Intel has moved forward in its plan to acquire AI chipmaker SambaNova Systems by signing a term sheet with the Palo Alto–based startup, WIRED reported.
The preliminary document is non-binding, and the deal remains subject to regulatory review, due diligence and financial scrutiny—processes that could take weeks or months to conclude.
While negotiations are advancing, the specific terms remain undisclosed, the report added.
Intel’s interest in the company was first revealed in October, when Bloomberg reported that early discussions were underway and that SambaNova could be valued below its 2021 peak of $5 billion.
The potential acquisition raises governance questions because Intel CEO Lip-Bu Tan is also the chairman of SambaNova. Intel Capital, now being spun out as an independent fund, is also an investor in the startup.
SoftBank, another major SambaNova backer, made a substantial investment in Intel earlier this year, deepening the web of cross-holdings.
SambaNova, founded in 2017 by Stanford professors Kunle Olukotun and Christopher Ré, along with former Oracle executive Rodrigo Liang, builds hardware and systems for AI inference workloads. The company has raised more than $1.14 billion to date, including major rounds led by BlackRock, GV, Intel Capital and SoftBank’s Vision Fund 2.
Its valuation, however, has fluctuated after hitting $5 billion in 2021. Recent disclosures suggest investors have marked down their holdings, with BlackRock reportedly cutting its valuation by 17% over the past year, according to The Information.
The downturn may have made SambaNova a more attractive target for Intel, which has struggled to keep pace with NVIDIA and other rivals in AI chip performance.
Intel has also received an $8.9 billion boost from the US government in August to expand domestic manufacturing.
Since taking over earlier this year, Tan has set out an AI-first strategy that includes reducing debt, selling non-core units, and improving Intel’s position in advanced semiconductor technology.
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CRED’s tryst with AI has been years in the making. Ever since the fintech company integrated AI in its CRED Protect feature four years ago to detect fraud and billing errors in credit card statements, the team has steadily stitched a nest of predictive analysis and real-time insights to offer a more premium customer experience.
The result was the Svalbard suite of features released earlier this year. But as its product lines grew, the challenge was no longer scale alone.
For an organisation to turn AI-native, it takes much more than just augmenting customer journeys with embedded AI. And Swamy Seetharaman, CRED co-founder and its head of engineering, knows that.
AI is becoming central to CRED’s functioning, and the executive pitches for AI to make every team member 10x more effective by giving them clarity, judgement, and context that helps with execution.
“With AI, this is no longer an abstract ambition; it is simply how we work,” Seetharaman told AIM. And OpenAI is helping the team shape its customer approach and product experience with its models.
Building Cleo with OpenAI
AI now sits horizontally across many of CRED’s workflows. Seetharaman said repetitive tasks are automated, context is easier to assemble, and teams move ideas into production faster. He frames AI as a steady reasoning layer that reduces friction and closes gaps—essential for growing companies like CRED, which boosted its revenue by 71% in FY24 to Rs 2,397 crore on the back of member engagement and monetisation.
The company developed Cleo as an AI companion to handle simple customer conversations and alleviate customer-facing teams. Built on OpenAI models, including GPT-4.0, GPT-5 and o3, Cleo interprets free-form messages, maps them to standard operating procedures with more than 97% accuracy, and executes them using APIs.
The turning point, Seetharaman said, came when it began handling multimodal voice and text conversations with multiple intents in one thread. This is where traditional bots struggle to push beyond scripted replies.
It began to diagnose, act, validate, and adapt in real time. “This shift, from answering questions to diagnosing, acting, validating, and adapting in real time, transformed Cleo to operate like a true concierge,” he said.
The impact shows up in numbers. According to the company, there has been a 31% decline in session drop-offs, a 14% rise in CSAT (customer satisfaction score), and more multi-intent conversations resolved in a single flow.
CRED also tracks repeat impressions, clarity of responses, and how often users need to add context again. All these markers improved as Cleo’s tone and helped its language flow naturally and more in tune with the company.
Being AI-First
While OpenAI models run customer-facing support, CRED has tapped into Anthropic’s Claude to strengthen its internal processes. Seetharaman described the approach as treating AI as a toolkit. They are picking the right model for the right task.
“Claude strengthens our engineering muscle and helps us ship features faster with higher reliability. We are building internal copilots on Claude that support developers with safer code generation, structured reasoning, and automation workflows,” he explained.
In a blog, Anthropic wrote how developers at CRED now rely on Claude Code to identify incremental solutions for writing, testing, and committing code across both new and existing projects. The team also uses the tool to generate documentation for existing codebases, in addition to breaking down complex problems into manageable steps.
CRED rebuilt its customer success stack with three systems: Stark, Thea, and Cleo. Seetharaman describes Stark as a tool that turns complex SOP creation into plain English workflows, optimising manual tasks.
Thea supports agents by giving them full conversation context, insights, and recommended actions. This matters as the company works with more than 20,000 APIs, and Thea saves agents from hunting for information across tools.
Cleo then understands member intent across informational, transactional, and personalised cases, and handles multiple needs within a single flow.
As these AI systems learn with each interaction, they enable CRED to track customer behaviour more closely. “When a conversation moves from AI to a human, the handoff becomes a source of insight that helps us identify new intents and onboard emerging use cases much faster,” Seetharaman explained.
The shift to an AI native organisation runs deeper than customer support. CRED is building foundational platforms that handle access, governance, security, privacy, compliance, and evaluations at scale.
Looking ahead, CRED plans to integrate Cleo across all business lines. It is also developing tools that detect data dead ends and feed those cases back into the knowledge base. The focus is on raising accuracy and reducing blind spots in real time.
“For us, incorporating OpenAI’s technology has been a true unlock across two of our values: compounding and being fast and right,” Seetharaman said. The early signals have been strong. The next steps are about scaling that impact.
The post Why CRED Turned to OpenAI for Cleo appeared first on Analytics India Magazine.
Setu by Pine Labs has introduced an agentic bill-payments experience on December 9. According to the company’s press release, it is designed to simplify how consumers track and pay their monthly bills.
Pine Labs is an API-enabled technology platform which offers digital public infrastructure solutions across payments, data and insights.
The new system tackles two of the biggest pain points in bill payments: the fear of wrong charges and the worry of missing due dates.
Most consumers spend only minutes paying a bill but hours tracking them each month. Setu’s new assistant automates that work by fetching every bill, checking for irregularities and ensuring payments are made on time.
“India’s digital public infrastructure has transformed how we pay, but the work behind monthly bills has remained manual. This agentic experience brings automation and trust together, giving people a smarter, safer way to handle their bills,” B Amrish Rau, CEO of Pine Labs, mentioned.
The system runs only on rules set by the user, ensuring that it never pays the wrong bill or crosses spending limits.
Meanwhile, earlier this year, Pine Labs and India’s private sector J&K Bank sought to revolutionise credit issuance in India by introducing a tech-first RuPay Credit Card for the bank’s customers.
The RuPay Credit Card can also be linked to UPI for seamless credit transactions. Developed using Pine Labs’ credit issuance platform Credit+, this integration will also enable seamless processing of pre-sanctioned credit lines on UPI.
Previously, SBI Payments and Pine Labs have also strengthened their long-standing relationship with an expanded strategic alliance, deepening a partnership that has already spanned 12 years. SBI Payments—a joint venture between State Bank of India, the country’s largest commercial bank, and Hitachi Payment Services—together with merchant commerce platform Pine Labs, currently powers more than two lakh digital checkout points across India.
With this renewed collaboration, the two companies aim to accelerate the use of digital payments and digital commerce solutions nationwide.
The focus will be on creating a smoother, more reliable checkout experience across a wide range of payment methods.
By bringing together SBI Payments’ extensive distribution network and robust acceptance infrastructure with Pine Labs’ merchant-focused innovations and comprehensive solution stack, the alliance is expected to enhance merchant capabilities and help drive sales growth significantly.
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Across India, this winter carries an unsettling undertone. With the festive season underway, along with the changing weather patterns, pollution levels have surged again across most of northern India. The National Capital Region, like every year, seems to be recording the worst numbers in its count of pollutants.
Delhi provides the most vivid warning of what this trajectory leads to. After Diwali and the crop burning season, the capital once again slipped into the ‘Severe’ and ‘Severe Plus’ AQI categories.
Several zones recorded PM2.5 levels above 450 and some touched 500. Visibility dropped, schools issued advisories and hospitals saw a jump in respiratory cases. This cycle has become so predictable that it is now considered part of Delhi’s seasonal rhythm.
Even urban centres once considered relatively breathable, like Bengaluru, are now showing worrisome signs of deterioration.
The city is experiencing a form of air stagnation, where polluted air stays trapped close to the surface, blocking sunlight and causing a deeper, unnatural dip in temperature.
Bengaluru’s air quality this winter has seen a sharp decline, with early December recording some of the city’s highest pollution levels of the season. The city witnessed its worst spike on December 7, when the AQI hit 161, falling into the “unhealthy” category and marking the highest reading during the period over the last five years (graph below).
While pollution levels eased slightly after this peak, dropping back toward the 80–100 range, the early-winter surge highlights a worrying trend: Bengaluru, during winter, is increasingly experiencing significant pollution episodes that resemble those typically associated with northern Indian cities.
Air purifier sales across urban India reflect this rising fear. Retailers report a sharp increase in demand in the weeks following pollution spikes. Popular electronics retailer Croma reported a 30% increase in the year-on-year sales of air purifiers across its online and offline channels.
According to IQAir’s 2024 assessment, India remains one of the world’s top five most polluted countries, with six of the 10 most polluted cities globally located here. Toxic air cuts more than three years off the average Indian’s life expectancy.
Despite the scale of the crisis, India’s air monitoring infrastructure has not kept pace. Most cities still rely on only one or two official AQI stations, leaving vast gaps in understanding how pollutants move across complex urban microclimates.
This gap in measurement is one of India’s biggest blind spots. As Amit Banka, founder and CEO of WeNaturalists, says, “AI models using satellite-derived intelligence can virtually sense pollution in areas without physical sensors, creating a scalable virtual monitoring network.” WeNaturalists is a global ecosystem empowering professionals and organisations working with nature with digital solutions.
Banka’s observation highlights a growing consensus among climate scientists that the problem in India is not the absence of data, but the absence of consistent, connected data capable of informing real-time governance.
How Can AI be Leveraged?
A 2024 study by Kuldeep Singh Rautela & Manish Kumar Goyal, published under Scientific Reports in the Nature journal, backs this urgency with hard evidence. Researchers used an AI/ML model, a convolutional autoencoder to forecast PM2.5 levels across India, even in regions with little to no ground monitoring. The model’s accuracy was unexpectedly high, with error margins under 10 µg/m³ and structural-similarity scores above 0.60, proving that AI can generate reliable pollution forecasts even when India’s sensor network falls short.
Rautela & Goyal warn that India’s AQI infrastructure is too sparse to capture true exposure levels, and that AI-driven forecasting is no longer optional; it is essential if cities like Bengaluru want to avoid the “Delhi trajectory.”
“India’s official monitoring network is geographically limited,” says Banka, adding that AI can override these limitations by combining historical data with real-time satellite imagery.
Professor A Damodaran of IIM Bangalore, believes this is where India must act decisively. Ease of driving is being prioritised over ease of breathing, he told AIM.
It is perhaps the clearest articulation of the dysfunction at the heart of India’s pollution problem. Traffic police prioritise vehicle movement. Pollution boards prioritise emissions reporting. Neither system is designed to work with the other.
AI offers the glue that India’s institutions have long lacked. According to Damodaran, India’s biggest pollutant is still suspended particulate matter produced by older vehicles, autorickshaws and trucks.
AI-powered camera-vision systems can already identify which types of vehicles pass through a junction and which ones are likely contributing disproportionately to particulate spikes. When correlated with traffic flow, such systems can recommend rerouting traffic in polluted corridors before the exposure becomes hazardous.
This shift is already visible in the startup ecosystem. Momentum Capital, which invests in climate and deep-tech ventures globally, sees AI as central to the next phase of environmental governance. Founder and managing partner Ankur Shrivastava says they are bullish on AI-led climate solutions that go beyond diagnostics and build mitigation tools, “from smart traffic-flow systems to industrial emission control.”
Shrivastava points out that scaling these systems requires navigating difficult regulatory terrain. “Air-quality startups must navigate a complex policy ecosystem, which is why we prioritise founders who can build teams capable of operating across regulatory environments.”
The future of urban environmental intelligence in India looks increasingly agentic. Banka believes, “India is moving toward agentic AI systems that will not just alert officials to smog, but automatically trigger interventions like rerouting traffic, or activating suppression systems.”
These systems can eventually power climate digital twins for major metros, giving decision makers the ability to run simulations that forecast the impact of electrifying buses, restricting construction dust or restructuring traffic before policies are implemented.
The Centre for Research on Energy and Clean Air analysis published a study last month that claimed that across 60% of Indian districts, “exposure to significant air pollution is not restricted to winter alone as is commonly believed.” The report noted that expanding the ground-monitoring network nationwide is essential.
India can no longer treat air quality as an episodic, wintertime inconvenience. The pollution crisis is structural, shaped by rapid urbanisation, weak monitoring, fragmented governance and climate volatility.
The post What If AI Can Monitor the Air You Breathe? appeared first on Analytics India Magazine.
India has moved closer to creating a formal royalty regime for artificial intelligence (AI) developers, with a government committee recommending a blanket licensing system for training models on copyrighted work.
The Department for Promotion of Industry and Internal Trade (DPIIT) has released a working paper on December 8 to that effect. The proposal marks a significant policy move on generative AI and copyright protection.
The eight-member committee, chaired by Himani Pande, an additional secretary at DPIIT, was tasked with assessing the adequacy of India’s copyright law in addressing AI-driven use of creative works.
The committee sides firmly with creator remuneration and rejects the push by tech firms for unrestricted text and data mining (TDM).
Safeguards Against Exploitation
The paper noted that India’s creative economy, spanning films, arts, music, digital content and informal folk traditions, contributes significantly to GDP and livelihoods and therefore requires strong safeguards against unremunerated AI exploitation of human-created works.
India is among the world’s fastest-growing AI markets and a major consumer base for generative AI systems.
The paper noted that the tech sector, led by industry body Nasscom, supported an opt-out rights framework. The Business Software Alliance, whose members include Google, Microsoft, Amazon Web Services, IBM, Salesforce and OpenAI, also argued for explicit TDM exceptions with opt-out provisions.
The proposal directly challenges the data practices of companies such as Google and OpenAI, which rely heavily on large-scale scraping of online material to train their models.
Google and OpenAI did not reply to emails sent by AIM seeking comment.
Content industry bodies such as broadcasters, music labels and creator organisations opposed the opt-out approach, arguing that it disproportionately benefits large platforms.
In a LinkedIn post, Kriti Sharma, director (regulatory, legal and compliance, India and Southeast Asia) at Dun and Bradstreet, said the proposal reflects an attempt to reconcile competing realities. “It feels like India is trying to reconcile two truths. AI needs data to grow. Creators need protection to survive,” Sharma said. She added that the paper approaches the question “thoughtfully, boldly, and practically.”
Rakesh Umarani, partner at Vidyam Legal, said the hybrid model offers a balanced pathway. “A clear licensing and royalty framework could prevent future disputes and give creators real confidence while allowing AI development to continue responsibly,” Umarani said.
After examining global developments and contrasting stakeholder submissions, the committee rejected a blanket text-and-data mining (TDM) exception, widely supported by technology firms, stating that allowing such an exception “would undermine copyright” and “leave human creators powerless to seek compensation.”
Hybrid Licensing System Proposed
The committee recommended a hybrid system allowing AI developers to train on “all lawfully accessed copyrighted works” as a matter of right, but with mandatory royalty sharing through a government-designated non-profit collective formed by rights holders.
Under this framework, creators will not have the option to withhold their works from AI training but will receive statutory remuneration.
Royalties would be administered through a single-window mechanism, the Copyright Royalties Collective for AI Training (CRCAT), with a government-appointed committee determining rate structures. Even non-members would be eligible for payments upon registering their works.
The proposal aims to reduce transaction costs, provide legal certainty, widen access to copyrighted datasets, mitigate AI bias and hallucinations, and create a level playing field between large platforms and startups.
Burden of Proof
Under the proposed framework, if a copyright owner alleges that their work was used to train an AI system without paying royalties, the law will presume the claim is valid unless the developer can prove otherwise.
Even though the blanket licence system gives developers little reason to hide training data, disputes may arise, for example, when a developer claims that only proprietary or separately licensed data was used.
In such cases, the burden of proof shifts to the AI developer, who must demonstrate that no third-party copyrighted material was used. Until they do, the presumption favours the copyright holder, it stated.
The working paper has now entered a 30-day public consultation phase.
The post India Moves to Regulate AI Training by Google, OpenAI appeared first on Analytics India Magazine.
Microsoft has announced a $17.5 billion investment in India over four years (2026–2029) to expand cloud and AI infrastructure, skilling programmes and ongoing operations.
This marks the company’s largest investment in Asia. This is in addition to the $3 billion announced earlier in 2025.
The announcement followed Microsoft chairman and CEO Satya Nadella’s meeting with Prime Minister Narendra Modi ahead of the company’s India AI tour. Microsoft said the new investment is aligned with three priorities — scale, skills and sovereignty.
The tech giant has also doubled its commitment to equip 20 million Indians with AI skills by 2030. Through its ADVANTA(I)GE India initiative, the company claims to have trained 5.6 million people since January 2025 and enabled over 125,000 individuals to secure jobs or pursue entrepreneurship opportunities.
“Had a very productive discussion with Mr. Satya Nadella. Happy to see India being the place where Microsoft will make its largest-ever investment in Asia,” posted PM Modi in a post on X.
He further said that the youth of India will harness this opportunity to innovate and leverage AI to build a better planet.
Union Minister for Electronics & IT Ashwini Vaishnaw said the investment signals India’s rise as a reliable technology partner for the world and will support the country’s move from digital public infrastructure to AI public infrastructure.
Puneet Chandok, president of Microsoft India and South Asia, said the company aims to translate India’s AI ambitions into “impact for every citizen.” He added, “Building on the $3 billion investment announced in January 2025, our new $17.5 billion commitment and deep partnership across India’s technology ecosystem are focused on turning India’s AI ambition into impact for every citizen.”
Expansion of AI and Cloud Infrastructure
A significant part of the investment will support the India South Central cloud region in Hyderabad, scheduled to go live in mid-2026. Microsoft said this will be its largest hyperscale region in the country, comprising three availability zones. The company will also expand existing regions in Chennai, Hyderabad and Pune.
Microsoft’s existing 22,000 employees across Indian cities contribute to product development, engineering, AI research and datacenter operations, the company noted.
AI Integration Into Public Platforms
The company also announced new AI integrations for the Ministry of Labour and Employment’s e-Shram and National Career Service (NCS) platforms. The initiative aims to benefit more than 310 million informal workers by supporting multilingual access, AI-assisted job matching, predictive analytics and automated resumé generation through Azure OpenAI Service.
It said that e-Shram, built on Microsoft Azure, has already helped expand social protection coverage in India from 24% in 2019 to 64% in 2025, according to ILO estimates.
Sovereign Cloud Offerings
The company also introduced Sovereign Public Cloud and Sovereign Private Cloud for customers in India. These offerings include Sovereign Landing Zones, governance controls, and support for connected and disconnected operations through Azure Local.
Additionally, Microsoft 365 Copilot will begin processing data within India by the end of 2025 for sectors requiring in-country data compliance.
Microsoft stated that its expanded infrastructure, AI adoption support and skilling programmes will help build a national ecosystem for “innovation, trust and opportunity,” positioning India to advance its AI capabilities at a population scale.
The post Microsoft Commits $17.5 Bn to Advance India’s AI Ambition appeared first on Analytics India Magazine.
