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Meta is creating a new Applied AI Engineering Group in its Reality Labs Division to speed up how AI is added to products and infrastructure with a focus on generating revenue quickly. Vice President Maher Saba will lead the team, which reports to CTO Andrew Bosworth. The group aims to connect research and product development through a flat structure, enabling each manager to oversee up to 50 engineers to move faster.  

Key Details of the Tactical Pivot  

• Purpose: The goal is to make AI models faster and more efficient using real-world information and feedback. Meta aims to create a cycle in which models continually improve across social platforms, smart glasses, and Meta AI.  

• The group comprises two teams: One develops internal tools and interfaces. The other manages data pipelines, model evaluation, and oversight.  

• The Applied AI team will collaborate with the Meta Super Intelligence Lab, led by former Scale AI CEO Alexander Wang, to accelerate the development of models such as Avocado for text and Mango for images and video.  

• This shift prioritizes immediate AI monetization, using data to improve ad targeting and engagement across Meta’s platforms, as well as to support AI-powered wearable products.  

• This pivot comes after Reality Labs’ major losses and aligns with Meta’s planned significant AI computing investments for 2026, marking a shift from metaverse to AI-powered wearables.  

This reorganization demonstrates that AI will drive all of Meta’s future products.  

On January 14, Meta laid off more than 1000 employees from its Reality Labs division. This is about 10% of its workforce. The layoff is part of a shift away from VR and metaverse projects. Meta will now focus on AI, Power Variables, and several VR game studios will be closed immediately as a result. The company is making these changes to address Reality Labs’ losses, which have topped $70B since 2021.  

On January 14, Meta announced a major shift in direction, laying off over 1,000 employees in its Reality Labs division as it moves away from its Metaverse focus. The cutback signals a clear transition within the company from Metaverse to AI-powered wearables and mobile technology initiatives.  

Workforce Reduction Details 

The layoffs affect about 10% of Reality Labs’ staff, with a spokesperson stating they are part of a broader plan to re-evaluate resources.  

This is part of that effort. We plan to reinvest the savings to support the growth of wearables this year. The spokesperson told Bloomberg the company had previously indicated this tactical shift in December.  

Strategic Change From VR To Wearables 

This represents a significant change from Meta’s previous Metaverse focus, which began during the pandemic and led to the rebranding from Facebook to Meta in October 2021. Reality Labs has since lost more than $70B.  

In an internal memo, CTO Andrew Bosworth said Meta wants to be more eco-friendly by moving its Metaverse investments towards mobile devices and cutting back on virtual reality spending. In December, the company had already announced plans to focus more on wearables than on the Metaverse.  

Gaming Operations Severely Impacted 

The restructuring has hit. Meta gaming plans are leading to the immediate shutdown of several VR gaming studios. The studios closed are Armichag, Sanzuru, and Twisted Pixel.  

The VR fitness app Supernatural will keep supporting its current features. However, new content and updates are on hold. Despite the closure, Tamara Sciamanna, director of Oculus Studio, stressed in an internal memo that gaming is still important to the company.  

Gaming remains the cornerstone of our ecosystem. With this change, we are shifting our investment to focus on our third-party developers and partners. This will guarantee long-term sustainability, she reportedly wrote.  

Previous Personnel Changes 

This is the latest round of job cuts at Reality Labs in April 2025. Meta also laid off staff working on the VR fitness game Supernatural. It did not share how many people were affected.  

This restructuring marks Reality Labs’ biggest step back from Metaverse projects. Meta is reallocating the Divisions’ resources as it pursues opportunities in the AI wearables market while seeking greater economic stability. 

Source: Meta Platforms Cuts Over 1,000 Reality Labs Jobs as Company Pivots from VR to AI Wearables

Quickly deploy a pre-configured OpenClaw instance with one click using an Amazon Lightsail Blueprint. Available in the US and other AWS regions, OpenClaw is an open-source, self-hosted AI agent for private use.  

How To Deploy With One Click 

Follow these steps in the AWS Lightsail console to set up OpenClaw.  

1. Log in to your Amazon Lightsail account.  

2. Go to Instances and select Create instance.  

3. Choose your region and availability zone (e.g., US East or West)  

4. Select the Linux/Unix platform.  

5. Select the OpenClaw Blueprint option.  

6. Pick your instance plan. Choose 4GB memory if available.  

7. Name your instance. Create an instance to launch.  

Key Features 

• Deploy privately to control your data and privacy.  

• The Lightsail instance has built-in security sandboxing for isolation and easy HTTPS access.  

• Default integration with Amazon Bedrock provides access via a post-deployment script in AWS CloudShell.  

• Recommended security practices, including: keeping the OpenClaw gateway closed to the Internet, generating your own SSH keys, applying patches promptly, and regularly rotating authentication tokens  

• Connect your AI assistant to Telegram, WhatsApp, and Discord.  

We are excited to share that OpenClaw is now available on Amazon LightSail. You can quickly launch an OpenClaw instance and connect it to your browser using AI features, and even link messaging channels if you want. Each LightSail OpenClaw instance comes ready with Amazon RedRock as the default AI model provider. After setup, you can start chatting with your assistant immediately. No extra configuration is needed.  

OpenClaw is an open-source, self-hosted AI agent that serves as your personal assistant on your own computer. Access OpenClaw in your browser to connect with messaging apps like WhatsApp, Discord, or Telegram. It manages emails, browses the web, organizes files, and more, not just answering questions.  

Many AWS customers have asked about running OpenClaw on AWS. Some have even shared blog posts about setting it up on Amazon EC2 instances. From my own experience installing OpenClaw on my home device, I found the process challenging and encountered security issues, including the need for strong SSH key management, secure network configuration to limit exposure, and promptly applying software security updates to protect sensitive data.  

Set up OpenClaw on your cloud easily and securely with Amazon Lightsail.  

Each Lightsail OpenClaw instance includes pre-configured security features such as session sandboxing.  

• device pairing for approved device access  

• automatic configuration backups  

You can securely access the dashboard in your browser with a single click. By default, OpenClaw uses Amazon Bedrock, but you can switch models or connect to Slack, Telegram, WhatsApp, or Discord.  

Amazon Lightsail is available in 15 AWS regions worldwide. Regions include:  

• US East (Northern Virginia)  

• US West (Oregon)  

• Europe (London)  

• Asia-Pacific (Tokyo & Jakarta)  

See Amazon Lightsail Documentation for the full list of regions. Go to the Lightsail console for pricing and more info. Check Lightsail pricing and Quick Start Documentation. 

Source: Amazon Lightsail now offers OpenClaw, a private self-hosted AI assistant 

Get started with OpenClaw on Lightsail

Microsoft will retire the managed Nginx Ingress with Application Routing Add-on for Azure Kubernetes Service on November 30, 2026, after the Kubernetes community’s decision to end support. The open-source Ingress Nginx controller support ends in March 2026.  

Key Dates 

• March 2026: The Community Ingress NGINX project will be retired and no longer receive updates or security fixes.  

• On November 30, 2026, Microsoft will end support for the NGINX Ingress Controller in Application Routing. Until then, only critical security patches will be provided.  

Migration to Gateway API 

Migrate to Kubernetes Gateway API alternatives. Gateway API provides stronger, more flexible L4 and L7 traffic management than the Ingress API.  

Microsoft offers several supported alternatives and migration options:  

• Application Gateway for Containers is a managed Layer 7 load balancer for containers. It supports Gateway API and Ingress API.  

• Microsoft is developing a new gateway API-based application for routing add-on.  

• If using a service mesh, consider the Istio add-on.  

Action Plan 

• Assess your progress: Check whether your AKS clusters use the community-maintained NGINX Ingress Controller or the NGINX Application Routing Add-on. Read the Microsoft LAM documentation to understand your migration paths to a supported platform.  

• Develop a migration timeline. Allocate resources and test the new solution in a non-production environment. Document changes and notify stakeholders. Migrate early to avoid security risks.  

Kubernetes SIG Network and the Security Response Committee are announcing that Ingress Nginx will be retired to help keep the ecosystem safe and secure. We will provide best effort maintenance until March 2026. After that, there will be no more releases, bug fixes, or security updates. Existing Ingress Nginx deployments will continue to work, and the installation files will remain available.  

We strongly recommend that users begin migration to alternatives as soon as possible to ensure continued security and support. Gateway API is the modern replacement for Ingress and is a good option to consider. If you need to keep using Ingress, you can find other Ingress controllers listed in the Kubernetes documentation. Read on for more details about Ingress, NGINX’s history, current status, and next steps.  

About Ingress NGINX 

Ingress directs network traffic to Kubernetes workloads. Gateway API now handles similar tasks. Using Ingress requires a controller. Choose among controllers by user and cloud compatibility.  

Ingress NGINX, created early in the Kubernetes project, became popular for its flexibility and features and is widely deployed across Kubernetes platforms.  

History and Challenges 

The wide range of features in Ingress NGINX has made it hard to maintain. As expectations for cloud-native software have changed, some features that were once helpful are now seen as security risks. For example, allowing users to add any NGINX configuration via snippets and annotations is now considered a serious flaw. What was once flexible has become technical debt too difficult to manage.  

Not enough maintainers supported Ingress NGINX. One or two people kept it running, mostly in their free time. Last year, maintainers announced plans to shut down Ingress NGINX and build a replacement for the Gateway API. The announcement did not attract new contributors. IN-GATE, the planned replacement, is also being retired.  

Current State and Following Steps 

Right now, Ingress NGINX is only receiving best-effort maintenance. SIG Network and the Security Response Committee have tried everything to secure additional support and keep Ingress NGINX running. To keep users safe, we have decided to retire the project.  

In March 2026, we will stop maintaining Ingress Nginx and retire the project. After that, there will be no more releases, bug fixes, or security updates. The GitHub repositories will become read-only but will stay available for reference.  

Current Ingress Nginx deployments will continue to work. Project files, such as Helm charts and container images, will still be available.  

To check if you are using Ingress NGINX, run `kubectl get pods -all-namespaces -l app.kubernetes.io/name=ingress-nginx as a cluster administrator.  

We want to thank the Ingress Nginx maintenance team for their hard work and dedication to this project. This Ingress controller has handled billions of requests across data centers and home labs worldwide. Kubernetes would not be where it is today without Ingress Nginx, and we thank the many years of effort that went into it.  

The Security Response Committee urges all Ingress Nginx users to migrate to Gateway API or another controller now. Review alternatives in the Kubernetes docs or through your vendors. 

Source: Ingress NGINX Retirement: What You Need to Know 

Google Cloud is rolling out new features in Semantic Search and AI-driven data tools. Vertex AI Search supports multi-modal search, and Vertex AI Agent Builder now offers better governance when used with Firestone and Vertex AI Vector Search. These tools enable advanced context-aware queries for auditing and content validation.  

Key Components and Capabilities 

• Vertex AI search and conversation let you build multi-modal semantic search and AI-driven chat agents. These help examine complex data in a complex control auditing setup.  

• Vertex AI agent builder now has improved governance features, making AI-powered auditing applications more secure and easier to control.  

• Firestone is a NoSQL document database for storing and syncing data. It can work with Vertex AI Vector Search to support advanced semantic queries.  

• Bringing these capabilities together unlocks new automation opportunities: quality control checks and information accuracy can now be carried out across both structured and unstructured data.  

• BigQuery and Cloud Dataflow support live data replication and processing, which is key to keeping records current and easy to audit.  

All of these technologies in the Google Cloud ecosystem enable the development of advanced AI tools for editing and quality control.  

We’re excited to share that the Vertex AI Agent Builder now includes advanced governance features enabled by the Cloud API Registry. With this update, administrators can manage which tools are available to developers right from the Agent Builder console. Developers can also use tools managed by the registry through the new API registry.  

• Following last month’s expansion of our Agent Builder Platform, we are introducing tools that accelerate every stage of the Agent Life Cycle with new ADK tools and enhanced visual features. Developers can build agents more quickly and with greater flexibility, and expanded agent engine services. Simplified scaling, while new session and memory support ensure smoother, more reliable agent interactions. These improvements help speed development and reduce operational hurdles. See below for more details.  

Together, these enhancements make Vertex AI Agent Builder a single platform for managing the full agent life cycle, making it easier to move from prototype to production. To learn more about the new features, check out the latest documentation and release notes.  

Expanding beyond data tools, Gle-tenant Cloud HSM is now generally available. This standards-compliant, highly available, and scalable HSM cluster gives you full control over your cryptographic keys and sensitive cloud work tools and general applications.  

Have full control over your cryptographic keys and can manage admin credentials using our Google Cloud APIs. Each customer receives a dedicated cryptographically isolated HSM cluster.  

In addition to these security advancements, Security Command Center (SCC) premium pay-as-you-go customers now have access to advanced AI data and compliance security features. These tools, previously available only to enterprise and premium subscribers, include: the AI security dashboard  

• data security posture management (DSPM)  

• compliance manager  

• security graph with graph search and correlated threats  

Integrating these updates, you can now manage new risks from general to AI and autonomous agents by providing integrated, automated protection for all your Google Cloud workloads. You can start a 30-day free trial to try the full SCC premium experience. 

Source: What’s new with Google Cloud – 2025 

NVIDIA GTC 2026, scheduled for March 16-19 in San Jose, will shift focus from GPU power to full rack-scale AI systems, spotlighting Blackwell Ultra architectures for agentic AI and throughput inference.  

Here are the main points about Blackwell Ultra Low-Power Optical Networking and Telco Reasoning Models for GTC 2026.  

Blackwell Ultra and Network Innovations 

• The system-focused AI column at GTC 2026 will highlight the move from counting individual cards to using rack-scale setups like NVL72 and NVL144, as well as the new NVL576, which will feature an orthogonal backplane design.  

• Blackwell Ultra (GB300) capabilities: major cloud developers use these systems for low-latency, long-running tasks. They use NVLinkswitch for scaling and NVFP4 precision for efficient inference.  

• LPO and Photonics debut as electrical internal interconnects hit their limits. NVIDIA is investing in optical connections, such as CPU and Photonics, for AI factories. Lumentum and Coherent are providing advanced CPUs to meet the high bandwidth demands of future AI systems.  

U.S. Telco Reasoning Models and Agentic AI 

• Agentic AI in Telco: NVIDIA is going beyond basic network automation and working on autonomous networks with telco reasoning models.  

• Tool-Calling Agents: These models enable AI agents to understand incidents, search databases, and take corrective actions in a controlled, trackable way, replacing old, hand-coded runbooks.  

• Industry partnerships: Telecom providers are working with NVIDIA to build 6G on Open Secure AI-based platforms. They are also using NVIDIA NEMO to fine-tune models for network operations center workflows.  

GTC 2026 Highlights 

• Keynote & Focus: CEO Jensen Huang will give the keynote on March 16, discussing the new AI Initiative software-defined infrastructure.  

• Key themes: The event will feature Vera Rubin for Agentic AI, Rubin CPX for rapid-throughput inference, and a new AI-native storage system called ICMS.  

• Sessions: Many sessions will focus on AI RAN, which brings AI to the edge of telecom networks.  

The 2026 GTC event marks a move toward treating inference as a regular operating cost, with attention on metrics such as time to first token, tokens per second, and energy efficiency.  

Telecommunications are quickly shifting toward autonomous networks, with 65% of operators viewing AI as essential for automation, according to the latest NVIDIA State of AI in Telecommunications report. Half also rank autonomous networks as the leading AI use case for return on investment.  

However, many telecom companies still lack enough AI and data science expertise. This gap makes it hard to safely scale closed-loop automation across complex networks.  

Most telecom NOCs use reactive alarm-based workflows. Engineers sift through numerous incidents with various tools, compiling data from different dashboards before resolving issues. NOCs are ideal for autonomous networks because the tasks are repeatable, allowing AI to reduce resolution time and costs.  

Tech Mahindra, a global technology and consulting solutions provider, is working with NVIDIA to help close the AI skills gap. Together, they are turning autonomous network building blocks, such as open models, tools, and guides, into resources telecom developers can easily use and adapt in their own networks. This post explains how to fine-tune reasoning models with NVIDIA Nemo so they can work like NOC engineers and safely manage closed-loop self-reasoning workflows. It covers how to:  

• Create synthetic incident data that closely matches real telecom scenarios.  

• Translate/Export Procedures into Systemic Reasoning Traces using Production-Grade Reference Workflows. This step teaches the model to coordinate tools, reason about network state, and execute end-to-end fault management tasks during fine-tuning.  

This approach gives telco teams a repeatable way to build their own AI agents for network operations. These agents can handle triage, root cause analysis, and resolution for many common incidents, helping operators move closer to TM Forum level 4, highly autonomous networks, and beyond.  

Why Do Network Operations Centers Need Reasoning Models 

Traditional NOC automation is mostly rule-based and open. Traditional NOC automation relies on rules and open-world scripts that trigger onset conditions. These scripts often struggle with noisy signals, cross-domain dependencies, and a system that can take on this work pattern in a controlled, auditable way. Instead of hard-coded runbooks and point scripts, the agent uses the model to interpret incidents, decide which tools to call, and adapt its actions based on live responses.  

Main features include:  

• AI reasoning in the tool-calling column takes over manual alarm triage by leveraging NOC tools for validation, root cause analysis, and issue resolution across current systems.  

• End-to-End Automation: Manages alarm validation, root cause analysis, and resolution for various incident types, including outages, flaps, congestion, and configuration problems.  

• Noise reduction, pull-on filters, self-clearing or low-value alarms that use historical patterns, so engineers can focus on higher priorities.  

• Resolution in seconds, not hours: Cuts down the time needed to resolve common high-volume incidents from hours to just seconds, greatly lowering MTTR.  

The result is a closed-loop self-healing network. NoC agents manage routine triage and resolution, allowing engineers to focus on proactive optimization and complex problem-solving.

Source: Building Telco Reasoning Models for Autonomous Networks with NVIDIA NeMo

OpenAI’s real-time API is now generally available following its official announcement and release in August 2025. This update includes support for remote model context protocol (MCP) servers and session initiation protocol (SIP).  

Key Features of the Real-Time API Now Available 

• General availability: The Runtime API is now production-ready and open to all paid developers.  

• Remote MCP Server Support Developers can connect AI voice agents to external tools and capabilities on any MCP-compliant server. The API automatically manages tool coils, making it easier to expand an agent’s features without manual integration.  

• SIP protocol integration: With native Session Initiation Protocol (SIP) support a common standard for initiating and managing voice communication over IP networks enterprises can connect AI voice agents directly to traditional PBX (Private Branch Exchange) systems and phone networks. This supports automated call handling, appointment scheduling, and customer service in contact centers.  

• New GPT Real-time model: The API uses the advanced GPT Real-time model, offering lower latency, more natural-sounding speech, and better performance with complex instructions.  

• Multi-modal inputs. The real-time API supports audio, image, and text inputs as well as audio and text outputs. This allows for a wide range of applications.  

For comprehensive setup and usage instructions and to explore how these new capabilities can accelerate your project, visit the OpenAI documentation today.  

OpenAI has introduced support for the remote model context protocol (MCP) server, which lets models access context from external sources, and for the Session Initiation Protocol (SIP), a widely used standard for starting and managing online voice and video calls. These technologies are integrated into its GPT-real-time speech-to-text model. These updates are available through a dedicated API. They are designed to help businesses create more autonomous voice-based agents.  

Support for remote MCP (Media Control Protocol) servers in the Real-Time API is now generally available. MCP enables communication with external applications. This lets developers program voice-based agents to access external capabilities or tools. These tools are listed as MCP servers on the internet or other servers, according to Charlie Dai, VP and Principal Analyst at Forrester.  

Remote MCP servers are not listed locally where the agent or application runs.  

OpenAI said enterprises can enable MCP support in an API session by entering the URL of a remote MCP server in the session configuration.  

Once you connect, the API automatically handles the tool calls, so you don’t need to manually wire up integrations. This setup makes it easy to extend your agent with new capabilities, the company explained in a blog post.  

Dai highlighted SIP as a standard for starting and managing real-time voice calls over IP networks, enabling AI voice agents to connect with PBX systems and phone networks.  

Examples of use cases where enterprises can take advantage of SAP support in the API comprise:  

• automated call handling  

• appointment scheduling  

• multilingual support for customer services in contact centers  

Dai added.  

Image Input And Additional Capabilities 

To make the GPT real-time model more useful for voice-based tasks, OpenAI now lets users include images, like photos, screenshots, or other visuals, along with text or audio in a session.  

This functionality enables the model to analyze and respond to image content. Users can ask questions such as “What do you see?” or “Can you read the text within this image?”, according to OpenAI’s blog post.  

Analysts say the ability to upload images is an important addition that will be useful to businesses.  

This can be seen as multi-modal support, meaning the ability to process and understand multiple forms of input, such as text, images, and audio, which is a key area in the market, Dai said. He added that competitors like Google, with Project Astra, are also focusing on multimodal live assistance. Besides image input, OpenAI has improved GPT’s real-time context awareness and memory.  

OpenAI also said the updated GPT real-time model is better at following complex instructions, calling tools accurately, and producing speech that sounds more natural and expressive.  

Dai said these improvements will help businesses use the API for fast, natural voice interactions in many areas. These improve real-time medical transcription, enhance booking assistance, improve customer service for banking, insurance, and telecom, and enhance employee support. Across industries, Penn AI said businesses using the API can now choose from two new voices: Cedar and Marin.  

Microsoft OpenAI’s largest investor also announced two text-to-speech models this week. The company said these will help unlock a wide range of enterprise uses.  

Source: OpenAI adds MCP and SIP support to gpt-realtime for smarter voice-based agents

Starting April 28, 2026, you must use Xcode 26 for App Store submissions. This version introduces new platform SDKs, including iOS SDK 26 and iPadOS SDK 26, designed to improve on-device LLM performance with the Foundation Modules framework. Swift 6.2 and macOS Sequoia 15.4 or later are required. Notable AI features include on-device integration with the cloud and ChatGPT, text summarization, entity extraction, and privacy-focused local inference.  

Key Requirements and Features 

• From April 28, 2026, all apps must be built with Xcode 26 and the latest platform SDKs, including iOS SDK 26 and macOS SDK 26. This requirement aligns with new local LLM features and the updated development workflow.  

• The new APIs let you run large language models locally, reducing reliance on cloud inference.  

• Xcode 26 loads workspaces 40% faster and has better compilation caching.  

• AI-powered tools in Xcode 26 automate inline code generation to accelerate development, generate documentation to improve code clarity, and fix bugs faster. This reduces manual work, letting developers focus on higher-level tasks and boosting overall productivity.  

• You will need a Mac with Apple Silicon running macOS 15.4 or later.  

Development Workflow Changes 

• To add on-device text generation, use the Language Model Session and always check the model’s availability before processing. Profile app uses the Foundation Models framework and tracks CPU with new tools.  

• New tools help you profile the Foundation Modules framework usage and monitor CPU usage in your app.  

• Always check model availability before starting on-device processing.  

Xcode 26 comes with Swift 6.2 and SDKs for:  

• iOS 26  

• iPadOS 26  

• tvOS 26  

• watchOS 8 or later  

• macOS Tahoe 26  

• visionOS 26  

You can debug directly on devices running iOS 15 or later, tvOS 15 or later, watchOS 8 or later, and visionOS. To use Xcode 26, your Mac must run macOS Sequoia 15.6 or later. You can debug directly on devices running iOS 15 or later, tvOS 15 or later, watchOS 8 or later, and visionOS. To use Xcode 26, your Mac needs to run macOS Sequoia 15.6 or newer.  

Xcode 26 provides advanced coding intelligence tools to streamline writing code, building tests and documentation, debugging, refactoring, and navigating projects. It supports integration with ChatGPT and cloud accounts, allows the use of custom API keys with providers that implement the chat completions of API, and offers local model operations on Macs with Apple Silicon.  

• Use the Coding Assistant to interact with code using natural language, featuring context awareness and conversation history.  

• Generate documentation, explain code, preview changes, and create playgrounds within the code editor.  

• Enhanced predictive code completion runs faster and leverages deeper code context on your Mac, resulting in more accurate and efficient suggestions that help speed up the coding process.  

Also in Xcode 26 

• The #Playground macro enables interactive debugging and code exploration in the preview panel, letting you test concepts, debug in real time, and make immediate adjustments to code logic.  

• Icon Composer simplifies icon creation from a single design file, letting you adjust depth and dynamic lighting and customize default dark and mono modes, which speeds up design iteration and ensures consistency across app icons.  

• Redesigned tabs improve project navigation, with in-tab navigation and file pinning to keep key files visible. This makes it easier to quickly access important code segments, improving workflow and reducing context switching.  

• Compilation caching reduces build times, especially when switching branches or performing clean builds, helping developers iterate faster and deliver updates more quickly.  

• New Instruments Enhance App Analysis, Processor Trace Records All Function Calls, Swift UI Profiler Analyzers View Updates, Power Profiler Tracks Battery and Thermal Impact, and CPU Counters, Identify Performance Bottlenecks  

• Swift concurrency debugging monitors async functions and threads, showing clear taps and properties.  

• String catalogs help with localization via tax-safe Swift symbols, string references, auto-compute support, and on-device AI-generated explanatory comments.  

• Voice control enables accurate Swift code dictation, recognizing syntax and automatically formatting code.  

New Features 

Hang and launch diagnostics now offer trending insights marked with a flame icon, making it easier to spot and prioritize performance issues.  

A new setting now controls how function names appear in C++ frames: Plugin.cpp.display.function-name-format.  

By default, the full function name shows, but you can include parts of the signature. For more, see http://les.idb.ibm.org/use/formatting.html#function-name-formats.  

LLDB now highlights C++ function base names by default in backtraces, helping you identify core functions. 

Source: Xcode 26 Release Notes 

Dell Technologies shared its strategy at the Morgan Stanley Technology Media & Telecom Conference 2026 on March 4. The company reported healthy financial results and stressed its focus on AI servers as a main growth area. Dell is positive about the future but also recognizes continuing challenges in the traditional market.  

Key Takeaways 

• Dell achieved over 20% revenue growth and 25% EPS gains in fiscal 2027.  

• The AI server business is growing quickly, and Dell now holds a $43B backlog.  

• Dell plans to give back more than 80% of its free cash flow to shareholders.  

• The traditional PC market continues to face challenges, including slow Windows 11 adoption.  

• Dell’s approach to capital management includes a large share repurchase program.  

Financial Results. 

• In fiscal 2027, Dell’s revenue and EPS grew by over 20% and 25%, respectively.  

• Q4 achieved record revenue, and EPS benefited from a $43B AI backlog.  

• Dell bought back 54 million shares last year, including 14.9 million in Q4.  

• Dell expects 25% EPS growth this year and a strong Q1.  

Operation Updates 

• Dell’s $34B surge in AI server demand marks a record-high pipeline driven by enterprise appetite for scalable AI deployments.  

• Dell expanded its enterprise AI customer base from 3,300 to over 4,000 within 90 days, indicating strong market momentum for its solutions.  

• Strong engineering and advanced power solutions underpin Dell’s AI Server for success, supporting efficient delivery and performance.  

• Project Lightning is on track for initial production and deployment in the year’s first half, aiming to rapidly address new customer needs.  

• Demand for a dense IP storage portfolio grew by 12% last year, expanding margins.  

Future Outlook 

• Dell is focused on maintaining and growing its strong AI server pipeline, with a five-quarter plan to drive continued growth and meet new market needs.  

• Dell aims to make FY2027 its strongest by leveraging AI, strengthening relationships, and driving innovation to address changing demand.  

• To address the ongoing memory market cycle, Dell is implementing proactive decision-making processes by adapting operational strategies to capitalize on rapid market changes.  

• Dell’s call is still to return more than 80% of its free cash flow to shareholders, and it has reached 84% since starting the program.  

Q&A Highlights 

Dell plans to actively pursue Grace Blackwell’s business opportunities and guarantee it can secure enough supply.  

Dell is focusing on investments in its sales teams and engineering capabilities.  

Dell’s Supply Chain is ready to meet its year-end targets.  

For more details, see the full conference call transcript below.  

Full Transcript Morgan Stanley Technology, Media & Telecom Conference 2026  

Erik Woodring, Analyst, Morgan Stanley: Let’s get started here. Welcome back to Day 3, the afternoon of Day 3. My name is Erik Woodring. I lead the hardware coverage here at Morgan Stanley, and I’m delighted to be joined by Dell Technologies CFO David Kennedy.  
 
Before we get started, a few things at my end:  

• Please see the Morgan Stanley Research Disclosure website at morganstanley.com/research disclosures.  

• If you have any questions, please contact Morgan Stanley Sales Representative.  

From Dell’s perspective, statements in this presentation regarding future results and events are forward-looking and based on Dell Technologies’ expectations. In some cases, you can identify these statements by such forward-looking words as “anticipate”, “believe”, “could”, “estimate”, “expect”, “intend”, “confidence”, “may”, “plan”, “potential”, “should”, “will”, and “would”, or similar expressions.  

Risks, uncertainties, and other factors, including those in Dell Technologies’ ICC filings, may cause factual results and future events to differ materially from those the forward-looking statements express or imply. Dell Technologies does not undertake to update these statements. David is attending the TMT conference for the first time. 

Source: Dell at Morgan Stanley Conference: AI Servers Drive Growth 

Key Takeaways 

• Amazon has joined the White House energy pledge to help strengthen the power grid and protect customers from higher costs.  

• We cover all of our data center energy costs, including expenses for new energy sources and grid improvements.  

• Since 2020, Amazon has been one of the world’s largest buyers of carbon-free energy.  

• Amazon operates more than 700 carbon-free projects providing over 40 GW of energy to communities.  

Today, Amazon is proud to have signed the ratepayer protection pledge at the White House. We appreciate the administration’s leadership on this issue. The pledge sets an important standard to protect ratepayers and support responsible long-term energy partnerships that strengthen the grid and the communities where our data centers are located.  

At Amazon, we build and run our data centers responsibly. We pay all our costs ourselves and invest in new energy sources that strengthen the grid for everyone. When we build a data center, we also invest in the local community.  

Because of these efforts, Amazon data centers create thousands of skilled jobs, open up new opportunities for local businesses, generate hundreds of millions in tax revenue for schools and services, and support programs that help people find good jobs.  

We understand that rising utility costs are a concern for American families. While data centers are often discussed, independent studies show they actually help keep rates lower even as other factors drive costs up.  

When Amazon builds new data centers, we also help upgrade the power grid. Meeting the country’s growing energy needs is key to keeping electricity affordable and reliable, supporting daily life, growing the economy, and keeping the U.S. competitive.  

Amazon’s Role in America’s Electricity System 

Amazon pays all of its electricity costs and invests heavily in new energy generation and transmission infrastructure that benefits our communities. We work with grid operators and other partners to ensure the grid can meet future demand and that costs are not passed on to customers.  

For example, we have participated in utility rate proceedings in Indiana, Missouri, Ohio, Oregon, and Virginia to ensure we protect ratepayers and pay the full cost to serve our data centers. For Amazon, paying the monthly power bill is only part of what it takes to run a data center responsibly. We also pay for the additional infrastructure to deliver the power to our data centers, including new transmission lines, substations, and other grid upgrades.  

We achieve this through long-term agreements with utilities that include things like:  

• minimum demand charges  

• financial guarantees  

• multi-year commitments  

These agreements ensure Amazon pays all the costs of serving our data centers, not households or small businesses.  

Amazon’s Great Modernization Benefits Local Communities 

Amazon’s long-term commitments gave utilities the confidence and funding to update the grid, supporting nearby communities, households, and businesses, for example:  

• In Indiana, regulators approved an agreement between Northern Indiana Public Service Company (NIPSCO) and Amazon that will save NIPSCO customers about $1 billion over 15 years near Amazon’s Project RAGNAR, one of the world’s largest AI compute facilities. Indiana Michigan Power (I&M) recently announced rate reductions. The extra revenue from data center growth is helping lower customers’ costs.  

• In Louisiana, Amazon partnered with Southwestern Electric Power Company (SWEPCO), to cover all costs related to our new data center campus. This includes fully funding new energy infrastructure and grid upgrades needed for Amazon’s data centers. These investments also improve dependability for all SWEPCO customers.  

• Entergy has launched a 300 million grid transformation project in Mississippi to improve reliability and aims to cut outages by half with investments from Amazon and other large customers. Residential customers will not incur any additional costs.  

• In Pennsylvania, our agreements ensure that we contribute to the transmission system. According to PPL Electric Utilities, this helps reduce costs and funds local grid upgrades, benefiting all energy users in the area.  

In Pennsylvania, our investment in the new Salem Township Data Center campus also provides major financial support for the nearby Susquehanna Nuclear Power Plant. This helps ensure the plant can keep producing safe, reliable nuclear energy for years to come.  

How Amazon Is Helping Power America’s AI Future 

The American economy is growing, and energy demand is rising due to advanced manufacturing, transportation, and new technologies such as AI. This shows economic strength and potential, but it also means our grid needs to modernize much faster than before. To do this, utilities, regulators, policymakers, and energy providers must work together, develop smart policies, expedite permitting, and continue investing in transmission infrastructure.  

Amazon invests in long-term energy commitments to build a stronger, more affordable energy future. We lead as one of the largest buyers of carbon-free energy and operate over 700 projects, providing more than 40 GW of capacity.  

These projects do more than add new carbon-free energy. They create thousands of construction jobs and hundreds of permanent jobs in their communities. They supply new energy for homes, hospitals, and schools, and modernize the infrastructure that keeps costs stable and affordable. They also support AI innovation and new technologies that are key to America’s leadership.  

We are also leading the way in new nuclear power in Washington. We signed an agreement to develop advanced small modular nuclear reactors (SMRs) with capacities up to 960 MW. We have invested in X-energy, a leading US company developing SMRs to help add more than 5 GW of new nuclear capacity by 2039. We are also working with US national labs, such as Idaho National Laboratory and Lawrence Livermore National Laboratory, to use our AI technology to help them develop the next generation of safe nuclear fission and fusion power.  

A Responsible Way Forward 

By signing the Red Pair Protection Pledge today, Amazon is committed to strengthening America’s energy future. We seek to improve affordability, reliability, and innovation by investing in new grid energy, building responsibly, and partnering with communities to deliver digital economy benefits for everyone.  

Even as we invest in modernizing the grid, more work is needed. Much of America’s electric grid was built for a different time, and now 70% of transmission lines are over 25 years old. Slow permitting and administrative delays are holding back important energy and infrastructure projects, causing higher utility bills and putting growth and US competitiveness at risk. That’s why we keep encouraging bipartisan network teamwork at all levels to unlock private-sector innovation and investment, strengthen America’s technology, and protect long-term economic and national security.  

The Ratepayer Protection Pledge is an important step toward a stronger grid that supports American families, drives our economy, and keeps the United States at the forefront of global innovation. We look forward to working with the Administration, Congress, utilities, regulators, and policymakers at every level to protect ratepayers, strengthen the grid, invest in national infrastructure, and build America’s energy future.  

Source: A responsible path forward for America’s energy future 

News Highlights 

• Meta and AMD have announced a multi-year partnership to expand Meta’s AI infrastructure using advanced, next-generation AMD Instinct GPUs for training and deploying high-performance AI models.  

• The partnership involves deploying up to 6 GW of AMD Instinct GPUs over several years.  

• The first phase will use AMD Helios rack-scale architecture, showcased at the 2025 Open Compute Project Global Summit. Shipments of these custom AMD Instinct MI450 GPUs, specifically designed for Meta’s AI workloads, will begin in the second half of 2026.  

• AMD and Meta are strengthening their partnership through aligning their plans for GPUs, CPUs, systems, and software.  

The 6 GW agreement will power Meta’s next-generation AI infrastructure using AMD Instinct GPUs.  

The initial Gigawatt-scale deployment features custom AMD Instinct MI450 GPUs and 6th Gen AMD EPYC Venice CPUs. These are integrated with ROCm software and AMD Helios rack-scale architecture, developed through collaboration in the Open Compute Project. Shipments start in the second half of 2026. etc.  

We are proud to expand our partnership with Meta as they advance AI at scale, said Dr. Lisa Xu, Chair and CEO of AMD. This collaboration aligns our roadmaps, delivering high-performance, energy-efficient infrastructure optimized for Meta’s workloads and accelerating a major AI deployment.  

We are excited to partner with AMD to deploy efficient compute and deliver personal super intelligence, said Mark Zuckerberg, founder and CEO of Meta. This partnership helps diversify our compute, and I expect AMD to be an important, long-term partner.  

Beyond GPUs, AMD and Meta are expanding their collaboration on EPYC processors. Meta has used millions of AMD EPYC CPUs and various MI300/MI350 GPUs. CPUs remain crucial for scaling complex AI systems. Meta will be among the first to use 6th Gen EPYC CPUs from AMD, optimized for performance and efficiency.  

As part of the agreement to further align strategic interests, AMD has issued Meta a performance-based warrant of up to 160 million shares of AMD common stock. The warrant will vest as certain shipment milestones for Instinct GPUs are met, with the first tranche vesting upon the initial 1 GW of shipments, expected to occur after shipments begin in the second half of 2026.  

• Additional tranches vest as Meta’s purchases scale to 6 GW.  

• Vesting is further tied to AMD achieving certain stock price thresholds.  

• Exercise is tied to Meta achieving key technical and commercial milestones.  

We expect this partnership to drive substantial multi-year revenue growth and be accretive to our non-GAAP earnings per share, representing another major step forward in delivering on our ambitious long-term financial model, said Jean Hu, EVP, CFO, and Treasurer, AMD. The performance-based structure also tightly aligns AMD and Meta around execution and long-term value creation.  

AMD and Meta are working together on hardware systems and software to build a global AI infrastructure. The goal is to speed up AI innovation and deliver AI-powered services to billions of people.