Experts have a long history of torturing us with predictions about how technology will wipe us out, first our jobs and then just getting rid of us altogether because humans are a bother. The AI panic around Large Language Models over the last three years is no exception.
Altman said the Pentagon agreed with his company's principles that OpenAI's technology would not be used for domestic mass surveillance or for autonomous weapon systems, affirming that humans would take responsibility for the use of force.
AI models like ByteDance's Seedance 2.0 can wolf down up to a dozen reference files-images, audio tracks, and camera movement samples-to flawlessly synthesize an alternate reality with no uncanny valley. And it costs only pennies do so.
Hegseth summoned Amodei and demanded that Anthropic AI be used any way he wants or said he'd cancel the company's existing $200 million contract and blacklist them from any further AI pacts. Hegseth gave Anthropic until 5 p.m. yesterday to bend the knee. Amodel didn't bend.
CoreWeave reported quarterly revenue of $1.57 billion, slightly beating estimates, but posted an adjusted loss per share of $0.56, far exceeding the anticipated $0.49 per share deficit. Full-year 2025 revenue hit $5.1 billion, up 168% year-over-year, yet the emphasis on ramping up spending to fuel AI infrastructure growth sparked fears of overspending in a competitive landscape.
Artificial intelligence is transforming every part of our society, but its energy appetite is unlike anything we've seen before. The electricity required to power AI datacenters is expected to double or triple in the coming decade, straining infrastructure that is already under pressure. ORNL is uniquely positioned to meet this challenge.
Red Hat AI Enterprise provides a foundation for modern AI workloads, including AI life-cycle management, high-performance inference at scale, agentic AI innovation, integrated observability and performance modeling, and trustworthy AI and continuous evaluation. Tools are provided for dynamic resource scaling, monitoring, and security.
It's surprising to me that we are, in my view, so close to these models reaching human level intelligence, and yet there doesn't seem to be a wider recognition in society of what's about to happen. It's as if this tsunami is coming at us, and it's so close we can see it on the horizon. And yet people are coming up with these explanations, 'oh, it's not actually a tsunami... that's just a trick of the light.'
Running AI infrastructure costs are astronomical. Back in 2023, it was estimated that OpenAI spends around $700,000 per day to run ChatGPT—about 36 cents per query. However, in 2024 with the release of its higher-performing o3 model, some queries cost over $1,000 of computing power. Consequently, OpenAI CEO Sam Altman reports the company is even losing money on its $200 ChatGPT Pro subscriptions.
Enterprises building autonomous agents powered by large language models face new challenges that traditional test approaches were not designed to address. Agents behave probabilistically, integrate deeply with applications, and coordinate across tools, making isolated accuracy metrics insufficient for understanding real-world performance.
AI could have significant upsides for productivity, but countries will first have to navigate a complicated and expensive landscape as they create digital infrastructure and support disrupted workforces. For countries that already deal with constrained public finances, AI's capital costs could end up sharpening the policy trade-off between assuming higher near-term fiscal risk and delaying participation in AI-driven growth opportunities.
Autonomous agents take the first part of their names very seriously and don't necessarily do what their humans tell them to do - or not to do. But the situation is more complicated than that. Generative (genAI) and agentic systems operate quite differently than other systems - including older AI systems - and humans. That means that how tech users and decision-makers phrase instructions, and where those instructions are placed, can make a major difference in outcomes.
Given the right permissions and with the proper plugins, it could create, modify, or delete the user's files and otherwise change things far beyond what most users could achieve with existing models and MCP. Users would use files like USER.MD, MEMORY.MD, SOUL.MD, or HEARTBEAT.MD to give the tool context about its goals and how to work toward them independently, sometimes running for long stretches without direct user input.
Laboratory safety goggles have finally joined the ranks of smart devices. That's the promise behind LabOS, an AI operating system for scientific laboratories built by the Stanford-Princeton AI Coscientist Team, a group led by Stanford University bioengineer Le Cong and Princeton University computer scientist Mengdi Wang, with founding partners that include NVIDIA. Powered by NVIDIA's vision-language models to process visual data, the system is designed to provide AI with real-time knowledge of lab work so it can determine what causes experiments to fail or succeed and rapidly train new scientists to expert levels by guiding them through experimental protocols.
DeepSeek, the Hangzhou-based lab backed by quantitative hedge fund High-Flyer, has forced a reckoning across the AI supply chain by releasing highly capable models at a fraction of the cost charged by OpenAI, Google, and Anthropic. The result: a cascading price war that is reshaping how companies budget for intelligence, how developers choose their tools, and how the entire economics of foundation models may evolve over the next 12 to 18 months.
OpenAI just announced a massive funding round of $110 billion, which is one of the biggest investment rounds in Silicon Valley history. The investors feature many of the usual suspects, including Amazon with $50 billion, NVIDIA with $30 billion and SoftBank with $30 billion. This investment brings OpenAI to a $730 billion valuation.
OpenAI has been clear about its priorities: infrastructure, compute, and talent. CEO Sam Altman has repeatedly pointed to the sheer cost of training and running frontier AI models as the primary bottleneck to progress. According to reporting from Bloomberg, a significant portion of this raise will go toward securing GPU capacity through partnerships with data center providers across the United States, Japan, and the UAE.
