Apple Patent Suggests Future iPads Could Combine Multiple Chips for More Power

Apple is exploring technology that would enable future iPads to interconnect multiple silicon chips, significantly enhancing computational throughput, according to recent patents.  

Key Findings from Apple’s Patent Activity: 

  • First, a patent for an optical communication bar reveals a modular photonic interconnect system. Here, data travels between chips or electronic assemblies via light rather than electrical wiring, enabling high bandwidth and low latency.  
  • Another related innovation is the patent for modular multiple-display electronic devices. This concept allows two iPads or other mobile devices to connect via an accessory, functioning as a single, more powerful computer in which one device serves as a display while the other serves as a digital keyboard or mouse, sharing computing resources.  
  • Looking further ahead, analysts indicate that Apple plans to work with Intel on future M-series chip production. Starting around 2027, the collaboration would use Intel’s Foveros (Direct 3D) stacking method to vertically bond chip components, aiming to boost performance and power efficiency.  
  • In addition, a separate patent envisions joining two iPads with a hinge to form a notebook-style device, allowing both iPads to function together and greatly enhancing the platform’s Pro features.  

These patent activities collectively highlight Apple’s clear focus on modular silicon approaches to realize more powerful, interconnected mobile devices.  

Apple is looking at an accessory that could let two iPads connect and work together like a notebook computer, according to a new patent filing reported by AppleInsider.  

Apple filed a patent application titled “Modular Multiple Display Electronic Devices.” Today, with the US Patent and Trademark Office, the filing describes how two iPads or phones could connect with an accessory. In this setup, one device could serve as a display while the other acts as a dynamic keyboard.  

The patent describes an accessory that uses two small connectors and a hinge, letting mobile devices attach on either side. This connector allows the devices to transfer data and work together as a single system.  

Patent images show that the accessory can create a notebook-style setup. One device lies flat on a surface while the hinged connector at the back props up a second device. Users can set up the devices in both portrait and landscape positions.  

Using a second display as a keyboard: a dynamic keyboard would allow the device to serve as both an input surface and a customizable interface, adapting its functions to what the user is doing much like the MacBook Pro Touch Bar. However, compared to a traditional keyboard, this display would likely lack the depth and tactile feedback that a dedicated physical keyboard provides.  

The patent also suggests that attaching two devices along their longest edges can create a book-style arrangement similar to the Microsoft Surface Duo’s design.  

Apple has filed many patents for second-screen devices, including one called “System with Multiple Electronic Devices.” This patent describes how two or more devices can act as a single device when they are close together, using proximity sensors. While many Apple patents never become products, they often reveal what the company is working on.

Source:  Apple Patent Suggests Two iPads Could Be Connected Together for Notebook-Style Computing 

Apple Explores Liquid Glass UI for iOS 27, Bloomberg Mark Gurman Reports

To summarize 

  • Macworld says Apple will keep the liquid glass design from iOS 26, even though some people think it is unpopular. Bloomberg’s Mark Gurman says adoption rates are actually high.  
  • IOS 27 will be designed for the new iPhone 4’s bigger design. It will support split-screen apps and offer better video and gaming experiences.  
  • Apple plans to add a system-wide liquid glass slider in iOS 27. This will let users control the visual effect, but the design will still be a main feature.  

Last year, Apple updated all its operating systems macOS 26, watchOS 26, visionOS 26, and iOS 26  with a major design change. The new “liquid glass” look added glass-like translucency and animations to every device. While not everyone liked it, a new report says Apple will keep liquid glass for now.  

Building on this, in his latest Power On newsletter, Bloomberg reporter Mark Gurman counters claims of liquid glass’s unpopularity.  

The idea that iOS 26 and Liquid Glass represent a crisis for Apple or constitute an unforgivable offense against good design that customers around the world despise is greatly overblown. He writes that the vast majority of users appear happy with the update, and the adoption rate has steadily climbed.  

He also says that liquid glass wasn’t merely the idea of a few people who have left Apple. According to Gurman, the executive team fully supported the design, and several current design leaders helped develop it over time. So, a sudden change is unlikely.  

But in a post on X, Gurman says Apple is still working on ways to let users control how glassy “liquid glass” looks. In iOS 26.2, Apple added a setting to choose between clear and tinted. He says Apple is still trying to give users more detailed control. Apple had been working on a system-wide liquid glass slider for iOS 26 to adjust the intensity of the glass effect, but it couldn’t be implemented for engineering reasons. Apple is trying again now for iOS 27  TBD if it lands.  

It’s clear that the opening keynote at WWDC26 will not include a big apology or a new design language. Still, the event should have some news. Gurman thinks one major change is coming to iOS 27 this year.

Source:  Apple plans to retain Liquid Glass UI in iOS 27 with new customization controls, improved multitasking, and design tweaks despite mixed user feedback. 

Google Adds Personal Context Memory to Gemini AI, Google Blog Confirms

Google is adding a new feature called Personal Intelligence to its Gemini AI. This feature aims to make Gemini’s answers more useful by using personal information from certain Google apps. If users approve, Gemini can access data from Gmail, Google Photos, YouTube, and search history to help answer questions and plan. Google says the goal is to help you daily with tasks like finding information or planning activities while letting users control how their data is used.  

Personal Intelligence: What Is It? 

With Personal Intelligence, users can link specific Google Ads to Gemini. After linking, Gemini leverages details from emails, photos, or past searches to deliver answers. For example, instead of searching your inbox or photos manually, Gemini records the information for you when prompted. Google states that the feature remains off by default, and users can choose which apps to link, disconnect them at any time, or disable personalization for any check.  

Personal Intelligence: How It Works 

Google states Personal Intelligence performs two core functions. It interprets data from multiple sources. It retrieves specific details from emails or images to answer queries. By integrating text, images, and videos, it tailors responses to better match user needs.  

Google illustrated the feature in action: Gemini helped a user identify their car’s tire size by checking prior data, then suggested options from travel photos, including ratings and prices.  

Google says this feature is useful for daily tasks like planning trips, shopping, or recording details you might not remember right away. However, the company admits the system may struggle with subtle or changing personal situations, such as shifting interests or relationships.  

Privacy and Data Controls 

Google says privacy remains a key part of how Personal Intelligence works. The company says Gemini does not print directly to users’ email inboxes, Gmail, or photo libraries. Instead, it uses that data only to answer specific requests on this blog. In its blog, Google explained that training uses limited information, such as prompts and responses, and that personal details are removed or hidden first.  

Gemini shows or explains where its answers came from, so users can check the information. If something looks wrong, users can correct it or ask for more details. They can also turn off personalization for a chat or use a temporary chat without personal data. Google also says Gemini avoids making guesses about sensitive topics like health unless the user asks.  

Availability and Rollout 

Personal Intelligence will be available over the next week for Google AI Pro and AI Ultra subscribers in the US. It works on the web, Android, and iOS, and supports all Gemini models in the modern figure. Google says it will also be added to AI mode in Search soon. The company plans to expand to more countries and free users later, but there is no set timeline for these. The feature is not available for Workspace or educational accounts and will not work with those account types at this time.  

  • Open the Gemini app.  
  • Tap on settings.  
  • Select Personal Intelligence, a tool that lets Gemini use information you allow from other Google services to answer questions.  
  • Choose which connector apps apps that link and share your data, like Gmail or Google Photos you want Gemini to access.

Source: Google introduces Personal Intelligence in Gemini: What is it, how it works 

Tesla Showcases Optimus Gen 3 Dexterity in New Engineering Update, Tesla Social Reveals

Optimus Gen 3 or V3 may look so real, you’ll need to poke it, says Musk. Wall Street is revising its models for possible major revenues. Meanwhile, roboticists warn that dexterity, safety, and cost remain big challenges.  

To set the context, this article breaks down the timeline, production plans, market outlook, and engineering challenges ahead of the upcoming reveal. We also look at what investors expect and the new career opportunities created by Tesla’s latest AI project. Let’s begin by reviewing the confirmed timeline milestones for Optimus.  

Confirmed Optimus Timeline Milestone 

Tesla’s October 22 statements give the most detailed schedule yet. Musk said, “We look forward to unveiling Optimus V3 probably in Q1,” hinting at a February showcase. Analysts expect a major public test of Tesla’s robotics plans in early spring 2026.  

First-generation production lines are already being installed at Freeman and Giga in Texas. According to the investor deck, internal pilots will deploy 7,000 robots across Tesla shops in 2025. Those points set the stage for later aspirations for one end production capacity  

The timeline now seems solid, but there are still risks to actually making it happen. To understand the scale of Tesla’s ambitions and operational steps ahead, let’s take a closer look at the company’s production plans.  

Tesla Scale Ambitions Stated 

Musk confirmed a goal of 1 million units in a few years. He called it key to global factory automation and management and set a long-term price target of $30,000 per robot.  

To hit the $30,000 price target, Tesla will cut costs and simplify supply chains. Its vehicle expertise will standardize parts and lower the cost of electronics. Executors believe costs could fall to $20,000 per robot, protecting margins.  

Suppliers warn that 10,000 parts per robot will make it hard to make the 1 million unique gold. Tesla believes in-house production will reduce delays and sets its unique mix of AI hardware and manufacturing skills.  

Big goals alone won’t solve the technical problems or main safety standards. Building reliable human-health robots depends on thorough management of parts and logistics with an eye on critical engineering challenges. Let’s identify the obstacles that make slow progress.  

Critical Manufacturing Hurdles Ahead 

Making a working robot is easier than scaling to millions. Rodney Brooks calls it a humanoid robot bubble and says dexterity remains unsolved. Touch sensors and joints are still in research, not mass production.  

Every Optimus contains roughly 10,000 K components, many of which are custom-designed for Tesla. Subsequently, a single late supply can halt an entire factory automation cell. Therefore, Musk conceded the ramp will be limited by the slowest part during Q&A.  

Battery performance, heat management, and the robots’ ability to withstand faults or need thorough testing before unit robots are ready for use. The next prototype should help identify the remaining problems to solve. These technical issues are important to consider as we turn to market demand and revenue forecasts.  

Global Market Forecast Variances 

Market researchers cannot agree on potential demand. Grandview Research banks humanoid robot sales at $4.04 billion by 2030, a conservative trajectory. In contrast, ABI Research models multi-value and revenue earlier, and RGB’s rapid adoption of factory automation.  

  • Q3 2025 Revenue column $28.095 Billion up 20% YOY  
  • Free Cash Flow: $3.99 Billion.  
  • Cash and Investments: $41 Billion.  
  • Vehicle Deliveries: 497,099 units  

Tesla’s goal of producing 1 million robots is well above either forecast. Management also promotes a long-term price target of $30,000 per robot, which could translate into $30 billion in annual hardware revenue. Because of this, some investors believe optimists could eventually surpass Tesla’s car business.  

Doubters argue that price, production volume, and regulations will slow down adoption. Still, even cautious estimates suggest that companies making human-like art robots would see strong double-digit growth. Next, to assess whether these ambitions are plausible, we examine how experts view the path forward.  

Independent Expert Skepticism Mounts 

Rodney Brooks argues hands remain the Achilles’ heel. He notes that decades of research have not produced affordable, reliable manipulation for 10k-component systems. Additionally, Brooks questions whether Musk’s $30,000 price target covers warranty costs and liability.  

Other experts note that no approval systems yet exist for robots in surgery or public places. Officials must test for false emergency stops, and cybersecurity testing of robots outside Tesla factories could take years.  

Expert warnings have made people more cautious about humanoid problems, yet their potential remains. Next, we’ll look at how success in this area could change other industries.  

Wider Strategic Industry Impact 

If successful, optimists could change work across the car, logistics, and electronics factories, enabling people and robots to collaborate on production lines. Consultants estimate that 1 million robots would boost productivity 20-40%.  

A $30,000 price could make Optimus cheaper than many traditional systems. Startups like Figure AI and Apptronik seek partnerships before Tesla scales. Older robotics firms target specialized markets rather than high-volume production.  

Government agencies will likely draft new guidelines for the development of humanoid robots in shared spaces. Nevertheless, clear economic incentives would accelerate regulatory harmonization across areas. Upskilling imperatives emerge, examined next.  

Essential Upskilling for Engineers 

Robotics engineers and operations managers must refresh their skills ahead of the mass adoption of humanoid robots. Additionally, cross-disciplinary competence in AI safety and mechanics will drive career mobility. Professionals can enhance their expertise through the AI Robotics certification endorsed by industry groups.  

Moreover, maintenance technicians will need fluency with 10K components, diagnostics, and predictive analytics. Training programs are surfacing at partner colleges and within Tesla’s own academies. Consequently, early adopters may secure leadership roles in factory automation rollouts.  

  • AI, Model, Tuning.  
  • Safety, Certification, Protocols.  
  • Actuator maintenance.  
  • Supply Chain Analytics  

Training programs need to grow as technology advances. To wrap up, here is a summary of the current situation and what comes next for Optimus and the sector.  

Tesla’s Optimus Gen 3 timeline crystallizes a key year for the development of humanoid robotics. The company targets 1M in production capacity, a $30K price target, and seamless integration with factory automation. However, the 10K components, complexity, safety certifications, and uncertainty remain challenges. If Tesla succeeds, the way manufacturing works around the world could change a lot. Professionals should watch for prototype demos and look for ways to keep learning. Getting certified now can help you prepare for the intelligent production lines of the future. 

Source: Tesla Optimus Gen 3 Spurs Humanoid Robotics Development Leap 

Researchers Discover Glassworm Malware Hidden in Invisible Unicode Aikido Security Warns.

The invisible threat we’ve tracked for nearly a year has re-emerged. While the PolinRider campaign compromised hundreds of GitHub repositories, we now see a sharp rise in glassworm activity impacting GitHub, NPM, and VS Code.  

Last October, we urgently warned about how hidden Unicode characters compromised GitHub repositories, a method unmistakably linked to glassworm. Now the situation is critical. Glassworm has resurfaced this month, and high-profile repositories are already affected, including those from Wasmer, Reworm, and OpenCode-Bench from anomalyco, the team behind OpenCode and SST.  

A Year Tracking the Invisible Code Campaign 

  • In March 2025, Aikido first finds malicious NPM packages that hide payloads using PUA Unicode characters.  
  • In May 2025, we will publish a blog post explaining the risks of invisible Unicode and how attackers can use it in supply chain attacks.  
  • On October 17, 2025, we found compromised extensions on OpenVSX that use the same technique.  
  • On October 31, 2025, we discovered that attackers had started targeting GitHub repositories.  
  • In March 2026, a new large-scale attack compromised hundreds of GitHub repositories, and NPM and VS Code were also affected.  

A Quick Reminder 

Before we uncover just how widespread this alarming new wave is, let’s quickly review how the attack works. Even with months of warnings, it continues to catch developers and tools off guard.  

The attack exploits invisible Unicode characters that escape detection in nearly every editor, terminal, and code review tool. Attackers conceal dangerous payloads within what appears to be empty strings. When the JavaScript runtime executes cold code, a disorder instantly extracts the real bytes and sends them straight to eval(), unleashing the full threat.  

Cybersecurity researchers have identified three new extensions linked to the Glassworm campaign, indicating continued targeting of the Visual Studio Code (VS Code) ecosystem.  

These extensions remain active threats and can still be downloaded right now. They are:  

  • AI-driven-dev.ai-driven.dev 3402 Downloads  
  • Adhamu.history-in-sublime-merge 4057 downloads  
  • Yasuyuky.transient.emacs 2431 Downloads  

Glassworm was first reported by Koi Security late last month. Attackers are exploiting VS Code extensions from both the Open VSX Registry and Microsoft Extension Marketplace to steal Open VSX, GitHub, and Git credentials. They actively drain funds from 49 cryptocurrency wallet extensions and install extra remote access tools, escalating the threat to urgent levels.  

This malware is particularly dangerous because it hides its code using invisible Unicode characters in code editors, stolen credentials fuel a self-replicating infection cycle that rapidly spreads across systems, making it difficult to stop the worm-like attack.  

Based on this evidence, Open VSX said it had found and removed all malicious extensions and had changed or revoked related tokens as of October 21, 2025. However, Koi’s Security’s latest report shows the threat has returned, using unusable Unicode characters to avoid detection.  

The attacker submitted a new Solana blockchain transaction that updated the C2 endpoint for malware downloads, according to security researchers Idan Dardikman, Yuval Ronan, and Luton Sery. This shows the resilience of blockchain-based C2 infrastructure. Even if servers shut down, the attacker can post a cheap transaction, and all infected machines get the updated location.  

The security vendor also found an exposed endpoint on the attackers’ server, revealing a partial victim list across the US, South America, Europe, Asia, and a major Middle East government entity.  

Further analysis found keylogger data that seems to come from the attacker’s own machine. This has provided some indications about where glassworms come from. The attacker is believed to be Russian-speaking and uses an open-source browser extension C2 framework called Redext as part of their setup.  

These are real organizations and real people whose credentials are being harvested now, whose machines may be serving as criminal proxy infrastructure and whose internal networks could be compromised at any moment, Koi Security said.  

The alarming news follows reports from Aikido Security that Glassworm is actively targeting GitHub, with stolen credentials being used to push malicious commits and cause immediate harm.

Sources: GlassWorm Malware Discovered in Three VS Code Extensions with Thousands of Installs 

Glassworm Is Back: A New Wave of Invisible Unicode Attacks Hits Hundreds of Repositories

Amazon Pushes ML-KEM Quantum-Safe Encryption for Future Internet, AWS Blog Reveals

Amazon Web Services (AWS) has deployed the latest hybrid post-quantum key agreement standards for TLS for 23 AWS services. AWS Key Management Service (AWS KMS), AWS Certificate Manager (ACM), and AWS Secrets Manager endpoints now support the lattice-based Key Encapsulation Mechanism (ML-KEM) for hybrid post-quantum key agreements in non-FIPS endpoints across all AWS regions. The AWS Secrets Manager Agent, built on the AWS SDK for Rust, now provides optimal support for hybrid post-quantum key agreement. This allows customers to use end-to-end post-quantum–enabled TLS when bringing data into their applications.  

These three services were selected because they are security-critical and require the highest level of post-quantum confidentiality. They previously supported Crystals Kyber, which ML-KEM now replaces. Crystals Kyber will continue until 2025, but will be removed from all AWS service endpoints in 2026 as ML-KEM becomes the standard.  

Our Migration to Post-Quantum Cryptography 

AWS is following its post-quantum cryptographic migration plan as part of this. AWS will add MLKM support to all services with HTTPS endpoints over the next few years. Customers need to update their TLS clients and SDKs to use ML-KEM when connecting to AWS HTTPS endpoints. This helps protect against future threats from quantum computing. AWS endpoints will select ME, ML-KEM when clients offer it.  

Our hybrid pAWS can negotiate hybrid post-quantum key-agreement algorithms thanks to AWS LibCrypto and AWS LC. Our open-source FIPS 143-validated cryptographic library and S2N TLS, our open-source TLS implementation. AWS LC has received several FIPS certificates from NIST: 434631, 4759, and 4816, and was the first open-source cryptographic module to include MLKM in a FIPS 140-3 validation.  

ML-KEM on TLS Performance 

Migrating from an elliptic curve Diffie-Hellman (ECDH) only key agreement to an ECDH plus ML-KEM hybrid key agreement necessarily requires that the TLS handshake send more data and perform more cryptographic operations. Switching from a classical to a hybrid post-quantum key agreement will transfer approximately 1,600 additional bytes during the TLS handshake and will require approximately 80 to 150 microseconds more compute time to perform ML-KEM cryptographic operations. This is a one-time TLS connection startup cost or amortized over the lifetime of the TLS connection across the HTTP requests sent over it.  

AWS is working to provide a smooth migration to hybrid post-quantum key agreement for TLS. This work includes benchmarking example workloads to help customers understand the impact of enabling hybrid post-quantum key agreements with ML-KEM.  

Using the AWS SDK for Java v2, AWS measured how many AWS KMS GenerateDataKey requests per second a single thread can send between an Amazon EC2 C6i bare metal client and the public AWS KMS endpoint, both in the US West 2 region. Classical TLS connections used the P-256 elliptic curve, while hybrid post-quantum TLS connections used the X25519 elliptic curve with ML-KEM-768. Your results may vary depending on your environment, including instance type, workload, parallelism, number of threads, and network setup. The tests measured HTTP request rates with TLS connection reuse enabled and disabled. The handshake is never amortized, and every HTTP request must perform a full TLS handshake. Enabling hybrid post-quantum TLS reduces transactions per second (TPS) by about 2.3%, from 108.7 TPS to 106.2 TPS.  

Results show that enabling post-quantum TLS has little impact on performance. For most workloads, the maximum DPS rates dropped by just 0.05%. In the worst case, with each request creating a new TLS handshake, the drop was only 2.3%.   

Removing Support for Draft Post Quantum Standards 

AWS Service Endpoints that currently support Crystals Kyber, the predecessor to ML-KEM, will continue to support it through 2025. AWS will gradually phase out Crystals Kyber after customers switch to ML-KEM. If you are using an AWS SDK for Java version that only supports Crystals Kyber, upgrade to the latest version with ML-KEM support. If your code uses a recent AWS SDK for Java V2 release, no changes are needed for the transition from Crystals Kyber to ML-KEM.  

Customers whose clients currently use Crystals Kyber must upgrade their AWS Java SDK v2 to a version that supports ML-KEM before 2026, as Crystals Kyber will be removed in 2026. Clients that have not updated will automatically revert to using classical key agreements to maintain connectivity but will lose post-quantum confidentiality.  

How to use Hybrid Post Quantum Key Agreement 

To enable hybrid post-quantum key agreement in the AWS SDK for Rust, add rustls to your crate and activate the prefer-hybrid-post-quantum feature flag.  

For AWS SDK for Java 2.x, enable hybrid postquantum key agreement by calling .postquantumtlsenabled(true) when building the AWS common runtime HTTP client.  

Step 1: Add the AWS Common Runtime HTTP client to your Java dependencies 

Add the latest AWS Common Runtime HTTP Client to your Maven dependencies. Use version 2.30.22 or higher for ML-KEM support.  

Step 2: Enable Post-Quantum TRS in your Java SDK client configuration 

Select AWSCRTAsyncHTTPClient in your AWS Client setup. Enable post-Quantum TLS.  

Things to Try 

Here are a few ways you can use this client with Post-Quantum support:  

  • Run, Load, Tests, and Benchmarks: AWSCRTAsyncHTTPClient is high-performing and uses AWS LibCrypto on Linux. If you are new to it, compare its performance to the default SDK client. Afterward, enable Post Quantum TLS and check whether it outperforms the default client without it.  
  • Test connections from various locations: Requests may be made via proxies or firewalls that use Deep Packet Inspection (DPI). If blocked, ask your security team to update rules for these TLS algorithms. Share feedback on how your network handles this traffic.  

Conclusion: We’ve added ML-KM Hybrid Key Agreement to 3 AWS Endpoints with TLS connection reuse, enabling hybrid post-quantum TLS, with minimal impact on performance in our tests. We saw only a 0.05% drop in the maximum transactions per second when using AWS KMS-generated data key.

Source: ML-KEM post-quantum TLS now supported in AWS KMS, ACM, and Secrets Manager 

Samsung Begins Shipping HBM4 Memory for Next-Gen AI PCs, Newsroom Confirms

Mass production of HBM4 commences with a consistent transfer speed of 11.7 Gbps with a maximum of 13 Gbps.  

Leading Edge DRAM with a 4nm logic-based die maximizes performance, reliability, and energy efficiency for next-gen data centers.  

Secure Process Technology and Supply Capabilities Strengthen Samsung’s HBM Roadmap Beyond HBM4.  

Samsung Electronics announced it has started mass production of its HBM4 memory and has shipped products to its customers. This makes Samsung the first company in the industry to reach this milestone and take an early lead in the HBM4 market.  

By using its cutting-edge 6th-generation 10-nanometer (NM)-class DRAM Process (1C), Samsung achieved stable yields and top performance from the start of mass production without needing any extra redesigns.  

Instead of using proven designs, Samsung chose the most advanced nodes, such as 1C DRAM and a 4nm logic process, for HBM4, said Sang Joon Hwang, Executive Vice President and Head of Memory Development. By leveraging our process strengths and design optimization, we deliver greater performance and can meet customers’ growing needs for higher performance when they need it.  

Setting the Bar for Maximum Effectiveness and Efficiency 

Samsung’s HBM4 operates at a data rate of 11.7 Gbps, which is approximately 46% faster than the prevailing industry standard of 8 Gbps. This represents a 22% increase over HBM3E’s maximum data rate of 9.6 Gbps. HBM4 can achieve peak speeds of up to 13 Gbps, alleviating bandwidth constraints as AI model sizes increase.  

The aggregate bandwidth per HBM4 stack is now 2.7 times the weight of HBM3E, reaching up to 3.3 terabytes per second (TB/s) across all I/O pins. Samsung’s HBM4 uses a 12-layer 3D stacking approach with capacities ranging from 24 GB to 36 GB per stack. Future 16-layer stacks will support modules up to 48 GB, enabling scalability based on system and customer requirements.  

To support increased power and heat from doubling data I/O from 1024 to 2048 signal pins, Samsung integrated advanced low-power circuitry at the core die. HBM4 achieves 40% higher power efficiency through low voltage, through-silicon vias (TSVs), and power distribution network (PDN) tuning, and realizes a 10% gain in thermal resistance and 30% better heat dissipation versus HBM3E. Samsung’s HBM4 delivers high performance, efficiency, and reliability, helping customers get more from their GPUs and control costs in new data centers.  

Comprehensive Yet Agile Manufacturing Capacities 

Samsung will build on its large-scale manufacturing resources to advance HBM4 and future technologies. The company will roll out enhancements and revisions to the roadmap in upcoming product generations.  

Close collaboration between Samsung’s factory and memory teams through design-technology co-optimization (DTCO) supports maintaining high quality and yield. Their in-house expertise in advanced packaging also shortens production cycles and lead times.  

Samsung also plans to expand its technical partnerships. The company works closely with global GPU makers and hyperscalers on next-generation ASIC development.  

Samsung expects its HBM sales to more than triple in 2026 compared to 2025. The company is expanding HBM4 production with HBM4E sampling to begin in the second half of 2026, following the HBM4 launch. Custom HBM samples will be delivered to customers in 2027 as needed, outlining a sequential roadmap: first launch, then sampling, then HBM delivery.

Source: Samsung Ships Industry-First Commercial HBM4 With Ultimate Performance for AI Computing 

NVIDIA Unveils Vera Rubin AI Platform for Efficiency Gains at GTC 2026

NVIDIA is unveiling the Vera Rubin platform, introducing a new era in AI as seven new chips enter full production to scale the world’s largest AI factories.  

The platform combines an NVIDIA GPU, a Vera CPU, a Rubin GPU, and an NVLink 6 switch. Connectex 9, SuperNic, Bluefield 4 DPU, Spectrum 6 Ethernet switch, and the new Groq 3 LPU. These chips work together as a single AI supercomputer, powering every single stage of AI from large-scale training and testing to real-time agent tech influence.  

Jensen Huang, Founder and CEO of NVIDIA, stated that Vera Rubin marks a generation with seven breakthrough chips, five racks, one large supercomputer, all built to support every phase of AI. According to Huang, the arrival of Agentic AI accelerates the latest infrastructure build-out in history.  

Enterprises and developers are using the cloud for increasingly intricate, agentic workflows and mission-critical decisions. “That demands infrastructure that can keep pace,” said Dario Amadei, CEO and co‑founder of Anthropic. NVIDIA’s Vera Rubin Platform gives us the compute, networking, and system design to keep delivering as we advance the safety and reliability our customers depend on.  

Sam Altman, CEO of OpenAI, affirmed that NVIDIA’s infrastructure is the foundation for continued AI advancements. With NVIDIA, Vera Rubin, and OpenAI expect to run more powerful models and agents at scale, delivering faster, more reliable systems to a broad user base.  

Shift to POD Scale Systems 

AI infrastructure is evolving from separate checks to servers to fully integrated rack-scale systems to POD-scale deployments to AI factories to sovereign AI. These changes deliver higher performance and make AI more cost-efficient for organizations of any size. They also enhance accessibility to powerful AI and improve energy efficiency, reducing costs for intensive workloads.  

By closely integrating compute, networking, and storage and working with over 80 NVIDIA MGX partners worldwide, NVIDIA and Vera Rubin deliver the most extensive POD-scale platform. This supercomputer combines multiple AI-focused racks into a single unified system.  

NVIDIA Vera Rubin NVL72, Rack 

The Vera Rubin NL72 delivers key benefits by combining 72 Rubin GPUs and 36 Vera CPUs connected via NVLink 6, along with ConnectX 9, SuperNic, and Bluefield 4 DPUs. It enables streaming large mixture-of-experts models with only a quarter of the GPUs required by the NVIDIA Blackwell platform while delivering up to 10 times higher inference throughput per watt and reducing the cost per token by 90%.  

NVL72 is built for large-scale AI factories worldwide. It works smoothly with NVIDIA Quantum X800 InfiniBand and Spectrum X Ethernet, keeping graphics processing utility unit clusters running efficiently while cutting training time and overall costs.  

NVIDIA Vera CPU Rack 

Reinforcement learning and agentic AI workloads depend on large numbers of CPU-based environments to test and validate results generated by models running on GPU systems.  

The NVIDIA Vera CPU Rack features a lens liquid-cooled setup powered by NVIDIA MyMJX and 256 Vera CPUs. It delivers scalable, energy-efficient performance and top single-core performance, enabling large-scale agentic AI.  

With Spectrum-X Ethernet Networking (a network infrastructure solution), Vera CPU Racks keep CPU environments in sync across the AI factory. Alongside GPU Compute Racks, they form the CPU base for large-scale agentic AI and reinforcement learning. CPUs in this system deliver results twice as efficiently and 50% faster than traditional CPUs.  

NVIDIA Groq3 LPX Rack 

Media, Groq3, LPX, Advances Accelerated Computing with Key Benefits. It is designed for low-latency, large-context, agentic systems and delivers up to 35 times higher inference throughput per megawatt and up to 10 times greater revenue potential for trillion-parameter models when used with Vera Rubin. When scaled up, many LPUs can work together as a single large processor for fast, predictable inference. The LPX rack has 256 LPU processors, 128 GB of on-chip SRAM, and 640 TB/s of bandwidth. Used with Vera Rubin, NVL72, Rubin GPUs, and LPUs, speeds up decoding by computing every layer of the AI model for each output token in parallel.  

The LPX architecture is optimized for Trillium parameter models and million-token contexts, working with Vera Rubin to maximize power, memory, and compute. Its higher throughput per watt and better token performance open up a new level of high-end trillion-parameter inference, creating more revenue opportunities for AI providers. LPX is fully liquid-cooled, built on MGX infrastructure, and will be available in the second half of this year as part of next-generation Vera Rubin AI.3.  

NVIDIA Bluefield 4STX Storage Rack 

The NVIDIA Bluefield-4 STX RackScale System is a storage solution created for AI, extending GPU memory across the POD and powered by Bluefield-4, which combines the NVIDIA Vera CPU and ConnectX-9 SuperNIC. STX provides a high-bandwidth shared layer for storing and retrieving a large key-value cache derived from data from language models and agentic AI workflows.  

NVIDIA DOCA Memos, a new DOCA framework that enhances BlueField for storage, allows dedicated KV Cache storage processing. This boosts inference throughput by up to 5x and greatly improves power efficiency compared to general-purpose storage. As a result, the system provides a broader context for faster multi-term interactions with AI agents, more scalable AI services, and better overall infrastructure utilization.  

Timothee Lacroix, Co-Founder and Chief Technology Officer of Mistral AI, commented that the NVIDIA BlueField for STX rack-scale complex memory storage system delivers the significant performance boost required to expand agentic AI. Lacroix highlighted that by introducing a storage tier designed specifically for AI agents, primary STX helps maintain coherence and speed during complex reasoning over large datasets.  

NVIDIA Spectrum 6 SPX Ethernet Rack 

Spectrum-6 SPX Ethernet is designed to accelerate east–west traffic (data transfer between servers in a data center) in AI factories. It can be set up with Spectrum X Ethernet (networking technology) for NVIDIA Quantum X800 InfiniBand switches (high-speed data connections), providing fast, high-throughput connections between racks at scale.  

Spectrum-X Ethernet Photonics with pro-packaged optics (advanced optical technology for networking) delivers up to 5× better optical power efficiency and 10× more resiliency than traditional pluggable transceivers.  

Improving Resiliency and Energy Efficiency 

NVIDIA and over 200 data center partners announced the NVIDIA DSX platform for Vera Rubin. DSX Max-Q dynamically manages power across the AI factory, enabling data centers to deploy 30% more AI infrastructure within the same power limits. The new DSX Flex software makes AI factories grid flexible, unlocking 100 gigawatts of unused grid power.  

NVIDIA also released the Vera Rubin DSX AI Factory Reference Design, a blueprint for AI infrastructure that maximizes tokens per watt and overall output. This design improves system resiliency (ability to handle outages) and speeds up time to first production.  

By tightly integrating compute, networking, storage, power, and cooling, this architecture improves energy efficiency and supports reliable AI factory scaling under heavy workloads while maintaining high uptime.  

Broad Ecosystem Support 

Vera Rubin based products will become available from partners in the second half of the year. Leading cloud providers. Amazon Web Services, Google Cloud, Microsoft Azure, and Oracle Cloud Infrastructure along with NVIDIA cloud partners CoreWeave, Cursoe, Lombard, Nebius, Nscale, and Together AI will offer these products.  

Global system manufacturers such as Cisco, Dell Technologies, HPE, Lenovo, and Supermicro plan to offer a variety of servers based on Vera Rubin products. Additional partners include Aivres, Asus, Foxconn, Gigabyte, Inventec, Pegatron, Quanta Cloud Technology (QCT), Wistron, and Wiwynn.  

AI labs and leading model developers, including Anthropic, Meta, Mistral AI, and OpenAI, intend to use the NVIDIA Vera Rubin platform to train larger, more advanced models. Their goal is to deliver long-context, multimodal systems with reduced latency and lower costs than previous GPU generations.

Source: NVIDIA Vera Rubin Opens Agentic AI Frontier 

OpenAI Signals AI Could Soon Run Apps on Your Computer

OpenAI is moving from a commerce system to agentic AI using an operator and its computer with a CUA model. The operator can independently control a computer to complete multi-step tasks, enabling AI to interact with websites and apps on the user’s behalf.  

With this new paradigm in mind, consider the following outline of OpenAI’s vision for autonomous computer tasks.  

To provide better context, let’s start by focusing on the first key area:  

  • An operator is an AI agent designed to take control of a user’s web browser and eventually their computer to handle repetitive or complex tasks.  
  • The operator uses a computer running the agent CUA model, which combines GPT-4’s Visual Reasoning with Reinforcement Learning. Unlike older automation tools that require API interfaces, CUA can view the screen via screenshots and interact with graphical interfaces, as a person does.  
  • An operator can fill out forms, order groceries, do research, create memes, and schedule appointments.  

The Shift to Agentic AI 

  • Operator denotes a shift from a checkbox that only talks to agents who can take action. It is built to manage long, multi-step tasks with little need for people to step in.  
  • A new ChatGPT Agent feature lets AI use a virtual computer to check calendars, book restaurants, and make slide decks.  
  • Once you set a goal, agents work on their own. For example, it could plan a weekend trip.  

Present Constraints & Safety 

  • The operator is still in the research stage and is primarily available to pro users in the US.  
  • The AI pauses for human approval before any action that can be undone, like sending emails or deleting calendar events.  

The agent can sometimes get stuck on streaky interfaces, capture or password fields, so it may need help from a person.  

Future Outlook discusses the upcoming directions and possibilities for the Operator platform and agentic AI. 

  • OpenAI plans to expand the operator to the Plus team and Enterprise users.  
  • OpenAI positions Customer Agents as a Foundation for Progress Towards Artificial General Intelligence (AGI).  
  • The aim is to move from a single tool to an ecosystem in which agents work independently across multiple systems seven times.  

The move to agentic AI is part of a broader trend in 2025, with companies like Anthropic and Google building similar capabilities.  

At the start of this year, OpenAI CEO Sam Altman predicted 2025 would be pivotal for AI agents—tools that automate tasks and act on users’ behalf.  

Building on this vision, OpenAI is now making its first real move in this area.  

OpenAI has announced a research preview of Operator, an AI agent that controls a web browser and autonomously performs tasks. It will initially be available to US users with ChatGPT’s Pro subscription and will expand to Plus, Team, and Enterprise plans, with dates to be announced.  

Operators will be available in other countries soon, though a specific launch date has not been announced. OpenAI CEO Sam Altman said during a live stream on Thursday that Europe will, unfortunately, take a while.  

Currently, the research preview is at operator.chatgpt.com. OpenAI plans to add Operator to all ChatGPT clients soon. The operator promises to automate tasks such as booking travel, making reservations, and shopping. The interface offers categories such as shopping, delivery, dining, and travel for different automations.  

When users activate Operator in ChatGPT, a dedicated web browser opens, allowing the agent to complete tasks and explain its actions. Users still control their own screen, as Operator operates in its own browser.   

OpenAI explains the browser runs on a computer using an agent or CUA model, combining GPT-4o’s vision skills with advanced reasoning. The CUA attempts to interact directly with website interfaces, bypassing developer APIs.  

This allows the CUA to click, navigate menus, and fill forms on web pages much like a person.  

OpenAI says it’s collaborating with companies like DoorDash, eBay, Instacart, Priceline, StubHub, and Uber to ensure operators comply with their terms of service.  

The CUA model is trained to ask for user confirmation before finalizing tasks with external side effects, for example, before submitting an order or sending an email, so that the user can recheck the model’s work before it becomes permanent. Open-air rights in materials provided for death crimes. It has already proven useful in a variety of cases, and we aim to extend that dependability across a wider range of tasks.  

But OpenAI warns the CUA isn’t perfect. The company says it doesn’t expect the CUA to perform reliably in all scenarios just yet.  

Currently, the operator cannot consistently handle many complex or specialized tasks. OpenAI adds support for tasks such as creating detailed slide shows, overseeing intricate calendar systems, or interacting with highly customized or non-standard web interfaces.  

To be extra careful, OpenAI requires users to supervise certain tasks, such as banking transactions, even though the CUA and operator could handle them on their own. For example, users must enter credit card information themselves. OpenAI also says the operator does not color or screenshot any data.  

On particularly sensitive websites, such as email, the operator requires active user supervision, guaranteeing users can directly catch and handle any potential mistakes the model might make, OpenAI says in its support materials.  

This does limit what an operator can do, but it also helps prevent mistakes like the agent accidentally spending your mortgage payment on edgy accent chairs. Google has taken a similar approach with its Project Marina AI agent, which also avoids entering sensitive information such as credit card numbers.  

Limitations 

The operator does have some important limitations.  

There are both daily and task-based rate limits. OpenAI says an operator can handle seven tasks at once, but there are dynamic limits on how many. There is also an AU total-usage limit that resets each day.  

For security reasons, the operator will not perform certain tasks at this stage, such as sending emails or deleting calendar events, even though the CUA can. OpenAI says this may change in the future, but there is no timeline yet.  

An operator can also get stuck if it encounters a complex interface, a password field, or a captcha. When this happens, it will prompt the user to take over.  

An Agentic Future 

Compared with competitors like Rabbit, Google, and Android, OpenAI has taken longer to develop an AI agent. This may be due to the technology’s safety risks.  

When an AI system can take actions on the web, it opens the door to much more dangerous use cases from nefarious actors. You could automate AI agents who orchestrate phishing scams or DDoS attacks or have them snatch up tickets to a concert before anyone else can. Especially for a tool as widely used as ChatGPT, it’s important that OpenAI takes steps to prepare for such exploits.  

OpenAI believes the operator is safe enough to release now, at least as a result review.  

The operator employs tools that seek to limit the model’s susceptibility to malicious prompts, consent instructions, and prompt injection. OpenAI explains on its website that a monitoring system triggers action if suspicious activity is detected, while automated and human-reviewed pipelines continuously update safety balances.  

Operator is OpenAI’s most ambitious effort so far to create an AI agent. Lastly, OpenAI launched Tasks, which gave ChatGPT basic automation features such as creating reminders and scheduling prompts to run at specific times each day. Tasks added some familiar but important features to ChatGPT, making it as practical as Siri or Alexa. However, the Operator introduces capabilities that earlier virtual assistants could not offer in AI. After ChatGPT, a new technology that will change how people use the internet and their PCs. Instead of simply delivering and processing information, agents can, in theory, take actions and actually do things.  

Now that OpenAI has released its first real AI agent, we will soon see how realistic this vision actually is.

Source: OpenAI launches Operator, an AI agent that performs tasks autonomously 

Microsoft Develops AI Agents to Automate Office Tasks, Report Reveals

When Generative AI emerged, It Jolted Our IT Engineering Team.  

We felt excitement, curiosity, skepticism, and had questions about what this technology meant for IT’s future.  

At Microsoft Digital, we didn’t start with a major transformation plan. We recognized AI wasn’t just another tool, but a big shift in engineering. This realization led us to a gradual, thoughtful approach.  

For years, our IT teams prioritized skill, reliability, and excellence. These priorities persisted. What shifted was our potential.  

Engineers could now generate code in seconds, digitize complex systems rapidly, or automate tasks that once took hours. This gave us a chance to grow our team’s AI expertise.  

This shift in capabilities prompted us to pause and consider tougher questions.  

How do you help thousands of engineers see how AI can perfect their daily work? How do you build trust after trying new things? And how do you use AI in a way that supports, not weakens, core engineering skills?  

We found our answers by taking a step-by-step approach focused on people, culture, and ongoing learning.  

Phase 1: Building Awareness and Giving Access 

It may seem unexpected for an engineering team, but our earliest challenge was not technology; it was understanding.  

When generative AI became a topic, most engineers noticed the news and tested some tools, but few understood its impact on their work. Some felt excited; others, cautious; and many were unsure where to start. Bridging the gap between knowing about AI and practical use was our first challenge.  

We realized ordering engineers to use AI without context would create skepticism. Instead, we focused on something both simple and difficult: exposure.  

We started by making AI visible and accessible in the tools engineers already used. GitHub Copilot, Microsoft 365 Copilot, and Early Copilots are embedded directly into engineering workflows. The goal wasn’t immediate productivity gains; it was familiarity, letting engineers see first-hand what AI could and couldn’t do.  

We also made sure to discuss AI’s limitations openly.  

AI wasn’t perfect; it sometimes invented facts or confidently made mistakes. Being honest was the key. By describing AI as an assistant, V-Rime reminded everyone that engineering judgment remained crucial. Engineers kept control. They just needed to know how.  

We also made it safe for engineers who experiment.  

New quotas, not First Adoption Matrix. Engineers were encouraged to try AI on low-risk tasks such as summarizing documentation, generating test cases, or examining unfamiliar code bases. Minor victories built confidence, confidence built curiosity, and curiosity drove organic adoption.  

As more people experimented, their mindset started to change.  

We encouraged the use of tools so people would experiment with AI, says Mukul Singhal, partner group engineering manager. Once they did, they saw value. The mindset shifted from “AI, replace me” to “AI can be my companion.”  

Over time, the conversation changed from asking “Should we use AI?” to “Where does AI help most?”  

Engineers began sharing prompts, tips, and lessons with one another. What began as individual exploration soon became community learning. Awareness turned into momentum.  

Ultimately, Phase 1 focused on giving people access to explore, ask questions, and learn. With this foundation in place, we were ready to build on what we had learned.  

Phase 2: Culture Shift 

When people gained access to AI, their increased use led to observable outcomes. Curiosity grew as individuals experimented, and we began to measure concrete ways AI enhanced productivity and problem-solving.  

As more engineers tried AI, we observed some teams working faster and more efficiently, while others initially slowed. Our analysis indicated that mindset, not technical skill, was the differentiator.  

We needed people to view AI as a standard part of modern engineering, not as an experiment.  

This meant making AI a trusted part of the engineering process.  

Leaders were key. Rather than call AI a shortcut, they described it as a way to improve basic engineering, clarify design talks, produce better documentation, provide faster feedback, and give more time for real problem-solving. The message was steady: using AI meant rethinking work, not shortcuts.  

We also had to deal with an early fear that using AI meant people would be repressed, not empowered.  

People have shifted from the mindset of “will AI work?” to “AI is working for me,” says Veera Mamilla, a Principal Growth Engineering Manager at Microsoft Digital. I think that was a transformative shift, and I believe many engineers in the organization began to believe in AI.  

How we talked about AI made a difference.  

As engineers used AI, success focused on results. Did AI help you learn systems, reveal risks, or free up time for critical work?  

Over time, AI use became routine. Observed outcomes included users setting new standards, peer-to-peer learning, and shared team success. Teams began discussing optimal ways to use AI, focusing less on adoption and more on effectiveness.  

Phase 3: Upskilling and Role Evolution 

Once AI was deployed, building new skills was essential.  

We deliberately chose upscaling and rescaling rather than replacement. The goal: invest in our current workforce.  

That choice influenced everything we did next.  

At first, upscaling focused on practical basics, including terminal learning the tools and observing how corporates and early agents worked in real-world situations. We encouraged every engineer to try these, no matter their specialty.  

But just developing new skills wasn’t enough. Evolving job roles changed how engineers contributed and worked together.  

In software, service, and cloud network engineering, work shifted from hands-on tasks to more oversight and coordination. Engineers learn to guide AI, review results, and decide on automation.  

As things changed, we looked at how the industry was changing engineering jobs. We just compared new job descriptions from the market with Microsoft’s own words. There was no official AI engineer job yet. Instead of creating a new title, we focused on updating expectations for current roles.  

The idea of an AI-native engineer became more about mindset than job title.  

An AI-native engineer still knows systems, architecture, and risk. Defense and routine tasks go to AI, while judgment, design, and responsibility stay with people. Engineers now oversee AI-assisted work instead of doing it themselves.  

Your title might be Software Engineer or Principal Engineer, says Ragini Singh, a Partner Group Engineering Manager in Microsoft Digital. But if you are acting like an AI engineer, what does that actually mean? That question helped us start defining how these roles were evolving.  

That evolution looked different across disciplines. Software engineers focused on AI-assisted coding, test generation, and spec-driven development. Service engineers relied on AI for incident response, knowledge capture, and out-of-the-ordinary decision support. Good cloud network engineers began moving from manual operation toward intelligent orchestration and agent-assisted troubleshooting. The common thread wasn’t identical to any of them; it was a shift toward higher-order work and robust code.  

Phase 4: Embedding AI Across the Engineering Life Circle 

At this stage, we realized that enabling individuals to be more productive would lead to greater benefits. Most AI users showed up in familiar places: code suggestions, document summaries, quick answers useful but fragmented. The bigger opportunity emerged when we stepped back and asked a harder question: What would it look like if AI were embedded across the entire engineering life cycle, not just used at isolated moments?  

We stopped thinking in terms of tools and started thinking in terms of flow, design, build, test, deploy, operate, and improve. AI needed to show up across all of it in ways that stringent engineers already worked.  

In Software Engineering, this meant using AI earlier to write requirements, explore design choices, and review code with a broader view. Coding help remains important, but is not the main focus.  

AI improved testing and quality, too, by creating tests, finding defects, and reviewing code to reduce repetitive work and spot problems earlier. This lets engineers focus more on quality and design.  

In service engineering, AI for incident management means summarizing findings, gathering information, and analyzing signals. In cloud network engineering, it enables simpler coordination and troubleshooting. The goal: AI should streamline processes.  

Expanding this approach showed that adding AI was more than technical. It changed the entire system.  

If AI shows up at one step, you don’t get the full value, says Sudhakar Sadasivuni, a principal growth engineering manager at Microsoft Digital. The real influence comes when it’s integrated across the life cycle, enabling engineers to design, build, operate, and learn faster as a system.  

With AI as part of daily work, engineers ensure results and next standards through checks, tests, and validations. Using AI raised expectations for judgment and integrity; responsibility and governance became even more important.  

Over time, these changes brought even more benefits.  

Faster design cycles, reduced network data testing, fewer operational problems, improved insights, and accelerated recovery. AI became a core engineering system component, not just an analytical tool, accelerating outcomes.  

Every AI story faces the same question. Does it really make engineers work better? For us, the answer showed up quickly when we saw less tedious work.  

At Microsoft, digital enemies have always had to do work that was needed but tiring. This included manual troubleshooting, repeating diagnostics, examining logs, and other routine tasks that kept things running but did not help the company move ahead.  

AI gave us an opportunity to change this.  

Using AI does not always deliver better results. We shifted our focus and began to ask what’s different now that our engineers wield AI?  

This shift changed how we measured success. Instead of just tracking tool usage, we looked at the bigger picture. We saw faster design cycles, earlier defect detection, less time on rotator tasks, quicker incident resolution, clearer documentation, fewer end-offs, and less work.   

These weren’t just abstract numbers. We witnessed these improvements every day in our work.  

We made sure not to impose a single definition of value on everyone. Software engineers, service engineers, and cloud network engineers all feel the impact in different ways. What was most important was that each team could see real improvements in how their work flowed. This mindset reshaped how our leader’s described success.  

Adoption was always the starting point, says Ullas Kumble, a principal group software engineering manager at Microsoft Digital. But we are clear from the beginning that usage isn’t the destination. The real goal is impact. Semicolon, more time for engineers to focus on the work that really matters. Over time, this approach elevated our conversations. Instead of debating whether AI working teams pinpointed where it helped and where it fell short, measurement shifted to a tone for learning and setting priorities.  

Gazing Forward 

As we look forward to the future, one thing is clear: this journey isn’t finished. We have new challenges and opportunities ahead.  

AI tools will improve, agents will get smarter, and engineering roles will evolve. So we must stick to the principles that guide us, invest in people, focus on the basics, use AI in real workflows, and be honest about what works.  

We did not try to build an AI-driven animating organization all at once. We built it bit by bit.  

We met the engineers who transformed our culture before redefining roles. We moved through the life cycle rather than simply hiding in town. We eliminated liquidity war and quantified impact where it mattered most.  

In summary, our key takeaways are: focus on real impact, adapt to evolving roles, invest in people, and remain honest about results by building step by step. By embedding AI throughout, we achieve better engineering powered by AI and guided by human insight.

Source: Powering the new age of AI-led engineering in IT at Microsoft