Dave Altavilla offers commentary on Nvidia's (NVDA) earnings minutes following the report. He says the company's record quarter shows that Nvidia is "powering" the AI trade on all fronts and helps ease fears of massive CapEx spending in hyperscalers like Amazon (AMZN), Microsoft (MSFT), and Meta Platforms (META). Dave also explains why he doesn't see margin compression in the cards for Nvidia.

The agreement represents one of the largest AI infrastructure commitments for the company to date and reinforces AMD’s growing relevance as hyperscalers look to diversify beyond Nvidia as a single dominant silicon supplier.

Electronic Design Automation leader, Cadence Design Systems is accelerating the shift toward autonomous chip development, moving artificial intelligence from a supporting role to the center of semiconductor design workflows. With the launch of its new ChipStack AI Super Agent, Cadence is introducing what it claims is the industry’s first fully agentic platform for front-end chip design and verification — a system capable of autonomously translating specifications into working designs, executing verification flows, and resolving issues alongside its human engineering counterparts. The company says the approach can deliver productivity gains of up to 10X, addressing both the surging complexity of modern silicon and a growing shortage of senior engineering talent.

In an industry often punctuated by megadeals among the larger semiconductor players, SiTime Corporation is making a different kind of strategic move. Its recently announced acquisition of Renesas Electronics’ timing business is a calculated bet on expansion that underscores how precision timing is becoming a critical performance lever in AI infrastructure, high-speed networking, next-generation communications and robotics.

In a move that could have major implications for future AI datacenters, SiFive, a leader in RISC-V processor IP, has announced a strategic partnership with Nvidia to integrate the company’s NVLink Fusion interconnect technology into its forthcoming silicon platforms.

One month in as my daily driver phone, the OnePlus 15 has proven itself to be less about flash and more about getting the fundamentals right. It’s a device that doesn’t try to break out with clever design hooks or gimmick software features. Instead, it focuses on the things phone shoppers actually feel every day: endurance, responsiveness, and a sense of consistency that some modern flagships ironically sometimes miss.

Demand still far outstrips supply" when it comes to Nvidia's (NVDA) data center growth, says Dave Altavilla. He expects that trend to continue as the Mag 7 giant sets its sights on margin expansion through its Blackwell and Blackwell Ultra sales. Dave adds that the A.I. will only grow, giving AMD Inc. (AMD) a chance to capture Nvidia's customers it can't take due to supply constraints. Tom White offers an example options trade for Nvidia.

With a tee-up from its amazingly accomplished CEO Dr. Lisa Su, AMD execs outlined a long-term growth model that calls for more than 35% compound annual revenue growth at the company level and over a 60% CAGR in its profitable data center business over the next three to five years.

Cadence Design Systems made a major advancement with it system chiplet, that may further accelerate the semiconductor industry’s migration toward evolving chiplet-based architectures. The company detailed the successful silicon bring-up of its system chiplet architecture, which is the cornerstone of a broader chiplet ecosystem vision designed to push modular silicon platforms forward. I first wrote about Cadence’s system chiplet earlier this year…

In the drive to build more powerful AI platforms and denser compute clusters with fast interconnects, one of the unsung, foundational technologies required is also becoming one of the most critical—precision timing. Every AI server, optical module, and high-speed network link (among many other things) depends on accurate, precision timing signals to keep hundreds or thousands of processors synchronized. Without them, latency increases, data errors multiply, and efficiency drops. That’s where SiTime has carved out its niche.

Over the last few days, the U.S. Department of Energy (DOE) announced a couple of strategic partnerships to build no less than four powerful AI supercomputers, spread across two national laboratories. AMD and Nvidia will be powering two major U.S. government-backed AI infrastructure projects—AMD with HPE for Sovereign AI Factory supercomputers and Nvidia with Oracle for the DOE’s largest AI system yet, though Oracle will also be involved with AMD’s project as well.

Nvidia and Uber further solidified a date the AV industry has been striving towards for years: large-scale robotaxis in regular service. The two companies plan to begin ramping a global autonomous fleet in 2027, growing toward 100,000 vehicles that will eventually roll directly onto Uber’s ride-hailing network. The backbone of the autonomous solution is Nvidia’s DRIVE AGX Hyperion 10 platform running the company’s DRIVE AV software stack, paired with a joint “AI data factory” leveraging Nvidia’s Cosmos development platform, that will train foundational AI models on “trillions” of real-world and synthetic driving miles.

With its new Maverick-2 accelerator, built on what the company calls an Intelligent Compute Architecture, NextSilicon is betting on a long-pursued but rarely realized approach to accelerating HPC and data center workloads, known as dataflow computing. A dataflow architecture is designed such that the data itself, not instruction sequences, drives computation. The company believes it’s finally solved the twin barriers that kept dataflow architectures confined to research labs: programmability and practicality.

The company unveiled two flagship processors, the Snapdragon 8 Elite Gen 5 for smartphones and the Snapdragon X2 Elite for PCs—that not only push performance and efficiency forward but will also hopefully serve as the foundation for a new class of AI-fueled personal agents.

In the world of high-tech electronics, timing is everything—literally. Long-standing precision timing solutions company, SiTime Corp, is aiming to reset the clock, so to speak, on how resonators are designed and deployed. The company’s newly announced Titan platform introduces a family of MEMS-based resonators that are markedly smaller, more resilient, and more easily integrated than traditional quartz designs.

In the semiconductor industry, virtually every major chip maker leverages physically accurate digital twins and simulation technologies throughout the design and manufacturing process, to gain invaluable insights into their devices, before a single wafer is prepped at the fab. When building chips, it is essentially a given that simulations and digital twins are used early and often, to ensure optimal performance, power, and area (PPA), but the same can’t be said in other industries. Even if we scale up only to the system level, for example, digital twins have been adopted by only a small fraction of companies. In this day and age of gigawatt AI factories and advanced data centers, however, it’s borderline silly to not leverage digital twins early in the design phase of complex projects.

SiFive just announced an array of new additions to its product stack that run the gamut, from tiny, ultra lower-power designs for far edge IoT devices, to more powerful engines for AI data centers, and everything in between. SiFive’s 2nd Generation Intelligence Family of RISC-V processor IP includes five new products -- the X160 Gen 2 and X180 Gen 2, both of which are brand-new designs, in addition to upgraded versions of the X280, X390, and XM cores.

The Google Pixel 10 Series arrives at a time when the smartphone market feels more than saturated, and even predictable. Yearly cycles bring faster processors, brighter displays, and incremental camera bumps. What stands out this year is not that the Pixel 10 Pro XL can match other flagships in hardware refinement, but that Google is repositioning its Pixel 10 series as a contextually aware assistant, with thoughtfully built, helpful AI at its core.

Nvidia’s Jetson line-up has long been the company’s proving ground for embedded AI computing at the edge, especially in robotics, industrial automation and autonomous vehicles. With the new Jetson AGX Thor developer kit, Nvidia is introducing a platform targeted at advancing machine learning in an arena where “physical AI”—robots, autonomous machines, and sensor-rich devices—must process vast amounts of data in real time.