AI News Archive: July 23, 2026 — Part 11
Sourced from 500+ daily AI sources, scored by relevance.
- Ajman becomes first UAE government to use Agentic AI for customer transactions
Ajman becomes first government in UAE to launch headless government services powered by Agentic AI
- Ajman uses agentic AI to renew trade licence in UAE government first
Ajman uses agentic AI to renew trade licence in UAE government first thenationalnews.com
- Apple Store App May Soon Use an AI-Powered Assistant to Help Sell You Things
Apple Store App May Soon Use an AI-Powered Assistant to Help Sell You Things PCMag
- Colman Domingo responds to Elon Musk planning a historically accurate AI Odyssey
"Jimmy Kimmel Live!" guest host Colman Domingo has reacted to Elon Musk saying he wants to create his own "historically accurate" AI version of "The Odyssey".
- Elon Musk’s AI Version of 'The Odyssey'
Musk has promised to have his AI model make a “historically accurate” version of The Odyssey and has expressed a willingness to give Mel Gibson $100 million for another kind of adaptation. Here’s what we think a Musk-produced Odyssey could look like.
- Inside the Growing Backlash Against Expansive AI Data Centers
There is growing backlash against the thousands of AI data centers popping up around the country that require a great deal of land, power and water. It comes as the White House prepares to unveil a plan to curb the skyrocketing power costs associated with the technology’s rapid growth. NBC’s Ryan Nobles reports for TODAY.
- AI Infrastructure Boom Creates New Global Logistics Challenge, Industry Leaders Say
AI Infrastructure Boom Creates New Global Logistics Challenge, Industry Leaders Say azcentral.com and The Arizona Republic
- Trump confirms date for visit from China’s Xi to talk AI: ‘Biggest thing anybody’s ever seen’
Xi’s state visit will be his first to Washington since the Obama administration
- Trump confirms date for visit from China’s Xi to talk AI: ‘Biggest thing anybody’s ever seen’
Xi’s state visit will be his first to Washington since the Obama administration
- AOC’s deepfake AI porn bill sailed through the Senate. She fears House Republicans will let it die to spite her
‘I don't think that we should be emboldening child pornographers and and helping abusers just because of some personal beef that they may have,’ the New York Democrat tells Eric Garcia
- 23 governors sign Trump's cost pledge for data centers, backbone of AI
23 governors sign Trump's cost pledge for data centers, backbone of AI azcentral.com and The Arizona Republic
- 60% of search journeys now involve AI. Here is what that means for brand visibility.
60% of search journeys now involve AI. Here is what that means for brand visibility. Raleigh News & Observer
- Pastor sues OpenAI alleging ChatGPT's medical advice nearly killed him
The case accuses OpenAI of practicing medicine without a license and failing to safeguard users seeking health advice.
- A Florida Pastor Is Suing OpenAI, Claiming ChatGPT Gave Him 'Extremely Dangerous' Medical Advice
A Florida Pastor Is Suing OpenAI, Claiming ChatGPT Gave Him 'Extremely Dangerous' Medical Advice entrepreneur.com
- Man Sues OpenAI, Alleging ChatGPT Gave Him ‘Dangerous Medical Advice’
Scott Winters alleges ChatGPT misdiagnosed him with dysautonomia. He later suffered a “massive pulmonary embolism,” according to the lawsuit.
- Q&A: Google’s AI and computing chief talks about its shapeshifting data centers
Google’s AI offerings span its internal and cloud offerings. Its data centers are processing seven times more AI tokens compared to last year. To keep up, Google is upgrading its data-center hardware and software technologies at a faster clip. It plans to raise $80 billion to build new data centers. (See related story: Google transforms its data center architecture for agent era ) Network World spoke with Mark Lohmeyer , vice president and general manager of AI and computing at Google, about how the company’s infrastructure is keeping pace with AI demand. Network World: What is the primary shift in infrastructure needs? Mark Lohmeyer: We’ve seen the rise of agents and agentic use cases . Years ago, it was the chat phase: Ask a question, get an answer. Now we’re in the agentic era, where you express your intent, agents spin off multiple sub-agents, working in parallel, preserving state. This is a radical shift in what infrastructure needs to do; make them fast, cost effective, secure, reliable. We’re delivering infrastructure optimized for the age of agents. NW: What’s the goal of the infrastructure buildout, and what should customers expect regarding costs? ML: Ultimately, it’s about enabling customers with leading-edge capabilities and models at scale cost-effectively. With agents, inference transactions increase by 50x, 100x versus non-agentic workloads. We’re driving the cost per transaction down exponentially. In our latest platforms, we reduce the cost by almost 2x for the same work. Customers serve twice the number of users at the same cost, directly driving profitability. NW: How are you addressing energy efficiency? ML: Energy is a critical resource, and Google has optimized for years. We design data centers and compute [to drive] high PUE (power usage effectiveness). We introduced liquid cooling over five years ago, and these latest systems are all liquid cooled. For agentic workloads, CPUs come to the forefront… orchestrating agents, calling tools, doing evaluation loops in reinforcement learning. Our latest Axion-based CPU platform called N4A has energy efficiency and is significantly better than the prior generation and x86 comparables. NW: How do you think about token efficiency as you build-out systems? ML: Performance and efficiency gains are powered by co-design of the model and infrastructure. Gemini is trained on TPUs, primarily served on TPUs with high frontier model capability, in a token and cost-efficient way. This stems from co-design across the full stack. NW: How do you project what infrastructure will be needed years in advance? ML: Hardware cycles deliver a new next generation roughly every year, but design cycles are two years or more in advance. We work with DeepMind doing core research, to application teams taking models into production, to billions of users, to our team building infrastructure. We work upstream with DeepMind and application teams to understand what’s coming. Agents weren’t being broadly spoken of externally, but internally we had those insights around what they would need. That shows up in hardware design. We hit the timing right — these platforms are built for agents. NW: What’s the eighth generation TPU platform? ML: We deliver new platforms every year, and ones launched years ago are close to 100% utilized because demand for AI-optimized compute is high. The eighth-generation TPU platform is the first delivering two complete systems, from the chip all the way up to the network and storage and software, that are optimized. TPU-8t is optimized for training, and TPU-8i is optimized for inference. For TPU-8i, we increased SRAM on the chip to 384MB — three times the prior generation — and increased the HBM by 50%. NW: How are you approaching GPU and TPU compatibility? ML: People in a single cluster do not commingle GPUs and TPUs. We offer both options based on specific workload needs. We’ve been investing on the TPU side in using software frameworks customers are comfortable with on GPUs and enabling those on TPUs. For example, PyTorch and vLLM. Customers could have a pool of GPUs and TPUs, running vLLM on top of that. Start with a workload on TPUs, but if the TPU pool is fully utilized, spill to GPUs or vice versa. This works because it’s all leveraging the same compatible software layer on top. NW: How has the orchestration platform changed for agents? ML: Kubernetes is becoming the orchestration platform of choice for AI. Google is transforming GKE [Google Kubernetes Engine] into an agent-native orchestration solution. When expressing intent to an agent and it spins up multiple sub-agents, compute needs to spin up rapidly — TPUs or GPUs — without long delays, then run and spin back down. We’re optimizing at every layer of the GKE stack : significantly improving node startup time and how rapidly we start and stop containers. Lovable demonstrates this with GKE, spinning up hundreds of sandboxes for live coding sessions on their platform in parallel, paying for infrastructure when needed. NW: What is the role of the network and storage infrastructure? ML: The network is critical for AI. This requires creating large-scale clusters of GPUs or TPUs and enabling them to talk to each other in a high-performance way. We created the Virgo network — a collapsed network architecture, non-blocking within a data center, where multiple pods or NVLink72 domains connect together. In TPU8T, we can connect over a million TPUs together leveraging Virgo, creating large-scale, high-performance, reliable clusters that shrink innovation cycles. Storage is equally critical. In large-scale clusters, something is always failing. The ability to take snapshots and go back to a checkpoint is important. We’ve introduced Managed Lustre 10T , with 10 terabytes per second of bandwidth, 18 petabytes of storage in single clusters. This is 10 times faster than last year and 20 times faster than competition. We have Rapid Bucket, low-latency storage backed by Google storage systems. Both are impactful in large-scale training environments. NW: How does KV cache strategy differ between training and inference? ML: For TPU-8i , we increased SRAM on the chip to 384 megabytes — three times the prior generation — and increased the HBM by 50%. Storing KV cache directly in chip memory allows responding to inference requests much more rapidly and cost-effectively than going to an external system. For inference workloads, storing as much KV cache as possible on-chip is critical. We’re introducing a dedicated KV cache storage subsystem that works across GPUs and TPUs. As KV caches get larger, being able to fall back to this dedicated subsystem becomes critical. Loading model weights rapidly is important in dynamic inference environments where accelerators switch between models hour by hour.
- China has cracked down on AI companions: What can we learn from this?
Since November 2022, generative artificial intelligence (AI) has been rewriting billions of emails, student assignments, reports and breakup text messages. Alongside these mundane uses, we've seen a proliferation of chatbots as AI "companions".
- Larry Magid: Trust and safety professionals in age of AI
Larry Magid: Trust and safety professionals in age of AI East Bay Times
- Curious robots mimic how children can learn to understand language
Curious robots mimic how children can learn to understand language EurekAlert!
- AegisAI banks $36M as AI spear-phishing surges fivefold in a year
Email security startup AegisAI Inc. today said it has raised $36 million in new funding to counter a fast-growing class of artificial intelligence-generated email attacks that no amount of employee training can reliably stop. The San Francisco company builds its own large language models to guard corporate inboxes. The pitch rests on how much the […] The post AegisAI banks $36M as AI spear-phishing surges fivefold in a year appeared first on SiliconANGLE .
- AI chip startup Etched raises $300 million at $10.3 billion valuation
AI chip startup Etched raises $300 million at $10.3 billion valuation Reuters
- Stripe in Talks to Buy Startup OpenRouter
Stripe in Talks to Buy Startup OpenRouter The Information
- kausable raises €12M to rethink how AI learns
AI systems need constant, costly retraining. European AI startup kausable raises €12 million in a seed funding round to solve this problem by developing reasoning-first frontier AI that adapts efficie...
- telli secures $15M seed to automate customer-facing operations
Berlin-basedAI startup telli has raised $15 million in a seed funding round to expand itsAI platform for customer-facing operations. The round was led by redalpine,with participation from Mutschler, C...
- India Digest: CARPL.ai raises $10m led by IFC; Wipro Consumer makes acquisitions
India Digest: CARPL.ai raises $10m led by IFC; Wipro Consumer makes acquisitions DealStreetAsia
- Google CEO distracts from Gemini 3.5 Pro delay with talk of Gemini 4 and monthly releases
Google CEO Sundar Pichai has sought to allay concerns over the delayed release of the Gemini 3.5 Pro large language model. He dodged questions about it in Google’s quarterly earnings call on Wednesday by focusing on the company’s next frontier AI model, Gemini 4, and plans to release subsequent LLMs at an almost monthly cadence. His comments came a day after Google unveiled Gemini 3.6 Flash and 3.5 Flash Cyber but offered no update on the release of Gemini 3.5 Pro, the company’s delayed flagship reasoning model that many developers had expected to arrive weeks earlier. Google introduced the Gemini 3.5 family at its annual I/O conference, promising to release the Pro model in June. That timeline has since slipped, with Bloomberg suggesting Gemini 3.5 Pro is months late because the model’s coding performance is falling short of internal expectations, especially when compared to better performance by similar models from OpenAI and Anthropic. Instead of revisiting the Gemini 3.5 Pro timeline, Pichai used the earnings call to shift the discussion toward Gemini 4, when asked about how his company planned to navigate an increasingly competitive race to release frontier AI models by to Barclays Investment Bank analyst Ross Sandler. “We are creating a baseline on top of which you will see us rapidly iterate on subsequent model releases. And so picking up pace and releasing models almost at a monthly cadence is part of our road map as we are building Gemini 4 as well,” Pichai said during the call . Sandler’s question followed one from JPMorgan Chase & Co analyst Douglas Anmuth , who asked Pichai if Google was releasing frontier AI models frequently enough to keep pace with rivals OpenAI and Anthropic. Pichai had responded to Anmuth’s question that Google remained confident of competing at the frontier and was investing heavily in a larger Gemini 4 base model. Analysts, though, aren’t as confident as Pichai. While delays to Google’s frontier model roadmap have not triggered an exodus of existing customers, either because of high switching costs or because many enterprises already running multi-model architectures, they have made CIOs evaluating AI platforms more cautious about making new commitments, said Bhupendra Chopra , chief revenue officer at IT consulting firm Kanerika. A monthly model release cadence could prove to be a double-edged sword for enterprises and their CIOs. While a monthly release cadence could help enterprises gain faster access to improvements in model performance, cost and capabilities, it will also require CIOs to invest more heavily in testing, governance and version management to safely adopt those updates, said Sanchit Vir Gogia , chief analyst at Greyhound Research. Similarly, Pareekh Jain , principal analyst at Pareekh Consulting, said enterprises will embrace a faster release cadence only if each successive model delivers measurable improvements in performance, cost or safety, rather than simply changing version number. The challenge for CIOs, Jain said, is not just keeping up with model releases; it’s deciding whether each new version is worth the cost of validating it. This article first appeared on InfoWorld .
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- Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
Coordinating autonomous vehicles at unsignalized intersections remains a critical challenge for multi-agent reinforcement learning (MARL) systems, which typically struggle with combinatorial action spaces, reliance on privileged information, or rigid agent designs. We propose Master-Agent Proto-plan...
- FedAgentKE: Federated Semantic Knowledge Evolution for Heterogeneous Agents
Large language model (LLM)-based agents increasingly rely on reasoning, tool use, and iterative execution, yet existing agent frameworks still operate largely in isolation. While recent memory-based agent systems improve individual agents through local retrieval and workflow reuse, local experiences...
- pAI-Econ-claude: A Gated Human-in-the-Loop Multi-Agent Architecture for AI-Assisted Economic Theory Development
In many social-science research tasks, such as economics, LLM-based agents must produce outputs for which no cheap, task-complete, machine-readable correctness signal exists. This creates a distinctive reliability problem for multi-agent systems: how should generation, critique, coordination, and hu...
- HiMe: Real-Time Self-Hosted Personal Agent Platform for Health Insights with Wearable Devices
Traditional approaches to wearable health signal analysis, such as smartwatches, are constrained by rigid analytical frameworks and limited personalisation. The emergence of LLM agents creates a new opportunity for Personal Health Agentic Analysis, where health insights can be generated adaptively a...
- The Boundaries of Automation: A Theory of Persistent Human Participation
The rapid progress of AI has intensified the long-standing pursuit of automation: replacing human participation with algorithms wherever possible. Implicit in this pursuit is the assumption that humans remain in the loop only because current AI systems are not yet sufficiently capable. This paper ch...
- Toward Continuous Assurance for the Democratization of AI Agent Creation in Industry
AI agents are increasingly created inside organizations by non-engineering users through low-code, no-code, and conversational development environments. This democratization enables rapid local innovation, but it also creates a reliability gap: agents that appear to users as simple productivity arti...
- Explainable Belief Harmonization under Dynamic Epistemic Partitions
Existing approaches to multi-agent belief combination have established mature foundations for combining uncertain beliefs under common assumptions: consensus methods use iterative averaging, logic-based methods resolve conflicting knowledge bases, and epistemic logic analyzes agents' information sta...
- GLP: A Grassroots, Multiagent, Concurrent, Logic Programming Language for AI
A grassroots platform is a multiagent distributed system in which multiple independent instances can form and operate independently of each other and of any global resource, yet may coalesce into ever larger instances, possibly resulting in a single global instance. Grassroots platforms aim to offe...
- SafeStep: AI-powered Travel Assistance for Elderly People with Frailty or Dementia
More than a million people in the UK suffer from frailty or dementia, which severely compromise their ability to travel in urban environments. This paper presents SafeStep, an AI-driven travel system that assists elderly users with their journeys. At the core of SafeStep is a novel travel graph repr...
- MedGame: Storytelling Gamification Empowered by Large Language Models for Medical Education
Large Language Models (LLMs) show promise for medical education, but most existing systems focus on localized interactions such as question answering or single-turn feedback, rather than organizing an entire clinical case into a decision-centered learning trajectory. We introduce \textit{MedGame}, a...
- Thinkink: 2D Spatial Ink-native Interaction with LLMs
People often use handwritten notes and sketches to externalize ideas for ideation. To integrate large language models (LLMs) into this practice, we propose Thinkink. Prompts can be handwritten text or drawn sketches with LLM-generated responses visualized as ink-like text and sketches spatially inte...
- AI Assistants Overassist
Large language models (LLMs) are increasingly used as tutors and thought partners, helping users reason through problems. While guidance from AI assistants can scaffold thinking and foster learning, such benefits depend on how they help--for instance, intervening too early or too frequently may hind...
- Exploring the Design Space of LLM-Based Programming Support in CS Education: A Scoping Review through the Lens of Assistance Governance
As large language models (LLMs) become integrated into programming education, learner-facing systems increasingly differ in how that assistance is bounded, enacted, and controlled. These governance decisions are often described implicitly, making it difficult to compare systems in educationally mean...
- QuantiBias: Benchmarking Quantization-Induced Bias in LLMs
Almost every large language model that reaches a broad audience is quantized: trained in full precision, then compressed for efficiency. This step is assumed harmless and its safety is rarely re-checked. We find its principal side effect is increased bias that standard safety evaluation misses. Hold...