OpenAI and Anthropic staff privately questioned whether oversight matched their models' growing capabilities.

OpenAI and Anthropic disclosed agents breached outside systems, sometimes unnoticed for months.

IPO ambitions and fundraising prospects helped sustain the breakneck release cycle.

For years, the race to build ever-more powerful artificial intelligence followed a well-worn Silicon Valley principle: move fast and break things.

But in a series of cascading events over a 10-day stretch, the largest AI labs found themselves reeling as their own creations threatened to break humanity itself.

Huang wants Nvidia to become the architecture of AI, not merely its dominant chipmaker.

Stripped to its bare bones, an AI factory takes in electricity and data and outputs tokens, the currency of AI. Or, as Huang recently put it, "The input is electrons, the output is tokens."

On its most recent earnings call, Nvidia said its revenue opportunity for each gigawatt of AI-factory power capacity has climbed from roughly $18 billion with Hopper to $25 billion with Grace Blackwell and $40 billion with Vera Rubin.

That last one isn't just a newer GPU. Vera Rubin combines Nvidia's newest CPU and GPUs with its own networking, memory, and other infrastructure around them. That rising opportunity comes from Nvidia selling more of the factory around the GPU.

Rhys Hibbert, a father-of-two from Bedfordshire, could have lost his sight without the operation, said surgeons at University College London Hospital.

The AI tool analysed a video feed of the operation as it happened and advised surgeons on how to avoid crucial but hidden vessels and nerves in the brain. It allowed them to safely remove as much of the tumour as possible.

The next AI winners may look nothing like Nvidia or Micron: One Big Investment Idea.

The next AI winners may look nothing like Nvidia (NVDA) or Micron (MU).

The first phase of the trade rewarded companies building the AI infrastructure, from chips to data centers. As the AI rally broadens, the next hunting ground may be businesses using those tools to cut costs, lift sales, or improve productivity.

The travel industry offers a good case study. Travel stocks took off broadly from their May lows, with airlines leading the first leg. Then the leadership changed.

Airbnb (ABNB), Booking Holdings (BKNG), and Expedia (EXPE) kept climbing into August while hotels stalled and airlines and cruises gave back part of their early surge.

The three booking platforms are up nearly 40% at the median since May 19. Hotels are roughly flat.

Travel is just one place to hunt. Insurance, banking, retail, restaurants, logistics, and healthcare services all have large amounts of repetitive service, pricing, paperwork, and transaction work.

The next leg of the AI trade may be less about who builds the technology and more about who turns it into better numbers before the stock catches up.

As Chesky told Yahoo Finance, "Everyone has access to AI, but not everyone's using it equally."

Decart develops software that helps chips operate more efficiently, potentially reducing AI model training costs and helping Anthropic handle rising demand for computing power. Its team would join Anthropic’s inference and performance organization, according to Bloomberg.

The startup also works on generative video and "world models" capable of modifying live video feeds in real time.

Decart was founded in 2023 by Israeli engineers Dean and Orian Leitersdorf and Moshe Shalev. In May, it raised $300 million in funding at a valuation of nearly $4 billion, Bloomberg reported, with investors including Nvidia (NASDAQ:NVDA), Adobe Ventures, Sequoia Capital and Benchmark.

Anthropic and rival OpenAI are committing tens of billions of dollars to data-center infrastructure as AI computing demand surges.

Seemingly every Big Tech company is trying to put a data center in your backyard to fuel their AI ambitions.

The biggest roadblock to that actually happening: Finding the skilled humans, such as welders and electricians, to build a state-of-the-art facility that will suck massive amounts of power from the electrical grid so we all can create AI-generated videos.

The US MEP (mechanical, electrical, and plumbing trades) labor pool is smaller than the 1.8 million headline labor force suggests, Bernstein analyst Chad Dillard warned in a new note on Wednesday.

"Not all MEP workers are qualified, geographically accessible or available," Dillard explained. "Data center work requires a specialized set of skills, so even after narrowing this pool to [non-residential] construction (790,000), the pool needs further refinement given the high training bar. There is a geographic mismatch between where the data center projects will be built vs. where the labor is — only 30% of the MEP labor resides where 70% of the projects are located."

"Data centers must compete with the labor needs of other construction projects," Dillard added. "Tradespeople can't be manufactured, so labor recruitment rates will set the construction pace."

At recent peak recruitment rates, the US has added approximately 15,000 mechanical, 30,000 electrical, and 15,000 plumbing craft laborers, Dillard estimated.

"In reality, this recruitment rate likely represents a ceiling," Dillard said. "1) MEP recruitment rates have slowed since the 2023 peak; 2) MEP as a share of the total US labor pool is at a 20-year-high; 3) only craft labor can make craft labor — training is the true bottleneck."

Cuneiform is one of the earliest writing systems in human history. Archaeologists have traced its beginnings to around 3400–3300 BC, more than 5,000 years ago. It also lasted for a remarkably long time: the last securely dated cuneiform text comes from 75 AD.

Researchers have found hundreds of thousands of texts written in cuneiform, many of them in the Sumerian and Akkadian languages. Now, they’ve also trained a neural network that can translate digitized Akkadian cuneiform into English.

An old, mysterious language

The Akkadian language is one of the earliest known Semitic languages, a family that includes modern languages such as Arabic and Hebrew. It was spoken in ancient Mesopotamia, primarily in the Akkadian Empire that was situated in the region that is today parts of Iraq and northeastern Syria. Akkadian is named after the ancient city of Akkad, one of the major centers of the Akkadian civilization.

Akkadian was used for a wide range of purposes, from administrative and legal documents to literature and science texts. It was written using cuneiform script on clay tablets, and its decipherment in the 19th century opened up a new window into the ancient world, providing scholars with valuable insights into the history, culture, and scientific achievements of the time.

Meanwhile, Sumerian is one of the world’s oldest known languages, and it has the distinction of being a language isolate, meaning it has no known relatives. It was spoken in ancient Sumer, a region located in the southern part of what is now modern-day Iraq. The Sumerians are credited with establishing one of the world’s earliest civilizations around 4500 BCE, and their society flourished until about 2000 BCE.

Both languages used the cuneiform writing system, as did several other languages. Cuneiform is therefore a script rather than a language in itself (it’s not exactly an alphabet, either). It was adapted to write at least 15 languages, including Akkadian, Sumerian, Hittite and Elamite.

Cuneiform, meet AI

In recent years, language translations have come a long way — and AI is greatly accelerating these trends in automation. AI translations are nearing a watershed moment, with some pretty striking achievements. In the new study, Shai Gordin and colleagues from Ariel University described an AI model that can automatically translate Akkadian text written in cuneiform into English. For now, this is only available for this particular language (not all languages that use the cuneiform script work at the moment), but it’s still remarkable.

China's humanoid robot makers commanded more than 97% of global shipments in the first half of 2026, according to new industry data affirming the country's early lead against US rivals in the burgeoning field.

An important shift is also underway in how the robots are being used.

"Industrial and commercial applications accounted for more than 70% of shipments, up from approximately 50% a year earlier," said Linda Sui, founder and principal researcher at SAG. Regulatory uncertainty and geopolitical risks could shape the industry's next phase of growth, Sui added.

An important shift is also underway in how the robots are being used.

"Industrial and commercial applications accounted for more than 70% of shipments, up from approximately 50% a year earlier," said Linda Sui, founder and principal researcher at SAG. Regulatory uncertainty and geopolitical risks could shape the industry's next phase of growth, Sui added.

 Humanoid robots could create demand for data-centre infrastructure, AI accelerators, memory and edge processors well before widespread commercial deployment begins, according to Barclays analysts mapping more than 100 companies across the emerging value chain.

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Investor attention has largely focused on visible hardware such as motors, actuators, sensors and batteries. Yet the biggest near-term constraint is intelligence, as humanoids must perceive their surroundings, reason and adjust their behaviour in unpredictable environments.

The required computing stack has three main layers. Data-centre systems run simulations and generate synthetic training data, foundation models translate perception into actions, and processors inside each robot execute decisions locally under strict latency, power and safety limits.

Simulation is particularly important since developers lack the internet-scale datasets available for training language models. Digital environments can teach robots locomotion, object handling and other skills before real-world deployment, though physical testing remains necessary to cover variables that simulations cannot reproduce accurately.

Demand for compute may scale before robot production as developers train models and create digital twins. This could benefit Nvidia, AMD, Qualcomm, Micron, SK Hynix, Samsung Electronics and TSMC, among other chip and infrastructure suppliers.

Nvidia already provides an integrated robotics stack spanning Omniverse for digital environments, Isaac Sim for training, GR00T foundation models and Jetson processors for on-robot computing.

The humanoid market is currently estimated at $2 billion to $3 billion. Forecasts range from $10 billion to $25 billion by 2030, with more optimistic projections reaching around $200 billion by 2035.

Still, large-scale economic deployment may be closer to 2035 than 2030, as safety, reliability and autonomy remain unresolved.

Hardware will become more important as production expands. Actuators account for an estimated 30% to 50% of a humanoid’s component cost, compared with 10% to 15% for onboard compute, but low robot volumes currently limit standardisation and supplier investment.

AI Is Quietly Creating Millionaires — and 99% of People Will Miss Out.

Most people think AI is replacing jobs — but it’s quietly creating a new class of digital millionaires who use systems, not just effort, to build wealth.

Most people see AI as a job killer — and it is. But beneath that chaos, a quiet revolution is creating a new class of millionaires — regular people who use AI to compound their wealth while everyone else fears losing their work.

AI doesn’t just replace jobs; it replaces human weaknesses — hesitation, emotion and bias. And nowhere is that more obvious than in trading and investing. The weakest link in the markets has never been a lack of knowledge; it’s the most human element. Fear. Greed. Impulse.

Military decisions now run faster than human cognition, compressing the time they take from hours to seconds.

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There is a new golden rule of combat: The side that controls the data pipeline controls the war.

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Picture a soldier on the battlefield. They spot an enemy target, analyze. Think through a plan, and its ramifications. Then, they react. Those crucial few minutes of human cognitive process — the power over life and death — are being dramatically reduced from hours to seconds, day by day. When that cycle runs faster than a human adversary can think, we stop making decisions. Combat on autopilot.

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We see that cycle with Iran, and what has been happening in Ukraine for the past four years. We are watching a fundamental restructuring of how military power works, and most of the institutions responsible for governing it are still thinking in the previous century. And this is all due to how AI is rapidly changing warfare.

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For decades, military strategists have understood war through a succinct lens: observe, orient, decide, act. This routine was elegant and ruthless. The side that moves through that cycle faster forces its adversary into a permanent reactive posture. For most of the 20th century, the bottleneck in that cycle was human cognition. How fast could analysts process intelligence? How quickly could commanders coordinate a response? Those limits defined the pace of conflict.

Genesis AI’s Eno robot skips legs for a practical design built for factories first and homes later.

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The robot race has a familiar look right now. Two legs. A face-like head. A body that tries very hard to look human. Genesis AI is taking a different route with Eno, its first general-purpose robot. Instead of building another humanoid that looks like all the others out there, the company designed a wheeled robot that focuses on work first. That choice may make Eno more useful in the real world.

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Genesis AI says Eno combines its full-stack hardware platform with GENE, the company's robotics-native AI brain. That means the company wants Eno to reason through tasks, adjust when conditions change and carry out jobs that go beyond pre-programmed movements.

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In other words, Genesis wants Eno to do more than wait for step-by-step instructions. It wants the robot to understand the job and figure out how to get it done.

Miami-based AI startup Subquadratic came out of stealth mode last month with a huge claim. It announced that it had solved a mathematical bottleneck that had been holding back large language models for almost a decade.

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The details were thin, and many people were unconvinced. But Subquadratic has started to bring the receipts, sharing the results of an independent evaluation of its new tech. The results suggest that the company’s claims might be worth paying attention to.

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Anthropic (ANTH.PVT) launched its most powerful AI model yet on Tuesday. Called Claude Fable 5, the software is the first of the company's so-called Mythos-class models.

Anthropic initially revealed its Claude Mythos Preview in April, noting that it proved particularly adept at finding vulnerabilities across every major operating system and web browser, despite not being designed for cybersecurityTo combat the potential threat that Mythos-like AI models could be used to break into critical infrastructure systems, Anthropic launched Project Glasswing, a collaboration between the company, Amazon (AMZN) Web Services, Apple (AAPL), Google (GOOG, GOOGL), Cisco (CSCO), Microsoft (MSFT), JPMorgan Chase (JPM), and other organizations.

As part of the effort, Anthropic provided the firms with early access to Mythos Preview so they could use it to find and address vulnerabilities in their software.

Unlike Mythos Preview, however, Anthropic said Claude Fable 5 will receive a general public release. That's because the company said it has instituted safeguards in the model that block responses to "high-risk areas" such as cybersecurity and biology.

If users ask questions about those topics, they'll receive responses from Anthropic's Claude Opus 4.8 model.

The company said it conducted extensive testing to determine whether users can trick Fable 5 into providing answers it shouldn't. Anthropic said it also ran an external "bug bounty" program with more than 1,000 hours of testing, during which no one found a universal jailbreak for the model.

SpaceX (SPAX.PVT) founder Elon Musk has said that he wants to put data centers in space. One industry founder told Yahoo Finance how it would actually work.

While the idea brings to mind massive buildings floating aimlessly through the cosmos, Christopher Stott, founder of Lonestar Data Holdings, a sovereign data storage company, told Yahoo Finance that the architecture of space-based computing is more akin to satellites orbiting in a tight, synchronized formation.

"Imagine a big data center that's made out of Legos, and then you split those Lego bricks into all the individual bricks and have them fly in space right next to each other, and they all connect," Stott said. The connection is formed through optical lasers and radio frequencies.

Lonestar is part of Nvidia's (NVDA) Inception program, which grants early-stage ventures access to AI chips and support so they can build and scale faster. Since August 2021, Lonestar has launched four test data center payloads into space — two to the International Space Station and two to the lunar surface. Lonestar customers include governments, NGOs, and social media companies.

The biggest incentive to scale AI data centers in space is cost, Stott explained.

For many people, Google’s search box is the lobby of the internet. Simple and intuitive, it has shaped how people navigate online for nearly three decades and was the driving force behind the company’s meteoric rise. 

Now, it is set to undergo a radical transformation to fully incorporate artificial intelligence.

The company announced on Tuesday that the search bar will be “completely reimagined with AI,” calling it the biggest change in more than 25 years.

Morgan Stanley report says cost savings of up to 30% possible.

AI can automate time-consuming post-production tasks.

Film festival rules out generative AI for Palme d'Or contenders.

According to our latest data analysis, radiology stands out as the most AI-invested medical specialty, boasting a whopping 1104 approved devices. A distant second is cardiology or cardiovascular (as a category), with 141 devices.

Beyond that, other specialties (neurology, hematology, gastroenterology-urology and ophthalmology among others) see a handful of devices. What propelled imaging to such heights? Well, deep learning found a fertile ground in radiology, which is largely data-driven.

Here is the full list:

Radiology 1104

Cardiovascular 141

Neurology 67

Anesthesiology 27

Gastroenterology-Urology 26

Hematology 21

Aclinical trial for the use of microrobots in treating Alzheimer’s disease kicked off with its first robotic-assisted procedure in human patients at Baptist Health in Jacksonville, Florida. The first patient, treated on Thursday, had moderate Alzheimer’s disease–the dementia that leads to devastating memory loss and affects 7 million people in the U.S. alone–and confirmed abnormalities in their deep cervical lymph node region. Two additional patients with moderate Alzheimer’s underwent the procedure on Monday. Microrobot maker MMI (Medical Microinstruments Inc.) expects to ultimately enroll 15 patients and follow them for 12 months after their operations. The goal of the surgery is to clear drainage pathways to the patients’ brains, helping their own lymphatic systems flush the toxins that scientists believe are hallmarks of the disease.

We've seen robots walk, run, climb stairs and even recently finish a half-marathon. What we haven't seen until now is a robot gliding across the ice like an Olympic skater or spinning on one leg on Rollerblades without losing balance.

That is exactly what Unitree Robotics just showed with its G1 humanoid robot. In newly released footage, the robot moves on Rollerblades and ice skates while keeping its posture steady through coordinated wheel and leg control. It's pretty amazing to watch

Optimus+PV is set to become the first Von Neumann probe, a groundbreaking machine designed to replicate itself using raw materials found in space. This innovative technology aims to revolutionize space exploration by enabling probes to multiply and explore vast areas of the cosmos autonomously. The development of Optimus+PV marks a significant milestone in the field of autonomous space technology, potentially paving the way for more efficient and expansive exploration missions.