Рубрика: AI

Google’s Nano Banana Pro dropped into the world like a quiet update, and within hours, the internet had turned it into a chaos engine. It quietly reminds us about the rise of deepfake and how OpenAI’s Sora turned social media into an AI feed.

Interestingly, Nano Banana Pro comes just days after Google made Gemini free for all Jio users in India for 18 months. Now, every Indian has a tool to be an AI fraud creator.

Google’s model can rewrite anything inside an image, match handwriting, bend layout, and slip new text into old paper. While some are calling it magic, there are others who find it a menace. People are also using it to fake refund chats and produce doctored receipts faster than they can finish a cup of tea.

In a post on X, a user mentioned how someone’s Swiggy Instamart order of a tray of eggs came with one of those cracked. Instead of reporting it, “they opened Gemini Nano and literally typed: ‘apply more cracks.’ In a few seconds, AI turned that tray into 20+ cracked eggs — flawless, realistic, impossible to distinguish.”

The Instamart team issued a full refund thinking the photo is real. “Just pause and think about that. Our refund systems were built for a world where photos were trustworthy. But now they’re up against 2025-level AI — and they’re getting absolutely destroyed,” the user added.

Someone ordered eggs on Instamart and only one came cracked.
Instead of just reporting it, they opened Gemini Nano and literally typed:
“apply more cracks.”
In a few seconds, AI turned that tray into 20+ cracked eggs — flawless, realistic, impossible to distinguish.
Support… pic.twitter.com/PnkNuG2Qt3

— kapilansh (@kapilansh_twt) November 24, 2025

All’s Well?

The funny part is that this behaviour didn’t need a breakthrough. One user wrote “FYI: Photoshop has been available since 1987!” but Nano Banana Pro changed the mood because the friction is gone. The tool doesn’t ask for layers, brushes, or patience. It just edits the world inside your photo like it has lived there for years.

Paras Chopra, founder of LossFunk, bumped up the hype when he posted a picture of a math problem, which he fed to Nano Banana Pro and it solved it back onto the paper. He said, “Gemini solves problems on the image itself… these AIs are getting smarter and weirder!”

This is insane.
Gemini solves problems on the image itself.
Think how crazy this is: it has to somehow fit all the steps within the space provided using a similar font size as my handwriting.
These AIs are getting smarter and weirder! pic.twitter.com/Z3wr1vbHx6

— Paras Chopra (@paraschopra) November 24, 2025

That was the moment the tone shifted. This wasn’t a party trick, but a rewriting of trust. Even Andrej Karpathy, the founder of Eureka Labs, pitched in his thoughts.

He said that he has been thinking about how this shift breaks not just refund systems but entire institutions. He told a school board recently that “you will never be able to detect the use of AI in homework. Full stop.” He said all detectors fail in practice and in principle, and that schools now have to assume any work done at home uses AI.

Karpathy argued that this forces a complete reset. Grading has to move back into the classroom, where teachers can actually watch students work. He said the point is not to ban AI because it is too powerful and too permanent, but to make sure students aren’t helpless without it, comparing it to calculators.

Read: Indian Developers Rank #1 in Cheating

Meanwhile, a flood of social posts followed. A user posted a fake Swiggy refund chat made using Nano Banana Pro and got a reply that said, “That’s fraud. Should not be encouraged.” Another wrote “Evil,” while someone else replied, “Free money glitch.”

When proof becomes editable, trust leads to vulnerability.

The alarms reached insurance companies, delivery platforms, and anyone who handles handwritten paperwork. This also creates trouble for legal documents, including Aadhaar or PAN cards in India.

Harveen Singh Chadha from Sarvam AI also expressed concerns. “Nanobanana is good but that is also a problem. It can create fake identity cards with extremely high precision. The legacy image verification systems are doomed to fail,” he posted on X, while sharing examples of it.

nanobanana is good but that is also a problem. it can create fake identity cards with extremely high precision
the legacy image verification systems are doomed to fail
sharing examples of pan and aadhar card of an imaginary person pic.twitter.com/Yx5vISfweK

— Harveen Singh Chadha (@HarveenChadha) November 24, 2025

There was an odd mix of amusement and dread, as if people knew they were watching a system glitch in real time. On Google’s credit, the company has done its part.

All images produced by Google tools will continue to include SynthID watermarking. Users can now upload an image in the Gemini app and “ask if it was generated by Google AI,” based on SynthID signals. Free and Pro-tier images will also include a visible Gemini watermark, which will be removed for Ultra subscribers and Google AI Studio developers.

The company said the goal is to support transparency. “We believe it’s critical to know when an image is AI-generated,” Google said.

But That’s Not Enough

A few others pointed out that this is not the first time the world has faced this. Going back to the deepfake era, people were creating a slew of fake videos of celebrities and others, and even using fake voices for fraud.

Read: How to Prevent Another ‘Jamtara’

It is simple and the examples are right. Tools don’t create fraud. Intent does. Nano Banana Pro just removed the effort barrier. Ankush Sabharwal from CoRover.ai put it bluntly: “AI isn’t the culprit here; a few people have always found ways to cheat.”

“Earlier, it might have taken longer with tools like Photoshop, but AI makes it quicker. The real issue is the intent behind using AI for deceit,” he pointed to MeitY’s draft rule that suggests a 10% disclaimer on AI-generated content.

“Platforms generating AI content should include disclaimers, and removing them should have legal consequences,” Sabharwal said, adding that this can be handled under Section 318 of the Bharatiya Nyaya Sanhita and Section 65 of the IT Act.

Jaspreet Bindra, co-founder of AI and Beyond, said that more than the power of AI, Nano Banana Pro reveals how outdated our verification systems are — “both AI and human.”

“We have built a world that trusts photos, screenshots, and receipts as proof, but in 2025, proof can be manufactured in seconds. AI is no longer just assisting us, it is convincingly mimicking reality,” he said, while adding that the challenge is not that AI can fake cracked eggs, it is that our systems cannot tell real from fake.

Bindra said this suggests that the need for a human in the loop for verification will always exist; in the Instamart case, at the customer service end.

The reactions online kept spinning. Some laughed, some panicked. Some wanted watermarks, while others wanted rules. Some asked for the prompt.

The story here is not only Google’s tool. It is how fast society recalibrates itself when a new piece of tech knocks the floor out from under shared trust.

The irony is that most of these people are not trying to commit crimes. Some are just trying the tool for fun. Many will stop the moment they know it is illegal. But the first reaction is curiosity. The second is chaos. The third will need to be clarity. Until that arrives, Nano Banana Pro will keep doing what it does best. It will keep turning proof into a suggestion.

The post Nano Banana Handed a Billion Indians the Ultimate Fraud Shortcut appeared first on Analytics India Magazine.

SAP’s footprint in India is large enough to support multiple developments across its portfolio, backed by partnerships with Mahindra & Mahindra, Asian Paints, Wipro, Infosys, Vahdam, Ola, Wakefit, DeHaat, Jaquar and others.

In an exclusive interview with AIM, Michael Ameling, president of SAP Business Technology Platform and member of the extended board, says the story of enterprise AI started long before the current hype wave.

Ameling’s two-decade journey at SAP spans radio frequency identification (RFID) experiments, early cloud services and the company’s integration architecture. But the shift he sees today is centred on one idea: AI without the right data is meaningless.

“If you don’t have the right data, then AI is meaningless. Just like machine learning—if you don’t have the right training data, you cannot achieve your goal.”

This belief has shaped SAP’s architectural bets, especially its effort to position HANA Cloud and the Business Data Cloud (BDC) as a unified “database for AI”, a platform where structured, unstructured, vector and graph data co-exist without losing context.

Why Databases, Not Models, Define Enterprise AI

Ameling is direct about why SAP chose not to build its own large language model. The goal was flexibility, not competing in a model arms race. “We said, let’s build an architecture where we can exchange the model, partner with the best vendors, and keep maximum flexibility. That strategy paid off.”

That flexibility sits in SAP’s data layer. According to him, enterprise differentiation comes not from the model but from the context preserved inside business data.

He pointed to the industry’s habit of copying data across lakes and silos: “Companies copied their data into a lake, rebuilt everything, added huge integration costs—sometimes storing the same data three times in different silos.” BDC was created to counter this, offering a federated layer that avoids copying and preserves semantics across finance, procurement, HR and supply chain data.

SAP has also invested heavily in HANA Cloud. “I would claim this is a database for AI, because in a single database you have so many different engines—vector, graph, document store and relational store.” This multi-model approach allows retrieval, reasoning, analytics and transactional workloads to run without splitting the data stack. Some SAP customers are already building AI-driven sales order optimisations directly through the integration of HANA and BDC.

Partnerships with Snowflake and Microsoft Fabric are expanding BDC’s connectivity and reinforcing the company’s open-data philosophy.

India: The Engineering Centre Behind SAP’s AI Data Stack

Ameling is unequivocal about India’s importance, considering many of the core capabilities behind SAP’s data and AI architecture are built here. “A large portion—everything I’ve talked about—is delivered out of India.” This includes HANA Cloud enhancements, anomaly-detection features in the integration suite and several of SAP’s new AI agents. The India teams now cover everything from Kubernetes workload distribution to applied AI use cases.

He said, “We have the right talent… We can build full end-to-end solutions, from deep technical work to delivering AI use cases in hackathons.”

India is not just building SAP’s AI stack; it is adopting it faster than global peers. About 93% of Indian enterprises expect AI ROI within three years, and they invest more aggressively than the global average.

“An average business in India is spending 31 million dollars on AI,” he said. From startups to conglomerates, adoption spans procurement, HR, design, operations and sales.

While JK Cement used BDC and SAP’s business AI to cut procurement processing time by 50%, ABB deployed Joule for real-time insights. Meanwhile, Wipro uses it to support consultants in client engagements.

To Ameling, India is both a builder and a beneficiary of SAP’s “database for AI” vision.

A Future Built on Context, Not Just Compute

As the world moves towards agentic systems and autonomous enterprise processes, Ameling believes SAP’s enduring differentiator will be business context.

He highlights SAP’s deep understanding of how enterprise processes interconnect—and how its knowledge-graph layer captures that logic. This allows customers to use their data with immediate context, tailored to their functional needs. And much of that intelligence is being engineered—and rapidly adopted—in India.
“India has a very bright future… For us, it’s a very, very important market.”

In Ameling’s view, SAP’s AI era won’t be defined by a single model or agent but by the database that holds the world’s business logic, and by the countries building it at scale. India, he believes, is already central to both.

The post ‘If You Don’t Have the Right Data, Then AI is Meaningless’ appeared first on Analytics India Magazine.

Amazon has announced two developments outlining upgrades to its satellite connectivity programme and a significant expansion of AI infrastructure for US government agencies.

The company said it will roll out an enterprise preview of Amazon Leo, its low Earth orbit satellite service. At the same time, it also committed up to $50 billion to build new AI and high-performance computing (HPC) capacity for federal customers from 2026.

Amazon confirmed that the newly launched Leo Ultra antenna will offer download speeds of up to 1 Gbps and upload speeds of up to 400 Mbps. At the same time, the AWS investment will add nearly 1.3 GW of compute capacity across AWS Top Secret, AWS Secret and AWS GovCloud (US) Regions.

Both initiatives aim to support organisations that need secure, high-speed connectivity and advanced compute resources. AWS CEO Matt Garman said, “We’re giving agencies expanded access to advanced AI capabilities that will enable them to accelerate critical missions from cybersecurity to drug discovery.”

Amazon Leo is designed to extend high-speed internet to businesses and public sector entities operating in areas with limited network access. The company has more than 150 satellites in orbit as it moves from deployment to early commercial testing.

Chris Weber, vice president of consumer and enterprise business for Amazon Leo, said, “We’ve designed Amazon Leo to meet the needs of some of the most complex business and government customers out there.”

Amazon revealed the final production design of the Leo Ultra, describing it as its fastest commercial phased-array terminal. The antenna includes a custom silicon chip, full-duplex operation, and integration with enterprise networks. It supports applications such as real-time data processing and cloud connectivity.

The service will also connect directly to AWS through options such as Direct to AWS and Private Network Interconnect. The enterprise preview includes partners such as Hunt Energy Holdings across energy, aviation, farms, and logistics. Select customers will begin testing Leo Pro and Leo Ultra hardware.

The $50 billion investment will result in infrastructure construction beginning in 2026. The plan will expand access to services like Amazon SageMaker, Amazon Bedrock, Amazon Nova, Anthropic Claude, AWS Trainium chips, and NVIDIA AI systems.

The company said the investment will support missions in national security, scientific research, and autonomous systems. It plans to enable agencies to process large datasets, model complex scenarios, and shorten research timelines.

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The White House has unveiled a sweeping new national initiative, dubbed the Genesis Mission aimed at using AI to accelerate scientific research and innovation.

By tapping into the federal government’s vast trove of research datasets and high-performance computing resources, the mission seeks to transform America’s research infrastructure and secure technological leadership.

“This pivotal moment … requires a historic national effort, comparable in urgency and ambition to the Manhattan Project,” the order released by the White House stated, calling Genesis a coordinated effort to deploy AI-enabled models and agents for hypothesis testing, automated research workflows, and scientific breakthroughs.

Under the directive, the US Department of Energy (DOE) will lead the effort, building out what is called the American Science and Security Platform. This platform will provide unified access to supercomputers, AI modeling frameworks, secure scientific datasets, and tools for autonomous experimentation.

The order directs the DOE Secretary to identify and integrate computing, storage, and networking resources, including cloud-based systems and national laboratories within 90 days.

By 120 days, the Secretary must propose a portfolio of initial data and model assets; by 240 days, review lab capabilities for AI-augmented research; and within 270 days, establish a working prototype of the platform for at least one national science challenge.

More than two dozen “science and technology challenges” have been identified for the mission, including areas such as advanced manufacturing, biotechnology, critical materials, nuclear energy, quantum information science, and microelectronics.

President Donald Trump has assigned overall leadership of the mission to the assistant to the president for science and technology (APST), who will coordinate interagency efforts through the National Science & Technology Council (NSTC).

The APST is also tasked with guiding strategy, funding prizes, and establishing partnerships with national laboratories, universities, and private-sector entities.

Genesis is expected to mobilise America’s research and development assets including DOE labs, academic institutions, and major technology companies to train scientific foundation models and build agentic AI that can “test new hypotheses” and “automate research workflows.”

The mission explicitly aims to “dramatically accelerate scientific discovery, strengthen national security, strengthen energy dominance … and multiply the return on taxpayer investment,” the order declares.

In addition, the order establishes new mechanisms for external collaboration and funding: it calls for cooperative R&D agreements, prize competitions, and standardised data- and model-sharing frameworks. It also outlines rules for data governance, IP licensing, export controls, and security approvals.

“We will harness … the world’s largest collection of scientific datasets … to train scientific foundation models and create AI agents to test new hypotheses, automate research workflows, and accelerate scientific breakthroughs,” the order reads.

To evaluate progress, the DOE Secretary must submit an annual report to the President, detailing platform performance, lab integration, user engagement, research outcomes, and partner collaboration.

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Cognizant has introduced Resilient IT Operations, a new platform-powered offering designed to help enterprises modernise complex technology environments using automation, AI agents and advanced analytics.

The company said the solution aims to reduce “operational debt” and shift technology teams away from routine maintenance toward higher-value innovation work.

As enterprises expand multi-cloud systems, integrate legacy infrastructure with modern microservices, and deploy generative-AI-driven business processes, traditional IT operations models are becoming harder to sustain.

Fragmented workflows and rising costs are now limiting innovation and increasing operational risk.

Cognizant said the emergence of agentic AI, AI agents capable of autonomous and semi-autonomous task execution, provides an opportunity to redesign IT operations for greater resilience.

These agents can accelerate the development of self-healing systems, enhance day-to-day self-service capabilities and assist incident-management teams in resolving complex issues with reduced disruption.

“Enterprises today need intelligent IT operations that are not just efficient but also enhance business resilience across complex estates,” said Prasad Sankaran, president of software and platform engineering at Cognizant.

He said the new offering brings automation and AI “to centre stage,” enabling organisations to improve reliability and reduce costs while unlocking new opportunities for innovation.

According to the company, the platform is designed to help organisations prevent critical incidents; accelerate incident resolution; reduce operational debt; and increase automation coverage across IT operations.

Cognizant said Resilient IT Operations enables proactive issue detection, automated remediation and data-driven insights that help prevent future outages.

By automating repetitive tasks at scale, enterprises can minimise unplanned downtime and deliver more reliable services.

The solution consolidates Cognizant’s experience in IT operations with its AI-native platforms, partner ecosystem and emerging technologies.

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Meta is considering deploying Google’s Tensor Processing Units (TPUs) in its data centres from 2027, a move that could challenge NVIDIA’s longstanding dominance in AI hardware, according to The Information.

Meta is reportedly in talks to spend billions on TPUs, exploring both long-term deployment and the possibility of renting Google’s chips through Google Cloud as early as next year, reported The Information.

The discussions come as major AI developers look to diversify suppliers amid soaring demand and concerns over dependence on NVIDIA GPUs, which are the current industry standard for training and running large AI models.

Interestingly, Google’s latest model Gemini 3, was also trained on TPUs.

Alphabet shares rose as much as 2.7% in late trading following the report, while NVIDIA slipped by a similar margin, reflecting investor expectations of a potential shift in market dynamics.

If finalised, the Meta–Google arrangement would bolster TPUs as a credible alternative in high-performance AI computing. Google has already signed a separate agreement to provide up to one million TPUs to Anthropic.

With Meta’s capital expenditure projected to exceed $100 billion in 2026, Bloomberg analysts estimate the company could spend $40–$50 billion next year on inferencing-chip capacity alone, potentially accelerating demand for Google Cloud services.

TPUs, designed more than a decade ago specifically for AI workloads, have gained traction as companies evaluate customised, power-efficient alternatives to traditional GPUs. While NVIDIA still commands the vast majority of the AI chip market and AMD remains a distant second, TPUs are emerging as a strong contender, especially as companies seek to mitigate reliance on a single dominant supplier.

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Wealthy, a wealth-tech startup based in Bengaluru, has raised ₹130 crore in a Series B funding round led by Bertelsmann India Investments. The round also included participation from existing investor Alphawave Global, new investor Shepherd’s Hill, and a group of prominent tech entrepreneurs.

According to the press release, this funding will support Wealthy’s goal of advancing India’s rapidly growing community of mutual fund distributors (MFDs) by providing advanced AI-powered tools and digital infrastructure.

Founded by IIT and IIM alumni Aditya Agarwal and Prashant Gupta, the startup processes over ₹300 crore in transactions monthly. It works with over 6,000 mutual fund distributors, serving more than 100,000 clients across 1,000 towns, and recruits over 350 distributors every month. The platform currently manages ₹5,000 crore in client assets.

In 2022, Wealthy secured Series A funding, also led by Alphawave Global. Over the past three years, the company’s assets under management (AUM) have surged from ₹200 crore to ₹5,000 crore.

The startup operates 20 offices across India, with a strong presence in major cities such as Bengaluru, Mumbai, Hyderabad, Ahmedabad, Surat, Jaipur, Gurugram, Delhi, Faridabad, Ghaziabad, Lucknow, Kanpur, and Kolkata, supported by a team of over 250 members.

“India has a fundamental advice gap that technology alone cannot solve. LIC serves over 40 crore customers, yet mutual funds have only five crore investors. This gap exists because we have too few advisors, and the ones we have lack the tools to scale,” Aditya Agarwal, co-founder of Wealthy, said.

Wealthy’s AI-powered platform offers a complete 360° solution that combines investments, mutual funds, stocks, PMS, FDs, and fixed-income securities, with protection products such as term and health insurance. Clients and distributors benefit from dedicated apps that provide access to over 200 financial institutions.

“Less than 15% of Indian households have any exposure to the Indian equities market, either directly or indirectly. As India marches on its way to being a developed country, we believe this number will move closer to 60% and catch up with developed markets,” Rohit Sood, partner at Bertelsmann India Investments, said.

Distributors are equipped with AI-powered workflows for real-time alerts and client engagement, quick KYC onboarding, and enterprise-grade tools to enhance their digital presence. The platform also provides advanced analytics to track portfolio performance and understand client behaviour.

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It has been more than a year since the IndiaAI Mission was announced, yet the country awaits its homegrown LLM. While there have been efforts across compute, datasets and research collaborations, the Mission’s progress remains far behind global labs that are already rolling out next-generation models.

Google just launched its latest model, Gemini 3, which it claims outperforms OpenAI’s GPT-5.1 and Anthropic’s Claude Sonnet 4.5.

Speaking at the Bengaluru Tech Summit 2025, IndiaAI Mission CEO Abhishek Singh acknowledged that India continues to lag behind the US and China. While global players release new LLMs, the Mission remains focused on building its seven foundational pillars, which are compute capacity, IndiaAI Innovation Centre (IAIC), datasets platform AIKosh, application development initiative, startup financing, IndiaAI FutureSkills and safe & trusted AI.

On foundation models, Abhishek said the government is currently supporting 12 initiatives. One of them, a homegrown sovereign LLM being developed in Bengaluru by Sarvam AI, is slated for release in December and will be showcased at the IndiaAI Impact Summit in February, 2026. The model is a 120-billion-parameter foundation model trained on more than 17 trillion tokens, including 17–20% Indian data.

Frontier AI cannot be built on short-term funding cycles, according to Jacob Joseph, VP of data science at CleverTap, an all-in-one customer engagement platform. He told AIM that it needs “deep, patient capital,” especially when India’s R&D spending sits at roughly 0.6% of GDP, compared to the 2–3% committed by countries at the forefront of advanced AI.

Joseph added that training a world-class model is a years-long, multibillion-dollar journey, with substantial investment going into foundational research that may take time to show commercial payoff.

Meanwhile, Abhishek said the mission released its AI safety framework on November 5 and is trying to balance innovation with safeguards. “We are developing tools for detecting deepfakes or limiting AI-generated content,” he said, adding that these tools will be available on the IndiaAI platform.

With that structure in place, “we will be able to do much more to ensure that whatever AI we develop improves efficiency, productivity and the reach of services,” Abhishek said.

Sanchit Vir Gogia, CEO of Greyhound Research, was more direct. He told AIM, “India does not need more policy PDFs or GPU ribbon cuttings.” He argued that infrastructure must perform, not just exist, which means subsidised compute that is fast, reliable and tiered for real workloads.

According to him, India also needs to back national AI bets in sectors like healthcare, agri-tech, and financial inclusion with funding that runs from data curation all the way to deployment.

IndiaAI is a well-structured starting point, but its impact will hinge on “velocity, usability, and signal clarity,” Gogia said. While the mission lays out the right pillars across compute, data, fellowships and startup support, the real test will be whether these translate into tangible traction.

IndiaAI is trying its Best

“Our ecosystem is still too fragmented,” said Ashutosh Singh, co-founder and CEO of RevRag.AI, explaining why India still struggles with frontier AI research. “India has strong talent, but lacks the density, long-horizon R&D funding, and tightly integrated research groups needed for frontier breakthroughs. Our ecosystem is still too fragmented.”

At the Tech Summit, Abhishek said that the government is scaling up compute capacity, foundation model development and skilling initiatives under the national AI mission, as it prepares for larger investments in the coming months. Abhishek said the mission has acquired 38,000 GPUs, bringing down the effective cost to ₹65 per GPU/hour after subsidies.

He added that teams from BharatGen, IISc Bengaluru and IIIT-Hyderabad are also progressing on their respective models. “For foundation models, we provide 100% of the compute support that is required,” Abhishek said, adding that the goal is to build models trained on Indian datasets to reduce dependence on foreign models.

Notably, BharatGen has secured 13,640 H100 GPUs and close to ₹1,000 crore in funding, the single-largest allocation in the country. It already has a series of early releases under its belt — Param-1, a bilingual 2.9-billion-parameter model, Shrutam for speech recognition, and Patram, a vision-language model for document understanding. But scaling to a trillion parameters is a different order of challenge.

The Next Moves India Cannot Miss

Joseph said that for India to catch up would require more than hardware and infrastructure. India needs to give researchers the freedom to run ambitious, high-risk experiments “without friction,” and to build hubs where talent, compute, and capital come together with a shared purpose. “That’s how global labs operate,” he said, adding that building that kind of rhythm will take time.

Meanwhile, under the FutureSkills pillar, IndiaAI is offering fellowships to undergraduate, postgraduate and research students from all disciplines who take up AI projects.

“These fellowships are not limited to only engineering or science students,” Abhishek said, adding that they also extend to fields such as medicine, law, commerce and liberal arts. The mission is also exploring partnerships with industry to expand training programmes.

MeitY, under the IndiaAI Mission, has also launched ‘YUVA AI for ALL’, “a first-of-its-kind free course that introduces the world of AI to all Indians, especially the youth.”

Gogia said that India must become “the centre of gravity for AI researchers, not their backup plan.” He argued that this requires far more than fellowships. It demands world-class labs, stable infrastructure, academic freedom, and career paths that don’t push talent abroad.

If India can move these pieces together, Gogia said, it can run its own race; if not, it risks becoming merely a customer in someone else’s system.

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