AI News Archive: August 3, 2026 — Part 7
Sourced from 500+ daily AI sources, scored by relevance.
- Tech-eager Canadian banks are coming for AI with gusto
AI is the latest in a long list of technologies that banks have used to increase productivity without cutting staff
- Why your context layer breaks the minute you use it for something new
Why your context layer breaks the minute you use it for something new InfoWorld
- Understanding Alignment in Multimodal LLMs: A Comprehensive Study
Preference alignment has become a crucial component in enhancing the performance of Large Language Models (LLMs), yet its impact in Multimodal Large Language Models (MLLMs) remains comparatively underexplored. Similar to language models, MLLMs for image understanding tasks encounter challenges like hallucination. In MLLMs, hallucination can occur not only by stating incorrect facts but also by producing responses that are inconsistent with the image content. A primary objective of alignment for MLLMs is to encourage these models to align responses more closely with image information. Recently…
- Unicorn, pelican, Middle-earth: OpenAI co-founder Karpathy is looking for the next AI vibe test
One paragraph of "Lord of the Rings" in, 5,500 lines of code out. Andrej Karpathy had Claude Opus 5 turn Tolkien's opening into a 3D browser scene. The article Unicorn, pelican, Middle-earth: OpenAI co-founder Karpathy is looking for the next AI vibe test appeared first on The Decoder .
- ChinAI #369: My Boss Wants Me to Run Kimi K3, What Should I Do?
Greetings from a world where…
- Elon Musk says Anthropic’s Dario Amodei ‘dug his own grave’ by calling Mythos terrifying
Elon Musk criticized Dario Amodei for alarming the public about AI risks. He stated Amodei's messaging about Mythos AI unnecessarily alarmed people. Musk agreed AI disruption would be a bumpy road for many jobs. He predicted AI would soon outperform humans at nearly all digital tasks. Musk also distinguished his views on Amodei from Sam Altman.
- Meet the coaches, measurers, and builders carving out a slice of the AI cost-saving business
Meet the coaches, measurers, and builders carving out a slice of the AI cost-saving business Business Insider
Score: 32🌐 MovesAug 3, 2026https://www.businessinsider.com/ai-cost-saving-businesses-startups-roi-2026-7 - Worried about AI jitters? Deutsche Bank says this tech subsector may offer some protection
Questions over the AI capex spend have upended bets on Big Tech, semiconductors and software this earnings season.
Score: 32🌐 MovesAug 3, 2026https://www.cnbc.com/2026/08/03/ai-deutsche-tech-downside-protection-stocks.html - Rivulo Named an OpenAI Select Partner
Rivulo Named an OpenAI Select Partner USA Today
Score: 32🌐 MovesAug 3, 2026https://www.usatoday.com/press-release/story/39004/rivulo-named-an-openai-select-partner/ - Self-Driving Cars Have An Aging Problem
AI workloads are pushing automotive sensors harder, forcing engineers to rethink how long safety-critical systems can be trusted. The post Self-Driving Cars Have An Aging Problem appeared first on Semiconductor Engineering .
- Governors association partners on $1M AI workforce project
The National Governors Association has joined with RAISE US, a nonprofit workforce initiative, to assist states in developing effective policies to reskill the workforce around AI technologies.
Score: 32💰 MoneyAug 3, 2026https://statescoop.com/governors-association-partners-on-1m-ai-workforce-project/ - IT Teams Always Need to Modernize. AI Is Changing the Game.
From helping in-source software development to streamlining resident-facing systems, AI is a new partner to governments overhauling legacy systems. Here's how IT organizations are using it today.
Score: 30🌐 MovesAug 3, 2026https://www.govtech.com/computing/it-teams-always-need-to-modernize-ai-is-changing-the-game - New tool can help utilities prepare for extreme weather
A model developed by Washington State University researchers can help utilities better prepare and plan for extreme weather to limit power outages.
- Irish Government hides behind ‘fiscal rules’ in holding back AI skills investment
Prize for swift delivery of training is substantial in terms of FDI competitiveness but cost of inaction is frightening
- Safe Pro Group Wins New U.S. Government Subcontract for Patented AI Threat Mapping and Drone Package
Safe Pro Group Wins New U.S. Government Subcontract for Patented AI Threat Mapping and Drone Package azcentral.com and The Arizona Republic
- Poll Finds Overwhelming Majority Disgusted by Trump’s Relationship With AI Industry, Saying He’s Using the Tech to Tear Down Democracy
"These Big AI goliaths think they can buy off our government, our elections, our leaders, and our future and nobody will notice." The post Poll Finds Overwhelming Majority Disgusted by Trump’s Relationship With AI Industry, Saying He’s Using the Tech to Tear Down Democracy appeared first on Futurism .
Score: 30🌐 MovesAug 3, 2026https://futurism.com/artificial-intelligence/polling-trump-ai-industry-democracy - AI’s measurement crisis is over. The translation crisis is next
Last fall, you couldn’t open a business publication without tripping over some version of the same headline: where is the ROI for AI? The anchor for most of that coverage was MIT’s “GenAI Divide” report , which found that despite $30 to 40 billion in enterprise generative AI spending, 95% of pilots delivered no measurable P&L impact. The bubble takes wrote themselves. Boards asked uncomfortable questions. More than a few AI budgets went into the freezer for the winter. Here’s the detail that got lost in the panic: the study defined success as measurable KPI impact within six months of the pilot. Read that again. A project that transformed how a team worked but was never instrumented to prove it counted as a failure. Researchers at UC Berkeley pushed back on exactly this point, arguing that the 95% figure may represent 95% of organizations measuring the wrong things at the wrong time rather than 95% of projects failing to create value. In other words, the AI ROI crisis of 2025 was never really about the AI. It was about measurable verification. Most enterprise AI projects didn’t fail. They were simply built in a way that made success unprovable. If you’re a CIO defending a budget line, that distinction is cold comfort, because “we can’t tell if it worked” and “it didn’t work” produce the same conversation with your CFO. But the diagnosis matters, because the treatment is completely different. You don’t fix an unprovable project with a better model. You fix it by picking a better problem. I’ve argued before that AI initiatives should start with problems that already have good data and trusted metrics, and over the first half of 2026, the market arrived at that conclusion on its own. The quiet correction of 2026 Watch where enterprise AI money actually went in the first half of this year and you’ll see a pattern that never made headlines: a hard pivot toward employee-facing use cases. Agents assisting support reps, sales teams, claims processors, IT help desks. The conventional read is that these are the safe choices, the training-wheels projects companies run while they work up the nerve for customer-facing AI. That read is wrong. The pivot to employee-facing AI isn’t about safety. It’s about scoreboards. Think about what an employee-facing workflow comes with that a greenfield AI initiative doesn’t. You already measure it. Average handle time, first-call resolution, cases closed per week, quota attainment. Those KPIs have years of baseline data behind them. More importantly, they’re politically real. In many organizations, people are bonused on those numbers. Nobody in the room disputes the methodology of a metric that’s been sitting on a comp plan for five years. When you drop an agent into that workflow and the KPIs move in the right direction across the entire employee population, ROI stops being a philosophy seminar and becomes back-of-the-envelope arithmetic. Headcount, fully loaded cost, percentage improvement, multiply. The survey data backs up what I’ve been seeing in the field. Foundry’s 2026 AI Priorities study found that improving employee productivity is now the single biggest business objective driving AI investment, cited by 55% of IT decision-makers. This publication’s own 25th annual State of the CIO research tells the same story from the measurement side: lack of clear ROI metrics remains a critical barrier to AI success, cited by 32% of IT leaders, and among organizations that measure AI success at all, operational efficiency and process improvement (40%), employee productivity (34%) and cost reduction (30%) dominate, while revenue impact trails at 27%. And Deloitte’s State of AI in the Enterprise found two-thirds of organizations reporting productivity and efficiency gains from AI, while only 20% can point to revenue growth. Notice what those numbers describe. The industry didn’t get better at measuring AI. It got better at picking problems that were already measured. The post-mortem question nobody asks first Which brings us to the diagnostic. When an AI project can’t demonstrate ROI, the instinct is to interrogate the technology. Wrong model. Wrong vendor. Insufficient context. Hallucinations. Sometimes that’s true. But the first question in the post-mortem should be about a decision that was made before a single token was generated: what problem did we pick? Did that problem have good data behind it? And did it have a scoreboard anyone trusted before the AI showed up? If the answer to either question is no, the project was never going to prove anything, no matter how well the technology performed. You can’t demonstrate improvement against a baseline that doesn’t exist, and you can’t win an argument with a metric that was invented the same week as the pilot. The MIT study’s 95% weren’t all technology failures. A meaningful share of them were selection errors, committed months earlier in a planning meeting, by people who chose an exciting problem over a measurable one. The bill comes due Here’s the uncomfortable part. Just as the industry figured out the measurability trick, the goalposts started moving. Futurum’s survey of 830 enterprise IT decision-makers in the first half of 2026 documents the shift: productivity gains fell from 23.8% to 18.0% as the primary ROI metric buyers use to justify AI investment, while hard financial measures, top-line revenue and bottom-line profitability combined, nearly doubled to 21.7%. The productivity argument carried the pilot era. CFOs accepted “the KPIs moved” as an answer for a while. Now, they want hard dollars. This is where the next generation of AI projects will separate winners from the pack, and it requires something almost no one negotiates up front: an ROI exchange rate. That’s the pre-agreed formula, signed off by finance before deployment, that converts KPI movement into currency. One point of first-call resolution improvement equals this many dollars. One hour of engineering time recovered equals that many. It sounds bureaucratic. It’s the opposite. The exchange rate is what lets a project claim its value the moment the KPIs move, instead of spending two quarters in a methodology debate trying to reverse-engineer credit after the fact. Without an exchange rate, even a well-instrumented project tops out at a productivity story. With one, the same project is a P&L story. Same technology, same results, entirely different conversation with the CFO. The award was won before deployment This month CIO celebrates the CIO 100 Awards , recognizing technology initiatives that deliver measurable business value. Study those winning projects and you’ll find plenty of impressive technology. But the thing they share isn’t a model or an architecture. It’s that “measurable” was engineered in at problem selection. The winners picked problems with real data and trusted scoreboards, and they agreed with finance on what the score was worth before they started playing. That’s the part of innovation that never makes it on stage, and it’s the part worth copying. So, flip the question that dominated last fall. Don’t ask where the ROI for AI is. Ask whether you picked a problem that could ever answer that question, and whether anyone wrote down the exchange rate. This article is published as part of the Foundry Expert Contributor Network. Want to join?
Score: 30🌐 MovesAug 3, 2026https://www.cio.com/article/4204035/ais-measurement-crisis-is-over-the-translation-crisis-is-next.html - Frontier AI will not break finance. Slow cyber decisions will
The scariest thing about frontier AI is that it gives lazy criminals better legs. That sounds flippant until you watch how cyber failure works. I have seen that weakness in many costumes: A server waiting for a patch, an access path nobody wants to touch, a supplier marked “low risk” because the contract said so, and a legacy system kept alive by one person who retired years ago. It is a known weakness with no owner. Frontier AI only needs to find them faster, join them better and act before the committee has finished admiring the heat map. On 15 May 2026, the Bank of England, the FCA and HM Treasury warned that frontier AI models carry serious cyber and operational resilience implications for regulated firms and financial market infrastructures. Cyber capability is getting faster and cheaper to scale. The European Systemic Risk Board (ESRB) warned in June 2026 that frontier AI models with cyber capabilities can discover vulnerabilities, generate working exploits and execute attacks at a speed, scale and accuracy beyond those of earlier models. It also warned that this may reduce response time, increase concentration risk and weaken resilience across the financial system. Three weeks earlier, a US executive order directed the Treasury, along with CISA and the NSA, to establish an AI cybersecurity clearinghouse and a pre-release evaluation framework for frontier models with advanced cyber capabilities. The clock has changed For years, cyber programmes lived on borrowed time. A weakness appeared. Someone logged it. Technology needed a change window. Procurement checked the supplier. Legal asked what could be said. Everyone was busy. Nobody was idle. Yet the decision moved like a suitcase with one broken wheel. Frontier AI punishes that rhythm. The Institute of International Finance (IIF) staff paper says frontier AI has lowered the barriers to discovering, exploiting and combining vulnerabilities. It also says the answer is not a new risk framework, but faster use of existing ones, with more senior ownership and faster remediation. A patching process that looked mature when attackers needed weeks may look quaint when exploitation can follow in hours. A vulnerability backlog that once looked like a queue can become a menu. And menus are for customers. Not attackers. When firm weakness becomes market fragility In finance, a cyber incident can travel. A bank does not sit alone. A payment system does not hum in a private corner. A firm and financial market infrastructure (FMI) does not clear and settle trades as a hobby. These institutions share technology, suppliers, market data, cloud services, open-source code, identity systems and habits. When one pipe shakes, another pipe may feel the vibration. That is why the ESRB treats frontier AI as a systemic risk, rather than a security issue. It points to shared technology stacks, common service providers, open-source dependencies and the risk of incidents spreading across critical functions. It also warns about asymmetry: Some firms and jurisdictions will have better skills, tools and access than others, while attackers may benefit sooner than defenders. For FMIs, the useful question is blunt: What failure would stop the market completing the day? Not “which system is red?” Not “which supplier scored medium?” If this breaks, who cannot pay, clear, settle, price, report or trust? In finance, one firm’s backlog can become another firm’s outage. Governance means naming the decision There will be a new policy. A renamed committee. A dashboard that tells directors what everyone already knows: the risk is high. Fine. Keep the dashboard. But do not confuse it with movement. Supervisors have already moved this to the top table. On 7 July 2026, the ECB, as banking supervisor, has asked significant institutions to assess the changed threat environment without delay and to deliver a full action plan by 31 October 2026. The ESRB says financial authorities should ensure boards are fully committed to mitigating frontier-AI-driven cyber risks, with clear governance, planned, timely responses and internal investment. Governance should name the decisions before the incident names them for you. Which important services are most exposed? Which vulnerabilities must be fixed first? Which patching risks will the board accept to avoid a worse cyber risk? Which suppliers can hurt the firm? Which defensive AI tools are safe enough to use, and under whose authority? Each important business service should have a Frontier AI Cyber Risk Position. One page. Service. Scenario. Owner. Gap. Decision. Funding. Date. Proof. If it cannot fit on one page, it may not be due to complexity. It may be fog. A policy says the firm noticed. A decision says the firm moved. Threat modelling must grow up Old threat models ask what an attacker might do. Useful, yes. But frontier AI adds a sharper question: What does the model make easier? The Frontier Model Forum says cyber risk frameworks use capability thresholds, capability assessments and extra safeguards when models reach levels that could enable serious harm. Two thresholds matter for finance: Models that give meaningful uplift to less-skilled attackers, and systems that can carry out parts or all of an attack chain with little human direction. So do not only ask whether phishing improves. Ask whether a novice can now perform work that once needed a specialist. Ask whether vulnerabilities can be discovered, chained, tested and used against hardened targets. The Frontier AI Risk Management Framework offers a useful lens: Deployment environment, threat source and enabling capability. In plain English: Where is the tool, who can misuse it and what does it let them do that they could not do before? Patching is now a resilience test Patching used to be treated like hygiene. Necessary, dull and easy to postpone. Not anymore. The ESRB warns that current patching practices in finance are largely reactive. They rely on periodic updates and ad hoc responses. That may fail if frontier AI increases the volume of critical vulnerabilities. Firms may then face an ugly choice: Leave systems exposed or reduce patch testing, risking outages. The IIF paper adds another sting. A published patch can become a signal. Attackers can inspect the fix, infer the weakness and move faster than firms can test and deploy it. In that world, “we are waiting for the next maintenance window” starts to sound less like discipline and more like hope in a suit. Firms need a patch-wave model: Asset visibility tied to critical services, component visibility, exploitability scoring, attack-path analysis, emergency change lanes, rollback plans and senior visibility when the clock collapses. Do not let CVSS become theatre. A lower-scored weakness on a live path to a critical service may matter more than a higher-scored weakness buried in a corner. A patch is not always the end of the story. Under pressure from frontier AI, it can be the starting gun. Your supplier map is part of your attack surface No firm owns its full risk anymore. Some of it sits on cloud platforms, in managed services and in open-source packages maintained by tired volunteers, software vendors and AI providers whose access decisions may depend on governments, export rules or commercial priorities. The IIF paper notes that weaknesses now being surfaced are not unique to financial services. They live in operating systems, browsers, cloud platforms and open-source software used across the wider economy. The capacity to fix many of them sits with technology developers, platform firms and governments. A contract clause does not patch a supplier. A right-to-audit clause does not restore settlement at 3 a.m. A service credit does not rebuild confidence. Firms and FMIs need sharper dependency maps. Which providers support important services? Which have production access? Which hold sensitive data? Which supplier failure would stop the day? Ask for proof. Patch proof. Incident routes. Recovery test results. Component lists. Exit options that can survive contact with reality. Procurement should not buy what resilience cannot recover. Your perimeter ends at the contract. The attacker’s path does not. Use AI for defence, but keep humans in authority Frontier AI can help search code, correlate signals, support testing and speed up triage. The ESRB accepts the defensive value, but warns that offensive gains may arrive sooner than defensive maturity. The IIF paper says firms that move faster to build defensive capability will be better placed as the threat shifts. So yes, use AI to test, find weak paths, help the SOC cut noise and scan code before deployment. But do not let speed smuggle in authority. If a containment action could affect payments, settlement, customer access or market operations, a named human must own the call. AI can suggest. AI can rank. AI can warn. It should not inherit a mandate by accident. Agents that write code, test controls, scan infrastructure or act in workflows need scoped permissions, monitoring, logs and kill switches. They also need owners who understand what the agent can touch. Use AI to gain speed. Do not let it become the ghost in the control room. Assurance must reconstruct the story After an incident, the question will not be, “Did you have controls?” It will be sharper. What did you know? When did you know it? Who decided? What did they reject? Why was the choice reasonable? Where is the proof? Assurance means following the decision trail from threat signal to board action to funding to remediation to test result. Evidence should include board papers, risk decisions, expired acceptances, supplier attestations, incident timelines, recovery tests and lessons learned. The scrutiny will keep moving. The ESRB will reassess these risks at each quarterly meeting of its General Board. Supervisors are calibrating expectations to the trajectory of AI capability because anything anchored to today’s models will be stale before it lands. One caution runs the other way. Firm-level disclosure of live vulnerabilities can itself concentrate targeting information. Push for aggregate reporting where the rules allow, and remediate before you broadcast. Internal audit should ask one brutal question: Could a competent stranger reconstruct the decision six months later? If the answer is no, you may have done work rather than built defensibility. Conclusion Frontier AI will not break finance by magic. It will test whether finance can move before its own processes turn against it. Frontier AI will punish firms that treat it as a chore and reward those that treat the next 12 months as a decision problem with a clock on it. The EU and the US reached the same conclusion by different routes: The rulebook already exists. DORA, the AI Act and the new US clearinghouse point to frameworks in place today. The variable is the speed, ownership and evidence with which firms apply them. The board questions are plain. Do we know our important services? Do we know the paths that can break them? Which suppliers and which models can hurt us? Can we patch in hours? Can we contain without guessing? Can we recover within tolerance? Can we prove who decided what, when and why? This article is published as part of the Foundry Expert Contributor Network. Want to join?
Score: 30🌐 MovesAug 3, 2026https://www.cio.com/article/4204031/frontier-ai-will-not-break-finance-slow-cyber-decisions-will.html - The SOC’s AI maturity model
The path to next-generation AI Security Operations Centers (SOCs), where AI works hand-in-hand with human analysts, is paved with ambitious goals. This ideal SOC incorporates AI across every task to stop fast-moving threats. But a fully AI-powered SOC isn’t a single deployment or a switch you just flip on. It is a staged rollout that’s built over time. There are levels of dependence on AI, starting with basic assistance, moving through automation, and ultimately reaching autonomous response. Each stage in the progression increases the model’s scope and narrows analyst involvement. This means that organizations embracing automation at any level must put their trust in the model. However, achieving trust depends on data . The data must be valid (and verifiable) so AI can propose logical conclusions, recommend appropriate actions for the organization’s needs and risk tolerances, and (at the autonomous stage) act on an analyst’s behalf without raising the risk of compromise and/or incorrect behavior. This article focuses on what enables movement between stages and how teams can build expertise alongside capabilities. Stage 1: Assistance In its simplest SOC use case, AI helps analysts interpret data faster and more accurately, analyzing data, explaining alerts, summarizing logs, and translating detection logic. Many mature SOCs already operate here. AI sits close to analysts but doesn’t directly make decisions; it improves comprehension and can influence outcomes. Stage 2: Automation This is where agentic AI enters the SOC. AI runs investigations, applies context, and proposes actions or interpretations while analysts retain oversight for accuracy and decision-making. AI starts shaping the investigative path rather than just explaining it, introducing efficiencies alongside uncertainty about when to trust (versus validating recommendations). Stage 3: Autonomy At level three, AI operates with near independence; analysts oversee strategy but aren’t involved in individual tasks. The key shift from stage 2 is the removal of case-by-case approval for routine decisions. Instead, agents run on continuous policy constraints, feedback loops, and auditability structures. Security teams are accustomed to validating evidence before acting, but autonomous systems invert that relationship, forcing teams to trust evidence they may only review after an action executes. Many operators will be wary, even as they recognize the efficiencies AI offers when deployed correctly. How SOCs are starting to trust AI Moving from assistance to automation is largely a data problem. Moving from automation to autonomy is about trusting the model and the decisions it produces. What’s necessary to progress is structural confidence in how outputs are produced, validated, and traced, rooted in reliable network data that turns doubt into decisions. If data is incomplete or overly interpreted, the model preserves and amplifies those issues. “If your data itself has bias,” says Vijit Nair, SVP of Product at Corelight , “the tools skew towards that judgment. AI does not fix poor inputs. It scales them.” Stan Kiefer, Senior Manager for Data Science at Corelight, adds that, “AI alone is not trustworthy at this point, and without data to reference back, it may never be.” Analysts should be able to inspect the evidence behind a model’s conclusion, which requires data and algorithms to be open to human inspection. Network security and AI To make that trust practical, teams need an evidence layer that is comprehensive, difficult to fake, and easy to audit. For many SOCs, that evidence layer is network data. Network data tells the most complete story of an environment, but it’s voluminous and easy to misinterpret without context. AI removes this complexity, letting analysts query network data without mastering every analysis technique, making it a knowledge multiplier rather than just a force multiplier. The difference: A knowledge multiplier helps humans operate above their current expertise; a tier-one analyst can ask a complex question and get an evidence-based answer, gradually building skills to operate at a higher level with more confidence. This has real implications for analyst development and team stability. Analysts can experience greater accomplishment, driving productivity and performance and reducing the chance of burnout. Analyst development and AI The SOC has long relied on repetitive work, especially for tier-one analysts. Their work is necessary but often neither instructive nor interesting, and it contributes to burnout and turnover. AI, especially agentic AI, can help mitigate that. AI doesn’t eliminate judgment; it removes friction and tedium. As Nair puts it, this is the difference between “craft” (manually working through data) and “art” (deciding what matters and what to do next). “AI ‘eats the craft’ so people can focus on the art,” he says. “It’s to have them spend less time on repetitive work and more on work that requires knowledge and judgment.” Kiefer estimates AI could cut time to competency by half or more. By replicating an analyst’s workflow, surfacing recurring steps, and explaining complex detections in plain language, AI accelerates the learning curve. The bottom line The real constraint on SOC AI maturity is trust, and trust depends on reliable data that makes outputs traceable and evidence inspectable. Moving through each stage reduces repetitive correlation work and shifts analyst effort toward the interpretation and validation that require human judgment. Key points for your team’s AI journey: Maturity is a staged process: assistance, automation, and autonomy reflect increasing delegation and trust requirements Trust is evidence-based: analysts need traceable outputs, not opaque recommendations Data quality is paramount: incomplete or low-context telemetry inhibits AI effectiveness Verification enables progression: auditability determines how far AI can safely move into decision-making Corelight: Provably better data AI is only as effective as the data behind it. Corelight network detection and response (NDR) delivers data that’s open, transparent, and explainable — in turn helping detect evasive threats, reduce triage time, and enable agentic AI throughout the SOC. Corelight’s structured network evidence preserves protocol-level context to produce a more complete dataset for investigation and AI. When analysts and AI can reason from evidence instead of isolated alerts or metadata, they can validate findings, reconstruct activity, and reach more reliable conclusions. Learn more about Corelight .
- Apple and the invisible wolf: AI slop drowns real security threats
Apple has had to introduce a quota on security researcher reports because its systems are being overwhelmed by low-quality warnings generated by AI. It’s a classic illustration of the rule of unintended consequences : a technology meant to help us has become a barrier to getting things done. After all, not only has AI driven the cost of consumer electronics higher, but it is also being used to identify and exploit security vulnerabilities — while also overwhelming security teams with low-grade reports, thus eroding their attention span. The cost of good intentions This is what’s happened at Apple, as security researchers use AI as a tool to identify new bugs . Perhaps the reports are well-intended. Hopefully, the researchers aren’t just motivated by the promise of easy bug bounties. Or maybe this is a cynical attempt to overwhelm platform security teams with low-grade bug reports — while holding back larger attacks for actual use by well-resourced state-backed actors. We can’t know whether attackers really are trying to overwhelm active platform defenses before going in for the kill. But given that it’s an actively used military strategy, it’s pretty hard to ignore the possibility. Apple’s response So, what’s happening at Apple? The company has put some limits in place to bug reporting as things got out of hand. It introduced a quota cap and a 30-day cool-off period for submitted reports, though researchers who exceed the cap can request an extension. This follows Apple’s recent decision to increase its top security bounty payout to $5 million for the most severe exploits. Apple has paid out more than $35 million to around 800 researchers since launching its bug bounty program. A Financial Times report tells us the many of the reports were about identical bugs, some already resolved, some trivial, but in combination comprising a fog of war that made it harder and more time-consuming to identify the really big flaws. The situation became so febrile the company made the decision to put limits in place in June. There is a little wriggle room to the approach: Apple has worked with the security community long enough to recognize some research teams. Those it trusts most can have their quota extended. Apple also deployed its own AI systems to triage incoming reports in an attempt to identify and remove AI-generated slop. The company also uses internal systems from Anthropic and OpenAI to help identify and fix vulnerabilities; that led to an extensive collection of fixes in its most recent software patch. The Times details an Italian company called Bynario, which identified a fairly nasty-sounding privilege escalation chain that lets attackers take complete control of a Mac. The company also reported a second bug, CVE-2026-43760, a macOS Screen Sharing flaw that allowed an authenticated VNC viewer to access protected data and create files with root privileges. Unfortunately, the hard-working research team was unable to report the first bug, as it had filed more than 50 reports in just three weeks thanks to AI. In other words, it’s possible some security researchers right now are unable to file warnings of critical vulnerabilities to Apple because the system is overwhelmed by slop. This is not just an Apple problem What makes this far more problematic is that it isn’t just Apple that is affected – security teams on multiple platforms are grappling with the same problem. Rafe Pilling, a security expert at Sophos, told the FT that bug bounty programs across the industry have had to shift from finding vulnerabilities to validating reports of them “at machine speed.” That follows comments from Jamf security expert Adam Boynton, who last week characterized AI use in security as, “an arms race between defenders and attackers who are both, increasingly, running the same kind of tools.” When it comes to platform security, it is possible that AI has added a new dimension of complexity to an already complex environment. Hopefully, the real threats will continue to be swiftly identified as they emerge, rather than being wrongly characterized as AI slop. When no one comes running To understand how this works, try reading Aesop’s fable about a shepherd boy who raised the alarm so often that when the real wolf arrived, no one came to help and the young shepherd? He was eaten. You can follow me on social media! Join me on BlueSky , LinkedIn , Mastodon and subscribe to The Core .
- When AI inherits your technical debt
The real challenge facing organizations isn't whether to adopt AI, but whether their existing technology foundations can support it at scale.
- Europe’s AI challenge is not invention, but momentum
Europe has the talent and the ambition to lead in AI, but it is struggling to translate that into sustained scale. The issue is whether it can keep moving and translate momentum into impact. Across industries, a familiar pattern is emerging: promising prototypes, successful pilots, and early traction, followed by a slowdown just as companies […] The post Europe’s AI challenge is not invention, but momentum appeared first on EU-Startups .
Score: 30🌐 MovesAug 3, 2026https://www.eu-startups.com/2026/08/europes-ai-challenge-is-not-invention-but-momentum/ - LWiAI Podcast #253 - Opus 5, Gemini 3.6, Kimi K3, Hugging Face Hack
Anthropic releases Opus 5 promising Fable 5-like capabilities, Google Releases Three New Gemini A.I. Models, and more!
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- The AI ‘news’ accounts hyping a blue-state dystopia
AI-generated videos are flooding YouTube with fake narratives about chaos in California and New York.
Score: 30🌐 MovesAug 3, 2026https://www.semafor.com/article/08/02/2026/the-ai-news-accounts-hyping-a-blue-state-dystopia - Consultants face backlash over AI plans
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Score: 30🌐 MovesAug 3, 2026https://www.telegraph.co.uk/business/2026/08/03/consultants-face-backlash-over-ai-plans/ - Inside the White House’s AI Framework, Apple’s Unusual iCloud Policy Retains Confidential Info — TITV [Video]
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- Outcome-first AI: redesigning enterprise service delivery at scale
Outcome-first AI: redesigning enterprise service delivery at scale YourStory.com
Score: 30🌐 MovesAug 3, 2026https://yourstory.com/2026/07/outcome-first-ai-redesigning-enterprise-service-delivery-at-scale - KAIST Develops AI That Generates Feasible Plans for Delivery, Production, and Workforce Scheduling
KAIST Develops AI That Generates Feasible Plans for Delivery, Production, and Workforce Scheduling EurekAlert!
- Rewriting Business Rules: Artificial Intelligence in Legal Tech and Compliance
Part 3: The Evidence Line — Digital Forensics and the Admissibility Battle How AI is changing forensics and evidentiary standards in the courtroom Every case, criminal or civil, eventually comes down to the same question: what happened, and can it be proven? For decades, this process ran almost entirely on people. In simpler times, evidence used to be physical — letters, documents and photographs. When these grew digital, so did the method of extracting, preserving and reconstructing data. Digital forensics emerged as its own discipline precisely because proving what happened digitally takes different expertise than proving it on paper. Whether that evidence becomes admissible in a courtroom is a separate question — and it’s the one AI is now forcing open. The Ground Law Firms Fight On Evidence isn’t just “what was found”. Evidence is what a record becomes once it’s put in front of a court. For it to be labelled as “admissible in a court of law”, that record has to clear a bar and that bar is called “chain of custody”. Every hand the evidence passes through, every system it touches, every step of analysis it undergoes, has to be documented and defensible. If for whatever reason, the chain breaks — a gap in the record, an unexplained access, an undocumented transfer — the risk is not just that the evidence can weaken but that it can be thrown out entirely, regardless of how compelling it looked on the day it was found. This is the real battlefield — whether the evidence can survive the walk from hard drive to courtroom and be upheld without a single question left unanswered. Everything AI adds to this process — speed, scale, pattern recognition, and traceability — needs to be judged against that same standard. Otherwise, a faster way to find evidence may also become a faster way to lose it. The Human Ceiling: A System Built to Run Out of Time When a lawsuit or investigation began, forensic examiners extracted the data (emails, chat logs, files, call records, social media posts) and handed the raw output to teams of lawyers and paralegals or specialized agencies. From there onwards, the process was mostly manual — keyword searches, followed by thousands of pages read line by line, looking for the phrase, the email or the fragment that proved intent or established a timeline of an event. As the world became increasingly online — conversations, transactions and record keeping started living on hard drives, servers, phones, and cloud accounts and this data had to be identified, preserved, extracted, and analyzed to reconstruct events. This was a critically important part of the lawsuit process because a single missed email or siloed context could either win or lose a multi-million-dollar court case or derail a criminal prosecution. Because it relied strictly on human eyes, it worked, but at a pace that dictated the speed, strategy, and cost of litigation. The human analysis, while competent in its own way, became a hold-up on three counts: the sheer volume of data which can run into terabytes, false positives or negatives in keyword searches and context recognition that a person reading line by line could overlook. An email where the words ‘project adjustment’ or a financial report that mentions ‘expenses: non-recurring’ instead of ‘bribe’ may walk past a keyword filter easily. These issues pointed to the same underlying problem — the process wasn’t broken because people weren’t careful. It was broken because it asked human reading speed to keep pace with a volume and subtlety of information that had already outgrown it. And a trained AI knows how to close that gap. From Evidence to Edge: How AI Enters Forensics and What it’s Worth Artificial intelligence excels at handling massive data sets and identifying complex patterns that escape human analysis. The first place this changes evidence review is “semantic and contextual discovery”. Traditional keyword search finds an exact match for a word; AI review tools replace that with something closer to intent understanding — pattern recognition, sentiment analysis, and shifts in tone or context across documents, emails, and text messages. Once trained to recognize it, AI can even flag a conversation as evasive or contradictory. It isn’t just faster at finding what’s already there, it scans for what the data is hiding. Evidence like that doesn’t just support a case — it has the power to turn the course of the whole lawsuit. The second important shift is AI’s expanding capability to scale across formats and recognize patterns across an entire digital footprint, also known as “advanced multimedia forensics”. Modern evidence is not only limited to text — it also includes image, voice and video information across sources. AI tools can now cross-reference this material, adding real inferential value on top of what a human investigator had already pieced together such as — matching a face or object across an archive of media, flagging the timestamp where a witness’s account shifts, or reconstructing a single timeline from every device an executive under investigation uses. What took a forensic team days of manual cross-referencing is now compressed into hours. The third place AI extends its reach is more complex analysis — geolocation of a person of interest, media authentication using metadata, and audio/visual enhancement. These, conducted by AI, bring the larger picture together, illuminating not just what happened, but where, when, and who knew it. Authenticating a single video’s metadata or reconstructing a suspect’s movements used to require outside experts, weeks of turnaround, and a substantial budget. With AI, that same analysis becomes viable for disputes that would previously have gone unexamined because of the overhead. Each of these is a genuine capability gain, and each one widens the range of matters a firm can afford to fight rather than fold. The next question remains — ascertaining the evidentiary quality of the data. The Verification Wall: What “Admissible” Actually Requires When presenting digital evidence, AI should be treated as a highly capable investigator, perhaps even a witness — but never a judge. For digital evidence to withstand cross-examination and uphold the deemed value, forensics follow a five-parameter code: Authenticity — Is it what it claims to be — did the evidence originate from the exact person, device, and time specified? Integrity — Has it been locked in a pristine state and has not been altered even slightly — since the moment it was collected Completeness — Does it tell the whole story and not selectively “cherry-pick” to support only one side of a narrative Reliability — Are the tools and methods used to collect, store and analyze data trustworthy, industry-standard and scientifically validated Strict Chain of Custody — This is the chronological, bulletproof paper trail documenting every single person who collected, transferred, analyzed, and secured the evidence AI and advanced cryptographic protocols help uphold and defend these mainstays of evidence — hash function protocols verify data integrity at the point of collection, system scans determine behavioral and network anomalies, stylometry and NLP-based communication profiling establish authorship and linguistic pattern, media authentication protocols detect deepfakes, and timeline reconstruction draws on thousands of unalterable, external data points such as ISP connection logs, cell tower pings, and router data. Together, these shift the legal burden from simple ‘visual trust’ to technology-backed validation. AI Governance on Trial: What’s the Verdict on the Proof? The intersection of admissibility and AI is fast becoming a defining battleground in modern law. Technology isn’t the problem — how it’s used, and what governs that use, is. AI can just as easily uncover the truth as it can be used to fabricate it, and governance is what tells a courtroom which one it’s looking at. A few tenets show up consistently across the governance frameworks: human accountability, process transparency and a clear line between assistance from and delegation to AI. Most matters falter in the application of the third because that’s the distinction that keeps surfacing across guidelines and rulings alike — who should make the final judgement call. In a first, this ‘decisive’ tenet is now being codified into law. The Federal Rule of Evidence 707 is currently being debated over in the U.S. federal rulemaking process and extends the Daubert standard that is applied to human testimony to AI generated evidence as well. Under FRE 707, to get an AI forensic model’s conclusion admitted, a lawyer must prove the algorithm relies on sufficient data, uses peer-reviewed principles, has a known error rate, and is generally accepted by scientists. Though not law yet, it is a clear signal of where admissibility standards are heading. Because AI models use probabilistic, predictive calculations, FRE 707 requires that an AI system’s output be treated with the same scrutiny as an expert’s opinion, rather than as neutral fact. Three cases show what’s at stake on either side of that gap. In Mata v. Avianca (2023) , a lawyer used ChatGPT to research case law for a filing. The brief cited six cases that didn’t exist — fabricated by the model and never checked against a real reporter or docket. When the court asked for copies of the opinions, the lawyer produced hallucinated excerpts to match. Three years later, the same failure showed up a level higher. In ARIHQ c. Santé Québec (2026) , a sole arbitrator’s award cited legal authorities that turned out to be entirely fabricated by an AI tool. The Quebec Superior Court annulled the award. While the court was careful to say AI use itself wasn’t the problem, rather it was delegation and letting the tool’s output stand in unverified for the arbitrator’s own judgment. Governance was absent in both the above cases and so, the evidence and the decision resting on it collapsed. Contrast these with Da Silva Moore v. Publicis Groupe (2012) which happened nearly a decade before GenAI made its way into courtrooms. The parties used predictive coding to search over three million documents in discovery. The court approved it — not because the algorithm was flawless, but because the process around it was defensible: a human-reviewed seed set, an agreed protocol both sides signed off on, and ongoing quality checks against the model’s output. When the reliability of the review was challenged, there was a documented process to point to and so, the evidence held. AI can strengthen admissibility in a court of law. But whether that evidence actually helps a court reach a fair, correct judgment comes down to governance that is built in advance into the mechanics of a legal case, not reconstructed after a challenge. If that is done correctly, AI doesn’t just make courts faster — it helps them get closer to the truth, which is the only outcome that was ever supposed to matter. This is the third article in a four-part series on shifts reshaping Legal Tech & Compliance as AI moves from a back-office tool to a strategic force — from talent and workflow , to contracts and arbitration , to digital forensics and admissibility, to cross-border governance . Rewriting Business Rules: Artificial Intelligence in Legal Tech and Compliance was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
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Professor Ingmar Posner and Oxford Robotics Institute showcase cutting-edge robotic arm at Royal Society Summer Science Exhibition pmb.ox.ac.uk
- The missing role in every enterprise AI strategy: The analytics engineer
Every enterprise AI strategy these days has mostly the same core cast: Software engineers who log online events data, data engineers who move data from online to offline data warehouses, data scientists who build machine learning models, AI/ML engineers who deploy these models to production systems and data analysts who consume these data outputs and help create dashboards and self-serve agents for product and business leadership for informed decision making. Despite this systematic setup, the same mode of failure still keeps recurring across industries: AI outputs contradict the dashboard, executives eventually stop trusting the numbers and there seems to be no clear owner of the gap between them. The missing role is not a brand-new role. It is a discipline that has existed for less than a decade, is still not clearly understood at the leadership level, and has no standardized hiring rubric at most organizations. It is the analytics engineer, and the absence of this role is why most enterprise AI deployments seem to stall before they scale. What is analytics engineering ? Analytics engineering sits right at the intersection between data engineering, data science and business intelligence — it is the discipline responsible for transforming raw data into a trusted, governed, reusable semantic layer with metrics and dimensions that both humans and AI systems can rely on. The role emerged from the dbt ecosystem as well as early data infrastructure work at Netflix around 2016-2018, but remains unclearly defined at the leadership level — most CIOs either conflate it with data engineering or product data science or business intelligence analysts, or don’t have a job family for it at all. The role is growing but poorly understood at the top: dbt Labs’ 2024 s urvey found only 14% of data professionals strongly agree their organization sets clear goals for their data team which is a number that holds steady across individual contributors and managers alike. The core function includes being able to speak both the language of core data engineering and product analytics while having a solid understanding of the business events to track for downstream end-user reporting. The analytics engineer plays a vital role in designing as well as reviewing data models to be used for reporting in conjunction with data engineers who are building these, often in SQL and Spark. This is not reporting work — it is infrastructure work and therefore analytics in production environments must be engineered as infrastructure, not assembled as reporting. From these defined business events and key objectives for tracking the health of the product, the analytics engineers need to be able to derive key metrics and dimensional slicing, validating the logic while ensuring those definitions are consistent across all central teams and geographies, and embedding the validation checks that make outputs trustworthy. The practitioner in this role can answer the question no one else can: “Why is the AI giving a different number than the dashboard, and who owns fixing it?” Why AI exposed the gap The metric governance problem has existed even before AI, with different teams using different definitions, regional inconsistencies, manual reconciliation cycles — but it was still controllable when humans were entirely responsible for all final reconciliation and data interpretation, and often any data inconsistencies were caught at the analysis stage. Now, with AI in the picture, it removes the human interpreter stage altogether. When an AI system consumes an ungoverned metric, it inherits the ambiguity at the data layer and amplifies it at the output layer. Executives receive different answers to the same question depending on which system they ask. Confidence in AI erodes independently of model quality — and the numbers bear this out: Foundry’s 2026 State of the CIO study found that fewer than half of enterprise IT leaders have established formal AI success metrics, and only 19% say AI initiatives have met or exceeded ROI goals. McKinsey’s 2025 State of AI survey found that nearly two-thirds of organizations have not yet begun scaling AI across the enterprise — and explicitly named the absence of platforms and guardrails, not model capability, as the reason. Popular semantic layer tools like dbt Metrics and LookML describe how metrics should be calculated but do not enforce correctness, which means there is no structural guarantee that the calculation is consistent across regions and can be traced to an authoritative source that is version-controlled on git or maintained by anyone accountable for its accuracy. With conversation and agentic AI systems embedded into the analytics workflow, the data inconsistency problem is further amplified where agents make sequential decisions, each one building on the previous output. A metric that drifts in a traditional pipeline generally produces one wrong number. The same drift in an agentic workflow can produce a chain of downstream decisions built on that wrong number, with no architectural checkpoint to catch it. This is not a model problem. It is a governance architecture problem — and it requires a specific type of data practitioner to detect and solve it. Ownership and enforcement The analytics engineer owns the semantic data layer: The governed, versioned, validated definitions of every metric that matters to the business. This includes standardizing metric definitions across teams and geographies, embedding validation logic directly into data pipelines, assigning ownership accountability for each metric, and ensuring AI systems consume only validated outputs. The technical signature of this role dives into the reconciliation controls that proactively detect and stall the data pipeline on failure rather than alerting after incomplete or incorrect data lands; This also includes financial reconciliation from upstream to downstream for all the data models trying all data values to financial statements and accounting ledgers, as well as jurisdiction-aware validation logic that treats regional regulatory differences as first-class properties supported by version-controlled metric definitions that create an audit trail. While data engineers are responsible for moving and transforming data from online to offline data warehouses, analytics engineers govern what that data means and ensure the meaning is consistent everywhere it is consumed. Data scientists, on the other hand, build machine learning models to detect anomalies, fraud or product marketing opportunities, while analytics engineers build the trusted data foundation those models depend on, making sure whether that data is accessed via manual querying, imported via dashboard tableau extracts or consumed via large language model (LLM), the end user receives consistent answers based on trusted and governed metrics. Data analysts are responsible for surfacing these metrics and building actionable dashboards and reports for leadership, while analytics engineers make sure that the data surfaced is of the utmost quality. Therefore, in the absence of this role, oftentimes the data engineer, the data scientist and the data analyst are working around a gap that none of them owns. What happens when the role is absent In the absence of this dedicated analytics engineer role, enterprises most often encounter the issue of the “which number is right” question where finance has one revenue figure, product intelligence has another and the LLM model has a third value, and none of these seem to reconcile. One of the common issues seen in AI projects that work in pilot and often break in production is that the pilot references clean, curated datasets and production data containing millions or even billions of records still reference the ungoverned data layer. The third and significant issue seen across enterprises is the analytics team burnout, where data engineers, scientists and analysts spend 60-70% of their time on reconciliation and firefighting rather than new pipeline creation and insight generation, because there is no governed layer to prevent these fires. The fourth issue is the hidden cost of delayed decisions, eroded executive trust and AI investments that deliver less than projected because the data foundation was never built. Most organizations recognize that they need this role only after something breaks in front of an executive, by which time the damage is already done. How to identify and hire talent for this role Analytics engineer, data governance engineer and metrics engineer are all applicable titles for this role. But what really matters technically is the experience with data modeling, semantic layer tooling (dbt, LookML, etc.), validation pipeline design, reconciliation architecture, data lineage and data governance. An ideal candidate is someone who thinks about data correctness as a structural constraint, not a quality preference, where the first instinct is to stop the pipeline rather than alert and continue with bad data to land and affect stakeholder dashboards. While interviewing, it’s critical to ask candidates to describe a time they caught a metric inconsistency before it reached a stakeholder. The answer will reveal whether they think in governance terms or reporting terms. This role belongs in the data platform engineering or analytics infrastructure team, not in BI or reporting — it is mostly infrastructure work, not data visualization work. If the role doesn’t exist in your org chart, it exists somehow informally, usually as the senior data engineer whom everyone asks when the numbers don’t reconcile. Key takeaways The enterprises that are scaling faster and winning with AI in 2026 are not the ones with the best models, best-in-class AI infrastructure or large budgets. They are the ones who invested the time and effort in successfully building the governed data foundation before deploying the LLMs, and they built it because someone in the organization understood that metric governance is the fundamental data foundation that defines the nervous system of data and insights. It is an architectural property, not a configuration setting in the model, and the data practitioner who helps embed this thinking as a design strategy is the analytics engineer. The role is the need of the hour, the discipline is established, and the gap it fills is not going away as AI systems become more autonomous. The question for every CIO is not whether this role is needed, as the AI deployment failures already answer that. The question is whether you should hire for it before the next deployment hiccup. This article is published as part of the Foundry Expert Contributor Network. Want to join?
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