United States President Donald Trump, on December 11, signed an executive order aimed at curbing state-level AI regulations and accelerating the creation of a unified federal framework.
The order, titled Ensuring a National Policy Framework for Artificial Intelligence, represents the administration’s most decisive move yet to preempt what it calls a “patchwork” of state rules that threaten US dominance in AI development.
In the order, Trump argues that the US is “in a race with adversaries for supremacy” in AI and that companies must be able to innovate “without cumbersome regulation”.
He directly criticises emerging state laws that seek to govern algorithmic discrimination, transparency or model outputs.
Citing Colorado’s recently enacted rules, the president claims such laws “may even force AI models to produce false results in order to avoid a ‘differential treatment or impact’ on protected groups”.
The executive order establishes a new AI Litigation Task Force inside the justice department, directing it to challenge state laws the administration views as unconstitutional or obstructive.
It also instructs the commerce department to publish, within 90 days, an evaluation identifying state AI laws that are “onerous” or inconsistent with federal policy.
Trump’s directive further calls on the Federal Communications Commission (FCC) and the Federal Trade Commission (FTC) to explore federal reporting and disclosure standards that could explicitly preempt conflicting state requirements.
“It is the policy of the United States to sustain and enhance the United States’ global AI dominance through a minimally burdensome national policy framework,” the order states.
The administration will also send Congress a legislative proposal establishing a federal artificial intelligence framework that overrides state regulations, except in areas like child safety and government procurement.
“We remain in the earliest days of this technological revolution,” Trump wrote, adding, “It is imperative that we act now to ensure that America wins the AI race.”
The order comes amid escalating friction between state governments and Washington over how AI should be governed.
In recent months, scrutiny from the states has intensified as 42 attorneys general, including those from Colorado, Florida, Massachusetts, Texas, Virginia, Washington and Illinois, warned that generative AI systems may already be violating consumer-protection and child-safety laws.
The bipartisan group has also demanded independent audits from Microsoft, Google, Meta and Apple, arguing that developers have not done enough to curb harmful or misleading outputs.
State legislatures have also moved aggressively to craft their own rules, creating the patchwork the White House says it is now trying to dismantle.
California passed its Transparency in Frontier Artificial Intelligence Act, which requires developers of large-scale AI systems to publish risk assessments and safety documentation.
Texas adopted a different approach, enacting criminal penalties for the possession or promotion of AI-generated obscene material involving minors.
The post Trump Cracks Down on State AI Regulations, Launches National Policy Push appeared first on Analytics India Magazine.
Indian companies have spent the last two years fielding bold claims about what AI can deliver. Every conference, pitch deck and internal strategy memo carries the same warning: AI adoption is no longer optional.
This urgency, however, has not always been matched by clarity. Many business owners and managers hear about ambitious use cases, yet struggle to see a clear path to implementation. As a result, they often fail to achieve the desired return on investment (ROI).
They also carry the belief that meaningful AI requires huge budgets and niche technical teams. This leaves SMEs unsure about where to invest and what returns to expect realistically.
A shift is underway. Instead of large standalone AI projects that demand parallel tech teams, businesses are turning to embedded capabilities within the tools they already use. Platforms such as Bitrix24 now offer summarisation, intelligent lead scoring and contextual recommendations directly inside existing workflows.
Integrated platforms are folding these features into their core systems so organisations can capture value without tearing their architecture apart. This marks a practical distinction. AI becomes useful when it augments processes rather than forcing companies to build a new operational layer dedicated to experimentation.
The Data Quality Gap
Every meaningful AI conversation eventually comes back to one unglamorous truth: models are only as reliable as the data they work with. Many firms believe they have data simply because they maintain CRM entries, spreadsheets or contact forms.
In practice, this information is often inconsistent, duplicated or incomplete. AI systems recognise patterns. When names vary, contacts repeat or histories are missing, the signals become unreliable. Even something a person would ignore, like a stray bracket or mismatched quote from a sloppy data transfer, can derail an automated process entirely.
For resource-constrained companies, the first step towards AI readiness is not a new model. It is an operational discipline. Standardised capture, consistent tagging, proper validation and regular hygiene create the foundation on which automation can function.
Modern CRM systems make this easier by enforcing structure. Bitrix24, for instance, offers duplicate detection, merge workflows, required-field rules and automation that fills or standardises fields during capture. Its activity log and telephony integrations consolidate calls, emails and chats into a single customer record so the system holds the full context.
With these basics in place, even modest automation produces real improvements in response times, conversion rates and error reduction. Data governance is not a technical footnote. It is the primary enabler of any scalable AI effort.
Fragmented Tools, Fragmented Value
Another barrier lies in the way many companies assemble their tech stack. Marketing teams use a content generator. Sales installs an analytics plugin. Customer support deploys a chatbot. None of these tools talk to each other. They create pockets of automation rather than an intelligent customer journey.
This fragmentation weakens ROI. A lead generated through marketing may not carry behavioural context into sales. A support ticket may not inform future product or marketing decisions.
For Indian businesses mindful of cost, adopting one specialised tool after another raises expenses and reduces the chance of system-wide improvement.
Integrated platforms solve this by merging communication, CRM, tasks and automation into one environment. When the entire workflow shares a common data model, AI can operate with continuity. Lead scoring can incorporate marketing signals.
Customer success tools update themselves after support conversations. Sales playbooks adjust based on outcome analytics.
The lesson is straightforward. AI delivers systemic improvement only when it operates on unified data inside connected workflows.
Anxiety About Automation
Technical barriers are only part of the challenge. Many employees fear that automation will replace them. This fear is not abstract. In Indian workplaces, concerns about job security are tangible. Teams often resist adopting tools they believe could make their roles redundant.
This leads companies to pay for AI features but never entirely use them.
Yet, practical deployments show that the most valuable AI does not replace judgement or relationship building. It removes repetitive chores—automating data entry, producing first-pass summaries of customer interactions or routing requests simply frees people to focus on work that requires nuance and experience.
Framing AI as support rather than displacement is essential. Teams adopt tools more readily when they see workload reduction in mundane tasks while retaining control over decisions.
Measuring ROI: Metrics that Matter
AI often enters organisations as an aspiration. Without clear metrics, it becomes a cost instead of an engine of efficiency. A metrics-first approach gives clarity. ROI should tie directly to operational change rather than abstract claims.
The most practical indicators focus on time saved, conversion lift, accuracy improvement, faster response times and revenue impact from AI-assisted engagements. Leading platforms now display these metrics on their dashboards. They translate automation into numbers that the business can act on.
Companies should define KPIs before deployment, run controlled pilots and compare post-implementation outcomes against baselines. If a gain cannot be measured, it cannot be managed.
A Pragmatic Roadmap to Adoption
Indian SMEs that want to move beyond hype benefit from a staged approach. High-leverage processes come first. These are repeatable tasks where automation cuts time to value, such as meeting summaries, lead qualification or ticket routing.
The next step is to get data in order with consistent fields, deduplication and tagging standards within the CRM.
After this comes tool selection. Integrated solutions that unify CRM, communication and workflow automation allow AI features to operate across the entire process.
A pilot with defined KPIs and the right internal champions sets the tone for scalable deployment.
Communication remains essential throughout. Positioning AI as augmentation builds trust and signals that the goal is human efficiency, not human replacement.
Practical adoption is a sequence of small improvements that compound over time. Features like automated lead scoring, contextual suggestions and instant summarisation are low-risk and high-reward. They deliver faster wins than grand AI overhauls.
The most effective AI is almost invisible. It blends naturally into workflows, improves reliability and shows its value through measurable change rather than loud announcements. It depends on disciplined data practices, unified systems and clear metrics. It succeeds when employees feel supported rather than sidelined.
Integrated platforms that weave AI into everyday tasks prove that companies do not need large specialist teams or inflated budgets to benefit. The businesses that succeed with AI will not be the ones chasing spectacle. They will be the ones stacking small, consistent improvements until they add up to real operational strength.
The post Beyond the AI Buzz: How Indian Businesses Can Use Realistic, Affordable AI Tools That Actually Work appeared first on Analytics India Magazine.
The duality of Indian cricket is hard to ignore. While money and opportunities flow under the high-mast floodlights of stadiums that host international ODIs and cash-rich cricket leagues, the real sport is played in the dusty district grounds and makeshift pitches, where players hone their talent with sweat and blood.
However, getting discovered in one of those cricket nurseries still factors more on chance than skill. A good performance might matter, but only if the right selector is watching.
Satyendra Kumar found himself on the wrong side of that uneven system. “I played eight years of cricket,” the 19-year-old said. “I was in the under-16 state team probables… but I didn’t get enough chances even being a left-arm fast bowler.”
Left-arm fast bowlers are rare in Indian cricket, but that didn’t guarantee opportunity. “There is no benchmark, no structure or no transparency in selections,” he recalled.
He kept pushing. He earned spots at special IPL camps with Royal Challengers Bengaluru (RCB), Delhi Capitals, and Lucknow Super Giants. But as his cricket career edged forward, life threw curveballs.
This experience inspired Kumar to create opportunities for others facing similar challenges, believing that if the system couldn’t be fair to him, it was time to build something new. In September this year, the teenager launched ScoutEdge as an AI-driven talent-scouting and video-analysis platform.
Building a Tech Backbone for Talent Discovery
Before starting up, Satyendra plunged into AI, realising Big Data and AI can assist scouts across India in overcoming biases related to geography, religion, background, or even physical appearance. Additionally, these technologies offer a cost-effective scouting solution, as travelling across the country can be expensive.
In just two months, he earned 84 Microsoft Learn AI badges through an upskilling programme offered by the tech giant.
He then became a familiar face at startup events and competitions, including Startup Singam (top 25 finalist), SRM Bootstrappers Research Council, Global Student Entrepreneurs Awards (top 3 finalist), CRFTHQ Fellowship, StudUp TBI, NextUnicorn Global Awards, ISB IVI, and media features like Sharks of India.
He is currently pursuing a Bachelor of Science in Data Science and Application at IIT Madras.
Over the last two years, Satyendra studied Python, MySQL, machine learning, and GenAI to architect the core of the AI platform. He also designed the database schemas for structuring player data, providing automated score computation pipelines, a dashboard for tracking growth, and an intelligent filtering system for athletes.
On the video analytics side, Kumar experimented with open-source frameworks OpenCV and MediaPipe for pose detection, AlphaPose for skeletal tracking, and YOLOv8 for real-time cricket action detection. Advanced deep learning frameworks like I3D and SlowFast enabled ScoutEdge to read motion across frames to classify bowling and batting techniques.
Budding players like Karan Kannan and Vaibhav Mishra swear by the platform’s possibilities.
Despite representing Andhra Pradesh at U-14, U-16, U-19 and U-23, and later joining Puducherry’s Ranji squad, Kannan hit the same invisible wall Satyendra once did. “Even if I was performing… I was not given that proper opportunity,” he told AIM.
ScoutEdge’s AI-driven insights gave him structured feedback, helping him identify strengths, address weaknesses, and train with greater purpose, he said.
AI Steps In Where Human Eyes Can’t Always Reach
ScoutEdge’s foundational idea is that technology can correct structural imbalance by producing consistent, objective assessments where subjective judgement once dominated. “We are mostly leveraging AI to get better insight for scouting,” Kumar explained. “We use deep learning, NLP, computer vision… to get better insight from the stats we have.”
The founder and CEO embedded this philosophy in the Player Growth Index (PGI), a multidimensional score that evaluates players through performance analytics, AI-driven video analysis and feedback processed using natural language processing, and fitness and mental resilience assessments.
For temporal analysis, understanding movement over time, video recognition frameworks like I3D and SlowFast allow the system to dissect a bowler’s run-up or a batsman’s backlift across frames.
Machine learning models like Linear Regression predict scouting index scores, while classification systems such as Random Forests and SVM (Support Vector Machine) bucket players into skill tiers. Together, these tools give ScoutEdge the analytical depth of a human coach paired with the precision and reproducibility of machine intelligence, the startup claims.
For players, this translates into meaningful, actionable insights. “Before ScoutEdge, it was very difficult for me to analyse what my strengths are, what my weaknesses are,” Kannan highlighted. “ScoutEdge changed this entirely.”
He adjusted his training based on what the platform flagged, “If I’m a little weak in a pull shot… if I could do that drill for 100 balls, now I would do it for 200.”
Mishra, an 18-year-old representing Bihar U-19, had grown up without access to even turf wickets. “Working with ScoutEdge has been a turning point for me. My training became smarter instead of just longer.”
The Chennai-based startup’s AI insights revealed nuances in his bowling patterns, such as seam position, release angles, run-up rhythm, and workload balance. “I’m hoping the platform keeps adding more tools, especially for bowlers, like advanced ball-tracking, fatigue prediction, and session planning,” he added.
For both players, ScoutEdge has become an accelerator—a structured, fair system they never had.
Funding, Scale, and Business Model
ScoutEdge’s traction has translated into significant financial backing. Through Tamil Nadu-based business reality TV show Startup Singam, three investors pledged ₹50 lakh each, totalling ₹1.5 crore. Two additional ₹25 lakh grants came from the Institute of Internal Auditor Delhi’s Ignite programme, followed by ₹10 lakh from Entrepreneurship Development and Innovation Institute Tamil Nadu, and a pending ₹40 lakh Genesis grant.
The startup is further supported by a looming six-year government scheme and incubated at IIT Madras, where Kumar can access research labs, technical support, and business mentorship.
The company’s revenue strategy is diversified.
Players access the platform through individual subscriptions for personalised analytics and improvement plans. The pricing starts from ₹199/month; however, coaches and academies can choose plans ranging from ₹999 to ₹2,499/month based on team size and analytics depth.
Meanwhile, scouts and teams can opt for custom enterprise plans featuring advanced dashboards and analytics that use a SaaS-based model tailored to talent-identification workflows.
The startup also has enterprise-scale contracts to enable leagues and franchises to integrate AI-enabled scouting into their recruitment pipelines. In addition, ScoutEdge is developing paid mentorship channels, trial facilitation services, and consulting offerings for emerging talent.
Currently, ScoutEdge has onboarded over 600 players from different regions and age groups, including district, state, and academy-level athletes. “We are onboarding more players every week as we prepare for a wider launch,” Kumar highlighted.
The startup’s 12-member team collaborates with over 20 academies and training centres, including the Sitamarhi District Cricket Association, the Dumra Cricket Academy, the Asha Indoor Academy, the Elite Sports Academy (Chennai), and the Nexus Cricket High-Performance Centre. It is also in early discussions with organisations like TNCA, BCCI units, and IPL teams like RCB and SRH to develop scouting and data-driven evaluation modules.
ScoutEdge’s key competitors in India include Vtrakit, which analyses cricket data for aspiring players; the Dartle App, a SaaS platform that enhances football academy operations and talent scouting; and Prorecruit India, which allows athletes to create digital profiles to improve recruitment opportunities.
Injury Prevention, Mental Resilience & Chatbots
ScoutEdge also positions itself as an athlete welfare platform. Its injury-prevention system allows players to log pain via a visual body map and receive targeted exercises, mobility routines, workload adjustments, and diet recommendations.
The platform includes a body-mapping system that allows players to indicate areas of pain or discomfort, rate their severity, and receive personalised recommendations. These suggestions include mobility routines, corrective exercises, workload adjustments, and sometimes dietary guidance to manage fatigue or overuse.
The Board of Control for Cricket in India (BCCI) uses injury and workload-tracking systems for national and under-19 players to prevent burnout among fast bowlers. Indian Super League clubs also use GPS vests and video analytics to monitor performance and develop strategies.
Additionally, the Khelo India initiative is creating a digital database of young athletes, recording their performance metrics to build a talent pool for future national teams.
Kumar is also building a conversational AI mentor designed to make expert guidance more accessible. The chatbot will eventually support multiple languages, allowing players across India’s diverse linguistic landscape to engage with frequently asked questions, analyse performance queries, and recommend drills or workload strategies.
Although still under development, it intends to democratise knowledge, giving disadvantaged players structured guidance once available only at elite academies.
ScoutEdge has also begun building a diverse mentorship ecosystem of over 50 people to give young players access to guidance that previously existed only within elite cricketing circles.
Among the mentors currently associated with the platform are Stephen Jones, a UK-based expert who has worked with IPL franchises like RCB and RR; Nishog Naik, widely recognised as India’s youngest scout; Mitans, who is part of the US national cricket setup; and Gor, a talent specialist connected with RCB.
Additionally, the startup is developing a mobile app, currently in beta and to be launched in early 2026.
A New Era of Discovery, Fueled by Stories and Precision
If ScoutEdge achieves its highest ambitions, cricket scouting in India and across the Indian diaspora could evolve into a system defined not by geography but by data.
Globally, sports analytics heavily rely on heat maps, biomechanics, action recognition, and fatigue modelling, and ScoutEdge signals India’s turn toward that future.
As Kannan said, “To be honest, this is just a start.”
The post How AI-driven Sports Tech Startup ScoutEdge is Democratising Athlete Scouting in India appeared first on Analytics India Magazine.
Global Capability Centres (GCCs) in India are entering a defining moment in their evolution. For years, they operated on human-driven models where people performed the critical steps and technology supported them. This aligns with the traditional Human-in-the-Loop (HITL) approach, where human oversight is essential for AI systems to function safely.
HITL now feels outdated. Advances in autonomous agents, enterprise-grade AI orchestration, and context-aware models have shifted the landscape. Among leading GCCs, Agent-in-the-Loop (AITL) is replacing HITL.
At the MachineCon GCC Summit 2025 held in Goa from Nov 29 to Dec 1, Jaywant Deshpande, Chief Solutions & Innovation Officer, Accion Labs, said, “This shift is not simply about replacing humans. It is about re-architecting how enterprises operate.”
As GCCs push for higher productivity and exponential scalability, they are discovering the limitations of human-dependent processes and the power of autonomous AI agents that don’t just assist but deliver end-to-end outcomes.
Why HITL Is No Longer Sustainable
The traditional HITL framework assumes that humans must check, validate, and correct the work performed by AI. This made sense when AI models were error-prone or lacked context. But in high-velocity GCC operations, where millions of transactions, tickets, lines of code, or data points flow every hour, humans appear to be the bottleneck.
Validation queues increase, QA cycles slow down automation, and the cost of human oversight rises linearly while AI output scales exponentially. Leaders across the ecosystem are observing the same phenomenon.
“We found that task-specific agents are better, faster, and 1000x cheaper than humans,” Jaywant mentioned.
This is not about replacing people. It is about repositioning them where their strengths matter most. Agents excel at repetitive, rule-driven work. Humans excel at strategy, judgment, and innovation.
As GCCs move towards AI-native operating models, keeping humans in every loop becomes inefficient, expensive, and unsustainable.
Task-Specific Agents
Unlike broad copilots, task-specific agents are tightly trained systems built for one job: code refactoring, test generation, compliance checks, document extraction, or infrastructure provisioning.
With specialized design, supported by validation agents and structured exception handling, they achieve extraordinary precision and speed.
“We migrated 2.3 million lines of code in under four months, 99% untouched by humans,” Jaywant added.
This level of automation was unthinkable a few years ago, yet it is already standard in advanced GCCs.
In the AITL model:
Agents execute work
Agents validate output
Only exceptions escalate to humans
By automating both task execution and quality control, GCCs are achieving throughputs that previously required hundreds or thousands of employees in HITL environments.
From Humans Checking AI to AI Checking Humans
The most profound shift in enterprise work is a reversal of responsibility. Instead of humans validating machine output, future GCC operating models will use AI to validate human work.
This change marks the arrival of the AITL era, where agents now handle 90%-99% of tasks, provide contextual recommendations, and flag anomalies only when human intervention is required.
“Humans transition from being the principal executors to becoming orchestrators and supervisors. This unlocks a workforce that is smaller but significantly more strategic. Only the GCCs that drive AI and innovation are going to succeed,” said Mandar Garge, SVP & Global Head of Strategy Consulting & Enterprise Transformation, Accion Labs.
Humans remain essential, but not for repetitive work, which often leads to fatigue, inconsistency, and cognitive overload.
Agents deliver:
perfect repeatability
real-time response
zero deviation
continuous execution
instant scale
This shift is not just a marginal efficiency gain but an exponential leap in capability.
For GCCs, the question is no longer whether agents will replace task-driven workflows, but how quickly they can be deployed.
In AITL models, humans focus on system design, policy setting, exception handling, strategic oversight, and innovation—areas where human talent creates real value, particularly in AI-first GCCs.
What GCCs Must Do to Prepare for Agent-Led Operations
A transformation of this magnitude requires more than technology. It demands new thinking, new architecture, and new operating models.
The most advanced GCCs are moving quickly on three fronts:
Automating with agents, not copilots Copilots assist individuals. Agents transform entire workflows.
Building context layers and Knowledge Graphs, not just data lakes Data alone is opaque to agents. Context enables accuracy, autonomy, and safety.
Designing AI systems with an engineering discipline A shared enterprise context to ensure the architecture stays reliable and scalable.
When Not to Use AI
“As far as possible, don’t use AI. Use code. Only use AI where nothing else works,” Jaywant added.
This mindset ensures that AI is applied where it delivers disproportionate value, rather than adding unnecessary complexity.
The GCC of the future will not be defined by real estate, scale, or headcount. It will be defined by AI maturity, particularly the ability to deploy autonomous agents that deliver outcomes at superhuman speed. HITL served the last decade of automation. AITL will define the next.
The post Human-in-the-Loop Is Out, Agent-in-the-Loop Is In appeared first on Analytics India Magazine.
Cognizant has begun work on an 8,000 seat campus in Visakhapatnam and opened an interim techfin centre that can host 1000 associates, marking its largest expansion move in Andhra Pradesh. The ceremony took place in the presence of Chief Minister N Chandrababu Naidu.
The company will invest ₹1,583 crore to build the 22 acre campus in 3 phases by 2033. Construction of the first phase starts in 2026 and will support 3000 associates when it opens in early 2029.
The next phases will take the total capacity to 8,000. The interim techfin centre will operate till the first phase is ready and will work on AI, machine learning, digital engineering and cloud solutions.
“We are proud to welcome Cognizant to Visakhapatnam, a landmark step in advancing our vision of transforming Andhra Pradesh into a world-class destination where global enterprises can build, innovate and grow,” Naidu said.
IT minister Nara Lokesh said the investment strengthens the city’s rise as a technology hub and opens new opportunities for local talent.
Cognizant CEO Ravi Kumar S said the expansion reflects the company’s confidence in the city’s potential and talent pool. The centre includes a client experience space and collaborative areas for teams.
Cognizant has added new delivery centres in Bhubaneswar, Indore and GIFT City since 2024. India remains a key part of its global delivery network, with existing hubs across major cities including Bengaluru, Chennai, Hyderabad, Mumbai and Pune.
Just recently, Cognizant also opened a new AI Lab and a Cognizant Moment Studio in Bengaluru, deepening its India presence as the company accelerates its $1 billion generative AI investment plan announced in 2023.
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2025 was a breakout year for NVIDIA. The company became the first ever to hit the $5-trillion market cap, cementing its role as the backbone of the global AI infrastructure boom.
This year, NVIDIA expanded far beyond chips, backing more than 50 startups and major tech players with multi-billion-dollar commitments. Its investments span AI research, cloud infrastructure, autonomous systems, scientific computing, and even fusion energy.
Here’s a breakdown of where NVIDIA’s investment interests lie and which companies it aggressively backed this year.
OpenAI
OpenAI has been one of the headline partners for NVIDIA. In September 2025, the two companies announced an investment framework under which NVIDIA intends to invest up to $100 billion in OpenAI, starting in 2026. The capital infusion will help build and deploy at least 10 gigawatts of NVIDIA AI data centre systems.
The ChatGPT maker expects the first phase of data centres to come online in the second half of 2026.
Synopsys
In December 2025, NVIDIA announced a $2 billion equity investment in Synopsys, a semiconductor EDA (Electronic Design Automation) and IP leader, tied to a broad multi-year partnership.
This deal goes beyond typical venture financing. It integrates NVIDIA’s accelerated computing into design tools used across chip and system engineering for faster simulation and shorter design cycles.
Both companies said the partnership addresses rising workflow complexity, higher development costs, and pressure to shorten time-to-market across sectors, including semiconductors, aerospace, automotive, and industrial engineering.
Intel
NVIDIA made a major move by investing $5 billion in Intel through the purchase of common stock, giving it a significant equity stake in one of its longtime competitors.
Under the agreement, Intel will develop custom x86 CPUs for NVIDIA to use in its data centres, while also producing x86 system-on-chips that integrate NVIDIA RTX GPU chiplets for next-generation personal computers.
Nokia
NVIDIA announced a $1 billion investment in Nokia in October to support the development of AI-driven 5G and 6G networks, fuelling the chipmaker’s expansion into AI-focused telecom infrastructure. As part of the partnership, Nokia will integrate NVIDIA’s CUDA platform—a parallel computing platform and API model—into its radio access network software.
As 6G standards take shape, combining NVIDIA’s compute platforms with Nokia’s RAN expertise could accelerate the shift toward AI-native wireless networks.
Reflection AI
NVIDIA invested in Reflection AI as part of a huge $2 billion funding round that pushed the New York–based startup’s valuation to about $8 billion.
Reflection AI, founded by former DeepMind researchers, is building advanced AI systems that automate software development and other complex engineering tasks. Its models use large-context reasoning and agent-style workflows to solve complex autonomous coding challenges.
Mistral AI
Mistral AI, a leading European generative AI company, was part of NVIDIA’s investment portfolio in 2025. The French startup raised Series C funding of €1.7 billion (≈$1.99 billion), with NVIDIA participating in the round.
The companies also partnered to launch a new family of open source models.
Mistral’s models compete with major global LLM players and reflect NVIDIA’s strategy of supporting diverse AI model ecosystems across geographies.
Thinking Machines Lab
Thinking Machines Lab, an AI startup founded this year and led by former OpenAI CTO Mira Murati, raised a massive seed funding round of about $2 billion at a $12 billion valuation, with participation from heavyweights including NVIDIA, AMD, Cisco, and Jane Street.
The San Francisco-based startup is working on new kinds of AI systems that can handle many different tasks by combining advanced models with new system designs. The startup aims to build more reliable, safe, and accessible AI, unlike autonomous systems.
CoreWeave
NVIDIA increased its stake in CoreWeave, reaching about 24 million shares worth $3–4 billion by mid-year. This made CoreWeave one of NVIDIA’s biggest investments.
The stake grew from 17.9 million shares after the company’s March IPO to 24.28 million by August, as NVIDIA secured a key customer and GPU cloud provider as AI demand rises.
The investment sits alongside a $6.3 billion cloud capacity backstop deal signed in 2023 but revealed in September this year. It obligates NVIDIA to pay CoreWeave through 2032 for any unsold data centre capacity.
Perplexity AI
NVIDIA, among other ventures, backed Perplexity AI in its $100 million financing that skyrocketed the AI search startup’s valuation to $18 billion.
This followed NVIDIA’s initial 2023 investment, with the chip giant joining heavyweights like SoftBank Vision Fund 2 and NEA to fuel Perplexity’s growth, including a massive $500 million infusion in late 2024.
NVIDIA skipped the follow-on September $200 million raise at $20 billion valuation, in September this year, but ties remain strong through integrations in its sovereign AI projects for multilingual search capabilities.
Wayve
Wayve, a UK-based self-learning autonomous driving AI startup, signed a letter of intent with NVIDIA for a $500 million investment.
Wayve focuses on developing generalisable driving skills via machine learning. NVIDIA’s involvement supports AI compute inside vehicles and in cloud training.
Lila Sciences
Lila Sciences, which works on scientific superintelligence and AI-driven laboratory automation, raised $115 million in an extension round that saw participation from NVIDIA’s investment arm, NVentures, bringing its valuation to more than $1.3 billion.
This investment extends NVIDIA’s reach into AI-augmented scientific workflows, where massive datasets and complex models accelerate discovery in chemistry, materials science, and biology.
Commonwealth Fusion Systems
NVIDIA participated in an $863 million funding round for Commonwealth Fusion Systems, a nuclear fusion energy startup.
The investment supports the US startup in achieving commercial fusion power by the 2030s through its GPUs that offer high-performance computing for simulations.
Figure AI
NVIDIA is an investor in Figure AI, a US robotics company developing AI-powered humanoid robots to perform physical tasks in industrial, commercial, and domestic environments.
The company first raised major funding in a large $675 million round in February last year, with participation from NVIDIA, along with Microsoft, Jeff Bezos, and other technology investors.
This year, Figure AI continued to attract investment as it expanded its technology lineup, including its Helix AI system and BotQ manufacturing facilities, and reached a $39 billion valuation in a Series C funding round, with NVIDIA participating alongside other major partners.
The post The Top 13 Companies NVIDIA Bet Billions On in 2025 appeared first on Analytics India Magazine.
The United States and eight partner countries have launched the Pax Silica Initiative to build a secure and innovation-driven global silicon and AI supply chain.
The initiative brings together Japan, South Korea, Singapore, the Netherlands, the United Kingdom, Israel, the United Arab Emirates, and Australia. They plan to coordinate on critical minerals, semiconductors, AI infrastructure, energy, logistics, and manufacturing.
The initiative was launched at the inaugural Pax Silica Summit and outlined the coordination, why it is needed, and what actions will follow.
Pax Silica leaders said the goal is to reduce coercive dependencies, support trusted technology, protect critical materials, and enable partner nations to develop and deploy AI at scale. The US said countries have “affirmed a shared commitment to pursue projects to jointly address AI supply chain opportunities and vulnerabilities”.
The initiative responds to rising demand from partner countries for deeper economic and technology cooperation with the ỦS. Officials emphasised that AI is reshaping the world economy and will drive new demand for minerals, semiconductors, energy systems, and infrastructure.
Partner countries plan to work together on semiconductor design, fabrication and packaging, logistics, compute systems, minerals refining, and power generation. They will also explore joint ventures and co-investment opportunities, and seek to protect sensitive technologies from “undue access or control by countries of concern”.
The United States said its diplomats have been instructed to turn summit discussions into specific actions. According to the release, the US Under Secretary of State for economic growth, energy, and the environment, Jacob Helberg, has directed teams “to operationalise this summit’s discussions through identification of infrastructure projects and the coordination of economic security practices”.
The announcement noted that the countries will also “build trusted technology ecosystems, including ICT systems, fibre optic cables, data centres, foundational models and applications.”
Pax Silica takes its name from the Latin pax, meaning peace and stability, and silica, the base compound refined into silicon for computer chips. The initiative aims to unite countries that host major technology companies and investors to build a “secure, resilient, and innovation-driven ecosystem” across the global supply chain.
The post US Leads Pax Silica Initiative to Secure Global Silicon Supply Chain appeared first on Analytics India Magazine.
Indian enterprise AI is entering a new phase. For two years the narrative has been full of optimism, swelling venture capital, and a rush of pilots across sectors.
The country is still bullish on AI, but its enterprises are beginning to treat this technology like a true business asset. Leaders want results, not promises. Investors want resilience, not showmanship. Startups want customers who stick, not just early demos. CIOs want tools that work in their industry, not broad platforms that promise magic.
This shift is expected to test the industry’s confidence and define the next year for enterprise AI.
A sense of inevitability hangs in the air. A LinkedIn and Microsoft survey shows that 93% of Indian business leaders plan to deploy AI agents in the next 12 to 18 months. This is not tentative exploration, but a flood. Local experts see this acceleration as the result of long term preparation.
Kalyan Kolachala, MD at SAIGroup, a global enterprise AI leader, says, “India is very well prepared for growth in AI investments.” He points to a young talent base, cost advantages, policy support and major bets such as the $1 billion AI innovation centre in India. These factors indicate the country’s strides towards becoming an AI hub.
The demand is rising inside the enterprise and the supply side is maturing simultaneously.
A Filter, Not a Freeze
The course correction will begin inside India’s startup ecosystem. After an intense surge of AI funding through 2025, investors are preparing for a slower, more deliberate year. Sanchit Vir Gogia from Greyhound Research calls it a shift from high velocity to high conviction.
The tilt is already visible. Indian AI startups raised nearly $780 million in 2024, a 40% jump over the previous year, but early stage investments fell 37% as funds placed their trust in companies with proven traction. Greyhound’s data suggests that term sheets in 2026 will be tighter. Milestones will be stricter, and performance covenants will become common.
Greyhound sums it up: “This isn’t a freeze. It’s a filter… By 2026, the real signal will be resilience, not rhetoric.”
This discipline is being felt inside enterprises as well. The age of pilots with fuzzy metrics is ending. Many companies already treat AI as a governed asset with the same seriousness as ERP or CRM systems.
AI projects are now expected to come with a clear return on investment, transparent audit trails and continuous monitoring. CIOs are setting up internal policy boards that work with compliance and legal teams to manage error rates and risk. Greyhound captures the mood well: “Either AI earns its place on the revenue sheet, or it gets demoted to the backlog.”
Srinivas Reddy, senior vice-president and head of EPAM India, a global provider of software engineering and digital transformation services, agrees with this. According to him, the most important enterprise buzzword will be AI-native enterprise: not as a good to have, but as an operating reality. This will be the year organisations move from asking “how do we use AI?” to “how do we run the business with AI?”
AI-Native Enterprises
“Now that AI has entered the revenue sheet, it will slowly make strides towards an ‘innovation spend’ in 2026,” Reddy says. “Organisations will look at it as a core business capability, just like cloud, security or data platforms. Boards will measure AI by impact: revenue contribution, time-to-market reduction, engineering productivity and operational resilience.”
Reddy says that EPAM expects four focus areas to dominate conversation and investment: AI-native engineering, agentic workflows, enterprise-grade GenAI copilots and responsible AI at scale.
The idea of AI as a playground for experimentation is fading. In 2026, these systems must tie themselves to revenue, cost savings, or compliance, or step out of the way. Sustainability pressures are also reshaping decisions. Energy firms now plan their training workloads around renewable power availability. Boards are tracking model right sizing as a serious metric.
Indian IT firms have also called out their increasing AI ROI from investments. More clear revenue might finally be visible across all firms.
What is most striking is that enterprises are no longer buying AI as a general purpose tool. They want vertical specificity. Across banking, manufacturing, healthcare, retail and logistics, the demand is rising for tools that come pre aligned with sector knowledge.
A bank does not want to spend months training a model on KYC. A hospital does not want AI that cannot show how it arrived at a diagnosis. A factory wants quicker throughput, not a platform that needs a year of integration. This is why vertical AI is expected to dominate the coming year.
Reports from the last cycle already show sector specific gains in areas like fraud detection, diagnostic imaging, and yield optimisation. Greyhound notes that enterprises have grown wary of platforms that offer everything and prefer solutions that solve measurable problems in their industry.
The next wave of winning startups will be the ones with deep domain insight, not the ones with the widest pitch decks.
“Vertical AI will be a major focus area. Sectors like legal, healthcare, insurance and security are full of delays, manual work and large backlogs,” Ujwal Sutaria, founder and general partner at TDV Partners, an early stage venture firm, said. “India has millions of pending court cases and an overburdened healthcare system. AI can help speed up processes, reduce errors and improve access.”
Sutaria said that startups building in India for the world will also become more prominent. “Whether it is in AI, financial services or consumer brands, Indian companies have a strong opportunity to go global. Just like India became the IT services hub for the world, it has the potential to become a major AI hub,” he said.
Sutaria said that the funding climate for 2026 carries a sense of disciplined optimism. After the turbulence of 2023 and 2024, India’s startup world is entering a rebalancing year where money moves toward models that work, profits matter more than breakneck expansion, and founders with a record of building steady companies get the advantage.
These pressures vary by industry, but all of them point in the same direction. Banks now insist on usage based contracts and full model traceability. Hospitals demand interoperability, explainability and clear lines of liability. Manufacturers want payback in two or three years, not distant promises.
Energy companies tie AI operations to renewable goals. Retail wants systems that work instantly with existing supply chains. Many old style software contracts are being reopened and renegotiated because the expected AI gains have not materialised. The theatre of transformation is over. An AI product that cannot move a central KPI will have no place in the contract.
What About Sovereignty?
This maturity of enterprise AI is also redefining India’s approach to AI sovereignty. The debate is not about isolation, but pragmatism.
Vikas Singh, chief growth officer at Turinton, a business consulting and services firm, captures this tension with clarity. “Sovereignty in AI is a legitimate concern, but we already have a proven playbook right here in India. Look at IT services. That industry didn’t win by reinventing the wheel or building inferior alternatives. It won by leveraging global best practices, assembling them smartly, and solving complex enterprise problems at scale.”
Singh adds that the real question is not whether a tool is Indian, but whether it solves the business problem better. Differentiation, he says, should come from architecture and domain expertise. He calls for pragmatism rather than protectionism. “We don’t need to reinvent foundational AI models. We need to build platforms that solve enterprise complexity better than everyone else.”
Jaspreet Bindra, co-founder of AI and Beyond, a firm providing AI and tech literacy to organisations, shares a similar view, while adding a warning. “India’s push for AI sovereignty is both timely and strategic, but it must not come at the expense of quality or innovation.”
He says sovereign models are making progress in linguistics and culturally relevant datasets, but parity is still some distance away. And that is fine because every ecosystem matures gradually. The danger, he says, lies in forcing enterprises to use local tools that are not ready. “The real win for India will be when sovereign AI and world class performance become the same thing.”
Indian enterprises will adopt AI faster than many regions because these models will be fit for purpose and cost efficient. The only condition is that the ecosystem must focus on depth, not just announcements.
India enters 2026 with clear advantages. Nearly all major enterprises have already begun their AI journeys. The government is pushing supportive policies. Talent is abundant. Data centres are expanding.
Global and local players are investing at record scale. The coming year will not be defined by how many pilots begin. It will be defined by how many will scale, survive scrutiny and prove their value.
Indian enterprise AI is growing up. The optimism is still around, but it now rides on discipline. That combination is what will drive the country’s next leap.
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