The new Google Pixel 11 Pro XL doesn’t dramatically reinvent the company’s flagship smartphone formula. From a hardware perspective, this year’s model represents an incremental update. But after spending time with Google’s latest full-featured Pixel, I’ve found that some of its most meaningful improvements for business users aren’t necessarily the things that stand out on a spec sheet or benchmark chart.

Starting at $1,299 with 256GB of storage, the Pixel 11 Pro XL features a 6.8-in. Super Actua LTPO OLED display, Google’s new Tensor G6 processor, a 5115mAh battery and an updated triple-camera system. Google also pledges seven years of operating system, Android security and Pixel Drop updates.

The hardware is very good, but Google’s deeper integration of Gemini with Android and other thoughtful software features are what make this device more interesting.

For much of the past several years, Intel has been defined more by what went wrong than by what the company accomplished. Manufacturing delays, product roadmap slips and growing competitive pressure from AMD and NVIDIA reshaped both the company's reputation and investor sentiment. The prevailing narrative became almost self-perpetuating: Intel was continually rebuilding, while everyone else was executing.

I don't believe that narrative accurately reflects where Intel stands today.

Of course, Intel's turnaround isn't complete. The company still faces significant competitive challenges, particularly in AI infrastructure, and Intel Foundry must continue proving itself with external customers at scale. However, after following Intel's engineering roadmap and manufacturing strategy closely over the past several years, I believe we've reached an inflection point where tangible improvements in manufacturing, product execution and long-term strategy are beginning to strengthen the company's competitive position. This is no longer a turnaround built primarily on promises. It's increasingly supported by measurable progress.

AMD executives took to the stage at its Advancing AI 2026 event in San Francisco today to detail the company’s next generation of AI infrastructure solutions, from Instinct MI455X AI accelerator GPUs and 6th Gen EPYC “Venice” CPUs, to Pensando networking, ROCm.AI software and its Helios rack-scale platform that ties it all together.

AMD has been working towards rack-scale AI system solutions for years. Its ZT Systems acquisition last year added valuable engineering talent and intellectual property that is now finally bearing the real fruits. Its Helios AI platform is a major platform evolution for AMD, with shipments scheduled to begin in the second half of this year (which is here and now).

Nvidia has expanded well beyond GPUs over the years, building an increasingly more diverse and comprehensive AI platform spanning networking, DPUs, high-speed interconnects, rack and pod-scale systems and software. With its upcoming Vera CPU, the company is adding another strategic product, a custom Arm-based server processor featuring its own custom “Olympus” microarchitecture, designed specifically for emerging agentic AI workloads.

One of the recurring themes across the AI industry this year has been making powerful hardware easier to use and develop on. AI accelerators continue to improve in performance and efficiency, but getting applications from concept to deployment can still require significant engineering effort. As edge AI adoption expands across industrial automation, robotics, retail analytics, healthcare and physical security, developer productivity is becoming an increasingly important part of the conversation.

That is the backdrop for Axelera AI's Voyager Wingman announcement. Voyager Wingman is a new AI-powered assistant built specifically for developers targeting the company's Metis edge AI accelerators through Axelera AI’s Voyager SDK.

As systems and AI infrastructure get more complex, the engineering challenges required to design and bring them to market extend well beyond silicon. Printed circuit boards, advanced packaging, power delivery, thermal management, materials and mechanical structures are all critical to system performance and reliability as well, and co-optimization of all aspects of a design is effectively a requirement today.

Cadence Design Systems believes the next level of AI-assisted engineering resides at the system level, and to that end, the company just announced its new AuraStack AI Super Agent, an agentic AI platform for PCB and advanced chip packaging.

Qualcomm's Investor Day in Manhattan last week marked what CEO Cristiano Amon described as the company's next major transformation—expanding from a leader in connected edge computing into a company building AI infrastructure that spans everything from ultra-low-power IoT devices to hyperscale data centers.

That's a broad, bold vision, but unlike many AI narratives circulating across the semiconductor industry, Qualcomm backed it with a coherent technology roadmap, meaningful customer engagement and measurable financial targets.

The handheld gaming market has evolved from a niche category into one of the most competitive segments in PC hardware. For years, AMD has powered many of the devices that defined the category, but Intel's Arc G3 Extreme marks something different. It represents the company's first truly dedicated handheld gaming platform and demonstrates just how much progress Intel has made in graphics, software and power management.

The MacBook Neo's launch has sparked much debate, with some arguing that PC manufacturers shouldn't copy Apple. Despite the Neo's praised $599 starting price, it achieves this by using older iPhone-class silicon, limited RAM, and basic connectivity. While its premium aluminum chassis stands out, many affordable Windows laptops offer superior specifications, including newer processors, more memory, and better connectivity, often at similar or lower price points.

By integrating Ansys simulation technologies more deeply into its core EDA workflows, Synopsys is making a strategic bet that future gains in semiconductor performance and efficiency will come less from optimizing individual components and more from understanding how increasingly complex systems behave as a whole. Simultaneous co-optimization will be key.