AI chipmaker Nvidia (NVDA) and pharmaceutical giant Eli Lilly (LLY) on Monday announced that the two companies would jointly invest $1 billion to create a lab in San Francisco focused on using AI to accelerate drug discovery.
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The $1 billion investment will be spent over five years on infrastructure, compute, and talent for the lab. Nvidia’s engineers will work alongside Lilly’s experts in biology, science, and medicine to generate large-scale data and build AI models to advance medicine development. The lab’s work will begin early this year, the companies said.
At CES 2026, Everything Is AI. What Matters Is How You Use It — from wired.com by Boone Ashworth Integrated chatbots and built-in machine intelligence are no longer standout features in consumer tech. If companies want to win in the AI era, they’ve got to hone the user experience.
Beyond Wearables
Right now, AI is on your face and arms—smart glasses and smart watches—but this year will see it proliferate further into products like earbuds, headphones, and smart clothing.
Health tech will see an influx of AI features too, as companies aim to use AI to monitor biometric data from wearables like rings and wristbands. Heath sensors will also continue to show up in newer places like toilets, bath mats, and brassieres.
The smart home will continue to be bolstered by machine intelligence, with more products that can listen, see, and understand what’s happening in your living space. Familiar candidates for AI-powered upgrades like smart vacuums and security cameras will be joined by surprising AI bedfellows like refrigerators and garage door openers.
After a year of bot battles, one thing stands out: There is no single best AI. The smartest way to use chatbots today is to pick different tools for different jobs — and not assume one bot can do it all.
Some enterprise platforms now support cross-agent communication and integration with ecosystems maintained by companies like Microsoft, NVIDIA, Google, and Oracle. These cross-platform data fabrics break down silos and turn isolated AI pilots into enterprise-wide services. The result is an IT backbone that not only automates but also collaborates for continuous learning, diagnostics, and system optimization in real time.
It’s difficult to think of any single company that had a bigger impact on Wall Street and the AI trade in 2025 than Nvidia (NVDA).
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Nvidia’s revenue soared in 2025, bringing in $187.1 billion, and its market capitalization continued to climb, briefly eclipsing the $5 trillion mark before settling back in the $4 trillion range.
There were plenty of major highs and deep lows throughout the year, but these 15 were among the biggest moments of Nvidia’s 2025.
Nvidia has officially become the first company in history to cross the $5 trillion market cap, cementing its position as the undisputed leader of the AI era. Just three months ago, the chipmaker hit $4 trillion; it’s already added another trillion since.
Nvidia market cap milestones:
Jan 2020: $144 billion
May 2023: $1 trillion
Feb 2024: $2 trillion
Jun 2024: $3 trillion
Jul 2025: $4 trillion
Oct 2025: $5 trillion
The above posting linked to:
Nvidia becomes first public company worth $5 trillion — from techcrunch.com by Ivan Mehta The biggest beneficiary of the ongoing AI boom, Nvidia has become the first public company to pass the $5 trillion market cap milestone.
At the most recent NVIDIA GTC conference, held in Washington, D.C. in October 2025, CEO Jensen Huang announced major developments emphasizing the use of AI to “reindustrialize America”. This included new partnerships, expansion of the Blackwell architecture, and advancements in AI factories for robotics and science. The spring 2024 GTC conference, meanwhile, was headlined by the launch of the Blackwell GPU and significant updates to the Omniverse and robotics platforms.
During the keynote in D.C., Jensen Huang focused on American AI leadership and announced several key initiatives.
Massive Blackwell GPU deployments: The company announced an expansion of its Blackwell GPU architecture, which first launched in March 2024. Reportedly, the company has already shipped 6 million Blackwell chips, with orders for 14 million more by the end of 2025.
AI supercomputers for science: In partnership with the Department of Energy and Oracle, NVIDIA is building new AI supercomputers at Argonne National Laboratory. The largest, named “Solstice,” will deploy 100,000 Blackwell GPUs.
6G infrastructure: NVIDIA announced a partnership with Nokia to develop a U.S.-based, AI-native 6G technology stack.
AI factories for robotics: A new AI Factory Research Center in Virginia will use NVIDIA’s technology for building massive-scale data centers for AI.
Autonomous robotaxis: The company’s self-driving technology, already adopted by several carmakers, will be used by Uber for an autonomous fleet of 100,000 robotaxis starting in 2027.
Nvidia (NVDA) and Uber (UBER) on Tuesday revealed that they’re working to put together what they say will be the world’s largest network of Level 4-ready autonomous cars.
The duo will build out 100,000 vehicles beginning in 2027 using Nvidia’s Drive AGX Hyperion 10 platform and Drive AV software.
Nvidia (NVDA) stock on Tuesday rose 5% to close at a record high after the company announced a slew of product updates, partnerships, and investment initiatives at its GTC event in Washington, D.C., putting it on the doorstep of becoming the first company in history with a market value above $5 trillion.
The AI chip giant is approaching the threshold — settling at a market cap of $4.89 trillion on Tuesday — just months after becoming the first to close above $4 trillion in July.
The Bull and Bear Case For the AI Bubble, Explained — from theneuron.ai by Grant Harvey AI is both a genuine technological revolution and a massive financial bubble, and the defining question is whether miraculous progress can outrun the catastrophic, multi-trillion-dollar cost required to achieve it.
This sets the stage for the defining conflict of our technological era. The narrative has split into two irreconcilable realities. In one, championed by bulls like venture capitalist Marc Andreessen and NVIDIA CEO Jensen Huang, we are at the dawn of “computer industry V2”—a platform shift so profound it will unlock unprecedented productivity and reshape civilization.
In the other, detailed by macro investors like Julien Garran and forensic bears like writer Ed Zitron, AI is a historically massive, circular, debt-fueled mania built on hype, propped up by a handful of insiders, and destined for a collapse that will make past busts look quaint.
This is a multi-layered conflict playing out across public stock markets, the private venture ecosystem, and the fundamental unit economics of the technology itself. To understand the future, and whether it holds a revolution, a ruinous crash, or a complex mixture of both, we must dissect every layer of the argument, from the historical parallels to the hard financial data and the technological critiques that question the very foundation of the boom.
From DSC:
I second what Grant said at the beginning of his analysis:
**The following is shared for educational purposes and is not intended to be financial advice; do your own research!
But I post this because Grant provides both sides of the argument very well.
In short, it’s been a monumental 12 months for AI. Our eighth annual report is the most comprehensive it’s ever been, covering what you need to know about research, industry, politics, and safety – along with our first State of AI Usage Survey of 1,200 practitioners.
Strategic partnership enables OpenAI to build and deploy at least 10 gigawatts of AI datacenters with NVIDIA systems representing millions of GPUs for OpenAI’s next-generation AI infrastructure.
To support the partnership, NVIDIA intends to invest up to $100 billion in OpenAI progressively as each gigawatt is deployed.
The first gigawatt of NVIDIA systems will be deployed in the second half of 2026 on NVIDIA’s Vera Rubin platform.
Why this matters: The partnership kicks off in the second half of 2026 with NVIDIA’s new Vera Rubin platform. OpenAI will use this massive compute power to train models beyond what we’ve seen with GPT-5 and likely also power what’s called inference (when you ask a question to chatGPT, and it gives you an answer). And NVIDIA gets a guaranteed customer for their most advanced chips. Infinite money glitch go brrr am I right? Though to be fair, this kinda deal is as old as the AI industry itself.
This isn’t just about bigger models, mind you: it’s about infrastructure for what both companies see as the future economy. As Sam Altman put it, “Compute infrastructure will be the basis for the economy of the future.”
… Our take: We think this news is actually super interesting when you pair it with the other big headline from today: Commonwealth Fusion Systems signed a commercial deal worth more than $1B with Italian energy company Eni to purchase fusion power from their 400 MW ARC plant in Virginia. Here’s what that means for AI…
AI filmmaker Dinda Prasetyo just released “Skyland,” a fantasy short film about a guy named Aeryn and his “loyal flying fish”, and honestly, the action sequences look like they belong in an actual film…
SKYLAND | AI Short Film Fantasy
Skyland is an AI-powered fantasy short film that takes you on a breathtaking journey with Aeryn Solveth and his loyal flying fish. From soaring above the futuristic city of Cybryne to returning to his homeland of Eryndor, Aeryn’s adventure is… https://t.co/Lz6UUxQvExpic.twitter.com/cYXs9nwTX3
What’s wild is that Dinda used a cocktail of AI tools (Adobe Firefly, MidJourney, the newly launched Luma Ray 3, and ElevenLabs) to create something that would’ve required a full production crew just two years ago.
The Era of Prompts Is Over. Here’s What Comes Next. — from builtin.com by Ankush Rastogi If you’re still prompting your AI, you’re behind the curve. Here’s how to prepare for the coming wave of AI agents.
Summary: Autonomous AI agents are emerging as systems that handle goals, break down tasks and integrate with tools without constant prompting. Early uses include call centers, healthcare, fraud detection and research, but concerns remain over errors, compliance risks and unchecked decisions.
The next shift is already peeking around the corner, and it’s going to make prompts look primitive. Before long, we won’t be typing carefully crafted requests at all. We’ll be leaning on autonomous AI agents, systems that don’t just spit out answers but actually chase goals, make choices and do the boring middle steps without us guiding them. And honestly, this jump might end up dwarfing the so-called “prompt revolution.”
A new way to get things done with your AI browsing assistant Imagine you’re a student researching a topic for a paper, and you have dozens of tabs open. Instead of spending hours jumping between sources and trying to connect the dots, your new AI browsing assistant — Gemini in Chrome1 — can do it for you. Gemini can answer questions about articles, find references within YouTube videos, and will soon be able to help you find pages you’ve visited so you can pick up exactly where you left off.
Rolling out to Mac and Windows users in the U.S. with their language set to English, Gemini in Chrome can understand the context of what you’re doing across multiple tabs, answer questions and integrate with other popular Google services, like Google Docs and Calendar. And it’ll be available on both Android and iOS soon, letting you ask questions and summarize pages while you’re on the go.
We’re also developing more advanced agentic capabilities for Gemini in Chrome that can perform multi-step tasks for you from start to finish, like ordering groceries. You’ll remain in control as Chrome handles the tedious work, turning 30-minute chores into 3-click user journeys.
Voice-powered AI meets a visual companion for entertainment, everyday help, and everything in between.
Redmond, Wash., August 27—Today, we’re announcing the launch ofCopilot on select Samsung TVs and monitors, transforming the biggest screen in your home into your most personal and helpful companion—and it’s free to use.
Copilot makes your TV easier and more fun to use with its voice-powered interface, friendly on-screen character, and simple visual cards. Now you can quickly find what you’re looking for and discover new favorites right from your living room.
Because it lives on the biggest screen in the home, Copilot is a social experience—something you can use together with family and friends to spark conversations, help groups decide what to watch, and turn the TV into a shared space for curiosity and connection.
Why it matters: GPT-5 embodies a “team of specialists” approach—fast small models for most tasks, powerful ones for hard problems—reflecting NVIDIA’s “heterogeneous agentic system” vision. This could evolve into orchestration across dozens of specialized models, mirroring human collective intelligence.
Bottom line: GPT-5 isn’t AGI, but it’s a leap in usability, reliability, and breadth—pushing ChatGPT toward being a truly personal, expert assistant.
…and another article from Grant Harvey:
GPT-5 is here… here’s everything you need to know (so far…). OpenAI launched GPT-5—described as its most capable model to date—now in ChatGPT (with higher usage limits for paid tiers) and the API, bringing stronger reasoning/coding/math/writing and safety improvements, yet, per Sam Altman, still short of AGI.
Why it matters: OpenAI’s move to replace its flurry of models with a unified GPT-5 simplifies user experience and gives everyone a PhD-level assistant, bringing elite problem-solving to the masses. The only question now is how long it can hold its edge in this fast-moving AI race, with Anthropic, Google, and Chinese giants all catching up.
OpenAI’s ChatGPT-5 released — from getsuperintel.com by Kim “Chubby” Isenberg GPT-5’s release marks a new era of productivity, from specialized AI tool to universal intelligence partner
The Takeaway
GPT-5’s unified architecture eliminates the effort of model switching and makes it the first truly seamless AI assistant that automatically applies the right level of reasoning for each task.
With 45% fewer hallucinations and 94.6% accuracy on complex math problems, GPT-5 exceeds the reliability threshold required for business-critical applications.
The model’s ability to generate complete applications from single prompts signals the democratization of software development and could revolutionize traditional coding workflows.
OpenAI’s “Safe Completions” training approach represents a new paradigm in AI safety, providing nuanced responses instead of blanket rejections for dual-use scenarios.
GPT-5 is live – but the community is divided— from getsuperintel.com by Kim “Chubby” Isenberg For some, it’s a lightning-fast creative partner; for others, it’s a system that can’t even decide when to think properly
Many had hoped that GPT-5 would finally unite all models – reasoning, image and video generation, voice – “one model to rule them all,” but this expectation has not been met.
GPT-5 marks a profound change in the human/machine relationship.
OBSERVATION #1: Up until yesterday, using OpenAI, you could pick the exact model variant for your task: the one tuned for reasoning, for writing, for code, or for math. Each had its own strengths, and experienced users learned which to reach for and when. In GPT-5, those choices are gone. There’s just “GPT-5,” and the routing decisions of which mode, which tool, which underlying approach is made by the model.
For a beginner, that’s a blessing. Most novice users never knew the differences between the models anyway. They used the same one regardless of the task.
For an experienced user, the jury’s still out. On one hand, the routing could save time. On the other, it introduces unpredictability: you can no longer reliably choose the optimal model for your purpose. If GPT-5’s choice is wrong, you’re stuck re-prompting rather than switching.
Why it matters:
GPT-5 signals a shift in AI’s evolution: progress through refinement, not revolution. While benchmarks show incremental gains, the real win is accessibility. Cheaper models (from $0.05 per million tokens) make AI more available to developers and casual users. This aligns with the Jevons Paradox, where lower costs could drive explosive adoption. However, the AGI hype faces reality checks. Fundraising may slow as investors focus on real-world utility over moonshots. For now, GPT-5 strengthens OpenAI’s market lead, proving AI’s future lies in practical applications, not just raw power.
I have had early access to GPT-51, and I wanted to give you some impressions of what it is and what it means. But the answer to the first question everyone will have is: Yes, this is a big deal. I asked GPT-5 to show you why by pasting this intro paragraph, exactly as you’re reading it, into ChatGPT, including the phrase: “you are GPT-5, do something very dramatic to illustrate my point, it has to fit into the next paragraph.”
That was the only prompt I gave it. It “thought” for 24 seconds and wrote:
Thunderstruck. Here, watch. I build worlds. See ideas become instruments. I code, compose, and converse. Stories synthesize science; solutions spark, swiftly. Asks arrive; answers appear, astonishingly, across domains. Behold: business plans, blueprints, briefs, beautifully built, briskly. I infer intent, integrate insight, and iterate instantly, indefatigably. Gaps get gauged; goals get generated, guarded, and guaranteed gracefully. During dialogue, disparate data distills down; decisions develop deliberately, deftly, dependably. Everything explained efficiently, even ethics: evidence examined, empathy engaged, everywhere, everyday, equitably. All at once, ambiguous arguments align as actionable, accurate advice awaits anyone anywhere. Look: language, logic, learning, and long-term memory link, lifting lives and labor limitlessly worldwide.
“Where generative AI creates, agentic AI acts.” That’s how my trusted assistant, Gemini 2.5 Pro deep research, describes the difference.
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Agents, unlike generative tools, create and perform multistep goals with minimal human supervision. The essential difference is found in its proactive nature. Rather than waiting for a specific, step-by-step command, agentic systems take a high-level objective and independently create and execute a plan to achieve that goal. This triggers a continuous, iterative workflow that is much like a cognitive loop. The typical agentic process involves six key steps, as described by Nvidia:
Our 2025 national survey of over 650 respondents across 49 states and Puerto Rico reveals both encouraging trends and important challenges. While AI adoption and optimism are growing, concerns about cheating, privacy, and the need for training persist.
Despite these challenges, I’m inspired by the resilience and adaptability of educators. You are the true game-changers in your students’ growth, and we’re honored to support this vital work.
This report reflects both where we are today and where we’re headed with AI. More importantly, it reflects your experiences, insights, and leadership in shaping the future of education.
This groundbreaking collaboration represents a transformative step forward in education technology and will begin with, but is not limited to, an effort between Instructure and OpenAI to enhance the Canvas experience by embedding OpenAI’s next-generation AI technology into the platform.
IgniteAI announced earlier today, establishes Instructure’s future-ready, open ecosystem with agentic support as the AI landscape continues to evolve. This partnership with OpenAI exemplifies this bold vision for AI in education. Instructure’s strategic approach to AI emphasizes the enhancement of connections within an educational ecosystem comprising over 1,100 edtech partners and leading LLM providers.
“We’re committed to delivering next-generation LMS technologies designed with an open ecosystem that empowers educators and learners to adapt and thrive in a rapidly changing world,” said Steve Daly, CEO of Instructure. “This collaboration with OpenAI showcases our ambitious vision: creating a future-ready ecosystem that fosters meaningful learning and achievement at every stage of education. This is a significant step forward for the education community as we continuously amplify the learning experience and improve student outcomes.”
Faculty Latest Targets of Big Tech’s AI-ification of Higher Ed— from insidehighered.com by Kathryn Palmer A new partnership between OpenAI and Instructure will embed generative AI in Canvas. It may make grading easier, but faculty are skeptical it will enhance teaching and learning.
The two companies, which have not disclosed the value of the deal, are also working together to embed large language models into Canvas through a feature called IgniteAI. It will work with an institution’s existing enterprise subscription to LLMs such as Anthropic’s Claude or OpenAI’s ChatGPT, allowing instructors to create custom LLM-enabled assignments. They’ll be able to tell the model how to interact with students—and even evaluate those interactions—and what it should look for to assess student learning. According to Instructure, any student information submitted through Canvas will remain private and won’t be shared with OpenAI.
… Faculty Unsurprised, Skeptical
Few faculty were surprised by the Canvas-OpenAI partnership announcement, though many are reserving judgment until they see how the first year of using it works in practice.
Call it the ultimate proving ground. Collaborating with teammates in the modern workplace requires fast, fluid thinking. Providing insights quickly, while juggling webcams and office messaging channels, is a startlingly good test, and enterprise AI is about to pass it — just in time to provide assistance to busy knowledge workers.
To support enterprises in boosting productivity with AI teammates, NVIDIA today introduced a new NVIDIA Enterprise AI Factory validated design at COMPUTEX. IT teams deploying and scaling AI agents can use the design to build accelerated infrastructure and easily integrate with platforms and tools from NVIDIA software partners.
NVIDIA also unveiled new NVIDIA AI Blueprints to aid developers building smart AI teammates. Using the new blueprints, developers can enhance employee productivity through adaptive avatars that understand natural communication and have direct access to enterprise data.
“AI is now infrastructure, and this infrastructure, just like the internet, just like electricity, needs factories,” Huang said. “These factories are essentially what we build today.”
“They’re not data centers of the past,” Huang added. “These AI data centers, if you will, are improperly described. They are, in fact, AI factories. You apply energy to it, and it produces something incredibly valuable, and these things are called tokens.”
More’s coming, Huang said, describing the growing power of AI to reason and perceive. That leads us to agentic AI — AI able to understand, think and act. Beyond that is physical AI — AI that understands the world. The phase after that, he said, is general robotics.
May 19 (Reuters) – Dell Technologies (DELL.N), opens new tab on Monday unveiled new servers powered by Nvidia’s (NVDA.O), opens new tab Blackwell Ultra chips, aiming to capitalize on the booming demand for artificial intelligence systems.
The servers, available in both air-cooled and liquid-cooled variations, support up to 192 Nvidia Blackwell Ultra chips but can be customized to include as many as 256 chips.
Nvidia (NVDA) rolled into this year’s Computex Taipei tech expo on Monday with several announcements, ranging from the development of humanoid robots to the opening up of its high-powered NVLink technology, which allows companies to build semi-custom AI servers with Nvidia’s infrastructure.
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During the event on Monday, Nvidia revealed its Nvidia Isaac GR00T-Dreams, which the company says helps developers create enormous amounts of training data they can use to teach robots how to perform different behaviors and adapt to new environments.
Essential AI tools for better work — from wondertools.substack.com by Jeremy Caplan My favorite tactics for making the most of AI — a podcast conversation
AI tools I consistently rely on (areas covered mentioned below)
Research and analysis
Communication efficiency
Multimedia creation
AI tactics that work surprisingly well
1. Reverse interviews Instead of just querying AI, have it interview you. Get the AI to interview you, rather than interviewing it. Give it a little context and what you’re focusing on and what you’re interested in, and then you ask it to interview you to elicit your own insights.”
This approach helps extract knowledge from yourself, not just from the AI. Sometimes we need that guide to pull ideas out of ourselves.
Isla Fulford, a researcher at OpenAI, had a hunch that Deep Research would be a hit even before it was released.
Fulford had helped build the artificial intelligence agent, which autonomously explores the web, deciding for itself what links to click, what to read, and what to collate into an in-depth report. OpenAI first made Deep Research available internally; whenever it went down, Fulford says, she was inundated with queries from colleagues eager to have it back. “The number of people who were DMing me made us pretty excited,” says Fulford.
Since going live to the public on February 2, Deep Research has proven to be a hit with many users outside the company too.
Nvidia (NASDAQ:NVDA) will open a quantum computing research lab in Boston which is expected to start operations later this year.
The Nvidia Accelerated Quantum Research Center, or NVAQC, will integrate leading quantum hardware with AI supercomputers, enabling what is known as accelerated quantum supercomputing, said the company in a March 18 press release.
Nvidia’s CEO Jensen Huang also made this announcement on Thursday at the company’s first-ever Quantum Day at its annual GTC event.
PARIS, March 21 (Reuters) – Pasqal, a fast-growing French quantum computer start-up company, announced on Friday a partnership with chip giant Nvidia (NVDA.O), opens new tab whereby Pasqal’s customers would gain access to more tools to develop quantum applications.
Pasqal said it would connect its quantum computing units and cloud platform onto NVIDIA’s open-source platform called CUDA-Q.
Today, we’re launching new speech-to-text and text-to-speech audio models in the API—making it possible to build more powerful, customizable, and intelligent voice agents that offer real value. Our latest speech-to-text models set a new state-of-the-art benchmark, outperforming existing solutions in accuracy and reliability—especially in challenging scenarios involving accents, noisy environments, and varying speech speeds. These improvements increase transcription reliability, making the models especially well-suited for use cases like customer call centers, meeting note transcription, and more.
From DSC: Look out Google, Amazon, and others! Nvidia is putting the pedal to the metal in terms of being innovative and visionary! They are leaving the likes of Apple in the dust.
The top talent out there is likely to go to Nvidia for a while. Engineers, programmers/software architects, network architects, product designers, data specialists, AI researchers, developers of robotics and autonomous vehicles, R&D specialists, computer vision specialists, natural language processing experts, and many more types of positions will be flocking to Nvidia to work for a company that has already changed the world and will likely continue to do so for years to come.
NVIDIA just shook the AI and Robotic world at NVIDIA GTC 2025.
CEO Jensen Huang announced jaw-dropping breakthroughs.
Here are the top 11 key highlights you can’t afford to miss: (wait till you see no 3) pic.twitter.com/domejuVdw5
For enterprises, NVIDIA unveiled DGX Spark and DGX Station—Jensen’s vision of AI-era computing, bringing NVIDIA’s powerful Blackwell chip directly to your desk.
Nvidia Bets Big on Synthetic Data — from wired.com by Lauren Goode Nvidia has acquired synthetic data startup Gretel to bolster the AI training data used by the chip maker’s customers and developers.
Nvidia, xAI to Join BlackRock and Microsoft’s $30 Billion AI Infrastructure Fund — from investopedia.com by Aaron McDade Nvidia and xAI are joining BlackRock and Microsoft in an AI infrastructure group seeking $30 billion in funding. The group was first announced in September as BlackRock and Microsoft sought to fund new data centers to power AI products.
AI Super Bowl. Hi everyone. This week, 20,000 engineers, scientists, industry executives, and yours truly descended upon San Jose, Calif. for Nvidia’s annual GTC developers’ conference, which has been dubbed the “Super Bowl of AI.”
