Tag: artificial intelligence

Adobe Agents Unlock Breakthrough Creative Intelligence With NVIDIA and WPP

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AI agents are transforming how work gets done across all industries, accelerating everything from content creation to decision-making.

NVIDIA’s expanded strategic collaborations with Adobe and WPP are bringing agentic AI to the center of enterprise marketing operations across creative production and customer experience orchestration. 

As demand for personalized customer experiences surges, brands require intelligent systems that can plan, create, produce and activate content continuously — without compromising control, governance or brand integrity.

Consider a global retailer delivering the right offer, image, copy and price, across millions of product, audience and channel combinations — updated in minutes instead of months. 

For marketing and creative teams, that means moving from one-size-fits-all campaigns to tailored experiences that are always on, always relevant and on brand. All of it is powered by intelligent systems that continuously generate and deliver content without sacrificing control, governance or brand integrity.

The expanded collaborations bring together three complementary strengths: Adobe’s creative and customer experience platforms and the new Adobe CX Enterprise Coworker, WPP’s global media and marketing expertise, and NVIDIA’s accelerated computing and software stack, including NVIDIA Nemotron open models, NVIDIA Agent Toolkit and the NVIDIA OpenShell secure runtime for building and running secure agentic AI systems.

As these agents begin orchestrating multistep workflows, tapping sensitive data and triggering actions across marketing stacks, enterprises need a way to enforce clear rules of engagement so every operation remains compliant, on brand and within defined risk boundaries.

Powered by the NVIDIA OpenShell runtime, every agent operates within a secure, isolated environment, delivering enterprise-grade control, consistency and auditability across the entire marketing lifecycle, with verifiable policy management, answering the question, “What can the agent do?” and not just, “What policy is in place?” 

In governed environments, enterprises can also keep key workflows and intelligence services inside their trust boundary, including securely invoking Adobe CX Intelligence as part of customer experience agents.

A live demo of CX Enterprise Coworker — powered by NVIDIA Agent Toolkit, including the OpenShell runtime and Nemotron models — will be featured during Adobe Summit’s day-two keynote taking place Tuesday, April 21, at 9 a.m. PT.

The collaboration enables:

  • End-to-end agentic workflows: Adobe is developing creative and marketing agents that can generate, adapt and version on-brand assets. Adobe’s CX Enterprise Coworker orchestrates downstream customer experience workflows from personalization to activation, closing the loop between content creation and customer engagement.
  • Controlled execution with NVIDIA OpenShell: Agents run in a policy-based, containerized sandbox designed to keep execution governed, observable and auditable, helping enterprises safely deploy long-running agentic workflows on premises or in the cloud.
  • Commercially safe content at scale: Adobe Firefly Foundry, accelerated by NVIDIA AI infrastructure, can help organizations deeply tune custom models on their proprietary assets, enabling agents to generate commercially safe content at scale and aligned to brand identity.
  • A 3D digital twins solution for scalable marketing production: Adobe’s cloud-native 3D digital twin solution is now generally available, built on NVIDIA Omniverse libraries and OpenUSD. 3D digital twins serve as persistent product identities that agents use to automate and scale high-fidelity content creation across formats, markets and configurations.

Creative Intelligence Meets Performance Intelligence With Policy-Governed Agents

Governed environments such as the ones enabled by this collaboration act as a set of “guardrails” that keep AI operations observable and auditable, preventing the system from acting outside of a company’s specific data boundaries or brand rules.

By combining Adobe’s creative platforms, WPP’s media and marketing expertise and NVIDIA’s secure infrastructure with CX Enterprise Coworker, brands no longer have to choose between speed and safety. Autonomous agents can now generate, adapt and activate content at scale while operating within governed, policy-driven environments.

The result is a new foundation for agentic marketing — where creative intelligence, performance and trust are built in from the start and delivered at global scale.

Watch NVIDIA founder and CEO Jensen Huang’s Adobe Summit fireside chat with Adobe CEO Shantanu Narayen below.

RTX to Spark: Gemma 4 Accelerated for Agentic AI

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Open models are driving a new wave of on-device AI, extending innovation beyond the cloud to everyday devices. As these models advance, their value increasingly depends on access to local, real-time context that can turn meaningful insights into action. 

Designed for this shift, Google’s latest additions to the Gemma 4 family introduce a class of small, fast and omni-capable models built for efficient local execution across a wide range of devices.  

Google and NVIDIA have collaborated to optimize Gemma 4 for NVIDIA GPUs, enabling efficient performance across a range of systems — from data center deployments to NVIDIA RTX-powered PCs and workstations, the NVIDIA DGX Spark personal AI supercomputer and NVIDIA Jetson Orin Nano edge AI modules.

Gemma 4: Compact Models Optimized for NVIDIA GPUs 

The latest additions to the Gemma 4 family of open models— spanning E2B, E4B, 26B and 31B variants — are designed for efficient deployment from edge devices to high-performance GPUs.  

All configurations measured using Q4_K_M quantizations BS = 1, ISL = 4096 and OSL = 128 on NVIDIA GeForce RTX 5090 and Mac M3 Ultra desktops. Token generation throughput measured on llama.cpp b7789, using the llama-bench tool.

This new generation of compact models supports a range of tasks, including: 

  • Reasoning: Strong performance on complex problem-solving tasks.  
  • Coding: Code generation and debugging for developer workflows.   
  • Agents: Native support for structured tool use (function calling).  
  • Vision, Video and Audio Capabilities: Enables rich multimodal interactions for object recognition, automated speech recognition, and document or video intelligence. 
  • Interleaved Multimodal Input: Mix text and images in any order within a single prompt.  
  • Multilingual: Out-of-the-box support for 35+ languages, pretrained on 140+ languages. 

The E2B and E4B models are built for ultraefficient, low-latency inference at the edge, running completely offline with near-zero latency across many devices including Jetson Nano modules. 

The 26B and 31B modelsare designed for high-performance reasoning and developer-centric workflows, making them well suited for agentic AI. Optimized to deliver state-of-the-art, accessible reasoning, these models run efficiently on NVIDIA RTX GPUs and DGX Spark — powering development environments, coding assistants and agent-driven workflows.  

As local agentic AI continues to gain momentum, applications like OpenClaw are enabling always-on AI assistants on RTX PCs, workstations and DGX Spark. The latest Gemma 4 models are compatible with OpenClaw, allowing users to build capable local agents that draw context from personal files, applications and workflows to automate tasks. Learn how to run OpenClaw for free on RTX GPUs and DGX Spark or using the DGX Spark OpenClaw playbook. 

Getting Started: Gemma 4 on RTX GPUs and DGX Spark 

NVIDIA has collaborated with Ollama and llama.cpp to provide the best local deployment experience for each of the Gemma 4 models.    

To use Gemma 4 locally, users can download Ollama to run Gemma 4 models or install llama.cpp and pair it with the Gemma 4 GGUF Hugging Face checkpoint. Additionally, Unsloth provides day-one support with optimized and quantized models for efficient local fine-tuning and deployment via Unsloth Studio. Start running and fine-tuning Gemma 4 in Unsloth Studio today. 

Running open models like the Gemma 4 family on NVIDIA GPUs achieves optimal performance because NVIDIA Tensor Cores accelerate AI inference workloads to deliver higher throughput and lower latency for local execution. Plus, the CUDA software stack ensures broad compatibility across leading frameworks and tools, enabling new models to run efficiently from day one.  

This combination allows open models like Gemma 4 to scale across a wide range of systems — from Jetson Orin Nano at the edge to RTX PCs, workstations and DGX Spark — without requiring extensive optimization. 

Check out the NVIDIA technical blog for more details on how to get started with Gemma 4 on NVIDIA GPUs and learn more about NVIDIA’s work on open models. 

#ICYMI: The Latest Updates for RTX AI PCs 

✨ Catch up on RTX AI Garage blogs for a host of agentic AI announcements from NVIDIA GTC, such as new open models for local agents. These models include NVIDIA Nemotron 3 Nano 4B and Nemotron 3 Super 120B, and optimizations for Qwen 3.5 and Mistral Small 4. 

 NVIDIA recently introduced NVIDIA NemoClaw, an open source stack that optimizes OpenClaw experiences on NVIDIA devices by increasing security and supporting local models.  

🚀 Accomplish.ai announced Accomplish FREE, a no-cost version of its open source desktop AI agent with built-in models. It harnesses NVIDIA GPUs to run open weight models locally, while a hybrid router dynamically balances workloads between local RTX hardware and the cloud — enabling fast, private, zero-configuration execution without requiring an application programming interface key. 

Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter. 

Follow NVIDIA Workstation on LinkedIn and X.  



AI Research Is Getting Harder to Separate From Geopolitics

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The world’s top AI research conference, the Conference on Neural Information Processing Systems—better known as NeurIPS—became the latest organization this week to become embroiled in a growing clash between geopolitics and global scientific collaboration. The conference’s organizers announced and then quickly reversed controversial new restrictions for international participants after Chinese AI researchers threatened to boycott the event.

“This is a potential watershed moment,” says Paul Triolo, a partner at the advisory firm DGA-Albright Stonebridge who studies US-China relations. Triolo argues that attracting Chinese researchers to NeurIPS is beneficial to US interests, but some American officials have pushed for American and Chinese scientists to decouple their work—especially in AI, which has become a particularly sensitive topic in Washington.

The incident could deepen political tensions around AI research, as well as dissuade Chinese scientists from working at US universities and tech companies in the future. “At some level now it is going to be hard to keep basic AI research out of the [political] picture,” Triolo says.

In its annual handbook for paper submissions, issued in mid-March, NeurIPS organizers announced updated restrictions for participation. The rules stated that the event could not provide services including “peer review, editing, and publishing” to any organizations subject to US sanctions, and linked to a database of sanctioned entities. It included companies and organizations on the Bureau of Industry and Security’s entity list and those on another list with alleged ties to the Chinese military.

The new rules would have affected researchers at Chinese companies like Tencent and Huawei who regularly present work at NeurIPS. The database also includes entities from other countries such as Russia and Iran. The US places limits on doing business with these organizations, but there are no rules around academic publishing or conference participation.

The NeurIPS handbook has since been updated to specify that the restrictions apply only to Specially Designated Nationals and Blocked Persons, a list used primarily for terrorist groups and criminal organizations.

“In preparing the NeurIPS 2026 handbook, we included a link to a US government sanctions tool that covers a significantly broader set of restrictions than those NeurIPS is actually required to follow,” the event’s organizers said in a statement issued Friday. “This error was due to miscommunication between the NeurIPS Foundation and our legal team.”

Before they reversed course, the conference organizers initially said that the new rule was “about legal requirements that apply to the NeurIPS Foundation, which is responsible for complying with sanctions,” adding that it was seeking legal consultation on the issue.

Immediate Backlash

The new rule drew swift backlash from AI researchers around the world, particularly in China, which produces a large quantity of cutting-edge machine learning papers and is home to a growing share of the world’s top AI talent. Several academic groups there issued statements condemning the measure and, more importantly, discouraging Chinese academics from attending NeurIPS in the future. Some urged Chinese academics to contribute instead to domestic research conferences, potentially helping increase the country’s influence in relevant science and tech fields.

The China Association of Science and Technology (CAST), an influential government-affiliated organization for scientists and engineers, said Thursday that it would stop providing funding for Chinese scholars traveling to attend NeurIPS and would use the money instead to support domestic and international conferences that “respect the rights of Chinese scholars.”

CAST also said it will no longer count publications at the 2026 NeurIPS conference as academic achievements when evaluating future research funding. It’s unclear if the organization will reverse course now that NeurIPS has walked back the new rule.

Washington Post’s Surveillance Pricing Under Fire From Dems Who Want to Ban the Practice

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Some subscribers to the Washington Post have been receiving emails that their subscription rates will be going up, according to the Washingtonian. That part isn’t surprising, given the fact that Post owner Jeff Bezos has reportedly been upset that the newspaper is losing money, especially since ditching about half of his workforce. But some folks who scrolled down to the bottom of the email were surprised when they read about how the new price was determined: “This price was set by an algorithm using your personal data.”

It’s a concept called surveillance pricing, and it’s not entirely new. People can often be charged different prices for the same product, depending on any number of factors. If your phone battery is low, rideshare companies like Uber or Lyft might charge more because they know you’re desperate. Instacart was recently caught charging up to 23% more to some shoppers based on unknown criteria.

Many Democrats aren’t happy about it, including Rep. Greg Casar of Texas. On Monday,  Casar wrote on Bluesky that surveillance pricing “should be illegal,” adding, “I have a bill to ban it.”

Last year, Casar and Rashida Tlaib of Michigan introduced legislation called the Stop AI Price Gouging and Wage Fixing Act. And last month, two other Democrats in the Senate, Ben Ray Luján from New Mexico and Jeff Merkley from Oregon, introduced very similar legislation called the Stop Price Gouging in Grocery Stores Act of 2026.

The Washington Post hasn’t explained how it determines pricing by using personal data. But there could be a number of factors, including zip code, estimated income, and purchase history. Bezos, the founder of Amazon, presumably has more data on what people buy than just about anyone in the country. And he’s a big supporter of utilizing AI to maximize profits.

The problem is that AI can’t really make up for losses any business might incur by offering a bad product. The newspaper first hemorrhaged subscribers—250,000 in one week alone—after Bezos stopped the Washington Post editorial board from endorsing Kamala Harris in the 2024 presidential election against Donald Trump.

The Washington Post had no reporters at the Academy Awards on Sunday, according to the paper’s former culture writer. And it was the last of the major news outlets to report that the U.S. had started bombing Iran late last month. The paper has purged any writer on the opinion side deemed to be liberal and has instead become a mouthpiece for the Bezos worldview—a worldview that happens to align perfectly with that of the Trump regime.

Bezos has been criticized for buying the distribution rights to First Lady Melania Trump’s “documentary” Melania for a whopping $40 million, but the movie itself helps explain why he’d bother. There are several shots of the Trumps with Big Tech oligarchs like Elon Musk, Tim Cook, and Bezos himself. All of these guys need something from Trump, whether it’s space contracts or just tariff relief.

News of what Bezos has in store for the future of his newspaper doesn’t instill confidence that it can survive much longer as a respected institution. The Washington Post’s news side still breaks major stories, but the New York Times reports Bezos’s big idea was to chop the newsroom’s budget in half and demand twice the productivity through AI. Columnist Dana Milbank and economics correspondent Jeff Stein both announced they were leaving the Post on Monday.

Businesses are increasingly turning to algorithms to set their prices, and it doesn’t look like that’s going to change anytime soon unless legislators get involved. At least a dozen states are considering legislation about surveillance pricing, but so far, only New York has passed a law in this area. Unfortunately, it doesn’t have much teeth since it only requires companies to notify consumers when a price has been set with AI.

On the other hand, New York’s law may be the only reason we know that the Washington Post is using AI for subscription rates. The paper has little other incentive to include the disclaimer: “This price was set by an algorithm using your personal data.” Notifying consumers may not fix the problem of surveillance pricing, but at least people can take it into account when deciding where they want to spend their money.

New NVIDIA Nemotron 3 Super Delivers 5x Higher Throughput for Agentic AI

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Launched today, NVIDIA Nemotron 3 Super is a 120‑billion‑parameter open model with 12 billion active parameters designed to run complex agentic AI systems at scale. 

Available now, the model combines advanced reasoning capabilities to efficiently complete tasks with high accuracy for autonomous agents.

AI-Native Companies: Perplexity offers its users access to Nemotron 3 Super for search and as one of 20 orchestrated models in Computer. Companies offering software development agents like CodeRabbit, Factory and Greptile are integrating the model into their AI agents along with proprietary models to achieve higher accuracy at lower cost. And life sciences and frontier AI organizations like Edison Scientific and Lila Sciences will power their agents for deep literature search, data science and molecular understanding.

Enterprise Software Platforms: Industry leaders such as Amdocs, Palantir, Cadence, Dassault Systèmes and Siemens are deploying and customizing the model to automate workflows in telecom, cybersecurity, semiconductor design and manufacturing. 

As companies move beyond chatbots and into multi‑agent applications, they encounter two constraints.

The first is context explosion. Multi‑agent workflows generate up to 15x more tokens than standard chat because each interaction requires resending full histories, including tool outputs and intermediate reasoning. 

Over long tasks, this volume of context increases costs and can lead to goal drift, where agents lose alignment with the original objective.

The second is the thinking tax. Complex agents must reason at every step, but using large models for every subtask makes multi-agent applications too expensive and sluggish for practical applications.

Nemotron 3 Super has a 1‑million‑token context window, allowing agents to retain full workflow state in memory and preventing goal drift.

Nemotron 3 Super has set new standards, claiming the top spot on Artificial Analysis for efficiency and openness with leading accuracy among models of the same size. 

The model also powers the NVIDIA AI-Q research agent to the No. 1 position on DeepResearch Bench and DeepResearch Bench II leaderboards, benchmarks that measure an AI system’s ability to conduct thorough, multistep research across large document sets while maintaining reasoning coherence. 

Hybrid Architecture

Nemotron 3 Super uses a hybrid mixture‑of‑experts (MoE) architecture that combines three major innovations to deliver up to 5x higher throughput and up to 2x higher accuracy than the previous Nemotron Super model. 

  • Hybrid Architecture: Mamba layers deliver 4x higher memory and compute efficiency, while transformer layers drive advanced reasoning.
  • MoE: Only 12 billion of its 120 billion parameters are active at inference. 
  • Latent MoE: A new technique that improves accuracy by activating four expert specialists for the cost of one to generate the next token at inference.
  • Multi-Token Prediction: Predicts multiple future words simultaneously, resulting in 3x faster inference.

On the NVIDIA Blackwell platform, the model runs in NVFP4 precision. That cuts memory requirements and pushes inference up to 4x faster than FP8 on NVIDIA Hopper, with no loss in accuracy. 

Open Weights, Data and Recipes

NVIDIA is releasing Nemotron 3 Super with open weights under a permissive license. Developers can deploy and customize it on workstations, in data centers or in the cloud.

The model was trained on synthetic data generated using frontier reasoning models. NVIDIA is publishing the complete methodology, including over 10 trillion tokens of pre- and post-training datasets, 15 training environments for reinforcement learning and evaluation recipes. Researchers can further use the NVIDIA NeMo platform to fine-tune the model or build their own. 

Use in Agentic Systems

Nemotron 3 Super is designed to handle complex subtasks inside a multi-agent system. 

A software development agent can load an entire codebase into context at once, enabling end-to-end code generation and debugging without document segmentation. 

In financial analysis it can load thousands of pages of reports into memory,  eliminating the need to re-reason across long conversations, which improves efficiency. 

Nemotron 3 Super has high-accuracy tool calling that ensures autonomous agents reliably navigate massive function libraries to prevent execution errors in high-stakes environments, like autonomous security orchestration in cybersecurity.

Availability

NVIDIA Nemotron 3 Super, part of the Nemotron 3 family, can be accessed at build.nvidia.com, Perplexity, OpenRouter and Hugging Face. Dell Technologies is bringing the model to the Dell Enterprise Hub on Hugging Face, optimized for on-premise deployment on the Dell AI Factory, advancing multi-agent AI workflows. HPE is also bringing NVIDIA Nemotron to its agents hub to help ensure scalable enterprise adoption of agentic AI. 

Enterprises and developers can deploy the model through several partners:

The model is packaged as an NVIDIA NIM microservice, allowing deployment from on-premises systems to the cloud.

Stay up to date on agentic AI, NVIDIA Nemotron and more by subscribing to NVIDIA AI news, joining the community, and following NVIDIA AI on LinkedIn, Instagram, X and Facebook.

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How to watch Jensen Huang’s Nvidia GTC 2026 keynote

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Nvidia kicks off its annual GTC developer conference in San Jose, California, next week with CEO Jensen Huang’s keynote scheduled for Monday at 11am PT / 2pm ET.

GTC — which stands for GPU Technology Conference — is Nvidia’s flagship annual event, where the chipmaker typically uses the spotlight to announce new products, champion partnerships, and lay out its vision for the future of computing. Huang’s keynote will focus on Nvidia’s role in the future of computing and AI. You can watch the two-hour address in person at the SAP Center or livestream the talk on the event’s website.

The broader three-day event is focused on what’s coming next for AI across industries including healthcare, robotics, and autonomous vehicles, among others.

On the software side, it’s rumored that Nvidia will release an open source platform for enterprise AI agents, dubbed NemoClaw, as originally reported by Wired. The platform would give businesses a structured way to build and deploy AI agents (software that can carry out multi-step tasks autonomously) and would position Nvidia to mirror similar offerings from companies like OpenAI.

On the hardware side, the company is also rumored to be releasing a new chip designed to accelerate the AI inference process — the process by which an AI model applies what it has learned to generate responses or make decisions, as distinct from the initial training process, which requires far more computing power. Faster, cheaper inference is widely seen as one of the last bottlenecks to scaling AI applications broadly. The chip, if confirmed, would represent Nvidia’s latest bid to dominate not just the training market, where it already commands an estimated 80% share, but the inference market as well, where competition from custom chips built by Google, Amazon and others is fast intensifying.

Kevin Cook, a senior equity strategist at Zacks Investment Research, told TechCrunch that attendees should also expect to learn what the company plans to do with its relationship with Groq, the inference company Nvidia reportedly paid $20 billion late last year to license its technology. There’s a lot of curiosity around this tie-up, given that Jonathan Ross, Groq’s founder, Sunny Madra, Groq’s President, and other members of the Groq team agreed to join Nvidia to help advance and scale that licensed tech.

There will, of course, also be a range of partnership announcements and demonstrations showcasing Nvidia’s AI capabilities across industries.

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Survey Reveals AI Is Delivering Clear Return on Investment in Healthcare

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AI is accelerating every aspect of healthcare — from radiology and drug discovery to medical device manufacturing and new treatment methods enabled by digital twins of the human body.

NVIDIA’s second annual “State of AI in Healthcare and Life Sciences” survey report reveals how the industry is moving from AI experimentation to execution, reaping return on investment (ROI) on core applications like medical imaging and drug discovery.

The industry is also embracing open source software and AI models to tackle specific use cases, as well as exploring using agentic AI to speed knowledge retrieval and research paper analysis.

Highlights from this year’s report include:

  • 70% of respondents said their organizations are actively using AI, up from 63% in 2024.
  • 69% said they’re using generative AI and large language models, up from 54%.
  • 82% said open source software and models are moderately to extremely important to their organizations’ AI strategy.
  • 47% said they’re using or assessing agentic AI.
  • 85% of executives said AI is helping increase revenue, and 80% said it’s helping reduce costs.

“Over the next 12-18 months, the most visible and scalable impact of AI will come from logistics and administrative streamlining,” said John Nosta, president of NostaLab, a healthcare think tank. “That’s where adoption curves are already steep — scheduling, documentation, coding, utilization management and care coordination.”

Read more below on some of the report’s key findings.

AI Adoption Ramps Up Across Healthcare and Life Sciences

AI adoption is up across every industry segment in this year’s survey — spanning digital healthcare, pharmaceutical and biotechnology, payers and providers, and medical technology and tools — with digital healthcare leading at 78%, followed by medical technology at 74%.

The top industry workload was generative AI and large language models, according to 69% of respondents. AI for data analytics and data science was the second most-used workload, followed by predictive analytics. New to the survey, agentic AI ranked fourth, with 47% of respondents saying they’re using or assessing AI agents.

“Scaling generative AI in healthcare starts with focusing on real clinical and operational problems, rather than the technology itself,” said Dr. Annabelle Painter, clinical AI strategy lead at Visiba U.K. “The organizations seeing impact are those that embed AI into existing workflows instead of layering AI on top as a separate tool.”

Healthcare and life sciences organizations are deploying these AI workloads across a variety of use cases, each specific to their primary functions. For example, 61% of respondents from medical technology said they’re using AI for medical imaging, such as radiologists using it to work more quickly and efficiently, while 57% from pharmaceutical and biotechnology said drug discovery is being driven by AI.

For the entire industry, the top AI use cases were clinical decision support (such as radiologists highlighting areas of concern on a scan), medical imaging and workflow optimization.

AI Budgets to Increase With Strong ROI

AI is helping healthcare and life sciences organizations become even better at their core competencies — underscoring strong ROI.

In addition to increasing annual revenue and reducing annual costs, AI is boosting back-office productivity through workflow optimization and is scaling across other key business operations such as patient interaction and administrative tasks.

For example, 57% of respondents from the medical technology segment reported seeing ROI from deploying AI for medical imaging. Nearly half (46%) of pharmaceutical and biotechnology respondents said AI for drug discovery and development was among their top ROI use cases.

The top ROI use case for digital healthcare providers was virtual health assistants and chatbots, according to 37%, while 39% of respondents from payers and providers (which include hospitals, primary care providers and insurance companies) cited administrative tasks and workflow optimization as their top area of ROI.

As a result of AI’s positive impact, 85% of respondents said their AI budgets would increase this year, with another 12% saying budgets would stay the same. For almost half of respondents (46%), AI spending will increase significantly, by more than 10%.

“Healthcare organizations that successfully integrate AI are those that explicitly fund and prioritize evaluation as a core operational function, ensuring AI delivers measurable improvements in safety, quality and patient care over time,” said Painter.

Using Open Source for Domain-Specific AI Deployment

Leaning into open source models and software allows enterprises to build domain-specific applications, lending them greater flexibility and efficiency while boosting business returns.

The healthcare industry has embraced open source, with 82% of survey respondents stating it’s moderately to extremely important to their AI strategy.

“Open models will shape the intellectual field,” said Nosta. “They are essential for exploration and for keeping the field honest. But in clinical environments where safety, liability and accountability are nonnegotiable, proprietary systems will remain necessary for validation, integration and trust. The key insight here is that discovery will be open, and deployment will demand stewardship.”

Download the “State of AI in Healthcare and Life Sciences: 2026 Trends” report for in-depth results and insights.

Sign up for NVIDIA’s healthcare and life sciences newsletter.

Know What Else Used a Lot of Energy? Human Civilization

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At last week’s India AI Impact Summit in New Delhi, industry leaders convened to discuss the future of artificial intelligence and how best to squeeze it into parts of your life you haven’t even considered. Notably absent was Bill Gates, who dropped out hours before his scheduled keynote over the ongoing scrutiny about his presence in the Epstein Files (though he continues to deny any wrongdoing). While the convention was reportedly a bit chaotic, what with the protests and all, the luminaries from around the tech world present nonetheless kept things upbeat and optimistic, declaring “full steam ahead” on the technological hype train carrying our species and planet off a cliff.

Also in attendance was OpenAI’s Sam Altman, who earned numerous headlines over the course of the event for his words and antics. His buzz blitzkrieg started on Thursday at a seemingly easy photo-opp layup with Indian Prime Minister Narendra Modi and other AI executives all raising their joined hands in a celebratory display of industry-wide solidarity. Altman and the former colleague and present CEO of Anthropic to his left, Dario Amodei, notably refused to complete the chain and hold each other’s hands, making for an all-too-poignant moment. Altman would continue to make news throughout the summit for his comments on the industry’s “urgent” need for global regulation and his sneaking suspicion that companies might actually be using AI as a scapegoat to whitewash their layoffs.

Ever the yapper, Altman has bagged yet another round of earned media for an interview with The Indian Express’ Anant Goenka, during which he posited some controversial rebuttals to concerns about AI’s environmental impact.

Altman started off by saying the claims about ChatGPT consuming “‘17 gallons of water for each query’ or whatever,” are “completely untrue, totally insane, no connection to reality,” before qualifying that, OK, maybe it was a valid concern when his company “used to do evaporative cooling in data centers.”

He went on to say that there is “fair” concern about the amount of energy data centers eat to crank out the most soulless slop you’ve ever seen, but suggested the onus of responsibility for dealing with AI’s ravenous appetite falls to the energy sector itself, which Altman feels needs to “move towards nuclear or wind and solar very quickly.”

Altman then stunned the crowd and firmly re-entered the discourse with a mind-blowing truth bomb for those who still felt AI was consuming too much energy.

“It also takes a lot of energy to train a human,” Altman rejoined euphorically. “It takes like 20 years of life, and all the food you eat before that time, before you get smart. And not only that, it took like the very widespread evolution of the hundred billion people that have ever lived and learned not to get eaten by predators and learned how to figure out science and whatever to produce you, and then you took whatever you took.”

It is true that every person and the sum total of human civilization have consumed a sizable amount of energy (and water) to get to where we are today. While the value comparison of a nascent tech industry and its models to the entirety of civilization and human beings may have elicited adulation at the summit, Altman got an icier reception from the internet. Social media quickly took to roasting the remarks as “dystopian” and “deeply antisocial and antihuman.”

Perhaps further illuminating the backlash, Altman’s energy comments butt up against the frustrating lack of transparency within the industry our collective futures now hinge upon. There are currently no regulations in place requiring data centers to disclose their water and energy consumption. Furthermore, center employees and business partners are typically muzzled by nondisclosure agreements. This has made reporting and research on the true expenditure levels a tricky figure to pin down.

At least we’ve got Sam to keep us informed while waiting for some clarity about what’s actually going on and being used in those centers.

Survey Reveals AI Advances in Telecom: Networks and Automation in Driver’s Seat as Return on Investment Climbs

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AI is accelerating the telecommunications industry’s transformation, becoming the backbone of autonomous networks and AI-native wireless infrastructure. At the same time, the technology is unlocking new business and revenue opportunities, as telecom operators accelerate AI adoption across consumers, enterprises and nations.

NVIDIA’s fourth annual “State of AI in Telecommunications” survey report unpacks these trends, underscoring strong AI adoption, impact and investment in the industry.

Highlights from the report include:

  • 90% said AI is helping increase annual revenue and drive down costs.
  • 77% said they expect to see AI-native networks launch before the deployment of 6G.
  • 65% of telecom operators said network automation is being driven by AI.
  • 60% said their organization is using or assessing generative AI, up from 49% in 2024.
  • 89% said open source models and software are important to their AI strategy.
  • 89% of telcos plan to boost AI spending in 2026, up from 65% a year ago.

“There is a seismic shift underway in the telecom industry driven by AI,” said Sebastian Barros, managing director of Circles, a Singapore-based telecommunications provider. “Communication service providers are converging on a new realization. Their role in society extends beyond moving bits across networks toward moving intelligence across local and regulated infrastructure. That transition defines the move from telco to ‘AICO’ — AI infrastructure companies operating at network proximity, not application vendors riding on top.”

Here are some more key findings from the report.

Tangible Revenue Impact and Return on Investment

The telecommunications industry is seeing a definitive revenue impact from the use of AI. Overall, about nine out of 10 respondents said AI is helping to increase revenue and reduce costs. Telecommunications operators, which represent about a quarter of the 1,000 responses in the survey, are also seeing the benefit, with 90% saying AI has had a positive impact on revenue and costs.

The top AI use cases cited for return on investment (ROI) were AI for autonomous networks (50%), followed by improved customer service (41%) and internal process optimization (33%).

“Autonomous networks deliver immediate ROI by eliminating human effort from repetitive, reactive workflows,” said Barros. “The fastest impact areas are energy management, fault prediction, configuration drift correction and capacity planning.”

This strong impact on revenue and ROI is leading telecommunications companies to increase their AI budgets in 2026. Overall, 89% of respondents said their AI budget will increase in the next 12 months, up from 65% in last year’s survey, with 35% saying their budgets would increase more than 10% from this year.

Focus on AI-Native Networks and Autonomous Operations

Network automation has overtaken customer experience as the leading use case for investment, deployment and ROI impact. This signals a bold step toward autonomous networks — AI-driven, self-managing systems that can self-configure, self-heal and self-optimize with minimal human intervention. Eighty-eight percent of organizations report being between levels 1-3 of autonomy, as defined by the TM Forum, and the use of generative AI and agentic AI is expected to accelerate the shift to level 5 autonomous networks.

“Autonomous networks are delivering return on investment faster than any other AI use case because they directly reduce outages, energy consumption and manual intervention,” said Chetan Sharma, CEO of Chetan Sharma Consulting. “Agentic AI accelerates this by coordinating decisions across domains in real time.”

A surge in edge computing investment is reshaping telecom network architectures, bringing AI inferencing closer to users through a distributed computing infrastructure. Telcos are stepping up investments in AI-native RAN and 6G — signaling a major industry intercept ahead of the traditional 6G deployment cycle, with 77% of respondents anticipating a much faster time to deployment of this new AI-native wireless network architecture.

The top drivers of investment are using AI to enhance spectral efficiency, improving the performance of the radio access network supporting edge AI applications and accelerating the research and development of 6G.

A Universal Boost in Productivity 

AI in telecommunications is advancing autonomous networks and business opportunities as well as improving internal operations. Nearly every respondent in the survey said AI is boosting employee productivity, with 26% citing major to significant improvements to their ability to complete more tasks with higher quality in less time.

The productivity gains are coming from generative and agentic AI solutions deployed across operations, from the back office to networks.

“Generative AI delivered fast productivity gains, but agentic AI is where telecoms begin to see structural ROI,” Sharma said. “Autonomous agents can act across networks, IT and customer journeys, turning insights into decisions without human delay.”

Download the “State of AI in Telecommunications 2026 Trends” report for in-depth results and insights.

Explore NVIDIA AI technologies for telecommunications.

India Fuels Its AI Mission With NVIDIA

Posted on by faz_business


India is the nexus of AI innovation this week as the host of the AI Impact Summit, which brings together global heads of state and industry to chart the future of AI.

At the summit, taking place in New Delhi, industry leaders, government agencies, educational institutions and startups are sharing how they’re working with NVIDIA to drive the AI industrial revolution in the world’s most populous country.

These initiatives support the IndiaAI Mission, a government effort that’s infusing India’s AI ecosystem with over $1 billion to bolster the nation’s compute capacity and foster the development of sovereign AI datasets, frontier models and applications. The mission also supports AI education, startup innovation and frameworks for trustworthy AI.

Read how NVIDIA is supporting IndiaAI Mission priorities including:

NVIDIA Cloud Partners Boost India AI Infrastructure

To achieve its AI ambitions, India is investing heavily in its computing infrastructure. Under the IndiaAI Compute Pillar, the nation is building out its AI cloud offerings with systems including tens of thousands of NVIDIA GPUs.

NVIDIA is collaborating with next‑generation cloud providers Yotta, L&T and E2E Networks to deliver advanced AI factories to meet India’s growing need for AI compute and enable it to develop AI models and services that drive innovation.

  • Yotta is a hyperscale data center and cloud provider building large‑scale sovereign AI infrastructure for India, branded as Shakti Cloud, powered by over 20,000 NVIDIA Blackwell Ultra GPUs. Its campuses in Navi Mumbai and Greater Noida deliver GPU‑dense, high‑bandwidth AI cloud services on a pay‑per‑use model, designed to make advanced AI training and inference affordable and compliant for Indian enterprises and public sector customers.
  • Larsen & Toubro (L&T) is building sovereign, gigawatt-scale NVIDIA AI factory infrastructure in India to reinforce the country’s position as a global AI powerhouse in alignment with the IndiaAI Mission. The roadmap includes initial expansions in Chennai to 30 megawatts as well as a new 40-megawatt facility in Mumbai. These facilities will power sovereign cloud workloads and hyperscale deployments, delivering secure, energy‑efficient infrastructure for advanced AI applications.
  • E2E Networks is building an NVIDIA Blackwell GPU cluster on its TIR platform, hosted at the L&T Vyoma Data Center in Chennai. The TIR cloud compute platform will feature NVIDIA HGX B200 systems and NVIDIA Enterprise software as well as NVIDIA Nemotron open models to supercharge sovereign development across agentic AI, healthcare, finance, manufacturing and agriculture.

India’s AI cloud infrastructure will host workloads as well as manufacture intelligence for model training, fine-tuning and high‑scale inference. Capacity within these data centers will be reserved for model builders, startups, researchers and enterprises to build, fine-tune and deploy AI in India.

Further expanding access to NVIDIA AI infrastructure in India, Netweb Technologies is launching its Tyrone Camarero AI Supercomputing systems built on the NVIDIA Grace Blackwell architecture. The NVIDIA GB200 NVL4 platforms — manufactured in India by Netweb under the government’s “Make in India” mission — feature four NVIDIA Blackwell GPUs and two NVIDIA Grace CPUs to power scientific computing, model training and inference.

NVIDIA and India AI-Native Companies Build the Nation’s Frontier AI Models

Another key goal of the IndiaAI Mission — led by its Innovation Center Pillar — is to develop and deploy foundation models trained on India-specific data and domestic AI infrastructure.

For a nation as multilingual as India — with 22 constitutionally recognized languages and over 1,500 more recorded by the country’s census — frontier AI models are a powerful tool to help its more than 1.4 billion residents interact with technology in their primary language.

Organizations across the country are building AI applications with NVIDIA Nemotron to support public-sector services, financial systems and enterprise operations in multiple languages.

NVIDIA Nemotron open models, datasets, tools and libraries enable organizations to build frontier speech, language and multimodal models at scale and across languages for government, consumer and enterprise applications. It includes India-specific datasets like Nemotron-Personas-India, an open dataset built from publicly available census data using NeMo Data Designer that includes 21 million fully synthetic Indic personas to enable population-scale sovereign AI development.

Adopters in India of Nemotron — and NeMo Curator, an open library for multilingual and multimodal data curation — include:

  • BharatGen, a sovereign AI initiative supported by the Government of India aimed at strengthening the country’s multilingual and multimodal AI ecosystem. As part of this effort, BharatGen has developed a 17-billion-parameter mixture-of-experts (MoE) model from the ground up, using the NVIDIA NeMo framework for pretraining and the NeMo RL library for post-training. The open source models are designed to power applications across public services, agriculture, security and cultural preservation.
  • Chariot, a company building AI systems for speech and multimodal communication. Using the NeMo framework, Chariot is developing an 8-billion-parameter model for real-time text to speech, supporting applications that improve accessibility and digital interaction across consumer and enterprise use cases.
  • Commotion, backed by Tata Communications, which has developed an AI operating system to automate complex enterprise workflows. By integrating NVIDIA Nemotron models and speech capabilities, the platform enables governed, production-grade AI deployments, helping enterprises scale AI across critical business operations.
  • CoRover.ai, which has deployed NVIDIA Nemotron Speech open models and NVIDIA Riva libraries for end-to-end, ultralow-latency speech AI — including the NVIDIA Riva Whisper v3 model for multilingual automatic speech recognition in English, Hindi and Gujarati. Powering customer service applications for the Indian Railway Catering and Tourism Corporation, CoRover’s platform supports around 10,000 concurrent users and more than 5,000 daily ticket bookings.
  • Gnani.ai, which offers enterprises a multilingual agentic AI platform that can interact with customers through voice and text. Gnani is building a 14-billion-parameter speech-to-speech model built on NVIDIA Nemotron Speech models, datasets and NeMo libraries including NeMo libraries through NVIDIA Cloud Partner E2E Networks — with plans to expand to a 32-billion-parameter model. By fine-tuning the NVIDIA Nemotron Speech model for Indic languages, Gnani has achieved a 15x reduction in inference costs, enabling the company to scale to support more than 10 million calls per day for customers in telecom, banking and hospitality.
  • National Payments Corporation of India (NPCI), which operates India’s retail payment and settlement systems and is deploying AI models to support digital financial services. Building on its production deployment of the AI-powered UPI Help Assistant — a pilot initiative for India’s Unified Payments Interface (UPI) — NPCI is exploring training FiMi, a financial model for India, using the NVIDIA Nemotron 3 Nano model and its own datasets. The model, fine-tuned with the NeMo framework, will support multilingual customer service across India’s banking ecosystem.
  • Sarvam.ai, a leader in full-stack sovereign generative AI that provides enterprise-grade multimodal, speech-to-text, text-to-speech, translation and reasoning models. The company is open sourcing its Sarvam-3 series of text and multimodal large language model variants, trained for 22 Indic languages, English math and code. Sarvam is using NeMo Curator to construct high-quality multilingual training data while adopting a subset of NVIDIA Nemotron datasets. The foundation models were pre-trained from scratch across 3B, 30B and 100B parameter sizes using the NVIDIA NeMo framework and Megatron-LM, and post-trained with NeMo RL. Training was conducted on NVIDIA H100 GPUs through NVIDIA Cloud Partners, including Yotta. With these sovereign models, Sarvam.ai’s new Pravah platform enables production-grade inference for Indian government and enterprise applications.
  • Soket.ai, which is using a modern large-model training stack on open NVIDIA Nemotron technologies, including NVIDIA Megatron and NVIDIA NeMo. These open source components enable scalable experimentation, training stability and efficient GPU usage, while preserving full control over the model’s data, design and life cycle.
  • Tech Mahindra, which has developed an 8-billion-parameter foundation model tailored for Indian languages and dialects. The model, built with Nemotron, is being designed for use in classrooms, where it can help make educational materials available in a wider range of Indian languages including Hindi, Maithili and Dogri. The team generated synthetic data with Nemotron libraries and tools such as NeMo Data Designer and conducted supervised fine-tuning with NeMo AutoModel.
  • Zoho, which is advancing its Zia LLM platform with proprietary models built using NVIDIA NeMo on the NVIDIA Blackwell and Hopper platforms, integrated across its software-as-a-service applications. This privacy-first architecture delivers contextual, production-grade AI for critical business workflows like customer relation management and finance, ensuring technology sovereignty and enterprise security at a global scale.

Developers building sovereign AI systems can access NVIDIA Nemotron and NeMo today. Nemotron models can be deployed anywhere on NVIDIA-accelerated infrastructure — including on NVIDIA DGX Spark, which is now available in India through qualified partners including PNY, RP tech India, Tech Data, a TD SYNNEX Company, as well as on NVIDIA Marketplace. A version manufactured in India as part of the “Make in India” initiative is available through Netweb.

DGX Spark also runs sovereign AI models by Indian model builders including Sarvam.ai.

Government and Academic Partnerships to Support Research in AI for Science and Engineering

Under its Application Development Initiative Pillar, the IndiaAI Mission is supporting high-impact AI applications — and its Startup Financing Pillar aims to democratize funding availability for AI entrepreneurs across the country.

NVIDIA is collaborating with government agencies, research institutions, venture capital firms and startups to advance projects aligned with these goals.

NVIDIA is collaborating with the Anusandhan National Research Foundation (ANRF), a statutory body under the Indian government, to spur even more cutting-edge AI research across the nation’s leading academic institutions. The initiative will support ANRF’s AI for Science & Engineering program and future AI programs.

NVIDIA will offer ANRF grantee institutions complimentary access to NVIDIA AI Enterprise software and specialized technical mentorship through the NVIDIA AI Technology Center. The collaboration will also include AI bootcamps, workshops and hackathons to strengthen India’s AI research ecosystem.

NVIDIA is also partnering with prominent venture capital firms including Peak XV, Z47, Elevation Capital,, Nexus Venture Partners and Accel India to identify and fund promising startups of all stages that are building AI solutions for India and international use. More than 4,000 of India’s AI startups are already part of the NVIDIA Inception program.

For more from the India AI Summit, learn how NVIDIA and global industrial software leaders are partnering with India’s largest manufacturers — and how India’s global systems integrators are building enterprise AI agents with NVIDIA.