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In late 2025 and early 2026, Google is developing new power grid resilience tools through Tapestry. This major project at Alphabet X is described as a Google Maps for electrons. The goal is to build digital twins of electricity distribution networks, offering operators a clearer way to manage local energy resilience.  

Building on this, Tapestry incorporates AI to create Unified Grid models. These models represent the grid’s physical structure and trace how energy travels across it, similar to how Google Maps displays roadways.  

By illustrating the flow of electricity using familiar mapping techniques, Tapestry provides operators with intuitive visibility into the system.  

In August 2025, Tapestry took its first significant step beyond high-voltage transmission planning, shifting focus to lower-voltage distribution networks. The team partnered with Vector in New Zealand to strengthen local network durability.  

By February 2026, this collaboration had enabled Tapestry’s digital twin systems to make virtual copies of distribution networks. These digital twins enhanced load management, supported improved planning, and delivered up to 20% faster restoration times.  

Forecasting Abilities: The AI tools can rapidly simulate complex scenarios for example, predicting low wind generation during heat waves up to 30 times faster than previous techniques. These tools analyze integrated data from weather systems, energy demand, and distributed resource outputs to optimize real-time grid management and proactively identify potential disruptions.  

Overall, this technology anchors Google’s broader initiative to use AI and mapping tools to make the power grid more reliable, supporting progress toward 24/7 carbon-free energy.  

History, a GoogleX project focused on the electric power grid (the network that delivers electricity from producers to consumers), has mainly partnered with others to bring artificial intelligence (AI tools to transition problems) (issues related to moving electricity over long distances). In Chile, the National Grid Operator uses Tapestry’s tools for yearly transmission planning. North America’s largest grid operator, PJM, a regional transmission organization, is also using Tapestry AI to help manage its large interconnection backlogs (the queue of projects waiting to connect to the grid).  

But as the project’s transmission efforts unfold, Tapestry has also been quietly developing tools for the distribution grid. Today, Latitude Media has learned that Tapestry is unveiling a key milestone in that work: a partnership with a New Zealand distribution service.  

Vector, the largest of New Zealand’s 29 distribution utilities, is now using Tapestry’s grid management and planning tools for daily operations. This marks the first wide use of the technology on a distribution network. The grid-aware Tapestry AI inspection tool has already cut Vector’s average inspection time from 45 minutes to about 5 minutes per asset. This faster, more accurate process gave Vector the insight it needed to use Tapestry’s grid planning tool. That tool helps stimulate future scenarios to plan for resilience and reliability.  

Tapestry’s transmission tools actually grew out of its distribution-focused work, according to Page Crahan, Tapestry’s general manager. When Tapestry started at X, the earliest goal was to address distribution-level challenges first.  

Distribution Grades are less understood, less measured, and less mapped with high confidence than the transmission network, Crahan told Latitude Media. One of the things that is really challenging for network distribution operators is getting a high-confidence representation of their current network from which they can make decisions.  

While these early years saw progress in distribution tools, development lagged; meanwhile, the global energy landscape shifted rapidly.  

More importantly, the conversation about load growth changed. In 2018, people focused on load growth from crypto or electric vehicles, but within a few years, load growth from artificial intelligence and industrial electrification became bigger and more urgent. At the same time, advances in AI and machine learning were also changing. Tapestry’s work greatly improved the team’s capabilities, Crahan said.  

Tapestry looked at that trend, probably a little bit early, and we knew that there was an all-hands-on-deck moment for transmission planning coming immediately. She explained, “When I think about managing resources on our team and where we should focus, it wasn’t about distribution being solved, so we should stop and put our pencils down. It was more about doubling down on things that seemed really urgent at the time.”  

Working Around the Data Problem 

In 2019, when Crahan and her team first met with Vector, the utility was touring innovation hubs around North America. The utility was looking for tools to prepare for future electrification needs in Auckland.  

Shortly after those initial meetings in 2019, the COVID-19 pandemic began, which changed how the teams could work together.  

Things went a little more slowly at the beginning, Crahan said. The upshot of that, she added, was that the Tapestry team really understood the problem before we started building things, because it was the best we could do remotely.  

For example, Tapestry and Vector initially set out to build a distribution planning tool. As they worked together throughout the slower months of the pandemic, they realized that Vector first needed a better understanding of the network’s immediate status before planning for future expansion. That need led to the creation of the grid-aware tool. The automation of inspections and defect detection is great for preventive maintenance, Crahan said, but, more importantly, it provides critical information to drive the planning tool.  

RIDA web enables partners to pull together images of their assets (from utility poles to transformers) from multiple sources, including satellite and street-view imagery, as well as images taken by field teams (in the case of Vector, the utility’s helicopter and drone images). The tool combines those various forms of visual inputs into a single view to simplify inspections.  

Crahan said the most important part of training the model was expert annotation. Experienced Vector field crews labeled features in images to teach the system important details. This human-in-the-loop method, she added, was essential. Ask someone who’s spent their entire career evaluating and maintaining a network that can look in less than 15 seconds and see things that are extremely difficult to train a machine learning model to do.  

The Distribution Impact 

With that foundational data in place, the team then turned to Tapestry’s grid planning tool. It was clear, however, that they could just replicate existing transmission-focused tools. Distribution required its own models, interfaces, and workflows tailored to its unique operations, Crahan explained.  . anna kopf nudes

Transmission and distribution planning do share key steps, she added, like:  

• Preparing future scenarios  

• Running power flow and economic simulations  

• Analyzing system constraints  

But the networks are fundamentally different.  

Transmission planning tools, such as Tapestry in Chile and PJMs, simulate scenarios for high voltage, long-distance power flows. It looks at large-scale expansions, such as load growth from industry or population centers. In contrast, a distribution planning tool must account for many local issues and operational constraints. These are often handled by different tools, which makes building scenarios and models more complicated, Crahan said.  

Now, seven years after Tapestry began deploying its tools on a distribution grid, it highlights the project’s broader strategy with Vector. Crahan said Tapestry is beginning to connect transmission and distribution tools and simulations to create an efficient, singular solution.  

Tapestry considers this deployment proof that AI can help the industry meet energy demand, not just increase it. According to Crahan, a field worker in Auckland may now perform tasks faster and more easily thanks to this machine learning work.

Source: Exclusive: Google’s grid moonshot is now tackling distribution 

In 2013, Caleb Hicks, who later founded School AI, taught nearly 300 students daily. He could connect with the top and bottom performers, but many students in the middle were overlooked. This concerned him. As class sizes grew and budgets shrank, teachers like Hicks found it harder to support every student. 

When ChatGPT launched in 2022, some teachers were concerned about cheating and safety. Still, Ben Hicks, drawing on his instructional design experience at Apple, saw how AI could help create more personalized learning and give teachers better resources. 

Building on that insight, he started School AI in 2023. The platform gives teachers quick updates on student progress and offers students bespoke support. In just two years, School AI has reached over 1 million classrooms in more than 80 countries and formed over 500 education partnerships. It uses OpenAI models to bring advanced technology to teachers and students. 

“We’ve worked hard to ensure AI doesn’t just do the work for you. If AI simply gives students the answer, we’ve missed the mark. Teaching is about coaching and keeping students engaged,” said Nate Sanders, Chief Experience Officer at School AI. 

Lesson 1: Building Trust by Keeping Teachers Involved 

To build trust, School AI’s system mirrors a real classroom. Teachers use DOT, an interactive assistant, to create captivating learning spaces for students. 

A teacher can ask Dot to create a reading activity for students at three different levels. Dot quickly builds a lesson. Teachers can also add interactive apps, letting students create, play, and learn in ways that match the lesson goals. 

Students use Sidekick and AI Tutor, powered by GPT-4.0 and GPT-4.1, to complete these lessons. Sidekick adapts to each student’s learning style, giving guidance, pacing, and encouragement along the way. 

As students work, teachers keep informed. Every School AI interaction is visible, giving teachers early insight into student needs before small gaps grow into larger ones. Built-in safeguards keep School AI safe, transparent, and consistent with classroom goals. 

One student who had just arrived in the US and spoke only Dari used Sidekick for instant translation. Within weeks, he was joining group activities, making friends, and feeling included. Early, confident engagement like this helps students build a base for long-term success. 

Lesson Number 2: Matching Models to Actual Tasks 

For teachers, the main question is not what AI can do, but how it can truly improve learning instead of just providing answers. 

“If AI gives the student the answer, we’ve failed,” says Hicks. “The point of teaching is to coach and keep students engaged in their work.” 

School AI made educator oversight a core feature, rather than relying on simple prompts and responses. Every student input goes through an agent graph with specialized nodes that use models, tools, or safeguards before providing organized support for real learning. 

OpenAI supports every part of this process: 

• GPT-4o drives DOTS Conversational Interface and real-time logic behind lesson construction and response generation.  

• GPT-4.1 handles more complex tasks, such as scaffolding and multi-step math problems. 

• Image generation creates custom visuals, such as photosynthesis diagrams or historical maps, to support lessons. 

• Text-to-Speech (TTS) provides spoken feedback in more than 60 languages. 

The system uses smart routing. Complex tasks go to GPT-4.1 or GPT-4o, while simpler ones are handled by smaller or nano models. This approach keeps costs stable while securing accuracy. 

Up-to-date information and nuance are especially important in schools. Algorithm outputs are visible in logs and shared with teachers in real time. Administrators get a consolidated report. This feedback loop supports School AI’s main idea: AI should coach students, not just give them answers. 

Lesson 3: Using One Tech Stack to Grow Quickly 

At School AI’s latest product showcase, over 10,000 educators attended. Just before the event, the team realized they were still limited by consumer-level restrictions. 

“We reached out to our OpenAI contact to see what we could do,” said Sanders, Chief Experience Officer at School AI. Within 10 minutes, they upgraded our storage and increased our limits as we started using GPT-4.1, assuring a smooth event as new models became more affordable. School AI reduced its costs from nearly $1 per student to just a fraction of that. This change allows the team to invest in the future and grow wisely, which is especially important in education, where budgets are tight. 

“We chose OpenAI because their models present unmatched accuracy, nuance, and flexibility. Hicks says we decided to scale with them because the support, we’ve received is second to none.” 

Gazing Forward: Molding the Future of Education 

For teachers, AI can be a valuable partner. It gives them more time for the important human side of teaching. Some teachers say School AI saves them over 10 hours a week. More importantly, they use that time to step in earlier, offer faster support, and spend more meaningful one-on-one time with students. said she used to depend on test scores to spot students who were struggling, but with school AI, she noticed a student who had stopped asking questions and joining discussions. That small sign led to a check-in and early help that might have been missed before.  

Student behavior is changing, too. Engagement is rising in lessons that use AI, and Sidekick is helping students become more confident and independent. The same Dari-speaking student who once needed real-time translation is now joining group work, joking with classmates, and feeling more confident. 

As more schools start using School AI, leaders are using live data to see what works and where extra support is needed, including new features for learning at home. School AI now connects students, teachers, and families through one trusted system. 

This mission has always been about helping every student feel noticed. Since HICS with OpenAI, we have been able to deliver on that promise consistently at the system-level schools’ needs.

Source:    SchoolAI’s lessons in building an AI platform that empowers teachers 

HomeKit Camera Security  

HomeKit cameras with an IP address send video and audio streams straight to iOS, iPadOS, TVOS, or macOS devices on the same local network. These streams are encrypted with randomly generated keys on both the device and the IP camera and exchanged over a secure HomeKit session when a device is not on the local network. The encrypted streams are sent through the Home Hub, which acts solely as a relay and does not decrypt them. When an app shows the HomeKit IP camera video to the user, HomeKit securely renders the video frames from a separate system process. This means the app cannot access or save the video stream, and apps are also not allowed to take screenshots of it.  

HomeKit Secure Video 

HomeKit offers a secure and private way to record, analyze, and view clips from HomeKit IP cameras without sharing video content with Apple or anyone else. When the IP camera detects motion, it sends video clips directly to an Apple device. Set up as a Home Hub, using a dedicated and encrypted local network connection. This connection uses a unique key pair for each HomeKit session, generated with HKDF-SHA-512. The Home Hub decrypts audio and video streams and analyzes video frames locally to detect important events. If something significant is found, HomeKit encrypts the video clip with AES-256-GCM using a randomly generated AES-256 key. Poster frames for each clip are also created and encrypted with the same key. The encrypted poster frame, audio, and video data are then uploaded to the iCloud server’s metadata for each clip, including the encryption key, which is uploaded to CloudKit using iCloud’s end-to-end encryption.  

For face classification, HomeKit stores all data used to identify a person’s face in CloudKit, protected by iCloud’s end-to-end encryption. This data includes each person’s name and their facial images. These images can come from a user’s photo if they choose to share it, or from an earlier IP camera video analyzed during a HomeKit secure video analysis session. This classification data helps identify faces in the secure video stream. From the IP camera, the identification details are added to the clips’ metadata as described earlier.  

When you use the Home app to view camera clips, the data is downloaded from iCloud, and the encryption keys are unlocked locally with iCloud’s end-to-end decryption. The decrypted video is streamed from the servers and decrypted on your iOS device before you see it. Each video clip session can be split into smaller parts, and each part is encrypted with its own individual key.  

Apple may be set to shake up the smart home market with its own security camera, despite already supporting third-party options through HomeKit Secure Video. For years, analysts and supply chain sources have suggested Apple is developing a camera on this system. If this could bring iCloud integration closer, stronger privacy, and better connections with Apple intelligence and automation  

This would be Apple’s first security camera, entering the market against Amazon’s Ring and Google’s Nest.  

Expected Release Timing 

Release timing remains to be clarified.  

Apple has scheduled a special Apple Experience event for Wednesday, March 4, 2026, with in-person sessions in New York, London, and Shanghai. Instead of a keynote, Apple plans to introduce two announced products over several days via press releases, then host hands-on demos at the event.  

Observers such as John Gruuber and Mark Gurman predict revelations daily before March 4, with the HomeKit camera likely to debut that week, either in a press release or at the event. The camera will ship immediately or later in the year. Apple often rolls out products in stages after announcements.  

With timing discussed, next is what this camera could actually offer. 

I still know confirmed specs, but rumors share some common themes:  

HomeKit integration: Rumors suggest the camera would work directly in the Home app with HomeKit Secure Video, including encrypted video streaming and recording through iCloud Plus. This aligns with Apple’s security camera standards. An Apple-made camera is expected to fit into this system.  

On-device intelligence: Multiple reports suggest Apple’s camera may use facial and motion recognition to improve automation. For example, some sources speculate it could trigger scenes when familiar people come or go. This would go beyond motion detection to provide greater context awareness, but the specifics remain unconfirmed.  

Automation and smart home: Rumors also indicate Apple’s security camera could connect with other smart home devices, such as presence of sensors, lights, or the upcoming HomePod hub, for tasks like turning on lights when someone enters a room. These integrations have not been verified.  

Current hardware features are not confirmed. Information on resolution, night-vision capabilities, or potential models for indoor and outdoor use remains unavailable.  

Price Expectations 

There are no confirmed leaks on pricing. Competing smart home cameras range from $100 to $300 or more, reflecting features like cloud storage or advanced sensors. An Apple camera would likely be positioned above basic third-party offerings, but pricing has not been confirmed.  

Apple is combining smart home features, cameras with face recognition, scene-triggering hubs, and multi-function speakers. The new camera may offer more than basic accessory features.

Source: Apple Security Camera Everything We Know: Specs, Price, and Release Date 

HomeKit camera security

At MWC 2026 in Barcelona, Lenovo is unveiling a new lineup of adaptive AI devices and innovative concepts for business professionals, creators, students, and gamers. Highlights include:  

• a modular PC  

• a glasses-free 3D laptop  

• a foldable gaming handheld  

• The first release of Lenovo Qira  

These products show how personal computing is moving toward systems that adjust intelligently to users and their surroundings.  

Across its portfolio, Lenovo continues to focus on delivering technology that is increasingly personalized, proactive, and protected while building a unified AI ecosystem that works naturally across devices.  

The AI era is not about one device or app, but about smart systems that work together across everything we use, said Luca Rossi, president of Lenovo’s Intelligent Devices Group. Lenovo and Motorola are fulfilling this vision in reality. By merging adaptive hardware with unified AI that works across species, smartphones, tablets, and wearables, and by introducing new mobile devices, innovative designs, and the launch of Lenovo Qira, we are building a broad AI portfolio to create better-connected, more intuitive experiences for everyone.  

Lenovo Qira Personal Ambient Intelligence 

Lenovo Qira is a personal ambient intelligence built at the system level and integrated directly into Lenovo and Motorola devices, rather than layered on as a standalone application operating across supported PCs, tablets, smartphones, and wearables. Qira is designed to help preserve continuity between tasks and devices while assisting based on user intent.  

Over the next few weeks, Lenovo Qira will become available on more than 20 Lenovo PCs, including Yoga, IdeaPad, Legend, and ThinkPad models. Through updates and pre-installed software, the IdeaTab Pro Gen 2 will be the first Lenovo tablet with Qira. At launch, Qira will support six languages in nine regions:  

• English (UK, US, India)  

• Spanish (Latin America, Spain)  

• French (France)  

• Italian (Italy)  

• German (Germany)  

• Portuguese (Brazil)  

Lenovo Qira will continue to evolve, reaching more devices and delivering new features and partnerships. In 2026, it will add more languages and devices, including its first appearance on Motorola smartphones, helping to build a unified ecosystem for both Motorola and Lenovo.  

Breakthrough Concepts: Exploring Adaptive Form Factors 

ThinkBook, Modular AI, PC Concept 

Lenovo is presenting a modern AI/PC concept that takes a flexible approach to business computing based on the idea of “carry small, use big”.  

This concept features a 14-inch ultra-thin base that supports various display configurations, detachable input modules, and modular ports. A second display can be attached in various ways or even replace the keyboard, expanding the workspace to about 90 inches while staying portable. This shows how modular design can support changing work needs and longer device life spans in AI-ready settings.  

Yoga Book Pro 3D Concept 

Lenovo is also showing a glasses-free 3D laptop concept that lets creators see and work with depth right on the screen.  

This concept combines dual-screen, AI-powered 2D-to-3D conversion, gesture controls, and easy-to-use creative tools. It makes immersive content creation smoother and shows Lenovo’s interest in spatial computing.  

Legion Go Fold Concept 

Lenovo is also introducing a foldable gaming handheld concept, expanding its work on flexible hardware.  

The device transitions from a compact handheld format into a larger immersive screen and supports multiple usage modes, including handheld play and split-screen multitasking. This device can switch from a small handheld to a larger immersive screen and support various use cases, including handheld gaming, split-screen multitasking, expanded display gaming, and desktop-style use. It shows how flexible screens can bring together gaming and productivity in one device.  

Commercial Portfolio: AI-Ready Platforms For Modern Work 

Lenovo is updating its commercial PC lineup to help organizations use AI-powered workflows more easily and at scale.  

The new ThinkPad T-Series comes with improvements that make it easier to service and use, and that better prepare it for AI. Some models have top iFixit repairability scores. These changes show Lenovo’s emphasis on long-term value, reduced downtime, and sustainable device management.  

The ThinkPad X13 detachable is built for flexible mobile work. It has pen support and replaceable parts, making it lightweight for people working on the go or in mixed settings.  

Lenovo is also moving into rugged devices with the new ThinkTab X11. This tough Android tablet is made for tough jobs in industrial and frontline settings.  

For small and medium businesses, the ThinkBook 14 2-in-1 Gen 6 offers flexible ways to work and AI-enabled tools for teamwork. The ThinkVision M16 portable monitor offers a lightweight, easy way to get more screen space for various work needs.  

Lenovo builds security, easy management, and supports services into all its business products. This helps organizations use AI-ready devices with more confidence and control.  

Consumer And Gaming Portfolio: Intelligent Experiences For Every Day 

Lenovo’s newest devices for creators, students, and gamers offer strong performance, easy portability, and smart features that simplify daily tasks.  

The Yoga 9i 2-in-1 Aura Edition (14” 11) has a high-end convertible design, vivid OLED screen, and smart features to help with creative work. The Yoga Pro 7A (15” 11) and IdeaPad Slim 5i Ultra (14” 11) add more AI-powered laptops to Lenovo’s lineup, offering both strong performance and easy portability for work and content creation.  

The Ideatab Pro Gen 2 brings smart learning and productivity tools in a flexible tablet design. It’s also the first Lenovo tablet to include Lenovo Qira.  

For gaming, Lenovo is adding the Legion 7A (15” 11 laptop) and the Legion Tab (8.8” 5 tablet). The Legion Tab has a 3K screen and optimized cooling for better performance.  

Advancing Smarter AI for All 

At MWC 2026, Lenovo is showing its plan to make AI more accessible for everyone by developing new hardware and rolling out Lenovo Qira. Lenovo aims to create a future in which technology adapts to people, not the other way around.  

To learn more about Lenovo’s new devices, solutions, and concepts from MWC2026, check out the Lenovo MWC 2026 Press Kit. 

Source: Lenovo Unveils Adaptive AI PCs, Modular Concepts, and Lenovo Qira Rollout at MWC 2026 

Local First Signals, data that tell search engines exactly who your business serves and where you operate, have become the new SEO goldmine. They directly address the modern AI-driven shift in search.  

Searches are now more hyperlocal and intent-based, such as “Near Me” or “Open Now”. By 2026, over 46% of all Google searches will have local intent. Businesses that use these signals can see over 500% ROI and often outrank bigger non-local competitors.   

Here is why local first signals are so valuable for SEO.  

High Intent Action-Oriented Traffic 

• Near me boom: Over 76% of people who conduct a local search on their smartphone visit or call a business within 24 hours.  

• Ready to buy column local searches show, and I need it now. Urgency showing up first in these searches attracts customers who are ready to buy, not just people browsing.  

AI and Voice Search Optimization 

• Contextual Answers: AI-powered Google searches use structured data, such as local business schema, to provide direct, accurate information about business locations, hours, and services.  

• Voice query alignment: local-first content that uses conversational, location-specific language, such as “best pizza in (neighborhood),” is more likely to be featured by smart assistants as voice search becomes more popular.  

The New Trust Layer (Beyond Links) 

• Reputation Management: By 2026, customer reviews will be a top-ranking factor. Having lots of recent positive reviews and consistent name, address, and phone number (NAP) details across directories shows that your business is real and trustworthy.  

• Verified Relevance: Creating local content, like blog posts about neighborhood events, partnerships with other local businesses, or community sponsorships, shows that your business is active in the area.  

Visibility In Zero-Click Searches 

• Pack dominance: local fast signals, such as an optimized Google Business Profile, help businesses appear in the local three-pack at the top of search results. This often means users get what they need without having to click through to a website.  

• Visual & entity-based: posting high-quality photos and videos and maintaining consistent updates on your Google Business profile help AI recognize your business as a real entity.  

The Startup Advantage 

• Speed & Agility: smaller, focused businesses can move faster than big brands, enabling them to update their content instantly to reflect local, seasonal, or community-based trends and gain a competitive edge.  

Key Local Signals to Maximize: 

• Google Business Profile (GBP) Optimization: The Top Ranking Factor in 2026.  

• Consistent NAP citations, ensuring name, address, and phone number are identical across all platforms.  

• Local reviews: focusing on frequency and content, noting particular services or neighborhoods  

• Local business schema markup: providing search engines with structured machine-readable data  

• Hyper Local Content: Blog posts about neighborhood events or local community engagement  

If you run a business in South Florida, you have probably noticed things are changing.  

Competition is tougher than ever, whether you are in the financial center of West Palm Beach, the busy hospitality scene in Delray, or the fast-growing tech community in Boca Raton.  

By 2025, just having a great product or service won’t cut it if customers can’t find you when they search for the best (service near me) on their phones. Your business might as well be invisible.  

That’s where local SEO comes in. Knowing how to use it is quickly becoming one of the most important skills in our local economy.  

What is Local SEO? 

Local SEO (Search Engine Optimization) means making your business easy to find in local online searches. While general SEO aims for an international audience, local SEO focuses on reaching customers in your area, like Jupiter, Wellington, or downtown West Palm.  

Local SEO targets the specific geographic map pack, while organic SEO focuses on global search results.  

Here is an example: General SEO might help a person in California find you. Local SEO helps the person walking down Clematis Street find you when they are looking for lunch or a graphic designer.  

Why Local SEO Is Do or Die in 2025 

The way people find businesses has changed a lot. Here’s what that means for businesses in Palm Beach County:  

• The “Near me” boom. Corona searches for “Near me” have jumped by 500% in recent years. If a tourist in Palm Beach looks up “Italian dinner”, Google won’t show them a restaurant in New York. Instead, it shows what’s just five minutes away.  

• Mobile roles: With so many tourists and seasonal residents, most people search on their phones. They are looking for things while at the beach, on the golf course, and even in their cars.  

• The Map Pack Advantage: Nearly half of all Google searches are local. The top three spots in the Google Map Pack: The map at the top of search results gets most of these clicks.  

Every local business is competing for a spot in the map pack. It is the most valuable place to be.  

The Three Pillars of Ranking Locally 

Ranking in Palm Beach County: Getting your business to rank in Palm Beach County isn’t magic. It takes technical know-how and good reputation management, and (GBP).  

Your Google Business Profile is like your new home page. It powers the map results, so make sure it is fully updated and within the right hours, services, and great photos.  

Reputation (Reviews) 

In South Florida’s service-driven economy, reputation matters a lot. Getting regular, genuine, positive reviews tells Google your business is trusted in the community.  

Technique 

This is where many businesses struggle. Google lowers the ranking of websites that are slow, hard to use on phones, or have bad code. If your site takes five seconds to load on a tourist’s phone, Google won’t show it.  

How Coding and Digital Skills Power Local SEO 

This is why marketing and web development need to work together. You can’t have strong local SEO without a solid technical base.  

At Park Beach Code School, we see our students use their skills to help local businesses grow and succeed.  

The Web Developer’s Role (The Technical Foundation) 

In our web developer program, students learn the behind-the-scenes skills that directly affect search rankings. First, design and code websites that look perfect on any screen size.  

• Site speed: producing clean HTML, CSS, and JS so pages load quickly, which is a big factor for Google rankings.  

• Schema markup: adding special code that tells Google exactly where your business is and what it offers.  

The Digital Marketers’ Role (the Strategy) 

In our Social Media Marketing Specialist Program, students learn how to attract more visitors and engage more people, rather than focusing solely on AC repair.  

• Content Creation: Column, Writing Blogs, and Social Posts That Show Your Business Is Relevant and Active in the Local Area.  

• Analytics: looking at data to find out exactly where your customers are coming from.  

The main takeaway: in 2026, local SEO rankings will focus on accuracy, clarity, and being active in your community to get started. Review your Google Business Profile and citations this week. Next week, publish a high-intent service page and begin a review request process that fits within your daily routine. If you want help with planning and tracking, you can find businesses by putting these local SEO strategies into action and supplying clear, measurable reports.

Source: Local SEO Ranking Factors Breakdown: How to Rank for Near-Me Searches 

Advanced Micro Devices (AMD) and Meta (Meta) announced a major multi-year agreement on February 24th, 2026, to run up to 6 GW of GPU capacity across Meta’s global data centers. This deal, valued between $60 billion and $100 billion over five years, marks the end of single-vendor dominance in high-end AI computing and positions AMD as an important player in global AI infrastructure.  

The deal centers on the mass deployment of AMD’s next-generation MI450 accelerators and sixth-generation EPYC Venice processors. By securing 6 GW of power capacity, roughly equivalent to the energy consumption of 6 million homes, Meta is doubling down on its capacity-open-compute philosophy while weaning its supply chain away from NVIDIA Corporation. For large-cap tech investors, the agreement represents the most significant validation of AMD’s AI roadmap to date, suggesting that the NVIDIA alternative narrative has officially transformed into a dual-vendor duopoly reality.  

The 6GW Era: A Breakdown of the Historic Partnership 

Today’s agreement is a result of three years of technical collaboration, starting with Meta’s early use of the AMD Instinct MI300X in 2023. In 2025, the companies worked together to create the Helios Rack Scale architecture, which improves power delivery and liquid cooling for large graphics processing unit clusters. The 6GW deal is beyond a purchase; It lays the foundation for Meta’s future personal superintelligence projects that require massive amounts of real-time computing power.  

Analysts say that 6 GW of power will support about 2.4 to 3 million GPUs. Unlike earlier, smaller orders, this agreement secures a long-term plan, giving Meta priority access to AMD’s CDNA4 and CDNA5 architectures until 2030 to strengthen the partnership. AMD has given Meta performance-based warrants for up to 160 million shares, which will vest as Meta meets certain deployment goals. This move further aligns the interests of both companies.  

The market responded quickly. AMD shares rose by 9.4% in early trading, and Meta’s stock gained 3.2% as investors welcomed the company’s efforts to lower its AI infrastructure costs. The deal also shows a change in how the industry measures scale. In 2026, the main benchmark for AI leadership is now gigawatts of deployed power rather than nearly counting chips, indicating the serious energy constraints facing data centers worldwide.  

Winners, Losers, and the Silicon Bifurcation 

AMD is the biggest winner in this deal, with its AI accelerator market share expected to rise from about 9% in 2025 to over 15% by the end of 2026. By bringing Meta on board as a major customer, AMD has overcome its biggest challenge: proving its software works at scale. Meta’s move to run its Llama 4 and Llama 5 models on AMD’s ROCm software now signals to other large cloud providers like Microsoft and Alphabet that they can do the same.  

The Chain of Events extends well beyond GPU designers. Networking giant Broadcom Inc. is a major hidden winner, as its high-end Ethernet switching and PCIe Gen7 fabric serve as the connective tissue for these 6 clusters. Similarly, Vertiv Holdings Corp and Eaton Corporation plc are expected to see a spike in demand for specialized liquid-cooling and high-voltage power distribution systems required to manage the high thermal profiles of MI450, which can draw upwards of 1.2 kW per accelerator.  

On the other hand, Intel is still struggling in the data center market. Meta’s decision to use AMD’s Venice EPYC CPUs with its GPUs shows that Intel is losing its usual hold on the server head node market, while NVIDIA remains the top player. This Meta deal brings real price competition for the first time in the generative AI era. NVIDIA may have to rethink its pricing for the upcoming Rubin chips to keep customers from leaving.  

Power as the new currency of AI 

This 6 GW deal fits into a wider industry trend in which electricity availability has become the ultimate bottleneck for AI progress in early 2026. Lead times for new grid interconnections in major hubs, like Northern Virginia, have stretched to nearly a decade. By securing 6 GW of capacity through a multi-year partnership, Meta is effectively land-grabbing power, making sure that its AI development will not be threatened by energy shortages that are currently stalling smaller competitors.  

The deal also spotlights the regulatory and environmental issues that large-cap tech companies face. Running 6 GW of computing power means companies must rethink how they get their energy. Meta says much of this new capacity will come from on-site solar, large battery storage, and even investments in small nuclear reactors. As tech firms start generating their own power, they are becoming more like utility companies, which is attracting more attention from regulators and legislators. While Microsoft and Intel once dominated the PC landscape, the AI era is proving to be more fragmented. The Meta AMD alliance proves that the software layer, specifically open-source frameworks like PyTorch, has become robust enough to bridge the gap between different hardware architectures, breaking the proprietary mode that many believed would protect NVIDIA indefinitely.  

The Road to 2030: What comes next? 

In the short term, the market will be watching for the first 1 GW milestone deployment, expected in the second half of 2026. Success here will depend on AMD’s capacity to maintain a steady supply chain amidst worldwide semiconductor fluctuations. For Meta, the challenge will be embedding this massive influx of compute into its consumer-facing products without a corresponding spike in energy costs. That could erode margins.  

Looking further ahead, the deal is likely to trigger a copycat effect among other Tier 1 cloud providers. If Meta can demonstrate that AMD hardware delivers equivalent performance at a 20-30% lower TCO, the pressure on Amazon and Google to diversify their own fleets will become irresistible. We may also see a strategic shift toward custom silicon partnerships in which AMD delivers foundational IP for Meta to build even more specialized chips. This move would further squeeze traditional merchant silicon providers.  

In the long run, the market is shifting from being limited by hardware to being limited by power. As computing becomes more common, thanks to AMD and Meta’s competition, tech giants will compete based on who can achieve the most efficient energy use and access the best data to train their AI models.  

Investor Takeaway: A New Market Equilibrium 

The 6 GW deal signed on February 24, 2026, ends the AI monopoly era. Investors should see this as proof that the AI infrastructure market is becoming more competitive and mature. AMD’s rise from an underdog to a $100 billion partner for Meta shows its strategy is working and sets up a direct rivalry with NVIDIA for the top spot among chip makers.  

Going forward, investors should focus on AMD’s quarterly AI data center revenue and how efficiently Meta spends on capital projects. The race for AI leadership is now about who can deploy the most computing power most efficiently, not just who has the fastest chip for the wider market. This tale shows that the AI boom is not yet over, with the biggest companies still investing at record levels.

Source: AMD Shatters Nvidia’s Monopoly: Landmark 6-Gigawatt GPU Deal with Meta Reshapes AI Landscape 

Artificial Intelligence is becoming increasingly important for small businesses, especially as AI tools are integrated into everyday apps like Microsoft 365 Pro Pilot. According to Microsoft’s latest survey, one-third of SMEs see AI as a top priority, and 71% plan to accelerate their AI investments over the next year.  

Although still new, the benefits of AI for SMBs, such as simplifying both common and complex tasks, are already clear. According to a Microsoft survey, 84% of respondents believe AI has a positive impact on their business, and those who regularly use it see a 40% in-product input. The real question is not if tools like Microsoft Co-Pilot can give SMBs a competitive edge, but rather how.  

How Does Microsoft 365 Co-Pilot Help Small Businesses Get the Most Out of Their Investment 

Get the most from AI tools like Microsoft 365 Co-Pilot. It helps to know what they can do. Microsoft keeps adding new features, but there are already many ways small businesses can use Co-Pilot today.  

There are some ways small businesses are using Microsoft Co-Pilot to boost productivity and encourage new ideas:  

• Creating an Effortless Experience Across Microsoft 365: Co-Pilot uses Graph Grounding to improve its responses by pulling in extra context extracted from your stored information. It does this by using Microsoft Graph, which connects to your data across Microsoft 365  

• Content Creation: Co-Pilot in Word saves time by paraphrasing text, providing further details, or including helpful statistics in your documents  

• Using email conversations: Co-Pilot can find emails by topic or form, summarize meetings, create to-do lists, and assist with crafting or editing messages, saving you valuable time.  

• Increasing Response Rates for Forms/Surveys/Polls: Canon Co-Pilot suggests follow-ups, such as sending reminders or writing messages, to encourage people to respond.  

• Reviewing and summarizing videos: for Teams meetings. Co-Pilot highlights key points adds time markers for topics or speakers, lists tasks, and can search specific topic words or phrases.  

• Automating Business Tasks: You can build Co-Pilot extensions or plug-ins to work with data from different sources and targeted searches. Use natural language queries and set up custom prompts based on your data.  

• Decreasing manual typing and formula work: Co-Pilot in Excel can use voice prompts to help analyze data, suggest formulas, show insights in charts and pivot tables, and add important data. It can automatically create and calculate formulas for complex spreadsheets based on your instructions.  

• Improving content management and data governance: Co-Pilot for restricted SharePoint search lets you turn off organization-wide search and limit enterprise search and Co-Pilot to only the SharePoint sites you specify, no matter which search option you use. Users can still work with files and content they own or have accessed before in Co-Pilot.  

• Gating Insights from Employee Feedback: Co-Pilot can record comments and help you add on suggestions using summaries, reports, and analysis from across Microsoft 365 Apps.  

• Making budget management easier: Co-Pilot in Planner (review) helps you plan, manage, carry out, and review major projects more smoothly.  

• Training employees to write better prompts: Copilot lab helps teams understand what drives successful AI-powered business practices.  

• To develop secure applications, Microsoft 365 Copilot can write and analyze code, troubleshoot code, and handle data or security issues. Knowing how to use fonts is important.  

This list is just a start. New features and updates are added every month. To learn more about what Co-Pilot can do, take a look at Microsoft’s AI Co-Pilot Success Kit.  

How SMBs Can Use Microsoft 365 Co-Pilot To Drive Greater Success 

Knowing what the product can do is just the beginning. A recent report on SMB technology adoption found that 77% of SMBs see a lack of knowledge as the main barrier to AI success. Early adoption and ongoing training with tools like Microsoft 365 Co-Pilot can help SMBs stand out.  

If you are new to AI and want to get the most from your investment, try Microsoft’s free Co-Pilot training modules. If you’re unsure how Co-Pilot could help your business, take a look at these industry-specific examples  

If you work in product-based or supply chain industries like retail, manufacturing, or transportation, Co-Pilot can help you:  

• Predict demand changes with greater accuracy based on your sales data.  

• Improve delivery routes through analyzing financial and geographical data.  

• Monitor weather and world events that impact the supply chain, including situations influencing your materials, carriers, or distribution networks.  

• Automatically draft emails to alert and engage partners or customers, reducing the likelihood of disruptions.  

• Support customer service through the creation and analysis of support forms, surveys, and polls.  

• Generate information on order status, delivery schedules, and shipping options.  

For service-based industries like insurance, legal, consulting, or real estate, Microsoft 365 Co-Pilot offers support in several ways:  

• Draft review and summarize essential documents based on business and industry data.  

• Compiling a deck for marketing campaigns, targeted sales outreach, or other important customer-facing messaging.  

• Translate documents, presentations, or sales and marketing collateral into 27+ languages.  

• Develop Captivating PowerPoint presentations based on industry data and notable themes.  

• Formulate comprehensive project management plans, including automated progress reports.  

• Respond to client inquiries more efficiently with AI-powered suggestions.  

Microsoft 365 Co-Pilot is more than just a product update; it’s a real gamechanger for SMBs. Microsoft continues to improve it with more language options, new features, and better performance. To stay up to date, check the Microsoft 365 roadmap.  

How Partners Can Make the Best Use of This Opportunity 

Microsoft 365 Co-Pilot gives resellers and managed service providers a great way to grow their businesses. Partners can boost their earnings by setting up productivity pools, adding business premium features, and offering training to help organizations start using Microsoft 365 Copilot. Partners can deliver important services, such as training users to write better prompts and use AI tools. Partners may also qualify for the Tier 2 Accelerator, which offers a 10.75% rebate above the user’s profit margin. Running SMB workshops and delivering ongoing support can make your services even more valuable and help you build steady revenue. This opportunity enables partners to earn more and take advantage of different incentives and rebates.

Source: How SMBs can use Microsoft 365 Copilot for a competitive advantage 

AMD is adding to its Ryzen AI line with the new Ryzen AI 400 Series and Ryzen AI Pro 400 Series desktop processors. These are the first processors designed for next-gen AI PC applications and support Co-Pilot Plus PC features.  

• OEM partners are launching new AI PCs, including enterprise notebooks and mobile workstations, powered by Ryzen AI Pro 400-series mobile processors.  

• Ryzen AI 400 series mobile processors offer up to 30% faster multi-threaded performance vs other processors. This helps professionals finish demanding tasks more quickly and still enjoy all-day battery life.  

• AMD now provides its widest range of enterprise PC solutions supported by AMD, the AMD PRO platform. This platform improves security, manageability, and resilience for large-scale AI PC rollouts.  

At Mobile World Congress 2026 in Barcelona, AMD introduced new additions to its Ryzen AI lineup: the Ryzen AI 400 series and Ryzen AI Pro 400 series desktop processors. These processors offer strong on-device acceleration and next-level performance, letting users run AI applications and large language models locally. The Ryzen AI 400 series can handle demanding tasks like design and engineering smoothly. AMD is also adding workstations to the Ryzen AI 400 series mobile lineup.  

With these new processors, OEMs can now offer next-generation AI PCs, including high-performance desktops, laptops, and mobile workstations, built for today’s workloads.  

Agent AI 400 series processors are the first desktop AI chips to support Microsoft Co-Pilot plus PC features. They include a neural processing unit (NPU) that delivers up to 50 TOPS3 of AI computing power. This lets users run AI assistants and productivity tools directly on their PCs, keeping sensitive data on their devices and offering better control, performance, and privacy.  

The desktop PC is evolving from a tool you use to an intelligent assistant that works alongside you, said Jack Huynh, Senior Vice President and General Manager of the Computing and Graphics Department. With the Ryzen AI 400 series of processors, the world’s first design to power New Co-Pilot + experiences on the desktop. They are bringing powerful AI acceleration that enables our partners to build systems that empower both enterprises and consumers to do more and create more.  

The World’s First Co-Pilot+ Desktop Processor For Next Gen AI Applications 

The AMD Ryzen AI400 series of desktop processors are built to ensure scalable performance and smart features for professional tasks. They combine high-performance Gen5 CPU cores, AMD RDNA 3.5 graphics, and a dedicated AMD XDNA2 NPU. This mix gives office professionals, developers, and power users the speed, efficiency, and local AI acceleration they need. Whether it’s multitasking, collaborating, software development, data analysis, or AI-assisted work, these processors provide continuous performance and on-device intelligence for today’s Desktops.  

Ryzen AI 400 Series Desktop Availability 

M5 desktop systems with Ryzen AI 400-series processors are expected to be available from OEMs such as HP and Lenovo starting in the second quarter of 2026.  

Extending Ryzen AI Leadership Across Notebooks And Mobile Workstations 

AMD’s commercial PC lineup is expanding, as OEM partners are launching notebooks powered by Ryzen AI Pro 400-series mobile processors. The Ryzen AI9 HX Pro 470 offers up to 30% faster multi-threaded performance than the Intel Core Ultra HX7 3581, helping users work through demanding tasks faster. These systems also provide strong power efficiency and all-day battery life, so productivity lasts throughout the workday. With these features, Ryzen AI Pro 400 series processors bring AI acceleration and Co-Pilot + PC experiences to mobile devices.  

As more organizations use AI in their daily work, Ryzen AI Pro 400 series mobile processors make it easy to run advanced AI tasks directly on commercial laptops and workstations. With a powerful NPU that delivers up to 60 TOPS of AI compute, these processors use on-device AI acceleration to boost efficiency. This helps businesses increase productivity while keeping the pace and responsiveness professionals need.  

Ryzen AI Pro 400 series mobile processors will also be used within next-generation workstations, bringing the same high performance to professional systems that support independent software vendors (ISVs). These mobile workstations are built to speed up professional applications by using all available computing resources (CPU, NPU, and GPU) for challenging tasks in engineering, creative work, and technical fields.  

Ryzen AI Pro 400 Series Mobile Workstation Availability 

Mobile workstations with Ryzen AI Pro 400 series processors are expected to be available from Dell Technologies, HP, and Lenovo starting in the second quarter of 2026.  

Strengthening Enterprise Security and Manageability with AMD Pro 

AMD Pro offers enterprise-level security, manageability, and dependability with hardware and software built to make IT operations easier and protect long-term investments. AMD is improving the Pro platform by upgrading both its hardware and software to help IT teams manage an AI-enabled PC fleet. New remote management features give IT administrators greater visibility and control, enabling them to resolve issues and keep the business running smoothly without visiting each desk.  

AMD systems work with most major commercial security solutions, making it easier to add them to existing enterprise environments. This helps organizations protect their device fleets within their current security systems.

Source: AMD Gives Consumers and Businesses More AI PC Options with Expanded Ryzen™ AI 400 Series Portfolio 

Ericsson and Intel are working together on AI-powered network innovation covering compute, connectivity, cloud, and standards across the core network, RAN, and Edge.  

• This partnership strives to make the move to 6G more open, efficient, and affordable for operators and the wider industry.  

Ericsson and Intel are combining their cutting-edge technologies to help the industry prepare for a smooth transition to AI-powered 6G networks and applications.  

The companies announced this extended partnership at Mobile World Congress Barcelona, May 2026. Building on their decades-long relationship, their work will cover mobile connectivity, cloud technology, and computing for AI-driven R&D and core network users. They will also focus on platform security and network features to accelerate cloud-native solutions for the industry.  

Borje Ekholm, President and CEO of Ericsson, says 6G is not simply an iteration of mobile technology. It is the infrastructure that will distribute AI across devices, the edge, and the cloud. Ericsson’s long history of network innovation and large-scale operator deployments enables us to lead practical integration across the value chain and move 6G from research into commercial reality.  

Indip Bhutan, CEO of Intel, says Intel’s ambition is to be the undisputed technology leader toward unifying RAM, core, and edge AI to enable an effortless transition to AI-native 6G environments. Together with Ericsson, we will continue to demonstrate that the future of network connectivity is open, power-efficient, secure, and grounded in intelligent AI influence. With future Ericsson Silicon powered by Intel’s most advanced process nodes, ongoing multi-year research plans, and flexible AI- and RAN-ready Cloud RAN powered by Intel Xeon, we are well on our way to delivering the future performance, efficiency, and supply security that the world’s leading operators require.  

A Joint Commitment 

6G moves from research to action. The industry needs to work together, leveraging global standards, to turn new ideas into operational infrastructure.  

This partnership will help develop high-performance, power-saving computing for both AI-powered networks and networks that support AI.  

AI-powered 6G will bring together smart, flexible networks, advanced computing, and real-time sensing. This will lay the basis for faster, more efficient, and capable services. In the future, sensing and computing could work even more closely together across the network.  

Showcasing Collaboration Results 

Xeon and Intel are key milestones together in Cloud R&D, 5G core, and open network infrastructure at MWC26. They will pursue this progress with several demonstrations at the Ericsson Pavilion(Hall 2), Intel (Hall 3, stand E3E31), and other partner event spaces, presenting their innovative teamwork.  

About Ericsson 

Fixing networks, connecting billions of people every day for 150 years. We have changed the way we communicate. We deliver mobile communication and connectivity solutions for service providers and businesses. Working with our customers and partners, we help build the digital world of tomorrow.  

About Intel 

Intel leads the industry by creating technology that advances progress and improves lives. Guided by Moore’s Law, we keep advancing semiconductor design and manufacturing to serve our customers’ biggest challenges. By adding Intel agents to the cloud, networks, edge, and all types of devices, we help data transform businesses and society. To learn more about Intel’s innovations, visit intnewsroom.intel.com and intel.com.

Source: Ericsson and Intel collaborate to accelerate the path to commercial AI-native 6G 

On March 2, NVIDIA announced a US$2 billion investment in Lumentum Holdings, a leader in optical components. This deal, involving the purchase of convertible preferred stock, constitutes a major change in building advanced AI data centers. The industry is now shifting from copper wiring to faster light-based optical connections.  

The news had an immediate impact on the semiconductor and networking industries, causing a strong rally among smaller optical companies that support the internet. Investors saw that the next stage of AI growth would rely on laser technology and silicon photonics. Shares of Applied Optoelectronics rose by 22.4%, and nLight increased by 16.4%. The market now sees light-based technology as key to building large-scale data centers.  

A Closer Look at the $2 Billion Photonics Deal 

Investment is more than a simple cash infusion; it’s a strategic marriage between the world’s most valuable chip maker and its primary supplier of light. Under the terms of the agreement, NVIDIA purchased 2,876,415 shares of Lumentum Holdings’ newly issued Series A convertible preferred stock at $695.31 per share. A parallel $2B investment matches this $2B layout in Coherent Corp., representing a total $4B bet by NVIDIA on the laser and photonics industry in a single day.  

The deal comes at an important time for AI development. In 2025, the industry found that copper cables, once standard for server connections, became too hot and slow for the high data rates needed by 1.6 terabit optical networks. By the end of 2025, leaders realized that scaling AI clusters to millions of GPUs would require moving data using photonics (light) rather than electronics (electricity). NVIDIA’s investment will help Lumentum build a modern US wafer fabrication facility, securing a domestic supply of advanced lasers for co-packaged optics (CPO).  

Market participants reacted with imminent fervor. While Lumentum Holdings itself rose nearly 10%, the REIT investors responded quickly to the news. Lumentum Holdings shares rose nearly 10%, and the impact on the rest of the sector was even stronger. The investment shows that NVIDIA is working to control more of the networking process, similar to its approach with the H100 and B200 GPU platforms. By securing supply from Lumentum and Coherent, NVIDIA is making it harder for competitors to get the key parts needed for high-performance AI clusters. Stakeholders call this a supply chain moat that provides the optical transceivers and laser diodes essential for fiber-optic communications. Applied Optoelectronics became the rally’s poster child, gaining 22 million as analysts highlighted its role in producing 800G and 1.60 transceivers that will now be in higher demand. Similarly, nLight saw a 16% boost, powered by expectations that its high-powered semiconductor lasers will be integrated into the next generation of NVIDIA’s NVLink interconnects.  

NVIDIA has announced new multi-year agreements with Lumentum Holdings to promote innovation in advanced optics technologies. The partnership will focus on research and development to support the next generation of AI infrastructure and system designs.  

The agreement is non-exclusive and includes a multi-billion-dollar purchase commitment from NVIDIA, as well as future access to advanced laser components. NVIDIA is also investing $2 billion in Lumentum to support research and development, expand capacity, and help build a new U.S.-based manufacturing facility.  

Optical interconnect technology and package integration are essential for scaling AI factories and making large AI networks more energy efficient and reliable. This partnership combines NVIDIA’s expertise in AI computing and networking with Lumentum’s strengths in optics and manufacturing. The investment will help Lumentum grow its manufacturing and research to support future AI data centers.  

AI has reinvented computing and is driving the largest computing infrastructure build-out in history, said Jensen Huang, founder and CEO of NVIDIA, together with Lumentum. NVIDIA is advancing the world’s most sophisticated silicon photonics to build the next generation of gigawatt-scale AI factories.  

The multi-year strategic agreement demonstrates our joint commitment toward advancing the optics technologies that will power the next generation of AI infrastructure, said Michael Hurston, CEO of Lumentum. In support of this cooperation, we are also investing in a new fabrication facility to increase capacity and accelerate innovation. We are excited to work together to expand what’s possible for the AI optical architecture of tomorrow.  

About Lumentum 

Lumentum is a global leader in optical and photonics technologies that support the networks and infrastructure for AI, cloud computing, and next-generation communications. With decades of experience in photonics, Lumentum provides high-performance lasers, modules, and optical sub-systems for expandable, energy-efficient data centers, advanced telecom networks, industrial manufacturing, and sensing. The company is based in San Jose, California, and has R&D, manufacturing, and sales facilities around the world.

Source: NVIDIA Announces Strategic Partnership With Lumentum to Develop State-of-the-Art Optics Technology 

The Photonics Pivot: Nvidia Injects $2 Billion into Lumentum to Secure the Future of AI Networking