Nvidia CEO Jensen Huang kept his company on top of the AI world this week.

At the company’s annual GTC developer conference this week in San Jose, Huang (pictured here on the exhibit hall floor with throngs of admirers seeking selfies with the tech rock star) introduced a series of new graphics processing units, new AI supercomputers big and small, more software to make it all work, and a vision of enabling AI factories to produce massive amounts of new intelligence — and revenue.

And that includes Nvidia, as investors well know. This week it splashed some of that cash it’s making on a $30 billion consortium to build more AI infrastructure, and reportedly on acquiring the synthetic data startup Gretel. Gotta keep those flywheels going!

Speaking of GPUs, AI hosting platform CoreWeave filed to go public, aiming to raise $2.7 billion. Another sign of the continued demand for GPUs: Even though Microsoft passed on the opportunity to lock in $12 billion worth of CoreWeave’s GPU capacity, OpenAI stepped in to snap it up.

Shadow IT? Try Shadow AI. Stealth use of AI models could present a big problem for companies needing to protect their data and their reputations.

SoftBank is buying AI chipmaker Ampere Computing for $6.5 billion — not a big surprise given all that demand for AI chips, not to mention SoftBank’s 90% ownership of Arm, on which Ampere chips are based.

Google finally managed to bag cybersecurity phenom Wiz after the deal faltered last year, but it cost a pretty penny: $32 billion, the company’s biggest acquisition ever. It’s another sign, if we needed any at this point, that it’s serious about bolstering Google Cloud. We’ll learn more about that next month when we cover its Google Cloud Next conference in Las Vegas.

The European Union’s campaign against U.S. Big Tech isn’t slowing down, as it separately found that both Apple and Google breached its antitrust rules.

Here’s more detail on that and the other big news this week on SiliconANGLE and beyond:

AI and data: Nvidia’s AI factory

Top of mind

Nvidia, of course! The graphics chip company — sorry, AI factory — held its GTC event this week, and no surprise, it was mostly about AI, with a day of quantum computing thrown in. Here’s all our coverage from GTC, the major AI conference of the year, with my own observations below:

Scaling up: Nvidia redefines the computing stack with new releases for the AI factory

Nvidia cranks up agentic AI with revamped Blackwell Ultra GPUs and next-gen AI desktops

Nvidia’s new reasoning models and building blocks pave the way for advanced AI agents

Quest for qubits: Quantum computing leaders make their case at Nvidia GTC

Nvidia announces new AI models for smarter, more adaptable robots

Nvidia expands Omniverse to simulate gigawatt AI data centers and drive robotic factories

Nvidia debuts new silicon photonics switches for AI data centers

The key takeaways from Nvidia CEO Jensen Huang’s GTC keynote

Nvidia GTC heats up: Catch the AI breakthroughs on theCUBE

Michael Dell calls AI a revolution, as Dell deepens Nvidia alliance

Dell aims new servers and software at Nvidia-powered AI applications

HPE’s Antonio Neri on AI, data centers and the future of infrastructure

HPE and Nvidia tighten partnership with broad infrastructure enhancements

Accenture debuts AI agent builder within its AI Refinery platform

Deloitte hops aboard the agentic AI hype train with Zora AI

Some of my observations from keynotes, panels, interviews and wandering the show floor:

* Huang is pushing hard on his AI Factory idea, and it’s not entirely a marketing term. Its AI factories are indeed producing the raw material of the AI era — tokens used to train and run the AI models to give us those mostly amazing answers to our queries — and they require the same energy and physical infrastructure as steel or autos. It’s just that the output isn’t hardware but software — which continues to eat the world. 

* The key to that for Nvidia is, of course, ever more powerful chips, and least surprising of all, Nvidia introduced Blackwell Ultra, the successor graphics processing unit coming later in the year even as the current Blackwell chips, which are 40 times slower, are still in short supply. But somewhat more unusual, Huang also tipped a chip not due until late 2026, called Rubin, to provide all its partners time to make sure they’re prepared for the tick-tock transitions to new chips. “We’re not building chips anymore,” Huang said in a press Q&A. “Now we build AI infrastructure that is deployed hundreds of billions of dollars at a time. So my planning needs to be upstream many years and downstream many years.”

* How long can this AI building frenzy last? There aren’t many signs of a slowdown, really, but these are big bets Nvidia and its customers are making. Like most semiconductor cycles, and like other tech infrastructure buildouts such as the internet boom, eventually things slow down. When that happens, the scale of this buildout means it could be a hard fall. Just not yet.

* Nvidia also introduced Dynamo, which Huang described as the “operating system of the AI factory.” The open-source “inference serving library” is intended to speed up inference, the process of running models, so they can prepare more accurate responses to queries in a process called “reasoning” — more on that in a second.

* A year after the last GTC when many AI projects were still prototypes or experiments, enterprises have turned a page with AI. “We’ve moved to a stage where we’re moving into full production,” Rahul Kulkarni, director of product management for compute and AI/ML infrastructure at Amazon Web Services, told SiliconANGLE, with solid results from clients such as Adobe, ServiceNow and Perplexity.

* At the same time, there’s still no easy button for AI. “There is no simple out-of-the-box solution for companies to get started,” Kulkarni noted. Many are working on that, including Nvidia, AWS and a gazillion system integrators, a sign that even AI-forward enterprises are still in the early stages of reinventing their businesses with it.

* The next big thing in AI — beyond agents, which themselves are still nascent — is “reasoning,” Nvidia confirms, and I put it in quotes because it still seems to anthropomorphize a process that still really doesn’t work the way humans learn and think. Reasoning models take more time — and of course more tokens and therefore more compute — to get to better answers. This is a big improvement over quick-and-dirty chatbots, of course. Just don’t fool yourself into thinking that they’re “thinking” or truly human-level intelligent.

* One reason that remains true is that animals such as people learn perhaps much more by living in the physical world than by language. As Jeff Hawkins and others contend, if we’re ever going to get to really capable AIs without burning up the world to provide energy for compute-intensive large language models, there’s much more kinds of learning we need to employ than ever more clever next-word prediction. And many of them involve interacting with physical objects in the real world — requiring what some call a “world model.”  “We have models of the physical world that we acquire in the first few weeks of life,” Meta Platforms Chief AI Scientist Yann LeCun said in an onstage interview. That’s going to require architectures that are completely different from LLMs, he argues. That’s one reason “physical AI,” a term Nvidia and others use as a wrapper for robots and autonomous vehicles, was a big focus at GTC. “The time has come for robots,” Huang said, who showed off a cute little robot onstage.

* Developers developers developers! That’s whom Nvidia is aiming for with its cute little DGX Spark “AI supercomputer” that starts at $2999 and can be added to a laptop for easy model development and other AI work such as creating proofs of concept. Oddly enough, it’s easier and often cheaper than renting the same compute in the cloud. “We’re essentially offloading work from DGX Cloud and other GPU cloud resources,” Bob Pette, VP and GM of enterprise platforms at Nvidia, told me.

* Quantum computing is coming — maybe a bit sooner than Jensen implied a couple months ago. He admitted that his offhand comment that practical quantum computing could be as much as 30 years away — tanking quantum stocks for awhile — was perhaps too negative. In a series of panels Thursday at GTC, the CEOs of more than a dozen quantum companies explained their various approaches, from using atoms and superconducting materials as qubits to finding new ways to correct quantum’s rampant errors.

* Still, the reality is that it’s still going to be a good number of years before quantum is solving a lot of practical problems at large scale. LeCun noted archly in a separate onstage interview that the main use case for quantum computers for now seems to be simulating quantum computers. And even a chastened Huang seemed mildly skeptical at times about how soon it will be useful — in fact, he didn’t exactly “walk back” his comments so much as explain that new technologies — such as, say, accelerated computing — just take longer to have a big impact than people realize.

* One sign of the nascent nature of quantum is simply that there are so many disparate approaches. The quantum CEOs made good cases for each of them, but it’s far from clear which if any will get to practical usefulness, or when. “Some of us may come together,” IonQ Executive Chairman Pete Chapman said in an admission that consolidation of quantum companies with similar approaches is likely.

* Quantum computing won’t replace classical computing. Instead, it will work in tandem, even perhaps employing a “quantum processing unit” in conventional computers like GPUs are used today. However, quantum computing isn’t just for running existing workloads faster but doing things such as drug discovery at massive scale that simply can’t be done now. “Quantum will be a big compute resource like classical computers are a big data resource,” said Ben Bloom, founder and CEO of Atom Computing. “We need to figure out how to use that.”

* One way is to enable a better approach to doing AI. “We have not been able to compute like nature computes,” noted Krysta Svore, a technical fellow at Microsoft. “Quantum takes us a step closer.”

* One last morsel: Denny’s has a new limited-time menu item: Nvidia Breakfast Bytes. Basically pigs in a blanket with pancakes and syrup, this was a combo order Huang loved when he was a dishwasher at Denny’s — where he started Nvidia with his co-founders.

On another note, check out this deep dive from Paul Gillin on what could be a ticking time bomb in the AI era: Shadow AI: Companies struggle to control unsanctioned use of new tools

Money matters

Nvidia, xAI join $30B AI investment consortium backed by Microsoft

Nvidia reportedly acquires Gretel for $320M+ to strengthen AI training tools

Money-hungry AI search startup Perplexity in talks to raise up to $1B in fresh funding

AI-driven commercial contractor platform BuildOps raises $127M at $1B valuation

Dataminr raises $85M for its real-time analytics platform

XAI acquires AI video generation startup Hotshot

Carbon Arc reels in $56M for its AI data platform

AI code assist startup Graphite raises $52M to try and keep ahead of the competition

Halliday raises $20M to build AI-driven blockchain agents to do away with smart contracts

Rerun gets $17M to build the essential data infrastructure for AI-powered robots, drones and cars

Silicon Data raises $4.7M for AI-driven GPU market insights

New models and services

Google Cloud is helping gaming startups use AI change the industry

Google Gemini introduces collaborative canvas and podcast-like audio overviews

Baidu debuts its first AI reasoning model to compete with DeepSeek

Mistral AI’s newest model packs more power in a much smaller package

Oracle lets customers create and modify AI agents across its Fusion application suite

Cisco debuts new AI-powered customer service features for Webex

Zoom introduces new AI capabilities and agents with major release

Adobe unleashes an army of AI agents on sales and marketing teams

Lyft to launch autonomous taxi service in Atlanta and Dallas

LogicMonitor improves visibility into AI workloads

Qualtrics says its new AI agents can satisfy most customer complaints without human guidance

Policy

Report co-authored by Fei-Fei Li stresses need for AI regulations to consider future risks

Court rules copyrighting AI-generated art is a no-go – even if you invented the software

There’s even more AI and big data news on SiliconANGLE

Around the enterprise: Softbank acquires Ampere

SoftBank agrees to buy Arm chipmaker Ampere Computing for $6.5B

AI cloud operator CoreWeave files for $2.7B IPO Meanwhile, Microsoft chose not to exercise $12 billion Coreweave option (per Semafor), but luckily for the GPU farm, OpenAI did

Dozens of tech firms urge EU to become more technologically independent

Virtual desktop startup Nerdio raises $500M at $1B+ valuation

Cloud infrastructure startup Evroc raises €50.6M to build new data centers

Google reportedly partnering with MediaTek for next-generation TPU production

Micron’s stock falls despite solid jump in revenue from AI memory chips

Confluent expands AI and analytics capabilities in Apache Flink and Tableflow

FCC opens probe into nine Chinese tech firms over US presence

We have plenty more news on cloud, infrastructure and apps

Cyber beat: Google finally buys Wiz

Top of mind

In its largest-ever acquisition, Google buys cybersecurity startup Wiz for $32B

More money matters

VulnCheck raises $12M to boost global expansion of exploit intelligence platform

Orion Security raises $6M to plug sensitive data leaks with AI smarts

New services

Cloudflare introduces Threat Events Feed to enhance cyberthreat visibility

JFrog’s Conan introduces Conan Audit to strengthen C/C++ dependency security

Prompt Security launches authorization features to strengthen AI data access controls

Attack & response

Enterprise AI adoption jumps 30-fold as organizations face growing cybersecurity risks

AI-driven threats fuel rise in phishing and zero-day attacks

Zimperium report warns that mobile rooting and jailbreaking still pose serious security risks

Flashpoint report highlights rising cyberthreats, with infostealers and ransomware leading the way

More cybersecurity news here

Elsewhere around tech: EU targets Apple and Google

EU finds Apple, Google breached DMA antitrust rules

Alphabet spins off its Taara laser-powered networking venture

D-Wave introduces quantum blockchain research that could greatly reduce energy use

FTC commissioners claim they were ‘illegally fired’ by Trump

Coinbase could reportedly acquire crypto derivatives market Deribit for $5B

Kraken enters agreement to acquire futures trading platform NinjaTrader for $1.5B

Skidattl wants to make augmented reality overlays ubiquitous with QR codes

Utila raises $18M to meet demand for its institutional digital asset MPC wallets

And check out more news on emerging tech, blockchain and crypto and policy

Comings and goings

Raj Aggarwal has left AWS after nearly three years as GM of generative AI and revenue acceleration, to do an unnamed startup (per TechCrunch).

Dr. Ann Kelleher, the executive vice president at Intel responsible for developing Intel’s fabrication technologies since 2020, announced plans to retire sometime later this year after three decades at the company, remaining an adviser (per Tom’s Hardware). She will be succeeded by Naga Chandrasekaran, who will be responsible for the development and implementation of semiconductor manufacturing processes. Navid Shahriari will be responsible for various back-end operations, such as advanced packaging.

Sources: Vision Pro creator Mike Rockwell will take over Apple’s Siri, which is being removed from AI head John Giannandrea after CEO Tim Cook lost reportedly lost confidence in the former Google executive (per Bloomberg).

What’s next

March 25: Axonius Adapt, Dallas: TheCUBE will be onsite with interviews and analysis.

March 26: Chainguard Assemble, San Francisco: TheCUBE will be onsite with interviews and analysis.

Photo: Robert Hof/SiliconANGLE