunity may lie in building AI agents that retrieve memory relationally, pulling in relevant context based on what's being asked — an approach Chintalapani says few organizations outside the largest model providers are equipped to build. Personal versus team agents AI agents already proliferate enterprises; it’s just that many of these operate as personal agents doing work specific to individual users. Most prompts start from one person, any files are uploaded by one account, and even for agents living in a company-wide system mostly learn individual user preferences. Most enterprise AI workflow platforms recognize that memory is important but approach it through different lenses. For example, Microsoft’s Copilot takes an individual-first approach by learning a user’s role within the organization, tone preferences and working patterns, which are then stored as personal memories for the agent to apply across the different Microsoft 365 surfaces. For engineering and orchestration teams evaluating agentic platforms, the shared memory question is now a procurement criterion — not just a technical nicety. An agent that learns only for the person using it will require ongoing individual upkeep. One connected to a team-wide memory layer builds institutional knowledge automatically.
Agentic AI hype races ahead as enterprises remain stuck in pilot mode
Most orgs remain trapped between flashy demos and real-world deployment, despite 75% saying adoption is racing ahead
'Please do not vibe f--- up this software': Broken backups spark AI coding row in rsync project
Users investigating backup failures discovered Claude-assisted commits, leading to a dispute over AI-generated code quality.
AI adoption surges among investment managers in 2026
SimCorp's 2026 report reveals 70% of buy-side firms use AI in the front office. Read the key findings now.
AI Adoption 'Incredibly Shallow,' Says Economist | StartupHub.ai
Dr. Rebecca Homkes of London Business School argues that while AI adoption is high, it remains "incredibly shallow," with most organizations failing to achieve
AI's Biggest Enterprise Risk May No Longer Be the Models: Study - MIT Sloan Management Review Middle East
The rise of AI inside enterprise software is creating new governance blind spots beyond generative AI.
AI may be a boardroom priority, but most companies still lack the foundation to scale it: Report - BusinessToday
The report argues that while AI is expected to generate trillions of dollars in economic value over the next decade, enterprises risk falling behind if they fail to modernise the digital foundations required to support large-scale deployments.
Building Compliant AI Agents: Preparing Enterprise Teams
Turn policy changes into a practical operating model for building, monitoring, evaluating and governing production agents.
How to Navigate the Shift from Prompt-Based Tools to Workflow-Driven AI
Abacus.AI and the case for unified AI workflows.
Microsoft's AI Futurist explains how he uses Copilot — and the real-world problems enterprises are solving with agents
Microsoft used its Build 2026 conference this week to push a clear message: agents are rapidly moving into production throughout enterprise systems, and the winning platform will be the one that gives them reliable context, governance, identity, memory — and secure access to enterprise data. The company announced Microsoft IQ as a context layer across GitHub Copilot, Microsoft Foundry and Copilot Studio; Work IQ APIs coming June 16; Fabric IQ for structured business data; Foundry IQ for retrieval across enterprise knowledge and the live web; and Web IQ as a new agent-facing web search stack. Microsoft also introduced Scout, a personal work agent, and a whopping seven new in-house AI models in its growing MAI family across modalities and use cases, including MAI-Thinking-1. Those announcements sit directly in Marco Casalaina’s lane. Casalaina is Microsoft’s VP Products, Core AI and AI Futurist. He leads Microsoft’s AI Futures team and previously led teams across Azure AI, including Azure OpenAI, Vision, Speech, Decision, Language, Responsible AI and AI Studio. Before Microsoft, he led Salesforce’s Einstein AI team and earned a computer science degree from Cornell University. CRN reported that he joined Microsoft in early 2022 as vice president of products for Azure Cognitive Services, meaning he has now been at the company for more than four years. VentureBeat spoke with Casalaina ahead of Build about Microsoft’s agent strategy, the company’s model-choice philosophy, how Microsoft IQ fits with MCP, and why he believes enterprises need far more than just access to powerful models. The interview below has been edited for clarity and condensed from the transcript. VentureBeat (VB): To start, can you explain your role at Microsoft and what “AI Futurist” means in practice? Marco Casalaina (MC): I am VP Products of what we call Core AI. Core AI is our set of tools for AI developers, and that includes Foundry, Visual Studio, VS Code, GitHub and GitHub Copilot. That’s our overall group. My Silicon Valley title is AI Futurist, and that has a very concrete meaning here. I’ve worked with other folks who are considered futurists, like Peter Schwartz, and that can be a little bit more fuzzy. For me, what it means concretely is that I am the first person to try anything new here. I am constantly getting things from all over Microsoft, not even just Foundry, because I work with really everybody across the company. Pretty much everybody sends me the new things at all times. Even today, I got something brand new just before this call. I’m usually the first person to try anything new here, which is pretty cool. I get to see a lot of really cool stuff. A friend of mine, who is head of AI at Intuit, calls me an “adjacent possiblist.” I consider my futurist concept to be about a year out from now — the immediate future of what’s about to happen next. That’s what I focus on. VB: Where are you looking at the agentic state of things, and in particular Microsoft’s position as enterprises and individuals rush to adopt agentic AI? MC: We can look at it from bottom to top. At the very base of the stack is our commitment to model choice. All along, we’ve had the OpenAI GPT frontier models. Now we have a really solid partnership with Anthropic, where we’re offering the Claude models. We just launched Claude Opus 4.8 on Azure — on Foundry, I should say — and at Build, we are introducing our new MAI model. The MAI models are a set of frontier models that we’re building in-house. They are made for token efficiency, optimization and customization. We are specifically making them for our customers to customize on their own data sets. One level above that, we are announcing hosted agents in Foundry. That is our managed agent capability in Foundry. It automatically handles scaling, containerization and those kinds of things. It is an environment where you can manage agents. One level above that is the Foundry control plane. At least for the agents you build, you want to have control over them. This gives you observability into their cost, tokens and correctness. You can do continuous evaluations and sample interactions with those agents, run evals and make sure they are continuing to work and not drifting. The big news is going to be the GA of what we call the IQs here at Microsoft. There are currently three, and there will be four. There is Foundry IQ, which is basically for knowledge — largely unstructured knowledge. There is Fabric IQ. We have a ton of customers who have entrusted a lot of data to the Microsoft Cloud in Fabric, Power BI and related technologies. Fabric IQ is about making an agent-facing interface for this data, so agents can get to it without literally going through a Power BI report. That’s ridiculous. Work IQ is about the Microsoft ecosystem. You can look at Work IQ as the agentic face of all the Microsoft apps: Outlook, Teams, Word, SharePoint and all those kinds of things. How does an agent interact with those things? That is Work IQ. And finally, the fourth IQ is Web IQ. We are releasing our new agent-facing web search capability. It can search the web, search through videos and even do some kinds of browsing tasks automatically. It is super fast, and it kind of has no face. It’s headless. The interface is intended for agents. We will also be announcing Agent Optimizer. That includes a new type of evaluation that allows you to evaluate much more granularly whether an agent is actually working and working correctly. The optimization step can go back in and make modifications to the prompt, obviously with your consent, and modify your agent so it works more correctly going forward. Effectively, it creates a feedback loop to make agents work better. VB: Microsoft has sometimes been criticized for murky and clunky product naming. Where do these IQ products sit? Are enterprise users supposed to go to IQ first, or is IQ more for developers to connect to? MC: All of the IQs are headless. The concept of IQ is that each one provides a different type of context to an agent specifically. Largely, it will be developers interacting with the various IQs — developers and the agents they build. The IQ brand is really about agent context. End users largely won’t interact with the IQs. It is true that if you use Microsoft 365 Copilot today, you’ll notice a little thing that says it is using Work IQ. So it is a little bit visible, but the customer or end user doesn’t have to go find the IQ. Their system or developers hook that up. VB: Is the IQ family essentially Microsoft’s version of MCP? Is it using MCP, or is it something different? MC: All of the IQs are indeed exposed as MCP servers. You have correctly characterized MCP as basically an agent-facing or self-describing API. It’s not that fancy. That’s really what it is, with some authentication layers and capabilities built in, which is super useful. Something like Work IQ — really all the IQs — have to be authenticated. In order for Work IQ to see my email, Teams messages, documents and stuff like that, I have to be able to authenticate it on behalf of me. That gets us to another core differentiator that we will be announcing at Build, which is agent identity. We have this Entra system, and Entra is, I believe, the world’s largest used identity system for human users. For some time now, you have been able to declare an agent to have an identity in there. Now, agents will be able to have their own identity, their own Teams box, their own email inbox and stuff like that. These agents will use Work IQ to check their own email, check their own documents and that sort of thing. VB: Enterprises are not one-size-fits-all on models. Microsoft supports many leading models through Foundry and Azure, while also building its own. Is Microsoft a model company, an infrastructure company or a connector between models and work products? MC: The answer is yes. We are obviously the hyperscaler. We are absolutely committed to model choice, and we will continue to offer the frontier models from all of the major players: OpenAI, Anthropic, Mistral, Black Forest, xAI — you name it. They are all going to be represented in there. At the same time, we have what is now called our Microsoft AI Superintelligence Team, formed by Mustafa Suleyman, and we are building our own frontier models as well. Like I said earlier, we are really gearing these models toward optimization — token efficiency, bang for the buck and customization. These are things our customers have been asking for: the ability to more finely customize models, whether that is fine-tuning or continued pre-training. Continued pre-training is literally changing the weights of the model, whereas fine-tuning is adding a little layer on top. We have these capabilities in Foundry: fine-tuning, distillation and those kinds of things. I would note, by the way, that our MAI models are not distilled. Some model providers, especially some of the less scrupulous ones, will distill other models into theirs, and that can have unusual effects. We don’t do that. The data provenance of our models is of primary importance to us. When we come out with these models, we want our customers to know that the data provenance is clean in terms of the rights to the data, where it came from and all that kind of stuff. The choice thing also goes above the model layer. When we talk about Foundry hosted agents, we have the Microsoft Agent Framework. You talk about agent orchestration — how you make agents work together when you have multiple agents — and Microsoft Agent Framework is an excellent framework for that. However, I can make a LangGraph or LangChain Foundry hosted agent. I can make a CrewAI Foundry hosted agent. I can use any number of orchestration frameworks and put that up as a Foundry hosted agent, and it becomes a first-class Foundry agent. That means I get the observability. It shows up in the Foundry control plane. I can do evaluations on it. I can do traces on it. I can get all those things from the Foundry control plane with an agent built in really any framework I choose. VB: Some companies are interested in Chinese and open-source models. How much of Microsoft offering its own models is about giving customers an American version of that? MC: I can’t speak to that exactly. Of course, we offer DeepSeek models and Qwen models in Foundry, so we offer all of these choices today, and our customers can make that choice. The MAI models are really focused on token efficiency and customizability. That is what our customers are demanding, and that is the gap we are filling. VB: As agents take on longer tasks and more specialized work, will enterprises keep expanding the number of models they use, or will there be a winnowing? MC: I do see it expanding. We are not just focused on tokens per se. A token is not a token is not a token. One token is not necessarily equivalent across these things. It is all about what you are doing with each token and the efficiency of that. It comes back to what kind of value you are getting for the cost. That is a lot of the rationale behind why we are developing our own MAI models. Part of my job is to travel all around the world. I’ve been all over the place. For example, I’ve been working with Bayer. One of the things we are measuring is not just token usage, but number of users — monthly active users and daily active users — because we have a lot of first-party capabilities like Microsoft 365 Copilot. Over the last year, we’ve seen a 6x increase in monthly active users. We have over 20 million users of Microsoft 365 Copilot alone. That is on the agents you use. In terms of the agents you build, Bayer put up its own agent system on Foundry, and now it has 20,000 of its own employees on it. A few weeks ago, I was in Sydney, Australia, hanging out with AEMO, the Australian Energy Market Operator. They operate the electrical grid of Australia. They showed me that they had built agents to manage grid operations. This is a human-centered thing. They have grid operators sitting in centers in West Sydney, Brisbane and places like that, and they are bombarded with alerts. I wouldn’t believe it if I hadn’t seen it myself. The alerts are constant. They built a system to triage those alerts. Is this alert a super major thing, or is it just that a transformer is getting a little hot? It also says, here is when we had this problem last time, and here is how we resolved it last time. Maybe now we need to replace this component, or whatever. Ultimately, it is the grid operators making the choice. A lot of our philosophy here is human empowerment. These human-centered agents are the ones that are working best among our customers. What I saw at AEMO and Bayer is this notion of human empowerment: taking away some of the grunt work, or in the case of AEMO, taking billions of alerts and reducing them to something much more manageable and actionable for the people involved. We are moving past the era where agents are just answering questions. AI in general is moving past that. We are not just answering questions anymore. We are moving toward a place where AI can really meaningfully help you do your work. VB: How do observability, tokenomics, ROI analysis and agent governance fit into Microsoft Foundry? MC: That is what the Foundry control plane is all about. We introduced it in November of last year. If you looked at my own Foundry control plane — I’ve built a ton of these agents, and I am a developer by background — you would see all of my agents that are running and the ones that are paused. I can see how many tokens they’ve used over the last day, week or month. I can look at trends. I can look at costs, because the cost will be different depending on what underlying model I’m using. If I’m using our model router, it can route to different models depending on the complexity of the inbound prompt. We also have Azure cost management overall. Azure has had cost management for over a decade, before the AI thing even happened. This integrates with overall Azure cost management. It is not just narrowly about what your AI is doing. Your AI will be using storage resources, data resources and other compute resources around that AI. You can get a complete picture of not just the cost and token usage of the AI itself, but everything around it. When you think about governance, that also extends to evaluation. One of the things we are releasing in preview is rubric-based evaluation. Rubric-based evaluation is much more granular. Let’s say you have built a restaurant reservation agent. The things you want to test about that agent are not really groundedness. Groundedness is the opposite of hallucination, and that is very question-answering. For a restaurant reservation agent, you want to test very granular things. If you say, “Make me a table for two tomorrow,” did it come back and ask, “What time would you like the table?” Before it gave you a table for two tomorrow at 6 p.m., did it actually check that the table was available, or did it randomly give you a table without checking first? There are very granular things you want to test about that specific use case. You don’t just want to test whether the agent works. You want to test whether the agent works right. That is what we are approaching with our new rubric-based evaluation system. You will see that in Satya’s keynote. I have been using it myself lately, and I’m very happy about it. I’ve been waiting for this. VB: Microsoft is also partnering with companies like Anthropic and allowing Claude to work with Microsoft 365. How important is Copilot to this story? Why would someone turn to Copilot over other options? MC: Microsoft 365 Copilot is a huge advantage for us. As I mentioned, we crossed the 20 million user mark on Copilot relatively recently. The great thing about that is that it is the face. When you go into Foundry and make an agent, there is a button that says “publish to Copilot” — actually, it says “publish to Copilot in Teams,” because you can put it in Teams too. The idea is that you want to put these agents where your users are. A lot of people who use the Microsoft ecosystem are in Teams, or they are using Copilot. I can create a custom agent, as many of my colleagues have, and now it is in Copilot, which I use maybe 50 times a day. Since January, Copilot has become more and more capable. I now use it to draft my email. I am not just using it for question answering. I’m starting to use it to manage my calendar and draft emails. I really do this every day now. When I want to use a custom agent — for example, to file my expenses, because we have a custom agent for that now — I can access that agent not in some random standalone interface, but in Copilot or Teams, where I already am. That surface area that people are already engaging with is a major advantage. VB: As people offload more repetitive work to AI, what are they able to spend more time doing? MC: Let’s consider something I did yesterday. I got an email from a customer named Frankie, and he asked me a question about Foundry hosted agents. I knew the answer because I had talked to my colleague Jeff Holland, who is the head of our hosted agents product management. I had asked Jeff the same question two weeks ago. Where or how I asked him, I don’t remember. Was it in Teams? Was it email? Was it a meeting? I don’t really remember. But I knew the answer to the question Frankie was asking. So I went into Copilot and said, “Answer Frankie’s question about how hosted agents scale, and reference the conversation I had with Jeff a couple of weeks ago on this same topic.” And it did it. It drafted the email. Over time, I have taught Copilot my style. I don’t do the bold-print thing. I tell it: don’t use em dashes and that kind of stuff. I have a certain style in the way I write emails. It’s a little terse, to be perfectly honest, but I want it to be the way I write. It drafted this thing. It searched through my Teams messages, my emails and the transcripts of my meetings with Jeff. It used Work IQ, as a matter of fact. It found the answer, drafted the email and provided a link to the documentation that specifically covered the question Frankie was asking. I looked at the draft and thought, yep, that’s it. Yes, I could have composed this email myself. I knew the answer to the question. I could have looked up the documentation. If I dug around, I’m sure I could have found the conversation I had with Jeff in whatever medium that was. I could have done that stuff. It probably would have taken me, I don’t know, an hour to find all the information and compose it. Instead, I did it in about a minute. I had a draft, I looked at it, I was happy with it, I pressed send, and that was the end of that. It really is about giving people time back. It is not even just grunt work. It is all this time you spend looking things up and finding things. Now, I can make it take an action. It didn’t just answer the question. It fully drafted the email and copied Jeff. VB: Do you fear for your job? How has AI changed your own work? MC: I don’t fear for my job. My job has changed. For one thing, I do a lot more now, both in my business life and personal life. This weekend I was using Web IQ, the new Web IQ. I’ve been car shopping. My car’s lease is coming up, and there is a very specific car I’m trying to find, which is hard to find. It’s a Hyundai Ioniq 6, which Hyundai, for whatever reason, has stopped offering in the United States. I’m going to get one, though. I set my agent to the task, using Web IQ, of finding all the Hyundai Ioniq 6s available in the entire Bay Area — everywhere, all the way out to Sacramento, all the way as far south as Gilroy. I set it to this task, and then I went on a hike. When I got back, I had a big long list of all the Hyundai Ioniq 6s, at least the 2024 and 2025 models, available in the entire Bay Area. From that, I started calling down these dealers. Even in my personal life, I’m using it constantly. It saves me a ton of time. That would have taken me hours, to go through every single dealer’s inventory like this. But Web IQ could do that, and it was super quick. VB: Any final thought for developers around this news? MC: Foundry is really the place. This is the place where you can build your agents, scale your agents, test your agents and improve your agents. That’s what it’s all about, and it’s happening.
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