Category: Artificial Intelligence

  • GPT-5.6 Sol Streamlines Quantum Computing Experiments with Automated Calibration

    GPT-5.6 Sol Streamlines Quantum Computing Experiments with Automated Calibration

    Researchers at MIT have successfully used OpenAI’s GPT-5.6 Sol model to automate parts of quantum computing experiments, reducing the manual workload for scientists and enabling overnight data collection. The AI agent, integrated via Codex, handled routine measurements and qubit calibration on real superconducting hardware.

    AI Meets Quantum Computing

    Quantum experiments often require hundreds or thousands of preliminary measurements before researchers can proceed to the main experiment. MIT researcher Beatriz Yankelevich tested whether GPT-5.6 Sol could automate these workflows. By connecting the model to lab software through Codex, it could interact directly with quantum experiments.

    Worked With Real Qubits

    The experiments involved superconducting qubits cooled to extremely low temperatures inside dilution refrigerators. Once the chip was prepared, researchers controlled it via software using microwave signals. This environment became a natural testbed for an AI agent capable of interacting with measurement software.

    GPT-5.6 Sol Could Run Measurements

    Yankelevich gave Codex specialized skills explaining how individual measurements should be performed and evaluated. GPT-5.6 Sol could then select parameters, operate the hardware, analyze measurements, and decide whether to refine the experiment or move forward.

    It Helped Calibrate Qubits

    The model worked on an uncalibrated six-qubit chip used by the research group. It identified transition frequencies, calibrated control and readout pulses, and measured how long qubits retained quantum information. These steps are essential for accurately controlling superconducting quantum processors.

    Overnight Experiments Became Possible

    One major advantage was time. Researchers could allow agents to run routine measurements for hours while they worked elsewhere in the laboratory. Yankelevich said she could check progress remotely and intervene when an experiment needed correction or a different direction.

    AI Still Has Limits

    GPT-5.6 Sol performed better when experimental signals were clear and workflows were well defined. Weak or noisy signals were harder, sometimes requiring guidance from an experienced researcher. The results suggest AI agents can automate routine workflows while humans remain important for ambiguous physical results.

    Researchers Move Up the Stack

    The biggest impact is not simply faster measurements. By handing routine calibration and analysis tasks to agents, researchers can spend more time interpreting results, designing experiments, and planning new research directions. OpenAI says the MIT group now regularly uses agents for routine measurements.

  • How AI-Led Productivity Is Redefining IT Hiring: Q&A with Han Digital CEO Saravanan Balasundaram

    How AI-Led Productivity Is Redefining IT Hiring: Q&A with Han Digital CEO Saravanan Balasundaram

    Saravanan Balasundaram, Founder and CEO of Han Digital Solution, sees a fundamental shift underway in India’s IT-BPM industry. In a recent interview, he outlined how AI-led productivity gains—not just weaker tech spending—are driving the sharp slowdown in net workforce additions. He also explained which roles will thrive, why flexible staffing is becoming the norm, and what graduates need to do to stay employable.

    The Numbers Behind the Hiring Slowdown

    According to Balasundaram, two forces are shaping H1 FY27 workforce trends. Around two-thirds of the decline can be attributed to AI-led productivity, which is reducing the number of people needed to deliver the same work, especially in traditional IT services and BPO. The remainder stems from cautious discretionary spending, reinforced by continued uncertainty in West Asia and its effect on sectors like retail, travel, and manufacturing.

    But overall technology spending has not collapsed. Global IT spending is expected to approach $6.3 trillion in 2026, and Indian enterprises are directing more than 40% of technology capex toward data modernization, AI, and cybersecurity. The result, says Balasundaram, is a shift from mass hiring to precision hiring—where each technology investment must deliver stronger capability and productivity.

    “The bigger imperative is to move the workforce up the value chain faster than the productivity curve is rising,” he said.

    Which Job Roles Will Grow or Decline?

    Hiring demand will center on professionals who can combine AI expertise with technical or domain knowledge. Balasundaram expects strong growth in AI/ML engineering, cybersecurity, robotics and AI, and platform-specific specialists across SAP, Oracle, Microsoft, AWS, Salesforce, and ServiceNow. He also flagged emerging roles such as Loop Engineers and Forward Deployed Engineers—positions built around deploying and operating AI systems autonomously.

    On the other side, repetitive and rules-based tasks will face the most pressure. L1 support, ticketing, transactional processing, basic QA, and some entry-level coding and testing roles will see their share of hiring fall significantly. Balasundaram doesn’t expect these jobs to vanish overnight, but he does see the hiring pyramid shifting from volume-based to skills-based.

    The Rise of Flexible Staffing

    Contract and project-based work is becoming a core part of India’s IT workforce, not just a tool for managing hiring cycles. As Global Capability Centers (GCCs) expand and AI-driven roles emerge, organizations need specialized talent quickly. Flexible staffing lets them access skills like AI, cloud, data, cybersecurity, enterprise platforms, and multilingual capability on demand.

    For mid-career professionals, this means more opportunities to work across projects and organizations. For fresh graduates, however, the entry bar is rising. Companies are moving away from high-volume campus hiring and looking for candidates who already have practical AI skills, domain understanding, and the ability to contribute in production environments. The staffing model, Balasundaram notes, is shifting from “people on demand” to “skills on demand.”

    What AI Readiness Really Means for Gen Z

    Balasundaram argues that AI readiness is too often reduced to prompt engineering. Prompting, he says, is becoming a baseline capability rather than a differentiator. Real readiness means knowing how to build, deploy, integrate, and manage AI systems that operate autonomously across business functions.

    He recommends three priorities for graduates: domain expertise layered with AI skills; technology and platform skills that go beyond using AI tools—including how to build production-ready code; and meta-skills like rapid learning, experimentation, problem-solving, and adaptability. Universities also need to move faster; static curricula risk producing graduates equipped for yesterday’s technology environment.

    When Will Stronger Hiring Return?

    Looking beyond FY27, Balasundaram says enterprise AI spending is surging globally, but ROI has not yet caught up. Workforce growth will rebound when AI moves from cost optimization to value creation through AI-native products, agentic systems, new digital businesses, and technology-led revenue streams. Until then, productivity gains will continue to suppress traditional headcount needs.

    GCCs will be a key signal to watch. As they evolve from executing roadmaps to shaping global strategy and owning products, demand for talent should strengthen—but with a very different skill mix. The next hiring cycle in IT-BPM will likely be smaller in volume, but potentially much higher in value.

    This article is based on insights from Saravanan Balasundaram, Founder & CEO of Han Digital Solution.

  • Meta’s Muse AI Agent Expands to Mac: Hands-Free Task Automation Goes Desktop

    Meta’s Muse AI Agent Expands to Mac: Hands-Free Task Automation Goes Desktop

    Meta has expanded its personal AI agent, Muse, to the Mac platform with a dedicated desktop application. This move significantly broadens Muse’s role in users’ daily workflows, allowing it to interact directly with files, messages, and applications stored on the computer, not just through a phone or web browser.

    Muse originally launched on September 8th in the United States across iOS, Android, the web, and WhatsApp. The Mac app is the latest step in Meta’s push into the agentic AI space, emphasizing AI that performs multi-step tasks rather than just responding to queries.

    More Than a Chatbot

    Muse is designed to handle real-world tasks. On the Mac, users can ask it to organize files, retrieve information from Messages, Calendar, or Notes, and fill out forms using saved documents. The agent works across a user’s entire digital routine, reducing the manual effort of moving data between applications.

    A standout feature is Muse’s ability to continue working even after the app is closed. It resumes when it needs user input or when a task is complete. This background operation distinguishes it from most current AI tools, which typically stop when the conversation ends.

    Built With Privacy in Mind

    Meta has granted users clear control over Muse’s access. Computer access is opt-in, and Full Disk Access is optional. Users can adjust permissions at any time through Mac settings.

    On the technical side, Muse operates within a dedicated virtual space called Muse Secure VM. This private environment gives the agent its own browser, storage, and computing resources. Meta states this setup keeps the agent away from sensitive login credentials and manages actions involving connected services.

    Also Read: Meta Launches Muse AI Agent, Can Send Emails, Book Travel: How It Works

    A Bigger Play for Agentic AI

    The Mac launch is part of Meta’s broader strategy toward AI that performs actions rather than just generating text. Muse is intended to execute multi-step tasks such as sending emails or booking travel with minimal user intervention.

    Currently, Muse is available only in the US. Meta has announced plans to expand the service to additional regions in the coming months.

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  • AI-Powered Startups Raise $310.7 Million in Latest Venture Capital Roundup

    AI-Powered Startups Raise $310.7 Million in Latest Venture Capital Roundup

    This week, five startups across artificial intelligence, semiconductor verification, enterprise software, HR technology, and interactive content collectively raised $310.7 million in fresh venture funding. The largest deals included a $200 million round for software development company Factory and a $53 million seed extension for enterprise AI services startup Hang Ten Systems. Investors continue to back applied AI solutions, with deals ranging from seed to Series B.

    Factory Leads with $200 Million at $5 Billion Valuation

    Software development startup Factory secured the largest disclosed round this week, raising $200 million at a $5 billion valuation. The financing included Blackstone, Khosla Ventures, Sequoia Capital, Insight Partners, Evantic Capital, Sound Ventures, NEA, Mantis VC, and Clearlake. The company said total funding now exceeds $400 million. Factory develops autonomous software-engineering systems for enterprises and plans to use the new capital for research, product development, and global expansion. Customers include NVIDIA, Blackstone, RBC, Palo Alto Networks, Adobe, and T-Mobile.

    Hang Ten Systems Adds $53 Million for Enterprise AI

    Enterprise AI services startup Hang Ten Systems, founded by former Infosys CEO Vishal Sikka, raised an additional $53 million in seed funding. The round was led by Xora Innovation, backed by Singapore’s Temasek, with participation from Mayfield and Aramco Ventures. This brings Hang Ten’s total funding to $85 million after its $32 million seed round in June. The company helps large enterprises apply AI to software development, migrations, finance, and analytics. The new funds will support engineering and consulting expansion, increased customer delivery capacity, and further development of its AI platform and reusable skills library.

    Flam Secures $40 Million Series B for AI Interactive Content

    AI interactive-content startup Flam raised $40 million in Series B funding led by QED Investors. Other participants included Claypond Capital, Martin Chavez, Olivier Pomel, Venky Harinarayan, and Shah Rukh Khan, with existing investors RTP Global and Dovetail increasing their stakes. Flam plans to direct the funding toward AI research, product development, and international enterprise expansion. The company reports more than 100 enterprise customers, including Google, Emirates, and KFC, using its technology for marketing, product visualization, learning, entertainment, and customer engagement.

    BoomerangHR Raises $12.7 Million for AI-Powered HR Platform

    AI-powered HR platform BoomerangHR raised $12.7 million in seed funding from Kalos Ventures, Album Ventures, Monta Vista Capital, Capital Eleven, and Strada Education Foundation. The company focuses on employee offboarding and maintaining relationships with former employees, enabling businesses to identify potential candidates for future roles. Its platform combines automated offboarding processes with verified alumni profiles, job matching, and reporting. BoomerangHR said the funding will support building long-term talent networks and expanding adoption among employers.

    VerifAIX Raises $5 Million for AI-Driven Semiconductor Verification

    AI-native semiconductor verification company VerifAIX raised $5 million in seed funding co-led by Endiya Partners and Bluehill VC. The company plans to use the capital for product development, customer deployments, and expansion of its engineering teams across the US, India, and Israel. VerifAIX is developing a verification platform for increasingly complex chip designs. Its technology, built around what the company calls a ‘Formal Brain’, combines AI with formal verification methods to help engineers assess whether chip designs meet intended specifications. The startup was founded in 2024 by Madhulima Tewari, Kenneth Roe, and Avner Landver.

    Closing Note

    Together, the week’s $310.7 million in disclosed funding shows continued investor interest in applied AI across multiple sectors, from enterprise software and chip verification to HR technology and interactive content. Capital is flowing into companies at various stages seeking to turn AI capabilities into commercial products and services.

  • Claude AI Now Drives 26% of Anthropic’s Internal Research, Company Reveals

    Claude AI Now Drives 26% of Anthropic’s Internal Research, Company Reveals

    Anthropic has disclosed that its Claude AI models now lead approximately 26% of the company’s internal artificial intelligence research and development work — a dramatic increase from under 1% just six months ago in February 2026. The data comes from a newly developed internal tool called the R&D Automation Index, which tracks how much AI contributes across different research tasks.

    The index uses a scale built by Epoch AI, ranging from AL0 (no AI involvement) to AL5 (fully autonomous operation). Claude currently operates at the AL4 level for 26% of the work, meaning the AI takes on most of a task after receiving a high-level instruction while a human remains in the loop to supervise. Anthropic emphasized that Claude is not yet running any operations fully on its own.

    More than 90% of the company’s measured AI research now occurs at or above the level where AI and humans collaborate side by side. These areas include model training, reinforcement learning, evaluation, and engineering.

    The scale of the operation is significant. In August, roughly 30,000 AI agents were active at any given time on Anthropic’s most-used internal platform. Over one billion agent decisions were tracked that month, of which approximately 0.002% were stopped by the company’s monitoring system. Additionally, about 6% of AI research compute was allocated to safety work during a week in July, rising to nearly 12% within AI-driven research specifically.

    Anthropic plans to publish these measurements regularly and is calling on other leading AI developers to release similar data. The company believes that shared methods and external verification can help the broader industry keep pace with the rapid evolution of AI development itself. This report represents one of the most detailed looks any major AI company has offered into how its own tools are shaping the work of building the next generation of AI.

  • Maximizing Siri AI on iPhone 18 Pro: Features, Tips, and What’s New in iOS 27

    Maximizing Siri AI on iPhone 18 Pro: Features, Tips, and What’s New in iOS 27

    Apple’s Siri AI has officially arrived in India with iOS 27, bringing a more intelligent and context-aware voice assistant to compatible iPhones. The feature launched on September 14 and comes pre-installed on the iPhone 18 Pro. Currently in beta, Siri AI supports English and requires users to join a waiting list. Apple also enforces daily limits on server requests to manage load.

    Deeper Access to iPhone Data

    The updated Siri can search across Messages, Mail, and Photos. Users can ask Siri to retrieve specific details, such as booking codes from older emails. During business calls, Call Context can display relevant information like a booking number automatically.

    Screen Awareness and App Integration

    Siri AI now understands content on your screen. For example, you can ask it to turn a sports fixture list from a webpage into calendar entries. Activate Siri via the wake phrase, side button, or a swipe down from the Dynamic Island.

    Visual Intelligence in Camera

    Visual Intelligence works through the Camera app as a Siri mode. Point your camera at a printed schedule, such as a wedding card, and ask Siri to extract relevant details.

    Third-Party App Support

    Siri AI integrates with third-party apps. Apple specifically mentions WhatsApp for sending messages via voice commands. Audible playback is supported, and apps like Microsoft Outlook, Notability, and Tripsy are slated for upcoming support.

    Writing, Dictation, and Shortcuts

    Writing Tools can draft and rewrite text in any app where you type. Siri can adapt its writing style to match yours. Dictation gets an upgrade via Apple’s AFM Core Advanced model, adding punctuation and formatting as you speak. The Shortcuts app now accepts plain-language instructions—just describe the action you want instead of manually building each step.

    Price Tracking and Personal Organization

    Safari’s Notify Me feature monitors webpages for changes, including restocks and price drops. Calendar and Reminders can add events and list items through spoken instructions. Siri AI maintains conversation history across devices via iCloud, so chats continue seamlessly on Mac, iPad, Apple Watch, or Vision Pro.

    Language Support in India

    Siri AI launched with English only. French, Japanese, Korean, Portuguese, and Spanish are scheduled to arrive next month, but Apple has not announced a timeline for Hindi. The feature works on iPhone 15 Pro and later models, including the iPhone 18 Pro and iPhone Duo.

  • How to Get a Google Gemini API Key in 2026: Setup, Pricing, and Security Guide

    How to Get a Google Gemini API Key in 2026: Setup, Pricing, and Security Guide

    Artificial intelligence has moved from a novelty to a core building block of modern software, and Google’s Gemini models sit at the center of that shift. Developers now rely on the Google Gemini API to add text generation, image understanding, and reasoning to their applications without training a model from scratch.

    The work begins with one credential: a Google Gemini API key. This key connects an application to Google’s generative AI infrastructure, and getting it right, in setup and security, decides how smoothly a project runs. This guide covers how to get a key in 2026, the free and paid tiers, and the practices that keep it safe.

    What is the Google Gemini API?

    The Gemini API is Google’s interface for sending requests to Gemini models and receiving generated responses. It accepts text, images, audio, and video inputs, making it one of the more flexible multimodal APIs available today.

    Access runs through two channels:

    • Google AI Studio — a browser-based tool built for fast prototyping
    • Vertex AI on Google Cloud — built for enterprise teams needing identity controls and audit logs

    Most independent developers start with Google AI Studio, since it offers a working key within minutes and skips a full Google Cloud setup.

    How to Get a Google Gemini API Key

    Creating a Google Gemini API key takes only a few steps, and Google has kept the process largely unchanged through 2026.

    Step 1: Sign in to Google AI Studio

    Visit aistudio.google.com and sign in with a Google account. First-time users must accept the Generative AI terms of service and a data usage disclosure. A second box about marketing communications is optional.

    Step 2: Open the API Keys Page

    Select “Get API key” from the left panel, or go directly to aistudio.google.com/apikey. This page lists any existing keys along with the Google Cloud project each one belongs to.

    Step 3: Create the Key

    Click Create API key. Two options will appear:

    • Create Key in a New Project: AI Studio generates a Google Cloud project automatically, the fastest route for testing.
    • Create Key in an Existing Project: Useful for developers managing Google Cloud resources who want billing to stay consistent across a team.

    Step 4: Copy and Store the Key

    The key appears only once in full view. Copy it immediately and store it safely, since Google does not display the full string again afterward.

    New users typically get a default project and key automatically after accepting the terms, so a working key is often ready before any manual setup.

    Gemini API Pricing and Free Access

    Google offers a genuinely usable free tier through the Gemini API, with no credit card required. As of mid-2026, this covers models such as Gemini 2.5 Flash-Lite and select Gemini 3 preview models, limited by rate rather than blocked outright.

    One trade-off is worth noting. Data sent through the free tier may be used to improve Google’s products. That makes it fine for prototypes and learning projects, but wrong for confidential or production data.

    Paid usage runs on a pay-as-you-go token model, with no subscription fee. Rates as of mid-2026 include:

    • Gemini 2.5 Flash-Lite: around $0.10 per million input tokens
    • Gemini 2.5 Flash: around $0.30 input and $2.50 output per million tokens
    • Gemini 2.5 Pro: $1.25 input and $10.00 output per million tokens under 200,000 tokens, with higher rates beyond that

    Pro-tier models apply a higher rate to the entire prompt once it crosses 200,000 tokens, not just the portion above it. Budgeting on a single flat number, without checking prompt size, is a common and costly mistake.

    Securing Your Gemini API Key

    An API key works much like a password, and mishandling it carries real financial risk. Google has flagged unrestricted keys as an open security concern for years, and the warning applies just as strongly to Gemini keys today.

    Sound practices include:

    • Never hardcode a key directly into source code that could be committed to a repository
    • Use environment variables to load keys at runtime instead of writing them into files
    • Restrict the key to only the Gemini API rather than every Google service
    • Rotate keys periodically, and delete any key suspected of exposure right away
    • Avoid client-side use in production apps, since browser or mobile code can be inspected by anyone

    Automated bots scan public code repositories continuously, and researchers have tracked tens of thousands of exposed API keys sitting in configuration files tied to AI development tools. A leaked key rarely stays unused for long, so treating it like a bank credential is a baseline requirement, not extra caution.

    Getting Started with Your First Request

    Once a key is generated, most developers test it through a simple text generation call using Google’s official client libraries for Python, Node.js, or REST directly. Google’s documentation provides starter code for each language, and a first response typically returns within seconds.

    From there, expanding into image inputs, function calling, or longer context windows is a matter of adjusting the request structure rather than starting over. This smooth progression is part of why adoption has grown quickly among smaller teams without dedicated machine learning staff.

    Final Thoughts

    Getting a Google Gemini API key is no longer a technical hurdle reserved for specialists. Google has simplified the process to the point where a working key can exist within minutes of signing in, and the free tier gives newcomers real room to experiment before spending a dollar.

    What separates a smooth integration from a costly mistake usually comes down to two things: understanding the pricing structure before scaling up, and treating the key as a sensitive credential from day one.

    As Gemini’s model lineup keeps expanding, fundamentals such as account setup, key creation, tier selection, and security hygiene will likely remain the foundation every developer returns to. Getting these basics right early saves both budget and headaches later, and sets any project built on Gemini up for a steadier run.

    FAQs

    Is the Google Gemini API free to use?

    Yes, Google offers a free tier through AI Studio with no credit card required. It includes rate-limited access to select models, though usage data may be used to improve Google’s products.

    Do I need a Google Cloud account to get a Gemini API key?

    No, Google AI Studio automatically creates a default Google Cloud project for new users. Existing Google Cloud users can import their own projects instead of using the default one.

    How much does the Gemini API cost beyond the free tier?

    Pricing runs per token with no subscription fee. Rates start near $0.10 per million input tokens for lighter models and rise for Pro-tier models on longer prompts.

    Can I use my Gemini API key directly inside a mobile or website app?

    It is not recommended. Client-side code can be inspected by anyone, exposing the key. Keys should be called from a secure backend server instead.

    What should I do if my Gemini API key gets exposed publicly?

    Delete the exposed key immediately from Google AI Studio and generate a new one. Review recent usage logs for unauthorized activity before resuming normal use.

  • Agentic AI Reshapes Banking, Payments, and Fintech: Key Insights

    Agentic AI Reshapes Banking, Payments, and Fintech: Key Insights

    Agentic AI is poised to revolutionize the financial sector by moving beyond simple customer service to autonomously handling complex banking tasks, payments, and fraud detection. This technology enables AI agents to analyze spending, execute transactions, and offer personalized financial advice, all while reducing manual effort and improving efficiency.

    Smarter Banking Decisions

    Agentic AI can analyze a customer’s spending habits, understand their financial goals, compare available options, and take approved actions. From moving money between accounts to managing recurring payments, these systems make everyday banking more automated, faster, and user-friendly.

    AI-Powered Payments

    Instead of manually completing every step of a payment, an AI agent can identify bills, check payment details, and complete transactions after receiving permission. It also monitors upcoming expenses and reminds users about payments, making digital transactions more convenient and organized.

    Fraud Detection Gets Proactive

    Traditional fraud systems react to unusual activity after it occurs. Agentic AI continuously monitors transactions, customer behavior, and account activity to spot risks earlier. It can investigate unusual patterns, request additional verification, and alert financial institutions, enabling faster threat response.

    Personalized Financial Services

    By studying a customer’s financial activity, Agentic AI offers tailored services such as spending pattern analysis, expense management suggestions, and savings planning. Unlike basic recommendation tools, AI agents can follow through on approved actions, turning suggestions into practical steps.

    Faster Fintech Operations

    Fintech companies handle large volumes of customer requests, transactions, compliance checks, and data. Agentic AI automates repetitive processes and coordinates tasks across systems, reducing manual work and allowing teams to focus on complex issues, product development, and business decisions.

    A New Role for Human Bankers

    Agentic AI does not eliminate the need for human employees. Instead, it handles routine tasks while bankers focus on areas requiring judgment, relationships, and complex financial advice. Human oversight remains essential, especially when AI agents make decisions involving money, sensitive data, or financial risk.

    The Road Ahead for Agentic Finance

    Agentic AI is pushing banking and fintech toward services that act rather than simply respond. Customers may increasingly rely on AI agents to manage routine financial tasks across accounts and services. This shift raises important questions about security, privacy, accountability, consent, and the degree of control customers grant AI systems.

  • AI Expansion, Chip Rivalry, and Deep-Tech Funding Lead Today’s Tech Landscape

    AI Expansion, Chip Rivalry, and Deep-Tech Funding Lead Today’s Tech Landscape

    The technology world is undergoing a rapid transformation as artificial intelligence reshapes industries, business workflows, and workforce requirements. Competition is intensifying across AI infrastructure, semiconductor technology, and specialized applications. From legal services and aerospace to financial markets and engineering education, companies and institutions are adapting to a market increasingly defined by AI adoption, hardware capabilities, and changing professional needs.

    What’s New Today

    The tech world is shifting from experimenting with AI to integrating it into real-world systems. AI is moving deeper into professional workflows, while competition around the computing infrastructure needed to support these applications continues to grow. Investment is flowing into deep-tech and aerospace innovation, and education systems are adapting to changing technology-related career requirements. Financial markets are also reflecting shifting investor sentiment around digital assets. Together, these developments show how AI is influencing not just software, but the wider technology ecosystem—from chips and infrastructure to jobs, industry applications, and investment.

    New OpenAI Model Brings AI Deeper Into Legal Workflows

    OpenAI is expanding further into the legal sector with a new AI model designed specifically for law firms. The move reflects the growing use of AI for tasks such as legal research, document analysis, and drafting. For lawyers, the technology could help reduce time spent on routine work while keeping human review central to complex legal decisions.

    Avaana Capital Leads Rs. 43 Crore Funding Round in DheyaTech

    Bengaluru-based deep-tech startup DheyaTech has raised Rs. 43 crore in a funding round led by Avaana Capital, with participation from Unimech Aerospace and Manufacturing. The company will use the fresh capital to scale production, build an integrated testing facility, and accelerate customer deployments. DheyaTech develops small gas turbine engines for aerial mobility, defense, and energy applications, strengthening India’s indigenous propulsion capabilities.

    Huawei Targets Nvidia With Next-Gen AI Chip Arriving in Q1 2027

    Huawei is accelerating its AI chip ambitions, bringing the launch of its next-generation Ascend 960DT forward to the first quarter of 2027 from the previously planned third quarter. The move comes as Huawei works to strengthen its position in AI computing and compete with Nvidia. The company is also developing its Peerium Computing Architecture, designed to connect large numbers of AI chips into powerful systems for training and inference. The push highlights China’s growing focus on building domestic AI computing capabilities amid ongoing semiconductor restrictions.

    Bitcoin Gains Ground While Ether and XRP ETF Flows Turn Negative

    Bitcoin is showing resilience above USD 77,000, even as investor flows into other major crypto assets weaken. Ether and XRP ETFs are facing outflows, highlighting a shift in short-term investor demand toward Bitcoin. The divergence comes amid broader market uncertainty and changing expectations around U.S. monetary policy, leaving traders closely watching ETF flows for clues about the next move.

    Software Jobs Face AI Disruption as Universities Prepare Students for Chip Industry

    As AI reshapes traditional software engineering roles, U.S. universities are preparing students for a different opportunity: semiconductor manufacturing. The shift comes as the country expands domestic chip production and faces a projected shortage of up to 157,000 semiconductor workers by 2030. Universities including Purdue and Arizona State are expanding chip-focused programs, giving engineering graduates a new path into the growing AI hardware economy.

  • How IoT and AI Are Reshaping the Automatic Door Industry in 2026: 5 Key Trends

    How IoT and AI Are Reshaping the Automatic Door Industry in 2026: 5 Key Trends

    The automatic door at your office entrance is no longer just a mechanical convenience. In 2026, it is a data-generating, self-diagnosing node in a building-wide intelligence network — and the companies manufacturing these systems are undergoing a transformation just as dramatic as the technology itself.

    The global automatic door market, valued at approximately USD 2.8 billion in 2025, is projected to grow at a CAGR of 6.2% through 2030. But the real story is not the market size — it is how IoT connectivity and AI-driven predictive maintenance are reshaping the entire value chain, from contract manufacturers to facilities managers.

    Here are five shifts redefining this industry.

    1. Predictive Maintenance Replaces Reactive Repairs

    Traditional automatic door maintenance follows a simple pattern: something breaks, someone calls a technician, and the building pays a premium for emergency service. IoT sensors are flipping this model. Modern door operators from brands like Dormakaba and Geze now embed vibration, temperature, and cycle-count sensors that stream data to cloud platforms. Algorithms detect degradation patterns — a worn drive belt, a drifting motor controller — weeks before failure.

    For facilities managers overseeing hundreds of entry points across multiple sites, this shift is transformative. Instead of scheduling reactive maintenance visits, they receive automated alerts ranked by urgency. The result: up to 40% fewer unplanned downtime events, according to early adopter case studies from European commercial real estate firms.

    2. Contract Manufacturers Become the Innovation Layer

    The brands that dominate automatic door installations — Dormakaba, Geze, Record, Assa Abloy, Gilgen, and BEA — rely on a network of specialized contract manufacturers to produce critical components. These manufacturers are no longer passive suppliers executing blueprints. Increasingly, they are co-developing next-generation parts: IoT-ready motor controllers, energy-efficient drive systems, and sensor arrays compatible with Building Management Systems (BMS).

    DoorDynamic, for example, serves as a contract manufacturer for several of the world’s leading automatic door brands, producing both genuine OEM components and certified compatible alternatives. This dual-track model — genuine brands alongside 100% compatible parts — gives distributors and facilities managers flexibility on cost without compromising on performance or warranty coverage.

    3. The Rise of Compatible Alternatives Disrupts Pricing

    One of the most significant trends in the automatic door aftermarket is the growing acceptance of compatible replacement parts. Historically, building operators had no choice but to purchase OEM components at premium prices. Today, manufacturers like DoorDynamic offer certified compatible alternatives for drive motors, controllers, drive belts, safety sensors, and door operators — all backed by warranties of up to five years.

    This shift mirrors what happened in the automotive aftermarket a decade ago. Independent manufacturers proved they could match OEM specifications at a fraction of the cost, and the market adjusted. In the automatic door industry, compatible parts are projected to capture 25% of the aftermarket by 2028, driven by cost-conscious facilities managers and distributors seeking better margins.

    4. Edge Computing Brings Intelligence to the Door

    Not every smart door use case requires cloud connectivity. Edge computing — processing data locally on the door controller — is emerging as the preferred architecture for latency-sensitive applications like safety sensor fusion and real-time obstacle detection. BEA’s latest sensor systems, for instance, combine radar and laser scanning to create a 3D detection zone that adapts in real time, reducing false triggers in high-traffic environments.

    This trend favors contract manufacturers with embedded systems expertise. The ability to integrate edge-capable controllers into existing door form factors — without requiring full system replacements — is becoming a key differentiator.

    5. Sustainability Drives Component-Level Upgrades

    Green building certifications like LEED and BREEAM are pushing building owners to evaluate the energy footprint of every system, including automatic doors. The good news: upgrading a door’s motor, controller, and sensors can reduce its energy consumption by 30-50% without replacing the entire door assembly.

    Contract manufacturers are responding with high-efficiency brushless motors, low-standby-power controllers, and LED-integrated sensor housings. For buildings pursuing net-zero targets, these component-level upgrades offer a fast ROI — often under 18 months — compared to full door replacements.

    What This Means for the Industry

    The automatic door industry is at an inflection point. IoT and AI are not just adding features to existing products — they are restructuring how components are designed, manufactured, and serviced. Contract manufacturers that invest in smart-component capabilities are moving up the value chain. Distributors that embrace compatible alternatives are unlocking new margins. And facilities managers that adopt predictive maintenance are cutting costs while improving reliability.

    For decision-makers evaluating building access solutions, the takeaway is clear: the intelligence of your automatic doors now depends less on the brand on the box and more on the capabilities of the manufacturers and suppliers behind it.