AI News Archive: August 28, 2026 — Part 5
Sourced from 500+ daily AI sources, scored by relevance.
- Pro.com co-founders reunite to launch OnTrade, an AI startup for the wealth management industry
OnTrade deploys AI agents to handle analytical work for wealth management firms, connecting their fragmented software systems into a single interface. The Seattle startup has been building in stealth for two years with backing from General Catalyst and Madrona. Read More
- Why finance leaders don’t fully trust AI and what they’re really checking for
There’s a specific moment every finance leader knows. A number is about to leave the building — headed for the board deck, the earnings call, or the audit committee — and right before it goes, you pause. You want to know where it came from, and you want to know it will still make sense if someone asks how you got it. That pause happens no matter what produced the number. That instinct shows up in the data too. In a Gartner survey of more than 200 CFOs, confidence across finance leaders’ top 2026 priorities averaged around 63%, while confidence in driving enterprise AI impact came in at just 36%. Leaders aren’t lacking confidence broadly. They’re confident about cost discipline and growth investment. The drop is specific to AI. It’s a broader measure than any single number leaving the building, but it points in the same direction: AI is the one place finance leaders can’t yet count on the confidence that usually comes easily. The four things every number has to pass That pause is a fast version of a test. Before you’d trust a number, you check four things: Where it came from Whether you could explain it simply Whether it would come out the same way twice Whether you could trace it back through the data if someone asked Most finance leaders have never written that test down. They’ve never had much reason to, because until now, the systems producing their numbers usually held up well enough that the check rarely turned into a real problem. AI doesn’t automatically pass that test. It can produce a plausible answer to almost anything, including things it has no real basis for knowing, and the answer looks the same whether the logic underneath is solid or made up. That’s the real source of the confidence gap. Leaders don’t doubt that AI can help. They doubt whether they could explain the answer if someone pushed back on it. The four things finance leaders already check for come down to four words: visible, understandable, repeatable, and auditable , or VURA . Those words succinctly describe what leaders were already checking for instinctually. Who owns the logic underneath Naming the test doesn’t resolve where it gets applied, though. That takes a harder answer about where the logic itself lives. Deterministic logic is defined by finance, not inferred by AI. A model can draft a variance commentary, summarize a forecast, or flag an anomaly worth a second look. It should never be the one deciding what counts as an exception, how revenue gets recognized, or which threshold triggers an escalation. Those are calls finance makes, and AI’s job is to work within them, explain them, and apply them consistently, not to invent them when it doesn’t have enough to go on. That distinction is where most AI disappointment in finance actually starts. The model usually isn’t failing at what it’s good at. The failure happens earlier: nobody defined the logic it needed, so it guessed, and it delivered that guess with exactly the same confidence it would use for a right answer. Looking at the output alone, you can’t tell the difference. That’s exactly what the four-question test catches. Ask where the number came from, whether you can explain it, whether it repeats, and whether you can trace it back to the data, and you’ll find out fast whether the AI applied logic finance defined or made something up that looks close enough. Building the standard into the workflow This is an architecture decision as much as a governance one. The four questions get easy answers when there’s a layer between raw enterprise data and the AI consuming it, one that prepares the data, holds the logic finance owns, and keeps every output traceable back to both. That’s the role Alteryx plays. It doesn’t compete with the model doing the reasoning, and it doesn’t replace the ERP or EPM system the data lives in. It’s the business logic layer that makes sure what reaches the model is something finance already stands behind, so the model’s output can be too. Build that in, and the pause before the number goes out changes what it’s doing. Instead of hoping the number will hold up, you can check that it does, every time, because the answers to those four questions are already built into how the workflow works, not something you have to reconstruct from memory. If you’re looking for a concrete way to see what that looks like on a real workflow, take a look at our AI-Ready Starter Kits : pre-built Alteryx workflows and synthetic datasets designed to demonstrate how Alteryx can be applied to specific business use cases. They prepare and structure data to produce analysis-ready outputs, which you can then extend using external AI tools such as large language models. Understanding why finance leaders hesitate to trust AI is only the first step. The next is building the governed foundation that gives AI reliable business logic to work from. Learn more in Building Finance AI You Can Trust , where you’ll explore the principles and practical steps behind AI-ready finance workflows. To learn more, visit us here .
- How Trackunit turns construction data into decisions with AI
Construction generates abundant data, from equipment telemetry and maintenance records...
Score: 45🌐 MovesAug 28, 2026https://www.databricks.com/blog/how-trackunit-turns-construction-data-decisions-ai - AI benchmarks have a trust problem and Google wants to fix it
Google Deepmind is testing a double-blind evaluation of a frontier AI model for the first time. Cryptographic protection through Confidential Space is meant to keep Google from seeing the test questions and keep evaluators from seeing the model weights. The pilot project with the Singapore AI Safety Institute uses a Gemini Flash Lite and could set a new standard for tamper-proof AI benchmarks. The article AI benchmarks have a trust problem and Google wants to fix it appeared first on The Decoder .
Score: 45🌐 MovesAug 28, 2026https://the-decoder.com/ai-benchmarks-have-a-trust-problem-and-google-wants-to-fix-it/ - HKEX Tech 100 adds Pony AI, WeRide in index revamp targeting AI stocks
Hong Kong Exchanges and Clearing (HKEX) will add 10 companies – including autonomous-driving firms Pony AI and WeRide – to its Tech 100 Index as part of a quarterly reshuffle that increases the benchmark’s exposure to artificial intelligence and other emerging technologies. Other additions include AI drug-discovery specialist Insilico Medicine, printed-circuit-board maker Victory Giant Technology, enterprise AI company Beijing Haizhi Technology and AI data-intelligence provider Mininglamp...
- China Life’s revenue surges as top insurer ramps up investment in AI, chips and biotech
China Life Insurance, the country’s largest life insurer, has posted record highs in its first-half results, and says it will scale up investments in technology-related sectors while seeking to become a long-term partner for innovative enterprises. The Shanghai- and Hong Kong-listed insurer recorded revenue of 434.3 billion yuan (US$64.6 billion), up 81.5 per cent year on year, according to interim results released on Thursday. Net profit surged more than 228 per cent to 134.5 billion yuan. The...
- Nicola Coughlan and Matt Lucas among stars backing campaign against AI voice cloning
About 80 people sign open letter to Andy Burnham calling for legislation to protect voice ownership Nicola Coughlan, Hugh Bonneville and Matt Lucas are among a group of actors backing a campaign against artificial intelligence voice cloning. Save Our Voices Now, which is also being supported by Luke Evans, Jen Brister, Siobhán McSweeney and Pearl Mackie, aims to stop the practice in which AI technology is used to replicate a real person’s voice to say anything it is prompted to. Continue reading...
- Open-weight AI companies are the Valley’s hottest acquisition targets
There's a lot of capital pouring into the business of giving models away.
Score: 45🌐 MovesAug 28, 2026https://techcrunch.com/2026/08/28/open-weight-ai-companies-are-the-valleys-hottest-acquisition-targets/ - Nvidia almighty: Chip riches flood through AI universe
Data: S&P Capital IQ Pro; Chart: Erin Davis/Axios Visuals Nvidia made billions selling AI's essential ingredient: chips. Now it's plowing those riches straight back into the AI ecosystem, betting on a buildout that craves ever more compute. Why it matters: Nvidia has become the AI industry's supplier, banker and kingmaker, feeding a self-reinforcing cycle in which chip profits finance the next wave of chip demand. State of play: Already the world's most valuable company, Nvidia is now worth more than five of the 11 sectors that make up the S&P 500. The chipmaker reported nearly $60 billion in quarterly profit Wednesday, prompting The Kobeissi Letter to call the results "the most impressive earnings in history." Nvidia believes its reign is far from over, telling investors to expect roughly 70% revenue growth even from today's extraordinary heights. Zoom out: Nvidia's vast chip windfall has enabled the company to take on a new role as financial patron of the entire AI industry. Nvidia is involved in more than $750 billion worth of AI investments, financing deals and partnerships, according to PitchBook — a staggering footprint for a company whose core business is still selling chips. That figure does not include this week's reported $13 billion acquisition of Hugging Face , which would give Nvidia control over one of the industry's most important model-distribution hubs. CEO Jensen Huang has enlisted Wall Street to mobilize more than $500 billion for AI infrastructure, channeling outside capital toward the data-center buildout that drives more than 90% of Nvidia's quarterly revenue . Between the lines: The strategy creates a powerful flywheel: The more money Nvidia helps pour into AI, the more compute the industry builds — and the more chips it needs. What they're saying: Huang argues Nvidia's expanding reach reflects a position no other company can match, calling its role in the AI market "singular." "We're the only company in the world that ... offers an entire AI factory platform," Huang said on Wednesday's earnings call. "Most companies just don't have the skills to do that." Reality check: Critics say Nvidia's flywheel looks uncomfortably circular. The company is helping finance customers and infrastructure projects that then spend heavily on its own hardware, raising questions about how much demand is being supported by Nvidia's own balance sheet. Huang has dismissed those concerns, telling CNBC Wednesday that Nvidia's investments will generate "tremendous returns" and that "the risk is low." Threat level: The cozy relationship between Nvidia and its biggest customers is becoming increasingly competitive. OpenAI, Google, Amazon, Microsoft and others are developing their own custom chips designed to reduce their dependence on Nvidia. OpenAI claims its new Jalapeno chip outperforms Nvidia hardware on some workloads. Nvidia, meanwhile, is spending billions developing its own open-source AI models, aiming to become the American champion in a field increasingly dominated by Chinese models. The bottom line: The result is an unusually tangled ecosystem in which Nvidia is simultaneously supplier, investor, partner — and increasingly competitor — to AI's biggest players.
Score: 45🌐 MovesAug 28, 2026https://www.axios.com/2026/08/28/nvidia-ai-chip-circular-finance-startups - Software and data firms rush to meet customers inside the chatbot
A new Salesforce plugin for Claude shows how software and financial services firms are adapting to the AI era, but monetization remains an open question.
Score: 45🌐 MovesAug 28, 2026https://www.semafor.com/article/08/28/2026/software-and-data-firms-rush-to-meet-customers-inside-the-chatbot - How to Modernize IT with Confidence: A straightforward guide to cloud, AI, and security readiness, powered by Dell and Intel
How to Modernize IT with Confidence: A straightforward guide to cloud, AI, and security readiness, powered by Dell and Intel IT Pro
- Cities terminate Flock contracts at record pace in August
Cancellations have accelerated.
Score: 44🌐 MovesAug 28, 2026https://arstechnica.com/tech-policy/2026/08/cities-terminate-flock-contracts-at-record-pace-in-august/ - Unsafe at any speed: AI optimists are turning cautious as safety concerns mount
And as for getting US government to help – uncontrolled AI is not as dangerous as AI under the control of the technically clueless
- Frontier AI access is a growing national security question for the UK
Two recent events should dramatically alter thinking and decision-making about the use of overseas AI models in the UK's critical national infrastructure, argues a new white paper from the University of Surrey. First, the U.S. government has demonstrated that its powerful AI models can be withdrawn from service by government dictate.
- Shared-memory AI system lets microscope components coordinate in real time
Arco Bast studies how neurons communicate. Earlier this year, the Janelia postdoc encountered a more mechanical version of the same problem: The components of his custom microscope could not easily communicate with one another.
Score: 44🌐 MovesAug 28, 2026https://techxplore.com/news/2026-08-memory-ai-microscope-components-real.html - Is ChatGPT down for you? Here’s what’s going on (Update: Back up)
OpenAI has acknowledged the issue and is working on a fix.
- Kimi K3 Tops New Benchmark of AI Models for Geological Reasoning
Kimi K3 Tops New Benchmark of AI Models for Geological Reasoning azcentral.com and The Arizona Republic
- Legal AI Gets an Upgrade: Inside LexisNexis' New Innovation Lab
In this episode of Legal Speak, LexisNexis CTO Greg Dickason discusses how LexisNexis' new Customer Innovation Lab in New York City brings legal customers, engineers and designers together to shape future products, alongside technology partners including OpenAI and Amazon Web Services.
- Kyndryl, Broadcom deepen partnership to build AI-ready private clouds
Kyndryl, Broadcom deepen partnership to build AI-ready private clouds verdict.co.uk
- How Toronto is using Clariti’s AI to speed up building permit approvals
Vancouver-based Clariti’s software, CivCheck, flags mistakes in initial applications. The post How Toronto is using Clariti’s AI to speed up building permit approvals first appeared on BetaKit .
Score: 44🌐 MovesAug 28, 2026https://betakit.com/how-toronto-is-using-claritis-ai-to-speed-up-building-permit-approvals/ - What happens when information theory accounts for reasoning?
What happens when information theory accounts for reasoning?
Score: 44🌐 MovesAug 28, 2026https://research.ibm.com/blog/information-theory-meaning?utm_medium=rss&utm_source=rss - The CFO’s playbook for building AI-ready finance data
Every CFO I talk to right now is under some version of the same pressure: the board wants AI, the business wants faster answers, and the finance team is often still reconciling spreadsheets. The promise of AI in finance is real. But so is the gap between that promise and what most organizations are able to deliver. I believe finance leaders need to be asking not simply, “How do we use AI?” but “What would make our data trustworthy enough for AI?” That distinction matters. AI-ready finance data is intentionally shaped for a specific business outcome, so we can trust what AI produces from it. In finance terms, it’s the difference between having transactions and being able to defend the numbers. Finance data is uniquely messy, and important Finance data is messy for rational reasons. We pull from multiple systems — ERP, CRM, payroll, procurement, planning tools, banks, data warehouses, and yes, still spreadsheets. We live through reorgs, acquisitions, new products, and chart of accounts changes. And when the business cannot wait, we create manual workarounds to keep moving. That complexity is the context in which we’re now being asked to use AI. It’s no wonder that so many initiatives stall. The non-negotiables of AI-ready finance data When Alteryx talks about AI-ready data , I translate it into a few non-negotiables. For finance leaders, this is where the concept becomes practical. Purpose-built, not “all the data” – AI-ready data should be scoped to the decision or workflow at hand. If I am building a cash forecast, I do not need every field from every ledger table. Clean and standardized – AI does not politely ignore bad inputs; it often amplifies them. That means your data needs to be deduplicated, standardized across dates, currencies, and units, and mapped to consistent hierarchies. Combined across sources, with business context – Finance work is inherently cross-source. AI-ready data is joined and enriched so the dataset reflects business reality, not just system silos. Traceable and transparent – This is where finance leaders should push harder than anyone else. AI-ready data has lineage . It is auditable and explainable, not just at the output layer, but in the data shaping behind it. Governed and controlled – AI readiness is about data risk management as much as data quality. AI-ready data should live inside a governed process, not a series of hero spreadsheets and copy-paste steps. Maintainable as the business changes – This is one of the hidden killers of AI initiatives. A one-time cleaned dataset is not AI-ready if it breaks the minute a new subsidiary is added, a cost center structure changes, or a revenue stream appears. AI-ready data has to be built through workflows that can be updated and re-run reliably, not through one-off cleanups. Where AI-ready data creates value in finance This is where the concept becomes real. AI-ready data is the difference between value and noise in some of finance’s most important workflows, including: Close acceleration: When trial balance data, mappings, intercompany logic, and exception rules are standardized, finance can generate more dependable variance flags and automate more of the financial close and reconciliation process. Cash forecasting: Better-connected bank data, AR/AP, billing schedules, and seasonality drivers make forecasts less likely to be derailed by missing or misclassified transactions. Anomaly and fraud detection: Clean, aligned vendor master data, payment runs, approval chains, and PO matching help teams reduce false positives and investigate issues faster. Revenue quality and leakage: When contracts, invoices, usage, CRM data, and credit logic are brought together in a way that reflects the actual economics of the business, AI can help surface patterns that matter. Narrative reporting: Grounding LLMs in curated, reconciled variance drivers and approved definitions allows teams to draft commentary responsibly within clear guardrails. Filling the AI data readiness gap I’ve found that in most organizations, there’s a constant friction point between data engineering and finance. Engineering understands the architecture, pipelines, and platforms. Finance understands the business context and logic — how revenue is recognized, how allocations work, where the exceptions hide. The handoff between those groups is often slow and messy. Analysts build fragile workarounds. Engineering teams inherit backlogs of finance requests that are actually business critical. What resonates with me about Alteryx is that it sits in that gap. It enables finance and business analysts to build repeatable data workflows for extracting, cleaning, joining, enriching, and shaping data for specific finance use cases. It emphasizes transparency and traceability, and it supports a model where IT can govern, and finance can execute. Just as importantly, it helps organizations turn their existing ERP, warehouse, and cloud investments into outputs that are actually usable for analytics, automation, and AI. How to get started If you want to make progress without boiling the ocean, my practical advice is simple: start small and start right. Pick one workflow that is high pain and highly repeatable (recs, allocations, forecasting inputs, reporting packs). Define what “trusted” means: the reconciliation rules, thresholds, approvals, and audit trail you need. Build the AI-ready dataset first cleaned, joined, governed, and repeatable. Then add AI where it makes sense (classification, summarization, exception explanation) inside the workflow, not as a free-floating tool. My bottom line is this: AI-ready data is an operating standard. It is how we scale AI without scaling risk. And for CFOs, that should be the real objective, not chasing the latest tool, but building the trusted data foundation that makes smarter automation, better decisions, and more resilient finance performance possible. To learn more, visit us here .
Score: 44🌐 MovesAug 28, 2026https://www.cio.com/article/4213439/the-cfos-playbook-for-building-ai-ready-finance-data.html - I've tested dozens of robot vacuums - this new $599 Roborock has all the features I need
The Roborock Qrevo 2 Pro is a midrange robot vacuum and mop with a hands-free cleaning experience.
- Matt Lucas and Hugh Bonneville among actors calling for law on AI voice cloning
The performers are asking the government to give every person in the UK a legal right to own their voice.
Score: 44🌐 MovesAug 28, 2026https://www.bbc.co.uk/news/articles/c4gv5gepxnyo?at_medium=RSS&at_campaign=rss - Reading the Body’s Signals: AI’s Ultimate Healthcare Opportunity
Reading the Body’s Signals: AI’s Ultimate Healthcare Opportunity MedCity News
Score: 44🌐 MovesAug 28, 2026https://medcitynews.com/2026/08/reading-the-bodys-signals-ais-ultimate-healthcare-opportunity/ - fileAI expands in Japan with new backing from SMBC and Singtel Innov8
Enterprise AI has spent the past two years trying to escape the demo room. For banks, insurers, manufacturers and telecom operators, the hard part is not producing a clever chatbot, but getting artificial intelligence to work reliably inside old, messy and highly regulated systems. Singapore-based fileAI is building for that less glamorous, but more valuable, […] The post fileAI expands in Japan with new backing from SMBC and Singtel Innov8 appeared first on e27 .
Score: 43💰 MoneyAug 28, 2026https://e27.co/fileai-expands-in-japan-with-new-backing-from-smbc-and-singtel-innov8-20260828/ - Tech bills of the week: identifying sponsored content in AI tools; Reshoring biotech manufacturing; and more
This week’s bills seek to label sponsored content in AI platforms, fortify emerging technology supply chains and spur adoption of open-weight models.
- Roborock’s new robot vacuum can remove its own mops before cleaning carpets
Roborock's new Qrevo 2 Pro robot vacuum can leave its wet mop pads at the dock before moving onto carpet, while its dock handles much of the maintenance afterward.
Score: 43🌐 MovesAug 28, 2026https://www.digitaltrends.com/home/roborocks-new-robot-vacuum-can-remove-its-own-mops-before-cleaning-carpets/ - Chinese Companies Are Unleashing AI-Powered Robo-Chefs
"What we are trying to do is turn the machine's parameters into numbers, and use that to standardize every single bowl." The post Chinese Companies Are Unleashing AI-Powered Robo-Chefs appeared first on Futurism .
Score: 43🌐 MovesAug 28, 2026https://futurism.com/robots-and-machines/chinese-companies-ai-robot-cooking-beijing - Can AI help the Marines simplify sustainment in the Pacific?
The service is testing a “logistics decision engine” at the theater level.
Score: 43🌐 MovesAug 28, 2026https://www.defenseone.com/technology/2026/08/can-ai-help-marines-simplify-sustainment-pacific/415709/ - The deepfake threat and beyond: 3 unconventional security crises every founder-led brand must prepare for
My journey began in engineering and corporate leadership, but a deep inner emptiness led me to seek God’s guidance, ultimately discovering an unexpected calling in the healing power of plants and holistic medicine. With only faith, perseverance, and S$10,000 in savings, I left a secure career to pioneer practitioner-grade Western herbal medicine in Singapore, overcoming […] The post The deepfake threat and beyond: 3 unconventional security crises every founder-led brand must prepare for appeared first on e27 .
- Where enterprise intelligence really comes from
Every new frontier model release seems to spur a fresh round of doomsday articles. Just Google “the end of white-collar jobs,” and you’ll be bombarded with discourse on the end of modern work, the unraveling of the social contract between employees and organizations. What I don’t see anyone talking about, however, and what I believe is a far more productive conversation, is the opportunity for knowledge workers. Nobody understands critical business processes better than your line-of-business (LOB) employees. Not executives. Not IT. Not even the most advanced LLMs. These are your business analysts and RevOps professionals, your supply chain managers and finance leaders, and the employees whose expertise has been forged over decades. For an enterprise to become truly intelligent, these workers must be involved in how AI workflows are built and deployed. Their guiding hand is the only way AI can learn and truly understand your business. But what does this transition look like, and how can organizations start operationalizing AI in a meaningful way alongside knowledge workers? Let’s take a look. What enterprise intelligence requires Imagine walking your board through a set of financials and recommending specific actions. Then, in your next meeting, you walk everything back because your AI layer got the numbers wrong. There is no faster way to kill an AI initiative than by delivering wrong outputs. Without trust, the whole system falls apart. In our recent survey of 1,400 business and IT leaders, we found that while over 90% of organizations are using AI, only 28% trust it to support decision-making. As for how many organizations scaled their AI pilots into production, the number was just under 25%, suggesting a very strong correlation between trust and operationalization. An intelligent enterprise, then, is an organization that has trustworthy AI embedded across the business. At Alteryx, we say the results of any AI system must follow our VURA framework: an AI system and its outputs must be visible, understandable, repeatable, and auditable. In other words, two people need to be able to go to AI with a question and arrive at the same answer; anyone who uses AI in their workflows must be able to explain how their AI system arrived at that answer. Who’s responsible for operationalizing AI? Enterprise intelligence is about trustworthy AI deployed throughout key business processes, but who’s ultimately responsible for these AI systems and processes: IT teams or knowledge workers? Let’s say you want to use AI in your Sarbanes-Oxley process, e.g., your journal entries , revenue recognition, access controls, etc. Before IT can help you build a new AI workflow, IT must first understand your Sarbanes-Oxley process in great detail. Then, they have to code a tool your finance team can trust. It’s possible, sure. But creating this solution would take an inordinate amount of time. Then, when a new regulation comes along or you have an acquisition, the whole thing falls apart. You have to get back in line with IT to retune everything. Moreover, if your books don’t balance out or if you fall out of compliance, IT does not want to have that responsibility fall on them. You can see why ownership of AI systems and workflows must sit with LOB workers. They are the only ones with the expertise to ensure the veracity of AI’s outputs. They are the only ones who can successfully shape and define its logic and oversee its ongoing execution. Data is the fuel. Business logic is what keeps AI on course. Finally, there’s the question of data. We’ve all heard “bad inputs, bad outputs.” Seeing as I’m the CEO of a data analytics company, you might expect me to say that reliable data is the end-all, be-all when it comes to trustworthy AI outputs. And while it’s absolutely essential, it’s only the first step. Aggregating your enterprise data into a cloud data platform is immensely useful. All of that data becomes readily accessible. You gain a single source of truth across teams and workflows. But you can’t point your LLM at a cloud data platform and ask it to make sense of your data for a complex business process. Again, you need the people who understand these critical processes to guide your LLMs to interpret the right data in the right way. This is what will make your AI systems visible, understandable, repeatable, and auditable. Yes, you need clean, reliable data. But more than that, you need business logic around that data, and that can only come from your knowledge workers. The five pillars of enterprise intelligence At the highest level, enterprise intelligence rests on five core pillars: Trustworthy, transparent data Empowered business analysts Shared responsibility across the C-suite Cross-functional collaboration Leadership that evolves alongside AI Each pillar reinforces the same core idea: AI only becomes valuable when it’s grounded in reliable data, shaped by real business expertise, supported by executive ownership, and scaled across teams that can put it to work to improve their daily processes. Tap into the intelligence all around you As a business leader looking to build an intelligent enterprise, the most important questions you can ask are the ones around operationalizing AI in key business processes. What would it take for you to trust AI’s outputs? What would make AI-powered processes superior to your current ones? Once you have those answers, engage your LOB workers immediately. Give them ownership and autonomy. Rather than asking AI to replace them, lean into their intelligence. Let your knowledge workers use their expertise to amplify, shape, and govern AI. Their business mastery is what makes enterprise intelligence possible. To learn more, visit us here .
Score: 43🌐 MovesAug 28, 2026https://www.cio.com/article/4215181/where-enterprise-intelligence-really-comes-from.html - Younger workers are more scared of AI than older employees, survey finds
Employee attitudes toward AI are closely tied to their age and generational concerns, according to Glassdoor.
- Gnani unveils sovereign AI stack ‘Artha’ featuring open-weight model, enterprise agents
Gnani unveils sovereign AI stack ‘Artha’ featuring open-weight model, enterprise agents
- Big Tech market power will cause UK to lose AI race, think tank warns
Britain's market watchdog criticized for not creating the conditions for competition to thrive
- BlackBerry lost the phone war. Now it's betting on cars and robots
BlackBerry CEO John Giamatteo joins CNBC's Arjun Kharpal on The Tech Download podcast.
Score: 42🌐 MovesAug 28, 2026https://www.cnbc.com/2026/08/28/blackberry-cars-robots-tech-download.html - Language was never the problem: Inside SEA’s real AI adoption gap
Ask most people what is holding back AI adoption in Southeast Asia, and the answer usually circles back to language. Bahasa Indonesia, Vietnamese, Thai and Malay are still treated as the great unsolved frontier for global models, the assumption being that once AI speaks the region fluently, enterprises will follow. Kai Yong Kang, Partner at […] The post Language was never the problem: Inside SEA’s real AI adoption gap appeared first on e27 .
Score: 42🌐 MovesAug 28, 2026https://e27.co/language-was-never-the-problem-inside-seas-real-ai-adoption-gap-20260827/ - Beatport blocks fully AI-generated music from its DJ marketplace
Effective immediately, the DJ marketplace Beatport is banning music that is entirely or largely generated by AI. The article Beatport blocks fully AI-generated music from its DJ marketplace appeared first on The Decoder .
Score: 42🌐 MovesAug 28, 2026https://the-decoder.com/beatport-blocks-fully-ai-generated-music-from-its-dj-marketplace/ - ChinaJoy 2026 shows how deeply AI is becoming embedded in gaming
Tencent, Mihoyo, and other game companies are taking different paths toward AI, but increasingly see it as a competitive capability.
Score: 42🌐 MovesAug 28, 2026https://kr-asia.com/chinajoy-2026-shows-how-deeply-ai-is-becoming-embedded-in-gaming - My Grantmaking Strategy for Surviving Superintelligence
AI is humanity's first through fifth largest problem, but one stands head and shoulders above the rest. Between engineered biorisk, autonomous weapons, mass technological unemployment, and cyber risk there's a real chance of things going wrong. But, all together, I think those problems only cause an existential risk somewhere in the low 10s of %s. Unaligned ruthless superintelligence , on the other hand seems like it would near-certainly cause an existential catastrophe [1] at anything like current levels of alignment theory, and that kind of unaligned superintelligence seems the default outcome of the transition away from AIs trained mostly to mimic patterns in human text towards lots of RL and continuous learning and the capabilities growth from massive investment . As a result, my grantmaking strategy is focused narrowly on interventions which seem like they might help delay or avert unaligned superintelligence, especially those that increase the odds of aligned superintelligence coming first. Classes of project I'm interested in Technical AI safety work that is sufficiently ambitious that it might apply even to strongly superintelligent systems. Such as Orthogonal [2] , Vanessa's agenda at ALTER , Abram and Sam 's work at MIRI then AFFINE , Richard Ngo , John Wentworth , some of the work at Resolution , PrinceInt , and Iliad Work to improve the chances of automated research systems being used to develop theory which is aiming towards proven alignment properties which aim to hold for strongly superintelligent systems, not just automating incremental empirical work. Resolution ? Strategic red teaming of Anthropic's plan? Attempts to prepare for worlds where timelines end up longer and there's more time to do ambitious theory work. e.g. Alignment Continuity Projects which improve the onboarding funnel , including courses, resources, and other materials that help people understand the risks of superintelligent AI (not just AI risk broadly). [3] e.g. AFFINE Seminar , Lens Academy , AISafety.info Attempts to delay the advent of superintelligent AI , despite it being the most economically productive and militarily powerful technology of all time. Work which clarifies and communicates the strategic situation e.g. IABIED , AI 2027 Classes of grantee I'm excited by Agentic individuals following strong inside views e.g. Towards Superintelligence Alignment , Steve Byrnes Highly competent people who are confused about what to do and will spend time figuring that out and upskilling and developing inside views rather than having a plan in advance. [4] People who have built useful things before seeking funding e.g. AI Plans , Mangrove Scrappy, money-efficient projects doing interesting/high leverage work AISafety.com , AI Safety Camp , AI Safety Quest Things I am mostly not excited by Evals [5] e.g. METR Interpretability [6] Control [7] ' Science of ML'-flavour theory without a clear backchained path to helping a lot with superintelligence alignment [8] Academics who didn't evaluate (or disagreed with) the arguments for misaligned superintelligence risk and therefore are not addressing what I see as the core bottlenecks to survival e.g. Reform AI Safety Undirected / unopinionated / deferring to consensus or Uniformitarian field-building , [9] especially the kinds that end up being largely a pipeline to the labs or fails to teach the big picture strategic situation e.g. MATS , BlueDot [10] Anyone with flags of being low integrity (including epistemic integrity) or who don't maintain good reputation through their actions and character. Classes of thing I don't consider particularly important Adverse selection. [11] Projects which would look weird to low context onlookers . [12] Mild CoIs , on the level of being friends or having collaborated with the grantee. [13] Context & me as a grantmaker A private donor has bought me into Lightcone Commons . I welcome more people boosting my regranting pool, [14] and expect to find good use for significantly more funds than I have been allocated so far. In my personal grantmaking [15] I have historically preferred to fund people I have seen doing good things in the wild rather than people who have good applications, tended to have at least a short call with most people I fund, positioned myself as a helpful mentor rather than overbearing assessor, and often provided extensive incubation, and networking for projects I have funded. Please don't pitch me via DMs, instead apply to Lightcone Commons and grantmaking.ai [16] . I'll be searching for the terms ' superintelligence risk' and 'superintelligence alignment' in applications and expect to read any application which uses those words. Also, unless I am allocated further funds, I will mostly be giving out grants in the sub $50k range, so individuals and very small/frugal orgs only. ^ Likely on the level of '... there will be no Earth and no biological life, but only a rapidly expanding sphere of darkness eating through the Milky Way as the AI reaches and extinguishes or envelops nearby stars. ' ^ Italic indicates organisations I have donated to with personal funds and/or spent significant time volunteering for. ^ Specifically, I think the Catastropist -end of funnel building has not been developed anything like as well as the more prosaic side. ^ Hard to identify with personal connection or having seen them excel in other domains. ^ Evals can give you warnings, but kind of fundamentally can't solve the problems of misaligned superintelligence, have major issues with situational awareness, and are imo massively overinvested in plus have notable capabilities externalities. ^ Interpretability has an ordering problem afaict, where it opens the black box enough to allow it to be optimised into world-ending capabilities before it is able to open the black box enough to align them, and therefore is mostly harmful to fund . Connor has a great talk about the challenges of using MI for AGI safety . There are rare exceptions, like I think Lucius 's work is plausibly the kind of thing that generates critical insights, but I mostly don't expect to fund interp. ^ Openly acknowledged by the founders of the field of control as not sufficient for superintelligence. Also, I think somewhat likely to backfire horribly in ways I keep meaning to write up. ^ This field risks being a potent capabilities enhancer / timelienes shrinker without sufficient gains to alignment to compensate. ^ Given that my previous categories excluded the vast majority of current work, it should not be a surprise that I am not excited about field builders who are going along with the current rather than building strategy formed by strong inside views. ^ They seem to be not just splitting attention between many AI risks, but specifically not including superintelligence misalignment risk and assuming business as usual on at least some courses . ^ What I have observed in personal grantmaking doesn't look like an efficient market where opportunities passed up by major grantmakers are often lemons , it looks like a lot of the best low cost opportunities going unfunded while huge amounts of money are poured into high legibility prestigious projects (with an often very questionable sign of impact on superintelligence risk). I think people focusing on adverse selection probably mostly in effect are managing PR in a way I expect to be net negative. ^ I am not nervously concerned about which stories might look bad to a very low context person, and side with e.g. the people who funded giving out lots of copies of HPMOR to math olympiads as an outreach program, not the people who thought that looked weird and bad. I do however have a strong filter for people who read as high dark triad or low integrity, because those are correlated with harmful mistakes and negative impact ^ My life kind of revolves around AI x-risk reduction and people I think are doing good work are often people I want to collaborate with, and therefore end up friends with, and also get to observe for skill and good character over a longer time. However, I will not grantmake to serious CoIs like partners or people who are plausible romantic interests. ^ Habryka is happy to onboard people planning to donate at least $50k this coming round, smaller donations can go via ARMF . I am happy to forward the normal 2% fee on to grantees rather than taking compensation, at least for the first round, as I still have adequate personal runway and have typically used spare money for donations anyway. I am also happy to share my identity with people who are seriously interested in grantmaking through me, please reach out via LW DMs. ^ I used to be a crypto millionaire before spending down and donating the vast majority of my wealth over the past 7 years of working full time uncompensated on reducing AI x-risk. ^ My sponsor has a preference for publicly viewable applications. Discuss
Score: 42🌐 MovesAug 28, 2026https://www.lesswrong.com/posts/whToGm8WFRqpHiFCB/my-grantmaking-strategy-for-surviving-superintelligence-1 - The test every AI explanation in finance has to pass
Say your reconciliation tool flags a break between two ledgers, and now there’s a number that needs an explanation. The AI-generated summary says the mismatch is a timing difference, transaction posted late on one side. Reasonable. You move on. Then your controller asks which transaction, on which date, and why it posted late instead of on time. And now you’re not looking at an explanation anymore. You’re looking at a sentence that sounded like one. The four part test behind every AI answer That gap is the same thing the last piece here named: can you explain where the answer came from, and would the explanation survive someone pulling on it? Most practitioners have been running that check for years, on spreadsheets, on junior staff’s work, on their own numbers before a review meeting. AI just hands you answers that sound complete far more often now, and faster than the checking can keep pace with. The test itself breaks into a few plain questions, and it’s worth naming them because most people run all four without thinking about them separately: Visible: Can you see where the number came from? Understandable: Do you actually understand the logic that produced it, or just the sentence describing it? Repeatable: Would the same input produce the same answer next time, or is this a one-off? Auditable: Could someone other than you retrace it if they had to? Four different failure modes, and an AI-generated explanation can fail any one of them while still reading like a good answer. Why the gap is widening faster than the checking The reconciliation example holds up because it’s ordinary. Nobody’s arguing AI shouldn’t touch reconciliation work. Matching balances, drafting a first-pass explanation for a variance, flagging what needs a human look — that’s real time back. The problem isn’t the AI doing that work. It’s that the logic behind “this is a timing difference” has to already be defined somewhere the AI can point to. If it isn’t, the model is pattern-matching its way to something plausible, and plausible is not the same as traceable. Deloitte’s Finance Trends 2026 survey of over 1,300 finance leaders found 63% have fully deployed AI in their departments, with only 21% reporting clear, measurable ROI. That’s a broader adoption figure than an explanation-quality study, but the gap it points to lines up with the reconciliation example: plenty of AI running, not much of it yet standing up to scrutiny. Where the logic has to live Closing that gap starts with what the AI is drawing from in the first place, before it ever produces an answer. Every explanation an AI generates borrows its logic from somewhere: a threshold for what counts as material, a rule for what makes something a timing difference, an assumption about which system wins when two ledgers disagree. When that logic lives only as a pattern the model has inferred from past examples, the explanation is a guess dressed in confident language. When it’s defined, owned, and applied the same way every time, the AI has something real to summarize. Finance has kept this kind of logic for as long as the job has existed, often in a spreadsheet somebody built years ago that everybody trusts without fully remembering why it works. That logic hasn’t changed. Who can now touch it, and how fast, has, and that means the definitions underneath it need to hold up to more traffic than they ever have before. Get that part right, and the reconciliation example flips. The AI’s explanation becomes a summary of logic that was already defined, applied consistently, and traceable back to where it came from — the version that survives the follow-up question. See trusted AI workflows in action If you want to see what that looks like in a live workflow rather than in the abstract, Alteryx’s AI-Ready Starter Kits are pre-built Alteryx workflows and synthetic datasets designed to demonstrate how Alteryx can be applied to specific business use cases. They prepare and structure data to produce analysis-ready outputs, which can be extended using external AI tools. The Reconciliation Exception Resolution AI-Ready Starter Kit shows the pattern from this piece in practice: exceptions routed to an owner, prioritized by materiality, and documented consistently enough that the resolution holds up when someone asks how you got there. To learn more, visit us here .
Score: 42🌐 MovesAug 28, 2026https://www.cio.com/article/4213433/the-test-every-ai-explanation-in-finance-has-to-pass.html - No special carve-out for states using fossil fuel to power datacentres, Chris Bowen insists, despite new conditions
Energy minister says states can apply to use coal and gas supply to power datacentres but must prove it is cheaper than renewables Follow our Australia news live blog for latest updates Get our breaking news email , free app or daily news podcast Chris Bowen insists federal Labor’s new national laws on datacentre power supply will not include carve-outs for Queensland and the Northern Territory, with states that are eager to use coal and gas required to make their case to the national regulator. The energy minister told Guardian Australia on Friday the only concession agreed at Wednesday’s meeting of national cabinet was that existing coal and gas supply could be used on the condition it could be shown to be cheaper than renewables. Continue reading...
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Score: 41🌐 MovesAug 28, 2026https://devops.com/cybersecurity-researchers-uncover-flaw-in-google-ai-coding-tool/ - Sigenergy Delivers Strong H1 2026 Performance as AI-Driven Energy Innovation Powers Global Growth
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