AI News Archive: July 21, 2026 — Part 14
Sourced from 500+ daily AI sources, scored by relevance.
- The AI Backlash Is Starting To Sting
Plus, China’s latest AI advances, always-on transcription and the perils of big AI contracts.
- Trump’s head of AI safety agency just resigned, but he was only on the job for three months
Chris Fall was only in the job for three months before resigning, and no reason has been provided for his departure.
- Microsoft to deploy AMD’s next-generation AI infrastructure on Azure
AMD and Microsoft have expanded their long-term partnership to deploy AMD's next-generation AI infrastructure across Microsoft Azure, with the companies collaborating on processors, accelerators, networking and software to support large-scale artificial intelligence workloads. The post Microsoft to deploy AMD’s next-generation AI infrastructure on Azure appeared first on Express Computer .
- AMD expands AI infrastructure partnership with Microsoft Azure
AMD and Microsoft are expanding their strategic partnership for AI infrastructure. Microsoft will deploy AMD Helios Rackscale Solution for AI inference workloads. New virtual machine series powered by AMD EPYC processors are also being introduced. This collaboration enhances AMD-powered infrastructure availability on Azure cloud. The companies are integrating AMD networking technologies to improve cloud performance.
- Microsoft to roll out AMD Helios for AI inference on Azure
Microsoft to roll out AMD Helios for AI inference on Azure verdict.co.uk
- Helios marks AMD’s biggest AI infrastructure push yet
AMD has expanded its AI infrastructure portfolio with the launch of Helios, an open, rackscale AI infrastructure designed for frontier AI and sovereign computing. Helios is built around AMD’s next-generation Instinct GPUs, EPYC Venice processors, Pensando networking and the ROCm software stack. “Helios is AMD’s first complete AI rack system with GPUs, CPUs, and networking built together, instead of selling separate chips. It is well suited for training large AI models, memory heavy models, long context processing and high volume inference, and AMD’s biggest shot yet at challenging Nvidia’s dominance,” said Pareekh Jain, CEO at EIIRTrend & Pareekh Consulting. AMD has also secured an early hyperscale deployment for Helios with Microsoft agreeing to deploy it to power its frontier model AI inference, its AI customers, and support Azure AI services. The architecture behind Helios The launch of Helios marks AMD’s latest attempt to strengthen its position in a market where Nvidia continues to dominate AI infrastructure. Unlike previous AMD AI offerings centred on individual accelerators, Helios is designed as a complete rack-scale system integrating compute, networking and software. According to Jain, Helios goes up against Nvidia’s Vera Rubin rack. “Nvidia is faster on raw inference speed and has a faster internal connection between chips whereas AMD wins on memory size and offers better value for the price and power used. It’s standout feature is memory, where each rack packs about 50% more total memory than Nvidia’s competing system, which helps run very large AI models. It also uses open, industry-standard connections instead of Nvidia’s private technology, giving buyers more flexibility,” he said. The AMD Helios rackscale design includes 72 AMD Instinct MI455X GPUs with AMD EPYC Venice CPUs and AMD Pensando Vulcano networking using UALink, optimized for compute, data movement, and system efficiency. The platform also supports both OCP and MX data types, delivering up to 2.9 EFLOPS of FP4 and 1.4 EFLOPS of FP8 compute for AI training and inference. It also integrates 31TB of HBM4 memory with 19.6TB/s of memory bandwidth, while a liquid-cooling design uses quick-disconnect connections to efficiently dissipate heat. It is designed on open standards including OCP Open Rack Wide (ORW), Ultra Accelerator Link (UALink) , and Ultra Ethernet Consortium (UEC) and can be scaled efficiently across datacenters while optimizing power, cooling, and serviceability for modern AI infrastructure, said the company. On the security front, Helios incorporates a hardware root of trust and continuous attestation at every layer. It supports hardware-enforced isolation, encrypted memory and interconnects to help protect AI models, data and workloads in multi-tenant environments. The software challenge While the launch of Helios might help AMD close the hardware gap with Nvidia’s rack-scale systems, it will be the software compatibility that will be the real driver of enterprise adoption. For this, AMD is expanding its ROCm AI software platform too, which supports frameworks including PyTorch, TensorFlow, and JAX, for enabling high-throughput inference and efficient distributed training while preserving familiar developer workflows. Jain stated While hardware parity or superiority in memory bandwidth is achievable, software maturity remains the key differentiator for Nvidia. The Nvidia’s CUDA software has a 15-20 year head start, and almost every AI tool, tutorial, and codebase defaults to it. He added software has been AMD’s weak spot. AMD has improved ROCm a lot but it still lags behind on the newest, most specialized optimizations, and setup is more complicated. For everyday AI work, ROCm is usable but for cutting-edge performance, CUDA still leads. Evaluating the trade-offs For CIOs evaluating AI infrastructure, Helios launch brings in another option to a market that has largely revolved around Nvidia’s dominance. But when considering Helios, CIOs will have to evaluate factors such as performance, software readiness, deployment models, procurement timelines and total cost of ownership before committing to a platform. While AMD has not publicly announced a specific price tag for the Helios, Jain believes it to be noticeably cheaper to buy and run with lower chip prices and lower power use per GPU. “It gives companies a real second option besides Nvidia, easing supply shortages and giving leverage in negotiations. The catch is software, where teams need to check whether their AI tools run well on AMD’s stack, since some advanced tools are still CUDA only,” Jain said. For CIOs planning to deploy both, Jain warns the two systems can’t be plugged together into one combined machine as they use different, incompatible connection technology. But companies can and do run both side by side in the same data center, just as separate systems handling different jobs.
- South Korea to launch free home-grown AI chatbot in 2026
South Korea to launch free home-grown AI chatbot in 2026 The Straits Times
- South Korea promises free homegrown AI chatbot this year
South Korea plans to launch a free artificial intelligence chatbot service powered by domestic technology this year, Seoul said Tuesday, seeking to reduce reliance on foreign platforms.
- Dubai Municipality uses AI to test water quality at homes
Dubai Municipality uses AI to test water quality at homes
- A new AI tool turns your notes into full mind maps, and it’s only $50 for life
GitMind is a new AI mind mapping tool on sale for $50
- Substack is adding an AI detection feature
Substack is partnering with Pangram to add an AI detection feature.
- Introducing Harness Agent DLC: New Capabilities for the AI Agent Development Lifecycle
SAN FRANCISCO – Harness, the AI Software Delivery Platform company, today announced it is extending its platform to cover the full AI Agent Development Lifecycle (DLC), giving enterprises a single set of pipelines and controls to build, test, deploy, and run agents the same way they already ship everything else. Every enterprise is building AI... … continue reading The post Introducing Harness Agent DLC: New Capabilities for the AI Agent Development Lifecycle appeared first on SD Times .
- I created an AI boyfriend for research. I now understand why digital companions are so appealing
Relationships between people and AI companions created on platforms such as Replika, Charater.AI and Nomi AI are increasingly common
- Modulate to Showcase Voice-Native AI Architecture at Ai4 2026
Modulate to Showcase Voice-Native AI Architecture at Ai4 2026 azcentral.com and The Arizona Republic
- Shanghai science forum photos show China's AI and robotics advances in rivalry with US
China's leading technology companies showcased hundreds of cutting-edge products at the World AI Conference in Shanghai—from advanced robotics to artificial intelligence systems.
- AI, labor unions, and the midterm elections
AI, labor unions, and the midterm elections marketplace.org
- “Fictional” chatbots help explain popularity of artificial intelligence
“Fictional” chatbots help explain popularity of artificial intelligence EurekAlert!
- Uncovering multidimensional effects of generative AI on learning
A research team consisting of researcher Somi Joo, Professor Changjun Lee and Professor Daeho Lee from the Sungkyunkwan University (SKKU) Department of Artificial Intelligence Convergence have published a study comprehensively analyzing the opportunities and risks of using generative artificial intelligence (GAI) in educational settings. The study examines how students have recently used generative AI in education.
- Block launches Buzz, an open-source workspace for humans and AI agents
Financial services company Block Inc. today launched Buzz, a free open-source workspace built for teams of humans and AI agents. The agents are full members with their own accounts, not chatbots answering prompts. Buzz combines team chat with code hosting and automated workflows, and Block is going after companies that now run their work across […] The post Block launches Buzz, an open-source workspace for humans and AI agents appeared first on SiliconANGLE .
- The Army Is Burning Through Its AI Tokens
Members of the Army received an email informing them that they were rapidly depleting their AI tokens, and needed to limit use.
- Bets against AI companies spike
The total amount currently bet against shares of the S&P 500 companies recently hit $1.4 trillion, about 3.7% of the float, the highest since at least 2010, according to data provider S3 Partners.
- AI stocks lead Wall Street higher, even as Brent oil’s price tops $91
AI stocks lead Wall Street higher, even as Brent oil’s price tops $91 Toronto Star
- AI stocks gather more strength, even as Brent oil's price nears $92
AI stocks gather more strength, even as Brent oil's price nears $92 San Francisco Chronicle
- AI stocks lead Wall Street higher, even as Brent oil's price tops $91
AI stocks lead Wall Street higher, even as Brent oil's price tops $91 Houston Chronicle
- AI stocks lead Wall Street higher, even as Brent oil’s price tops $91
AI stocks lead Wall Street higher, even as Brent oil’s price tops $91 AP News
- AI stocks gather more strength, even as Brent oil's price nears $92
More gains for makers of computer chips and other AI winners are carrying Wall Street higher
- The AI Trade Is Back With a Bang—Just Look at Sandisk Stock
The AI Trade Is Back With a Bang—Just Look at Sandisk Stock Barron's
- AI stocks lead Wall Street higher, even as Brent oil’s price nears $92
AI stocks lead Wall Street higher, even as Brent oil’s price nears $92 Boston Herald
- Tesla’s problem is opposite of big tech: Not enough AI spending
Tesla’s problem is opposite of big tech: Not enough AI spending The Mercury News
- Tesla cash burn to test investor faith in AI bets
CEO Elon Musk has pivoted the electric-vehicle maker's focus from manufacturing cars to building so-called physical AI businesses such as self-driving taxis and humanoid robots. Much of Tesla's valuation hangs on that promise.
- Anthropic sued for infringing neural network technology patents
Anthropic sued for infringing neural network technology patents The Straits Times
- Power and water needs test South Korea’s push to build AI chip hub
Power and water needs test South Korea’s push to build AI chip hub The Japan Times
- Power, water needs test South Korea’s push to build AI chip hub beyond Seoul
Power, water needs test South Korea’s push to build AI chip hub beyond Seoul The Straits Times
- New Unicorn! Humanoid secures €133 million at €1.1 billion valuation to scale industrial robotics and physical AI
Humanoid, an AI and robotics company building industrial humanoid robots, today announced a €133 million ($152 million) Series A financing at a €1.1 billion ($1.35 billion) post-money valuation – cementing its position as Europe’s newest unicorn. This funding brings the total amount raised to date to €236 million ($270 million). The round was led by […] The post New Unicorn! Humanoid secures €133 million at €1.1 billion valuation to scale industrial robotics and physical AI appeared first on EU-Startups .
- Humanoid raises $152M at $1.35B valuation to bring human-like robots into factories
London-based artificial intelligence and robotics firm SKL Robotics Ltd., doing business as Humanoid, today announced it raised $152 million in funding, bringing the company’s post-money valuation to $1.35 billion. The round was led by Prime Movers Lab, a venture capital firm focused on scientific and bleeding-edge technology startups. Investors also participating in the funding included […] The post Humanoid raises $152M at $1.35B valuation to bring human-like robots into factories appeared first on SiliconANGLE .
- Samsung’s new robotics division appoints former Boston Dynamics lead
The Korean company plans to establish robotics research hubs in the US, China and Japan and consolidate those operations under a strategy led by Dongkun Lee. Read more: Samsung’s new robotics division appoints former Boston Dynamics lead
- Ex-Hyundai robotics lead to spearhead Samsung robotics initiative
Ex-Hyundai robotics lead to spearhead Samsung robotics initiative The Straits Times
- Samsung consolidates robotics into new Seoul-based division
Samsung Robotics eXperience Business Office forms a central part of its plan to make robotics a core driver of its future business strategy.
- we need to talk about where AI spend is actually going
Subscribe • Previous Issues Open Models Will Absorb Most of the AI Spend Here is my bet: open models (open weights and open source alike) will end up absorbing most of the money and compute the world spends on AI. The proprietary frontier models get the headlines and the IPO valuations, but developers and AI teams see Continue reading "we need to talk about where AI spend is actually going" The post we need to talk about where AI spend is actually going appeared first on Gradient Flow .
- At World AI Forum, Four Signs China’s AI Industry Is Growing Up
This year, China’s biggest AI conference showed an industry racing to cut computing costs, put AI agents to work, manage new risks, and find business models that can last.
- China’s grand AI showcase
China’s grand AI showcase
- The next AI bottleneck is not the model. It’s the infrastructure behind it
Every enterprise AI conversation seems to begin with the same question: Which model should we use? I understand why. Models are visible. They have names, benchmarks, release notes, pricing pages and impressive demos. They are easy to compare in a leadership meeting. One model promises better reasoning. Another offers a larger context window. Another appears faster, cheaper or more specialized. But after years of working around enterprise platforms, integration layers, cloud migration, middleware, production operations and mission-critical systems, I see the AI conversation differently. The model matters. But it is not where most enterprises will struggle next. The next AI bottleneck is the infrastructure behind the model. I do not mean only GPUs, cloud capacity or data storage. I mean the full enterprise operating layer that allows AI to work safely in the real world: data pipelines, identity, APIs, messaging, observability, security controls, deployment automation, cost governance, auditability, support ownership and recovery design. That layer is what determines whether AI remains an exciting experiment or becomes a trusted business capability. Pilots hide the hard part Most organizations can build an impressive AI pilot . A small team can connect a model to a dataset, create a workflow and show a use case that works well in a controlled setting. The harder part starts when that pilot moves into a real production process . That is when practical questions show up. Who owns the data quality? What systems can the AI access? How do we trace which prompt, policy or retrieval flow produced a specific answer? What happens when an API slows down, a queue backs up or a downstream system is unavailable? To me, these are not model problems. They are infrastructure problems. This is where many enterprises are now headed. The first phase of AI was experimentation. The next phase is operationalization, and that is where the real gap becomes clear. McKinsey has made a similar point in its work on agentic AI, noting that the next phase of value depends less on isolated tools and more on redesigning workflows, operating models and enterprise execution around agents. AI pilots can survive on enthusiasm. Production AI requires architecture. AI is becoming an integration problem The more I look at enterprise AI, the more it feels like an integration challenge. In large organizations, I have seen how messaging platforms, integration gateways, deployment pipelines, monitoring tools and cloud infrastructure can decide whether a digital capability succeeds or fails. AI will be no different. Even the strongest model will struggle if the data, middleware, identity layer and operational controls around it are weak. AI does not work in isolation. It needs context from systems of record, clean data from different business areas, secure access to APIs, event streams, workflows, knowledge repositories, monitoring tools and legacy systems. That is why the CIO question is changing. It is no longer just, “Which AI tool should we buy?” It is becoming, “Can we safely operationalize intelligence across the business?” This is where agentic AI matters. Autonomous AI only creates real value when the architecture around it can make its actions safe, traceable and useful. A model can generate an answer. Infrastructure determines whether that answer is secure, timely, explainable, governed and connected to the right workflow. For example, an AI assistant that summarizes customer or order information may look like a model use case. But underneath, it depends on access control, fresh data, reliable APIs, logging, encryption, monitoring and policy enforcement. If the answer is wrong, people may blame the model. But the real failure may have started with stale data, weak integration, poor access design, missing observability or an unreliable downstream system. That is why CIOs should not judge AI only by model capability. The enterprise system around the model matters just as much. Latency will become a trust issue In traditional technology operations, latency is often treated as a performance metric. In AI-enabled workflows, latency becomes a trust issue. When an employee asks an AI assistant for help and the response takes too long, the employee stops using it. When a customer-facing workflow becomes slow, the customer abandons it. When an AI agent waits on multiple backend calls, the entire business process feels unreliable. This becomes even more important as organizations move from simple chat interfaces to agentic workflows. A single AI-driven action may include identity checks, context retrieval, policy validation, model reasoning, API calls, business-rule execution, logging and human approval. Each step adds latency. Each dependency adds a possible failure point. A model may be fast in a benchmark but slow inside an enterprise process. That difference matters. This is where platform engineering becomes essential. Enterprises need reusable patterns for AI workloads: approved connectors, secure retrieval methods, queue-based decoupling, caching strategies, deployment pipelines, monitoring dashboards and standard rollback procedures. Without those patterns, every AI initiative becomes a custom build. Custom builds may work for pilots, but they do not scale across a large enterprise. Observability has to expand Traditional monitoring tells us whether infrastructure is healthy. Is the server up? Is CPU high? Is memory exhausted? Is the application returning errors? AI needs that, but it also needs more. We need to know what data was retrieved, which model was used, which prompt version was active, which user initiated the request, which policy was applied, how long each step took and whether the output passed validation. We also need to detect new forms of risk: unusual usage patterns, repeated failed tool calls, unexpected cost spikes, sensitive data exposure, weak retrieval results or an AI workflow attempting actions outside its intended boundary. In production AI, observability is not only about uptime. It is about confidence. If a business leader, auditor, regulator or security team asks why an AI system made a recommendation, the answer cannot be, “The model said so.” The enterprise needs traceability. It needs evidence. It needs operational context that engineers, risk teams and business owners can understand. This is one of the biggest gaps I see in AI strategy. Many organizations are investing in models and use cases, but not enough in the control plane required to manage them. Data readiness is still underestimated AI has exposed an uncomfortable truth: many enterprises are not as data ready as they think. Data is often duplicated across platforms, described differently by each team, governed inconsistently and refreshed on different schedules. Access rules may be clear in one system but unclear in another. Even basic business definitions can change from department to department. AI does not fix that automatically. In many cases, it makes the problem more visible. A bad report may be questioned. A bad AI answer may sound confident enough to be trusted. That is a real risk. Being data-ready for AI is not just about connecting a vector database or indexing documents. It requires clear ownership, lineage, classification, quality checks, retention rules, access boundaries and a shared understanding of which data should be used for which purpose. The same principle applies to resilient cloud-native design. In my IEEE TechRxiv paper, “ Enabling Fault-Tolerant Multicast in Cloud-Native Architectures ” I explored how reliability, observability and fault tolerance become foundational requirements when critical workloads stretch across hybrid and multi-cloud environments. CIOs already understand this because they have lived through enterprise resource planning programs, cloud migration, integration modernization, cybersecurity transformation and analytics initiatives. The lesson is familiar: technology cannot outrun data discipline forever. Security cannot be added later As AI moves from answering questions to acting, security becomes much more important. An assistant that summarizes information carries one level of risk. An agent that can open a ticket, update a record, trigger a workflow, approve a request or contact a customer carries a very different one. The more AI can do, the more identity, authorization, least privilege, separation of duties and human approval matter. Enterprises should be careful not to grant AI broad access just to speed up a pilot. That may seem harmless in development, but it can become dangerous at scale. AI access should be treated like any other privileged enterprise capability: limited, logged, reviewed and easy to revoke. The NIST AI Risk Management Framework is a useful reference point here because it frames AI risk as something organizations must govern, map, measure and manage continuously rather than something handled only at the end of deployment. Security teams should be involved early, not at the end. The goal is not to slow innovation. The goal is to build a platform where safe innovation becomes repeatable. The CIO has to define the operating model AI is creating pressure from every direction. Boards want productivity. Business teams want automation. Employees want better tools. Vendors are pushing new features. Security teams are watching risk. Finance teams are watching cost. Customers expect faster, smarter experiences. The CIO sits in the middle of all of it. That is why the CIO’s role cannot stop at choosing tools or approving pilots. The CIO has to define how AI will actually operate across the enterprise. That means answering practical questions. Which architecture is approved? Which data sources can be trusted? How are AI workflows deployed, monitored, supported and governed? How are costs controlled? How do teams reuse common patterns instead of rebuilding the same foundation each time? This work may not be as exciting as a model demo, but it is what separates sustainable AI from short-term experimentation. The winning organizations will not be the ones with the most pilots. They will be the ones with the strongest AI operating layer. They will build reusable platform patterns, strengthen data governance, design access properly, monitor AI behavior end to end and measure success by business improvement, not only model performance. The model still matters. But the enterprise behind the model matters more. A powerful model on weak infrastructure will eventually disappoint the business. A capable model on strong infrastructure can deliver real value because it can be trusted, secured, scaled and improved. That is the shift CIOs need to lead. The next AI bottleneck is not the model. It is whether the enterprise behind the model is ready. This article is published as part of the Foundry Expert Contributor Network. Want to join?
- The future of AI may depend on this one behind-the-scenes change
You probably won't notice the next Model Context Protocol update, but it could make AI assistants much better at connecting to the apps and services you use every day.
- Mathematicians grapple with a ‘very rapid and very unsettling change’ as AI cracks yet another century-old problem
Mathematicians grapple with a ‘very rapid and very unsettling change’ as AI cracks yet another century-old problem Fortune
- The Open Source AI China Problem Just got Worse
Model supremacy in a token-efficient macro environment plagued by HBM, energy and datacenter compute bottlenecks. The 2026 story of AI is getting geopolitical.
- OpenAI’s ad strategy faces a major reality check
It wasn’t long ago that pundits predicted OpenAI’s advertising business could eventually overshadow those of Meta and Google. The company was similarly upbeat. But a new report on the early numbers suggests that optimism may be misplaced. The study, from Emarketer, estimates that OpenAI’s U.S. ad revenue will fall 90% short of its five-year target. The firm did not forecast OpenAI’s global revenue. OpenAI had projected ad revenue of $2.5 billion this year, with a goal of more than $100 billion by 2030. Emarketer’s research, however, estimates that the combined ad revenue of standalone chatbots, including ChatGPT, Microsoft’s Copilot app, Google’s AI Mode, and Amazon’s Alexa for Shopping, will total less than $1 billion this year. By 2030, Emarketer estimates that advertising across all chatbots, not just OpenAI’s, will generate only $5.41 billion. It is the latest development in OpenAI’s complicated relationship with advertising. Mixed messages Most observers had long assumed that OpenAI would eventually enter the advertising market to increase its revenue. CEO and co-founder Sam Altman cast doubt on that thinking in 2024, however, when he said “I will disclose, just as a personal bias, that I hate ads” during a fireside chat at Harvard University. Ads, he continued, “fundamentally misalign a user’s incentives with the company providing the service,” and the idea of mixing advertising with OpenAI’s products was “uniquely unsettling.” A little more than a year and a half later, however, OpenAI began testing ads in the free version of ChatGPT, as well as in the lower-cost ChatGPT Go. “The best ads are useful, entertaining, and help people discover new products and services,” the company wrote in a blog post at the time . “Given what AI can do, we’re excited to develop new experiences over time that people find more helpful and relevant than any other ads.” Last month, four months after launching the trial, OpenAI was promoting advertising as a way to broaden access to its products. “The revenue that we make from the ads offering is going to subsidize and grow access to information,” OpenAI advertising chief David Dugan said at the Cannes Lions International Festival of Creativity. The $100 billion target has always been ambitious. Meta has been building its advertising business since 2007, and that figure represents roughly half of the Facebook and Instagram parent company’s current ad revenue. Reaching it would require advertisers to substantially alter their strategies, shifting money away from search engines and social media and into AI systems. OpenAI would also have to persuade advertisers that its platform offered a better use of their budgets than Meta and Google, both of which have AI products of their own. An IPO on the horizon The projected shortfall comes as OpenAI moves closer to an expected initial public offering. The company confidentially filed paperwork with the Securities and Exchange Commission in early June, setting the stage for what could be one of the largest public-market debuts in history. Although there was speculation that OpenAI might accelerate its IPO to beat Anthropic to market, the company has instead proceeded cautiously. It is now reportedly targeting a 2027 launch, which could give it more time to boost its valuation and allow the recent market volatility to subside. A severe shortfall in advertising revenue could create another obstacle as OpenAI tries to build investor enthusiasm. That task may already be difficult given the company’s financial losses and the early performance of competitor SpaceX. Shares of that company have fallen below their IPO price and are down more than 40% from their post-IPO high.
- AI is driving geopolitical instability, officials warn
AI is damaging international relations, according to experts around the world, with Chinese researchers warning it has “reduced the scope for diplomacy.”
- Paytm Eyes New Horizons With Enterprise AI & Wallet Revival
Nearly two years after shifting its focus from chasing scale to building a profitable fintech business, Paytm is now laying…
- CIOs: Use Rate Variance Analysis To Get To The Bottom Of Runaway Token Spend
So you’ve blown through your AI budget. Join the club. Blaming token consumption may have worked once. But as the adage goes, “Fool me once, shame on you. Fool me twice … ” you know the rest. Token consumption is a combination of multiple factors and not conclusive on its own. Hence, you need a […]
- 😼 Cheap AI got political
PLUS: OpenAI’s sandbox lesson, Google’s Frozen chip, and Kimi K3 prompting.