Eternal Partners with OpenAI to Embed AI Across Zomato, Blinkit and Its Digital Commerce Ecosystem

 In a decisive move to future-proof its digital commerce empire, Eternal Limited has entered into a strategic collaboration with OpenAI, embedding advanced artificial intelligence across its vast ecosystem — from consumer apps to partner platforms and internal systems.

The alliance will power AI-led innovation across Eternal’s marquee brands, including Zomato, Blinkit, District, Hyperpure, Feeding India, and its AI-native venture Nugget. At its core, the partnership aims to position AI not as an add-on feature, but as foundational infrastructure driving efficiency, intelligence and next-generation commerce experiences.

AI as Core Infrastructure

Under the collaboration, Eternal will leverage OpenAI’s Enterprise API platform to reimagine customer and partner interactions. Expect AI-assisted workflows for merchants and delivery partners, contextual AI copilots embedded within partner portals, and next-generation search and discovery interfaces across its consumer platforms.

The objective is clear: faster decisions, smarter operations and seamless user journeys — all while maintaining the speed and reliability that define Eternal’s platforms.

AI tools will also be deployed across partner-facing applications to strengthen compliance, operational oversight and real-time business intelligence. The initiative reflects a broader shift toward data-driven commerce ecosystems where automation and insight move hand in hand.

Powering Product Innovation

The collaboration extends to Nugget, Eternal’s AI-first venture, where OpenAI’s models will support rapid experimentation and product innovation. By integrating advanced generative AI capabilities, Nugget aims to accelerate development cycles and prototype next-gen solutions at scale.

Internally, Eternal is evaluating the integration of OpenAI’s advanced coding models — including GPT-5.3-Codex — into its proprietary orchestration and automation platform, Stitch. The move is expected to significantly compress software development timelines, automate complex workflows and reduce manual intervention across both engineering and operational teams.

Upskilling the Ecosystem

Beyond technology deployment, the partnership also carries a strong ecosystem focus. Eternal and OpenAI are exploring a structured Partner Upskilling Program designed to drive AI adoption across its restaurant and delivery network. The initiative will introduce intelligent assistants and automation tools directly into partner systems, empowering businesses to scale more efficiently.

Albinder Dhindsa, Group CEO of Eternal, emphasized that the collaboration opens doors to experimentation across both software development and on-ground operations. Meanwhile, Oliver Jay, Managing Director, International at OpenAI, highlighted the opportunity to apply cutting-edge AI advancements across Eternal’s large-scale consumer and partner platforms.

A Signal to the Market

More than a technology upgrade, this partnership signals Eternal’s larger ambition: to embed AI deeply within India’s rapidly expanding digital commerce landscape. As competition intensifies and customer expectations evolve, AI is no longer optional — it is foundational.

With OpenAI as a strategic collaborator, Eternal is positioning itself at the forefront of AI-driven commerce, building a future where intelligence is woven into every transaction, interaction and operational decision.

BY: Nirosha Gupta 

The New Delhi Synthesis: Where Global Governance Meets the Silicon Frontier

 The Political Vanguard: A New Kind of Diplomacy

When we see names like Emmanuel Macron and Pedro Sánchez on the roster, it signals that Europe is no longer content to simply regulate AI from the sidelines; they want to be part of the architectural phase. Macron, in particular, has been a vocal proponent of "technological sovereignty," and his presence suggests a desire to find a middle path between the rampant commercialism of the West and the state-controlled models of the East.

The inclusion of Luiz Inácio Lula da Silva brings a vital Global South perspective to the table. For Brazil and India, AI isn't just about chatbots; it’s about agricultural yields, managing urban sprawl, and democratising healthcare. This sentiment is echoed by the presence of regional leaders like Tshering Tobgay of Bhutan and Anura Kumara Dissanayake of Sri Lanka. For these nations, the summit is a high-stakes masterclass in ensuring that the digital divide doesn't become an unbridgeable chasm.

When UN Secretary-General António Guterres stands at the podium, the conversation will inevitably pivot toward ethics. How do we ensure that an autonomous system respects a human right? His role is to be the conscience of the room, reminding the "techno-optimists" that progress without a moral compass is merely a well-funded catastrophe.

The Architects of the Future: The Silicon Valley Contingent

While the politicians discuss borders and laws, the technologists in attendance are the ones building the reality we all have to live in. The presence of Sundar Pichai feels like a homecoming of sorts, but one with immense weight. Google’s deep integration into the Indian digital ecosystem makes his insights into "AI for Bharat" particularly poignant.

Then, we have the pioneers of the current "Gold Rush." Sam Altman of OpenAI and Dario Amodei of Anthropic represent the two sides of the generative AI coin—rapid scaling versus rigorous safety. Seeing them in the same room as Yann LeCun, a man who often challenges the very foundations of their current models, promises some of the most intellectually stimulating debates of the decade.

Interestingly, the inclusion of Alexandr Wang (Scale AI) and Arthur Mensch (Mistral AI) highlights a shift toward the "infrastructure" and "open-source" movements. They are the ones providing the raw materials and the transparent blueprints that allow smaller nations to build their own bespoke AI systems without being beholden to a single corporate entity.

The Indian Powerhouse: Domestic Giants on the Global Stage

India is no longer just a "back office" for global tech; it is the laboratory. The Indian industry leaders attending this summit—Mukesh Ambani, Natarajan Chandrasekaran, and Salil Parekh—represent the bridge between traditional industrial might and the digital future.

Reliance and Tata: These aren't just companies; they are ecosystems. When Ambani speaks about AI, he is talking about its integration into the lives of hundreds of millions of consumers.

The Global Diaspora: Leaders like Nikesh Arora and Shantanu Narayen bring a unique "dual-lens" perspective. They understand the ruthless efficiency of Silicon Valley but maintain a profound connection to the scale and complexity of the Indian market.

Their participation ensures that the summit isn't just a talk shop for foreign dignitaries, but a catalyst for domestic investment that will likely define India’s economic trajectory for the next thirty years.

Why This Matters: The Human Element

It is easy to get lost in the sea of names and titles, but the true heart of the India AI Summit 2026 is agency. For the first time, we are seeing a diverse coalition of nations refuse to be "users" of a finished product. Instead, they are demanding to be "co-creators."

We are moving away from the era where AI was something that "happened" to us, and into an era where we deliberately shape it. Whether it is a farmer in rural Maharashtra using a predictive model to save his crop or a doctor in Lisbon using a diagnostic tool to catch an early-stage illness, the decisions made by these leaders in New Delhi will eventually filter down to the most granular levels of human existence.

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The 2026 summit is an admission that the challenges posed by artificial intelligence—job displacement, deepfakes, and the alignment problem—are too big for any one company or country to solve in isolation. It requires a "Digital Non-Aligned Movement" of sorts, where the focus is on the collective benefit of humanity rather than the quarterly earnings of a few.

A Note on the Setting

New Delhi in February provides the perfect backdrop—a city where ancient monuments stand alongside cutting-edge data centres. It serves as a visual metaphor for the summit itself: an attempt to marry our historical human values with the cold, fast-moving logic of the machine.

Perspective: "The India AI Summit is not about the machines we build; it is about the values we choose to program into them."

Death by Algorithm: Preparing for the New Age of Legal Liability

The era of digital globalisation is hitting a hard border. For decades, the tech industry operated under the assumption that a single, massive intelligence—housed in a handful of data centres in Northern California—would eventually serve the entire planet. But as we move deeper into 2026, that dream is dissolving. According to the latest strategic forecasts from Gartner, Inc., the future of AI is not global; it is fiercely, stubbornly local.

The Death of the 'One-Size-Fits-All' Model

For years, the "closed U.S. model" has been the gold standard. However, the tide is turning toward Digital Sovereignty. Nations are no longer content to outsource their cognitive infrastructure to foreign powers. By 2027, Gartner predicts that platform lock-in will skyrocket from a mere 5% to 35%.

This isn't just about protectionism; it’s about cultural accuracy. A model trained on the archives of the American internet often fails to grasp the legal subtleties of a Parisian courtroom or the linguistic nuances of a Riyadh classroom.

"Trust and cultural fit are emerging as key criteria," notes Gaurav Gupta, VP Analyst at Gartner. "Decision makers are prioritising AI platforms that align with local values and regulatory frameworks over those with the largest training datasets."

The Sovereign Tax: 1% of GDP

Independence comes at a premium. Building a "Sovereign AI Stack"—which includes everything from domestic "AI factories" and specialised data centres to models aligned with local sensitivities—is an expensive endeavour. Gartner estimates that nations committed to this path will need to earmark at least 1% of their GDP by 2029 to keep pace.

While this ensures national security and cultural relevance, it creates a "Market Fragmentation" risk. We are witnessing a massive duplication of effort where different regions build overlapping technologies, potentially stifling the cross-border collaboration that defined the early internet.

The Rise of the Multiagent Economy

As the infrastructure becomes localised, the application of AI is becoming total. We are moving beyond chatbots into the age of Multiagent Systems.

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The 80% Rule: By 2028, up to 80% of all customer interactions will be managed not by humans, nor by simple scripts, but by autonomous AI agents capable of complex reasoning.

The $15 Trillion Handshake: AI-driven B2B commerce is projected to dominate global spending, fundamentally altering how supply chains and corporate contracts function.

Programmable Money: Perhaps most radical is the convergence of finance and autonomous logic. By 2030, Gartner foresees that 22% of financial transactions will be "programmable"—incorporating built-in AI conditions that dictate when and how money is released.

The Human Cost: 'Death by AI'

With deeper reliance comes darker consequences. As AIsystems take control of critical infrastructure, healthcare, and transport, the legal landscape is shifting from "user error" to "algorithmic liability."

Gartner predicts that by the end of this year (2026), we will see over 1,000 global "death by AI" legal claims. These are not mere technical glitches; they are life-and-death legal battles that will force a complete rewrite of international law and corporate accountability.

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Strategic Survival: The Agnostic Executive

For the modern CIO, the message from Daryl Plummer, Gartner’s Chief of AI Research, is clear: Adapt or be stranded. The advice is to move away from rigid, single-provider systems and toward model-agnostic workflows. This allows a business to pivot between different regional LLMs as geopolitical winds shift. More importantly, leaders must focus on "human adaptation." The bottleneck in the next five years won't be the speed of the silicon, but the ability of the workforce to navigate a world where the money thinks for itself and the machines have a national identity.

By - Aaradhay Sharma 

DATA AT BREAKING POINT: The Hidden Cost of India’s AI Ambition

India is no longer just "preparing" for an AI-led future; it is actively architecting it. According to the recently unveiled 2025 State of Data Infrastructure Report, the nation has surged past global benchmarks, transforming from a hub of experimental pilots into a powerhouse of enterprise-scale deployment.

However, beneath this bullish momentum lies a sobering reality: the sheer weight of data is beginning to strain the digital foundations of even the most ambitious firms.

The Momentum: Beyond the Hype

The report, which surveyed over 1,200 global leaders including a significant cohort from India, paints a picture of a nation in high gear. While the world averages a 69% adoption rate for AI, a staggering 89% of Indian organisations have integrated AI as a critical component of their operations.

Unlike previous tech cycles where ROI remained elusive for years, the Indian market is seeing immediate returns. Nearly 63% of local enterprises report "strong or established" ROI, proving that workflow automation and data-driven insights are already paying dividends.

The Complexity Crisis

But speed comes at a price. As Indian enterprises race ahead, their data environments are becoming increasingly labyrinthine.

Growing Pains: 87% of Indian firms report that infrastructure complexity is spiralling "rapidly or faster"—a rate that eclipses the global average.

The Petabyte Pressure: Roughly 40% of Indian organisations are now juggling between 50 and 200 petabytes of data.

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The Investment Surge: AI spending in India is projected to skyrocket by 75.6% over the next two years, yet storage requirements are expected to climb by nearly 74% in tandem.

The Readiness Divide

Despite the optimistic headlines, a "Readiness Gap" is carving the market into two distinct camps. While 55% of Indian organisations possess "managed or optimised" infrastructure, a significant 45% are lagging behind. These firms risk falling into a trap where AI initiatives become too resource-intensive to sustain or too fragile to scale.

"Only 32% of organisations currently possess the predictive, automated scaling necessary to handle the looming data deluge."

Lessons from the Leaders

What separates the "Data Mature" from the rest? The report identifies three pillars of success:

Strategic Vision: 87% of leaders in mature markets treat AI as a strategic priority rather than a siloed IT task.

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Automation: Mature firms are more than twice as likely to have automated infrastructure (65% vs 27%).

Resilience by Design: Over 80% of top-tier companies have built-in sustainability and resilience, compared to a mere 19% of those with weaker data practices.

As India enters this next phase of the industrial revolution, the message is clear: the winners won't just be those with the best algorithms, but those with the sturdiest foundations.

By – Aaradhay Sharma

OpenAI’s Quiet Hardware Revolution: A Screenless AI Device May Arrive in 2026

 For years, OpenAI has lived almost entirely on screens—inside browsers, apps, and developer dashboards. That’s about to change.

Behind closed doors and guarded conversations at Davos, OpenAI has confirmed what the tech world has been whispering for months: the company is preparing to launch its first-ever AI device, with a tentative unveiling planned for the second half of 2026.

The confirmation came from Chris Lehane, OpenAI’s Chief Global Affairs Officer, during discussions at Axios House. He didn’t show a prototype. He didn’t drop specs. But the message was clear—OpenAI no longer sees AI as just software. It wants AI to live with you.

Not a Phone. Not a Screen. Something Else.

This isn’t another smartphone, and it’s definitely not trying to replace one. In fact, the device is expected to move in the opposite direction—away from screens altogether.

That philosophy traces back to Jony Ive, the former Apple design chief whose company OpenAI acquired last year. Ive has described the project as a “peaceful” AI device—one designed to reduce digital noise rather than add to it. No endless scrolling. No app clutter. Just an intelligent presence that works quietly in the background.

A teaser video released by Ive’s design studio hinted at a 2026 debut, reinforcing the timeline now echoed by OpenAI leadership.

What Might It Look Like?

For now, OpenAI is keeping the form factor deliberately vague. Early reports suggest the company has been experimenting with small, screenless prototypes, possibly wearables. Think less “gadget” and more “companion.”

Whether it ends up as an earpiece, a pin, or something entirely new remains an open question. Lehane has only said that details will come “much later,” suggesting the company is still refining how humans should physically interact with advanced AI.

Why 2026 Matters

The timing isn’t accidental. The AI hardware market is finally starting to find its footing after a few high-profile missteps. Devices like Humane’s AI Pin struggled to resonate, but industry leaders believe the real wave is just beginning.

Qualcomm CEO Cristiano Amon recently revealed that around 10 million AI-powered smart glasses are already shipping annually—and that number could jump tenfold in the near future. From smart glasses and camera-equipped earbuds to AI-infused jewellery, the industry is searching for a post-smartphone interface.

OpenAI clearly wants to be at the center of that shift.

Partners, Chips, and the Bigger Vision

While it’s still unclear which chips will power OpenAI’s device, Qualcomm has confirmed ongoing collaboration with the company on hardware initiatives. That alone signals how seriously OpenAI is taking this transition.

More importantly, OpenAI sees devices not as side projects, but as a core pillar of its future. Software may remain its foundation, but hardware could become the bridge between powerful AI models and everyday human life.

A New Way to Meet AI

If OpenAI gets this right, its first device won’t just be another piece of consumer electronics. It could redefine how people meet AI—less typing, less tapping, more listening, speaking, and understanding.

In an industry obsessed with screens, OpenAI’s boldest move may be building something you barely notice at all.

And that might be exactly the point.

By- Nirosha Gupta

India’s AI Spending Surge Signals Confidence—But the Real Battle Is Human, Not Digital

 As India steps into 2026, one thing is clear: artificialintelligence has moved from boardroom buzzword to business backbone. Corporate India is spending with confidence, scaling budgets, and placing AI squarely at the centre of growth strategies. This is no longer about testing what’s possible—it’s about building what’s next. Yet behind the bullish outlook sits a quieter concern that refuses to be automated away: people.

Accenture’s latest Pulse of Change survey captures this contradiction perfectly. Almost nine in ten Indian C-suite leaders plan to raise AI investments in the coming year, signalling a belief that AI will unlock new revenue streams rather than merely optimise costs. Even amid geopolitical instability and economic headwinds, Indian enterprises are choosing to push forward, not pause.

Big Vision, Fragile Foundations

The ambition is undeniable. The readiness, less so.

Over a quarter of Indian executives admit that shortage of skilled AI talent is the biggest obstacle preventing them from extracting real value from their AI initiatives. What makes this especially striking is that this gap persists even as AI tools become easier to access and deploy.

Most organisations are still stuck in training-lite mode. Only 24% have made continuous AI learning part of everyday work, and fewer than one in ten are rethinking job roles for an AI-first future. The result is a pattern many enterprises know too well: successful pilots that never quite graduate into enterprise-wide impact.

No Fear of the Bubble Bursting

Unlike past tech cycles, Indian business leaders appear unfazed by talk of an AI bubble. Six out of ten CXOs say they would keep increasing AI spending even if the hype deflates, and half would continue hiring regardless.

The confidence runs deeper. 79% expect to grow their workforce in 2026, while 76% are betting on faster revenue growth, despite anticipating more disruption than this year. AI, in this context, is no longer a gamble—it’s seen as essential infrastructure, much like cloud or broadband once were.

AI Grows Up: From Trials to Transformation

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India’s AI journey is also maturing. About 41% of enterprises are already deploying AI agents across multiple functions, while 24% are redesigning entire processes with AI at the core. For senior leaders, AI is becoming part of daily work—nearly four in ten Indian C-suite executives now use generative AI tools regularly.

What’s surprising is the alignment between leadership and the workforce. Executives are confident their teams are AI-ready, and employees largely echo that sentiment. Nearly half already use AI to boost productivity, and a strong majority believe it can deliver meaningful business impact.

Why Skills Will Separate Leaders from Laggards

Indian leaders feel well-prepared to handle technological disruption, with AI and digital investments topping their priority list. Confidence dips, however, when it comes to environmental and geopolitical uncertainty—areas where adaptability, judgment, and leadership skills matter as much as algorithms.

As Saurabh Kumar Sahu, MD and Lead for India Business at Accenture, notes, the equation has changed. The challenge is no longer about access to cutting-edge AI—it’s about whether employees feel empowered, prepared, and included as work itself is redefined.

Heading into 2026, India’s AI narrative is shifting gears. The question is no longer who is adopting AI, but who can translate it into sustained value. Turning ambition into execution. Turning technology into outcomes. And above all, turning investment into skills. Those who get the human side right won’t just use AI—they’ll shape the future of India Inc.

By Advik Gupta

Iron Mountain Starts Building 85MW AI-Focused Hyperscale Data Center in Mumbai

Iron Mountain has taken a decisive step in strengthening India’s AI and cloud infrastructure by beginning construction on a large-scale hyperscale data center campus in Mumbai. Built specifically for the demands of the AI era, the new facility will deliver a substantial 85 MW of IT load, catering to power-hungry, compute-intensive workloads. The campus is expected to go live in 2027.

This upcoming Mumbai site isn’t just about raw capacity—it’s about readiness. Drawing on Iron Mountain’s global experience in building carrier-neutral and sustainability-focused data centers, the campus is being designed from the ground up to handle extreme power densities and advanced cooling requirements. That makes it a natural fit for hyperscalers, AI platforms, and enterprises running next-generation workloads.

A strong emphasis has also been placed on reliability and governance. The facility is being engineered for industries with strict regulatory requirements, ensuring continuous uptime while meeting global compliance benchmarks such as HIPAA, FISMA, and ISO standards. For customers operating in finance, healthcare, or government-linked sectors, this translates into a highly secure and regulation-ready environment.

Sustainability is another key pillar of the project. Through Iron Mountain’s Green Power Pass, customers will be able to match their energy usage entirely with renewable sources—helping them move closer to their ESG goals without added operational burden.

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Speaking on the development, Rajesh Tapadia, CEO of Iron Mountain Data Centers India, described the Mumbai campus as a clear signal of the company’s long-term vision for the region. He highlighted that the project is designed to deliver the scale, rapid deployment, and sustainable performance that global hyperscalers expect—while also forming a backbone for India’s growing AI ecosystem.

The Mumbai hyperscale campus is part of Iron Mountain’s broader expansion strategy across the country. The company already operates data centers in Bangalore, Hyderabad, Pune, Mumbai, and Noida, with additional locations planned in Chennai and Noida. Once fully built out, Iron Mountain’s India footprint is expected to reach 152 MW of potential capacity, positioning it strongly to meet the nation’s accelerating demand for AI, cloud, and digital services.

As India pushes forward in its ambition to become a global AI and data hub, projects like Iron Mountain’s Mumbai campus could play a pivotal role in shaping the country’s digital future.

  By Advik Gupta

Vertiv has identified artificial intelligence (AI), digital twins, and advanced liquid cooling technologies

 Vertiv has identified artificial intelligence (AI), digital twins, and advanced liquid cooling technologies as the primary forces redefining the design, deployment, and operation of next-generation data centers, according to its newly released Vertiv™ Frontiers report.

The report examines how accelerating AI adoption, combined with increasing compute density and rapid deployment requirements, is fundamentally transforming global data center infrastructure—trends that are becoming increasingly relevant for the UAE and wider Middle East, where large-scale digital transformation and AI investments are underway.

AI Workloads Driving Structural Change

Vertiv notes that AI and high-performance computing (HPC) workloads are placing unprecedented demands on power and thermal management systems. Traditional hybrid AC/DC power architectures are reaching their operational limits as rack densities increase, prompting a gradual shift toward higher-voltage DC power systems that offer improved efficiency, reduced conversion losses, and greater scalability.

“The data center industry is rapidly evolving to address the density and speed requirements of AI-driven facilities,” said Scott Armul, Chief Product and Technology Officer at Vertiv. He emphasized that advanced power architectures and liquid cooling solutions are becoming critical enablers of gigawatt-scale AI deployments.

"The data center industry is continuing to rapidly evolve how it designs, builds, operates and services data centers, in response to the density and speed of deployment demands of AI factories," said Vertiv chief product and technology officer, Scott Armul. "We see cross-technology forces, including extreme densification, driving transformative trends such as higher voltage DC power architectures and advanced liquid cooling that are important to deliver the gigawatt scaling that is critical for AI innovation. On-site energy generation and digital twin technology are also expected to help to advance the scale and speed of AI adoption." Global digital infrastructure leader Vertiv has released its latest Vertiv™ Frontiers report, detailing the technology trends and macro forces shaping the evolution of data centers, particularly in response to the rapid growth of artificial intelligence (AI). The report highlights innovations in power, cooling, energy autonomy, and digital twin technology, which are enabling data centers to meet the demands of AI workloads.

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Scott Armul, Vertiv’s Chief Product and Technology Officer, noted that the industry is evolving rapidly to meet the “density and speed of deployment demands of AI factories.” He highlighted transformative trends including higher-voltage DC power architectures, advanced liquid cooling, on-site energy generation, and digital twin technology, all of which are critical for supporting gigawatt-scale AI deployments.

“The data centre industry is rapidly evolving how it designs, builds, operates and services facilities in response to the density and speed of deployment demands of AI,” Armul said.

“Extreme densification is driving transformative trends such as higher-voltage DC power architectures and advanced liquid cooling, which are critical to achieving the gigawatt-scale capacity needed for AI innovation.”

By Advik Gupta

Amazon Bets on Ambient AI Again — Meet Bee, the $50 Wearable That Listens, Learns, and Logs Your Life

By redefining what an AI wearable should be, Amazon is quietly making its way back into a market it once struggled to crack.

Amazon is stepping back into the wearable arena — this time with a very different philosophy. After acquiring AI wearable startup Bee in September 2025, the tech giant is backing a minimalist, always-listening device that promises to work in the background of your life, not compete for your attention.

Priced at just $50, Bee’s new AI wearable doesn’t flash notifications, vibrate constantly, or try to replace your smartphone. Instead, it listens — quietly.

A Wearable That Fades Into the Background

Bee’s device is designed as “ambient AI” hardware. It can be worn on the wrist or clipped to clothing, and it continuously records and transcribes daily activities. From casual conversations to work discussions, Bee turns real-life moments into automatic to-do lists, summaries, and personal insights — all without the user needing to tap a screen or say a wake word.

There’s no display. No camera. No endless prompts.

That’s intentional.

Unlike previous AI wearables that aimed to be the next smartphone — and failed — Bee positions itself as a digital memory and daily journal, quietly working in the background while you live your life.

Learning from Past AI Wearable Failures

The AI wearable market has had a rocky start. High-profile launches like the Humane AI Pin and Rabbit R1 struggled with software bugs, short battery life, and limited real-world usefulness. Many users quickly realized their phones already did most of what these devices promised — and did it better.

Bee takes a different approach. It doesn’t try to compete with your phone. It complements it.

With a claimed battery life of up to one week, the device avoids one of the biggest pain points that plagued earlier AI gadgets.

Smarter, More Proactive — With Amazon Behind It

Since joining Amazon as a small, eight-person team, Bee has rapidly expanded its feature set. New updates include voice notes, allowing users to capture ideas with a single button press, and daily insights that reflect patterns in mood, energy levels, and even relationship dynamics.

The company is also making Bee more proactive. A new “actions” feature connects the assistant to your calendar and email, enabling it to draft emails, suggest meetings, or create calendar invites automatically.

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Co-founder Maria de Lourdes Zollo says the goal is simple: reduce friction between thought and action.

A Quiet Comeback

Amazon’s earlier attempts in wearables delivered mixed results. But with Bee, the company appears to have learned an important lesson — sometimes the best technology is the one that gets out of the way.

If Bee succeeds, it won’t be because it demands attention — but because it quietly earns trust, one day at a time.

BY- Nirosha Gupta

CrowdStrike Acquires SGNL to Bring Real-Time Identity Security Into the AI Era

On January 8, 2026, cybersecurity heavyweight CrowdStrike (NASDAQ: CRWD) revealed plans to acquire SGNL, a fast-growing company focused on continuous identity security, in a deal valued at roughly $740 million. The transaction, funded mainly through cash along with a smaller equity component, is expected to close in CrowdStrike’s first quarter of fiscal year 2027, which concludes on April 30, 2026.

The acquisition marks a strategic shift in how digital identities are protected, particularly in environments increasingly dominated by AI-driven workloads and autonomous agents. Rather than relying on static access controls, CrowdStrike intends to push enterprises toward a Zero Standing Privilege (ZSP) approach—where access is temporary, contextual, and continuously reassessed.

Why SGNL Matters to CrowdStrike’s Strategy

As organizations deploy AI agents capable of acting independently and at machine speed, traditional identity models are struggling to keep up. SGNL’s technology is designed to address this gap by enforcing real-time authorization decisions instead of long-lived permissions.

Key goals of the acquisition include:

Safeguarding AI and Machine Identities

AI agents often operate with elevated privileges. CrowdStrike plans to treat every agent—human or non-human—as a dynamic risk that must be continuously evaluated.

Ending Permanent Access Rights

The ZSP model replaces standing privileges with Just-in-Time (JIT) access, ensuring credentials exist only for the precise moment they are required and are revoked immediately afterward.

Creating a Unified Identity Layer

SGNL’s capabilities will allow the Falcon platform to span identity systems such as Active Directory, Microsoft Entra ID, Okta, AWS IAM, and a wide range of SaaS applications—bringing them together into a single, cohesive identity framework.

How the Technology Fits Together

At the core of SGNL’s platform is a continuous authorization engine that leverages the Continuous Access Evaluation Protocol (CAEP). This allows access decisions to change instantly based on real-time risk signals supplied by CrowdStrike’s Falcon telemetry.

Additional capabilities include:

Identity Data Fabric

Aggregates signals from IT service platforms, cloud environments, and SaaS tools to deliver a consolidated view of both human and machine identities.

Policy-Based Access Control

Simplifies identity governance by replacing sprawling role-based systems with clear, readable policies that automatically adapt to context and risk.

A Growing Market Opportunity

CrowdStrike’s move comes as identity security rapidly becomes one of the most critical pillars of enterprise defense. According to IDC, the global identity security market is on track to reach $56 billion by 2029, fueled by cloud adoption, remote work, and the rise of autonomous AI systems.

By integrating SGNL’s continuous authorization technology into Falcon, CrowdStrike is positioning itself to lead the next phase of identity security—one built for AI-native, zero-trust environments rather than legacy access models.

By – Aaradhay Sharma

Microsoft Unleashes Agentic AI to Turn Copilot Into a Full-Scale Shopping Engine

In January 2026, Microsoft unveiled a new generation of agent-driven artificial intelligence tools designed to reshape the retail experience from discovery to checkout. Built around the Microsoft Copilot ecosystem, the initiative aims to collapse fragmented shopping journeys into a single, intelligent flow—while simultaneously automating complex retail operations behind the scenes.

How Microsoft’s AI Is Changing the Shopper Experience

At the center of the rollout is Copilot Checkout, a conversational commerce feature that lets users explore products, receive tailored suggestions, and complete purchases directly inside the Copilot chat window—eliminating the need to jump between multiple apps or websites.

To enable frictionless payments, Microsoft has integrated Copilot with leading commerce and payment platforms such as PayPal, Shopify, and Stripe. This allows in-chat transactions for well-known retail brands including Urban Outfitters, Anthropologie, Etsy, and Ashley Furniture.

Retailers can also deploy custom shopping agents using templates available in Copilot Studio. These AI agents are designed to assist customers with tasks like outfit coordination, product discovery, and personalized recommendations in real time, closely mimicking the experience of a knowledgeable in-store associate.

AI Tools Powering the Seller Side of Retail

For merchants, Microsoft introduced several backend-focused AI agents aimed at improving efficiency and scalability:

Shopify Brand Agents act as brand-specific digital assistants, trained on a retailer’s product catalog and tone of voice. These agents handle product questions and customer support directly on the merchant’s website.

The Catalog Enrichment Agent, now in public preview, streamlines product onboarding by automatically extracting attributes from images, correcting metadata issues, and improving search visibility across digital storefronts.

Designed for brick-and-mortar environments, the Store Operations Agent allows retail staff to query inventory levels, manage staffing needs, and receive operational recommendations influenced by factors such as weather patterns and customer footfall.

Microsoft is also preparing to roll out the Dynamics 365 Commerce MCP Server in preview by February 2026, exposing critical retail logic—such as pricing, inventory, and order management—to AI agents for executing advanced workflows across both digital and physical environments.

Early Results and Business Impact

Early performance signals show meaningful gains. Microsoft notes that customer journeys involving Copilot result in 53% more purchases within 30 minutes compared to conventional shopping paths.

Merchants can measure and refine agent effectiveness using Microsoft Clarity, which tracks conversion uplift, average order value, and engagement across AI-powered touchpoints.

By Aaradhay Sharma

DeepSeek V4 Set to Redefine AI Coding Power as China Enters the Global AI Frontline

Chinese AI firm DeepSeek is gearing up for the debut of its next flagship model, DeepSeek-V4, with industry chatter pointing to a mid-February 2026 release. The timing is believed to align closely with the Chinese New Year, signalling a strategically timed launch aimed at maximum global visibility.

Rather than chasing leaderboard dominance alone, DeepSeek-V4 is being positioned as a developer-first AI system, designed to push the boundaries of software engineering, complex reasoning, and large-scale code comprehension.

What to Expect from DeepSeek-V4

Projected Release Window: Mid-February 2026

Model Lineage: Successor to the V3 family rolled out incrementally across 2025

Core Strengths: Deep logical reasoning, advanced programming support, and efficient handling of long, multi-file codebases

Competitive Positioning

Early internal evaluations reportedly indicate that DeepSeek-V4 could outperform leading Western models—including OpenAI’s GPT-5 variants and Anthropic’s Claude 4.5/Opus—particularly in real-world coding workflows rather than synthetic benchmarks.

One of DeepSeek’s defining advantages continues to be architectural efficiency. V4 is expected to refine technologies introduced in earlier releases, such as Multi-head Latent Attention (MLA) and DeepSeek Sparse Attention (DSA), both of which dramatically reduce inference costs while preserving accuracy across extended context windows.

This approach reinforces DeepSeek’s reputation for delivering frontier-grade AI at significantly lower operational costs, a strategy that previously triggered what many in the industry dubbed the “DeepSeek Shock” in early 2025.

Research-Driven Foundations

DeepSeek-V4 is likely built on several research breakthroughs published by the company over the past few months:

Manifold-Constrained Hyper-Connections (mHC): Introduced in January 2026, this training architecture aims to improve stability and scalability in very large models.

Self-Verification Systems: Adapted from DeepSeekMath-V2 (December 2025), enabling the model to critique, revise, and strengthen its own reasoning and code generation in iterative cycles.

Together, these advances suggest that DeepSeek-V4 is less about raw parameter counts and more about precision, reliability, and developer trust—a combination that could reshape competition in the global AI race.

By Aaradhay Sharma

As voice becomes a primary interface for digital identity, consent, and authorization, it has

As voice becomes a primary interface for digital identity, consent, and authorization, it has simultaneously emerged as one of the most exploited attack vectors. Advances in generative AI have made it possible to clone a person’s voice using a few seconds of audio, generate speech indistinguishable from human voices, and replay or manipulate recordings to bypass traditional voice authentication systems. In this environment, recognizing a voice is no longer sufficient. Authenticity must be proven.

FaceOff’s 10th AI, Synthetic Audio Detection, is designed to address this exact challenge by determining whether an audio signal originates from a real, live human speaker or from an artificial, manipulated, or replayed source. This AI does not focus on voice identity matching alone. Instead, it evaluates the intrinsic authenticity of the audio itself, making it a foundational layer of trust for voice-based digital interactions.

Dr.Deepak Kumar Sahu, Founder- FaceOff Technologies Inc says, Synthetic Audio Detection operates by analyzing deep acoustic, temporal, and behavioral properties of speech that are typically altered or imperfectly reproduced by text-to-speech engines, voice cloning systems, neural vocoders, and replay mechanisms. While synthetic voices may sound natural to human listeners, they inevitably leave behind subtle artifacts across frequency bands, phase alignment, temporal continuity, and signal entropy. FaceOff’s AI is trained to detect these signals with high precision.

At the signal level, the system examines spectral consistency, harmonic structure, phase coherence, jitter, shimmer, and micro-prosodic variations that are difficult for generative models to replicate accurately. At the temporal level, it analyzes rhythm stability, pause patterns, response latency, and continuity anomalies that indicate non-human generation or replay. These features are evaluated using deep neural networks trained on diverse datasets covering modern text-to-speech models, voice conversion systems, diffusion-based speech generators, and real-world replay attack scenarios.

The AI operates in real time and is channel-agnostic, enabling deployment across live microphone input, telephony networks, IVR systems, call-center recordings, mobile applications, and uploaded or streamed audio files. This makes it suitable for both synchronous interactions, such as live authentication calls, and asynchronous processes, such as consent recording validation or post-event forensic analysis.

A key strength of FaceOff’s Synthetic Audio Detection lies in its adaptability. The AI is designed to evolve alongside emerging deepfake technologies through continuous model retraining, ensemble detection strategies, and adversarial learning techniques. As new voice synthesis models enter the ecosystem, the detection framework adapts without requiring changes to user workflows or system architecture. This ensures long-term resilience against rapidly advancing audio deepfake threats. Dr. Sahu Said.

Within enterprise and regulated environments, Synthetic Audio Detection plays a critical role in safeguarding high-risk voice-driven workflows. In banking and fintech sectors, it protects voice-based customer authentication, transaction authorization, and telephonic KYC processes from voice cloning and replay attacks. In call centers and IVR systems, it prevents large-scale impersonation, account takeover, and social engineering campaigns that exploit automated voice channels.

Legal and compliance functions rely on this AI to ensure that recorded verbal consent, declarations, and authorizations are genuinely provided by a real human and have not been synthetically generated or manipulated. Telecom operators use it to secure voice channels and prevent SIM-linked fraud, while government and public service platforms apply it to protect citizen interactions conducted through voice interfaces.

Synthetic Audio Detection is also designed with auditability and regulatory alignment in mind. It generates explainable risk indicators, maintains tamper-proof logs of detection outcomes, and supports configurable decision thresholds based on sectoral risk appetite. When integrated with FaceOff’s Adaptive Cognito Engine, its outputs are correlated with facial, behavioral, and physiological signals, enabling cross-modal validation and significantly reducing false positives and false negatives.

Ultimately, FaceOff’s 10th AI transforms voice from a vulnerable identity signal into a verified authenticity factor. It ensures that when a voice is used to authenticate, authorize, or consent, the system can confidently answer a critical question: whether that voice is real, live, and human.

In a world where voices can be cloned at scale and deception can be automated, FaceOff’s Synthetic Audio Detection establishes a new standard of trust for voice-based digital identity, making authenticity provable rather than assumed.

By Advik Gupta 

AI Model Referee LMArena Climbs to $1.7B Valuation as Trust Becomes AI’s Next Battleground

 The AI industry has spent the past several years obsessing over scale—bigger models, more parameters, and ever-expanding compute budgets. But LMArena’s rise to a $1.7 billion valuation following its latest funding round suggests the next phase of the AI race may be defined less by raw capability and more by trust, measurement, and accountability.

LMArena has carved out a unique position in the AI ecosystem by focusing on a problem that grows harder as models improve: evaluating them in ways that actually matter. Instead of relying purely on synthetic benchmarks or narrowly defined test suites, the company operates a crowdsourced, human-in-the-loop platform that lets users compare large language models side by side. These comparisons capture real human preferences—how people perceive usefulness, clarity, accuracy, and overall experience—providing a signal that traditional benchmarks often fail to deliver.

This distinction is becoming increasingly important. As enterprises roll out AI across customer support, software development, marketing, data analysis, and creative workflows, the question is no longer “Which model scores highest on a leaderboard?” but “Which model can we safely and reliably trust in production?” Small differences in model behavior can translate into major business risks, from hallucinations and bias to compliance failures and unexpected costs.

The funding momentum behind LMArena reflects a broader shift in how investors view the AI stack. While headline-grabbing investments continue to pour into model training and specialized chips, there is growing recognition that the industry’s long-term winners will include the “picks-and-shovels” companies—those providing the tools that help others deploy AI responsibly. Evaluation platforms sit at the center of this shift, acting as arbiters in an increasingly noisy market filled with overlapping claims and opaque performance metrics.

Another factor driving LMArena’s relevance is the growing difficulty of measuring progress itself. Many leading models now perform similarly on established benchmarks, making incremental improvements hard to interpret. In some cases, benchmark gains reflect optimization for the test rather than genuine capability improvements. As marketing narratives race ahead of verifiable evidence, independent evaluation grounded in human judgment offers a counterbalance—imperfect, but closely aligned with real-world use.

LMArena’s success also highlights a deeper structural challenge for the AI industry: performance alone is no longer sufficient. Enterprises must consider cost efficiency, reliability under edge cases, safety guardrails, bias exposure, and regulatory readiness. Choosing the wrong model can have downstream consequences that extend far beyond technical performance, affecting brand reputation, legal compliance, and customer trust. In this environment, evaluation becomes a strategic decision, not a technical afterthought.

Looking ahead, LMArena appears well positioned to expand beyond public-facing model comparisons into enterprise-grade offerings. Continuous monitoring, internal benchmarking, audit trails, and compliance reporting are logical extensions of its core platform. As regulators tighten oversight and boards demand clearer explanations of AI-related risk, independent evaluation may become a standard requirement rather than a nice-to-have.

By Advik Gupta

The bigger shift is strategic: cooling is no longer a component decision.

AI may dominate headlines, but heat is quietly becoming the defining constraint of the AI era.

As model sizes grow and workloads intensify, thermal limits are shaping how—and where—AI can scale.

GPUs are rapidly crossing 1,500Watts and heading toward 2,000Watts and beyond.

At these power levels, cooling is no longer a background infrastructure concern.

It directly affects performance stability, energy efficiency, water consumption, site selection, and total cost of ownership.

Traditional air and liquid cooling approaches are reaching their limits.

Dense AI clusters generate extreme, uneven heat profiles that conventional cold plates were never designed to handle efficiently.

This has created urgency to rethink semiconductor manufacturing itself.

New processes adapted to metal wafers now enable 3D short-loop jet channel microstructures, multistage cooling, and hybrid 3D cell designs.

These architectures allow cooling systems to be precisely matched to GPU power maps, targeting hotspots rather than treating the chip as thermally uniform.

The result is dramatically improved heat extraction where it matters most.

Early implementations show materially higher thermal performance alongside up to 50% weight reduction compared to conventional cold plates—an advantage critical for dense data centers and even space-based systems.

The bigger shift is strategic: cooling is no longer a component decision.

It is becoming a system-level enabler for AI scale, efficiency, and sustainability—quietly determining the future pace of AI progress

By Advik Gupta 

Tiiny AI positioned Pocket Lab as a companion device rather than a replacement for laptops

 Tiiny AI has introduced a pocket-sized personal AI computer designed to run large artificial intelligence models locally, positioning the device as an alternative to cloud-based AI services amid growing concerns over data privacy, recurring usage costs and dependence on remote infrastructure.

The product, called the Tiiny AI Pocket Lab, was unveiled this week at Consumer Electronics Show (CES), where it attracted attention from developers and analysts exploring ways to deploy AI without relying on cloud platforms. Unlike most mainstream AI services that require internet connectivity and usage-based pricing, Pocket Lab is built to operate entirely on-device, without subscriptions or token-based fees.

At CES demonstrations, the company showed the Pocket Lab running large language models with up to 120 billion parameters fully offline, delivering decoding speeds exceeding 20 tokens per second. Tiiny AI said the performance is intended for practical, everyday use rather than experimental or proof-of-concept workloads.

The launch comes as enterprises and individual users increasingly reassess how AI systems handle sensitive data and how costs scale over time. While cloud-based AI platforms offer flexibility and scale, they often require users to send data to third-party servers and commit to ongoing usage fees. Advances in inference efficiency, however, are making local AI deployments more viable, even on compact hardware.

Samar, go-to-market director at Tiiny AI, said the company sees a shift in how people think about AI ownership. As users become more conscious of data governance and long-term costs, he said, local AI systems offer a model closer to owning a personal computer rather than renting AI capabilities on demand.

Tiiny AI positioned Pocket Lab as a companion device rather than a replacement for laptops or desktops. The system connects via plug-and-play and offloads AI inference externally, enabling even older computers to access advanced AI models without requiring hardware upgrades.

Alongside the hardware, the company also introduced TiinyOS, an on-device software platform that allows users to download and run open-source language models and AI agents with minimal setup. TiinyOS also includes developer tools for building and deploying local AI workflows without reliance on cloud infrastructure.

Pocket Lab is scheduled to launch on Kickstarter in February, with an early-bird price of $1,399. The company said the pricing is intended to make local AI more accessible rather than position the device as a high-end workstation. The system includes 80GB of LPDDR5X memory, a configuration that typically accounts for a large portion of the overall hardware cost.

Interest in local-first AI devices has been growing, particularly as discussions around enterprise security, data sovereignty and the economics of AI services intensify. Tiiny AI said it has received confirmation from Guinness World Records that Pocket Lab will be certified as the smallest mini PC capable of running a 100-billion-parameter language model locally.

By Advik GUPTA

Digital learning platforms face challenges not only in identity verification and content protection, but also in accurately

Digital learning platforms face challenges not only in identity verification and content protection, but also in accurately understanding student engagement, comprehension and preparation. FaceOff, an artificial intelligence identity and behavior intelligence platform, can significantly enhance the on-line learning ecosystem by adding trust, safety and deeper learning analytics.

Below is a complete breakdown of how FaceOff can help.

Real Time Student Identity Verification

FaceOff verifies the identity of students instantly when they log in to classes, tests or assignments.

Benefits for :

● Prevents proxy attendance

● Confirms genuine participation

● Protects assessment integrity

Liveness Detection During Live Classes

FaceOff checks that the student is actually present, attentive and participating.

Highly relevant for online education companies

● Ensures active attendance

● Reduces passive logins

● Maintains discipline in large digital classrooms

Secure Examinations and Assessments

FaceOff monitors identity, attention, gaze behavior and interaction during tests.

Advantages:

● Prevents cheating

● Ensures correct student completes the test

● Makes test scores more accurate and trustworthy

Teacher Verification and Classroom Safety

FaceOff verifies the identity of teachers before they enter virtual classrooms or student groups

Benefits:

● Protects student privacy

● Prevents unauthorized persons from teaching or accessing student data

● Strengthens parent trust in the system

Understanding How Much a Student Understands the Lecture    

This is where FaceOff becomes a powerful learning intelligence tool. FaceOff can track visual behavior and attention patterns during a lecture to understand:

A. How focused the student is

The artificial intelligence based attention model can detect:

● Long moments of distraction

● Frequent gaze shifts away from the screen

● Reduced engagement during difficult topics

These signals help measure engagement level in real time

B. How well the student is absorbing the content

FaceOff can integrate with On-line learning analytics to map:

● Attention drops during complex explanations

● Confusion patterns

● Topic wise engagement score

This gives teachers and parents a clear view of which chapters need revision.

C. Informed teaching adjustments

With this data, On-line educators can:

● Modify lecture styles

● Improve explanations for weak topics

● Personalize learning paths

FaceOff does not invade privacy. It only interprets behavior patterns to support better learning.

Measuring Student Preparation Before an Exam

FaceOff can help On-line Education understand how prepared a student is for an upcoming test by analyzing:

A. Time spent reviewing a topic

If a student spends very little time on a chapter, it may indicate poor preparation.

B. Attention levels during revision videos

Consistent distraction suggests a lack of readiness.

C. Confidence and hesitation during practice tests

FaceOff can identify stress patterns or uncertain behavior during mock exams.

D. Consistency of study habits

Regularity of logins and frequency of lecture views contribute to a preparation score.

On-line Education organisations can use these insights to automatically encourage the student to revise areas where readiness seems low.

Protecting Digital Content and Preventing Misuse

FaceOff monitors for:

● Multiple logins from different locations

● Shared accounts

● Unauthorized downloads

This protects the value of On-line education premium content.

Safe Online Environment for Younger Learners

FaceOff verifies that only authorized parents or guardians access dashboards, protecting sensitive student data.

Fast and Secure On-boarding

FaceOff offers quick identity verification for students and parents, reducing queues, support load and documentation errors during registration.onclusion: A Complete Trust and Learning Intelligence Layer fr On-line Education companies

FaceOff can redefine the online learning experience by providing:

●Verified student identity

● Monitored engagement

● Accurate understanding of student comprehension

● Insight into student preparation levels

● Cheating free assessments

● Secure teacher access

● Protected digital content

● Safer learning spaces

● Strong real time fraud detection

Most importantly, FaceOff allows On-line education to understand not only who is learning, but how well they are learning.

This creates a smarter, safer and more effective digital classroom for millions of learners.

By Advik Gupta