Год: 2025

OpenAI president and co-founder Greg Brockman announced that Sachin Katti, a senior technology leader formerly at Intel, is joining OpenAI.

Brockman shared the update on X, saying he is “incredibly excited to work with him on designing and building our compute infrastructure, which will power our AGI research and scale its applications to benefit everyone.”

According to Reuters, Intel CEO Lip-Bu Tan will now oversee the chipmaker’s AI and advanced technologies efforts following Katti’s departure. “We thank Sachin for his contributions and wish him all the best. Lip-Bu will lead the AI and Advanced Technologies Groups, working closely with the team,” Intel wrote in a statement.

Katti previously served as senior vice president, chief technology and AI officer, and GM of the Network and Edge Group (NEX) at Intel Corporation, where he led the company’s overall AI strategy, product roadmap, and research efforts through Intel Labs. He was also responsible for strengthening Intel’s engagement with startups and developers, as well as advancing networking and edge technologies.

Before joining Intel, Katti had an accomplished academic career as a professor of electrical engineering and computer science at Stanford University, conducting pioneering research in wireless communications, networking, and applied coding theory.

His work earned him several honours, including the ACM Doctoral Dissertation Award (honourable mention) and the William Bennett Prize for Best Paper in IEEE/ACM Transactions on Networking.

Beyond academia, Katti is a successful entrepreneur and industry influencer. He co-founded Kumu Networks, known for its breakthroughs in self-interference cancellation technology, and later Uhana, which built AI-driven solutions for mobile network optimisation and was later acquired by VMware.

Katti holds a PhD in electrical engineering and computer science from MIT and a BTech in electrical engineering from IIT Bombay.

The post Intel AI Chief Sachin Katti Joins OpenAI appeared first on Analytics India Magazine.

India’s strides in Artificial Intelligence (AI) are often described in terms of venture funding, unicorn valuations, or the global race to access GPU clusters. But, beneath these headlines lies a quieter, foundational movement, one powered not by governments, but by private startup accelerators.

These accelerators are rapidly becoming the unsung infrastructure of India’s AI ecosystem, filling critical gaps that money alone cannot solve.

As compute costs soar, regulatory frameworks lag, and public infrastructure remains strained, private accelerators are stepping in to democratise access to compute, mentorship, and enterprise markets.

Their role is becoming crucial in ensuring that AI innovation in India is not restricted to well-funded founders in metros, but is accessible to the country’s broad and diverse talent pool as well.

Clarity Before Capital

While most accelerators position themselves as funding pipelines, founders increasingly seek something deeper, execution support.

As Manas Pal, cofounder of PedalStart, puts it: early-stage startups “don’t fail because they run out of money. They fail because they lose clarity.” He notes that the “biggest gaps… lie in problem validation” and the absence of founder-problem fit.

“At PedalStart, our belief is simple: clarity before capital, and execution before valuation,” he adds.

For AI startups, these gaps are amplified. GPU costs in India can be prohibitive; NVIDIA A100 instances often cost three to five times more in India than in subsidised US or EU zones. Access to high-quality domain data and regulatory clarity in sectors such as fintech, health, and legal only adds to the friction.

This is why accelerators are becoming infrastructure builders.

One of their crucial contributions has been democratising access to compute credits and cloud architecture support.

InsightAI, an AI-driven AML and fraud-detection startup accelerated by PedalStart, emphasises how transformative this was.

“Through PedalStart’s network, we secured substantial AWS and Google Cloud credits, which enabled us to run large-scale model training and experimentation without worrying about compute costs,” said Akash Chandra, co-founder of InsightAI. For early AI teams, this can shave lakhs off burn rates and drastically accelerate iteration cycles.

This model is being replicated widely. According to Inc42’s 2024 Startup Report, a large percentage of private accelerators now offer compute support. Cloud providers, eager to establish early loyalty among AI-native founders, have likewise expanded credit limits. AWS Activate offers up to $100,000 in credits, and Google for Startups offers up to $350,000 over multiple stages.

Historically, India’s startup ecosystem has been deeply metro-centric. But private accelerators are broadening the map.

As Pal explains, “One of the biggest gaps … isn’t access to capital, it’s access to visibility.” To counter this, PedalStart now runs community programs across Lucknow, Coimbatore, Jaipur, and Chandigarh. This outreach has already created what Pal calls “a ripple effect that can transform entire regions,” particularly for talented founders who lack access to investors or exposure.

However, in 2024, the creation of new tech startups in India increased by 2.1 times compared to 2023, with emerging hubs continually expanding their share. The Indian ecosystem now has over 800 incubators and accelerators, reflecting a growth of approximately 1.5 times since 2019, according to a NASCOMM report.

Physical Hubs as Execution Engines

To overcome infrastructure inequality, private accelerators are also building physical spaces.

PedalStart’s Innovation Hub in Bangalore, free for portfolio startups, is more than a co-working space. It is a site for what Pal describes as “spontaneous discussions, rapid problem-solving, live investor sessions, and startup events that thrive in shared environments.” A second hub in Gurugram extends this model to the Delhi-NCR region.

For founders outside metros, these hubs level playing fields: “A founder from Jaipur in our portfolio gets the same workspace access, mentorship, and ecosystem exposure as someone in Bangalore,” says Pal.

Acceleration is increasingly evolving into a full-stack shared-services ecosystem. Pal explains that PedalStart didn’t wait for this trend. “Most early-stage founders don’t just need capital but also hands-on execution support across areas like legal, finance, product, growth, and hiring… our ecosystem reflects that vision, a shared backbone of support where founders, mentors, and investors don’t just connect, but actively co-build.”

This model is particularly effective for AI startups facing specialised compliance burdens in sectors like fintech, insurance, or healthcare.

Public accelerators, while well-intentioned, often operate at a slower pace. Many founders describe long wait cycles, procedural mentorship, and delays in fund disbursement.

InsightAI experienced this firsthand: “Our startup was selected for the Startup India Seed Fund… however, the fund disbursement process faced repeated delays and… the allocation never reached us,” Chandra says.

The contrast with private acceleration was stark. “PedalStart has been extremely agile… their approach is hands-on and execution-driven,” he adds.

Chandra believes that the balance between public and private accelerators in India’s AI ecosystem is evolving, with a current preference for private programs. Public accelerators, although well-intentioned, often lack flexibility and have slower turnaround times.

In contrast, he says, private accelerators like PedalStart are results-oriented and collaborative, acting as an extension of our team to provide hands-on support with go-to-market strategy, technology, and partnerships, beyond funding.

Where Speed Meets Regulation

In highly regulated sectors, accelerators serve dual roles, speeding up execution while enabling compliance.

Aditya Pandranki, founder and CEO of Doqfy, a global contract lifecycle management SaaS platform, describes private accelerators as essential for iteration speed: “Private accelerators give us the agility to experiment, validate fast, and build investor and customer networks quickly.”

But he notes that legal tech also requires alignment with frameworks like eSignatures, KYC, and data localisation. Hence, “government-backed accelerators become invaluable… They open doors to policymakers, regulatory sandboxes, and institutional buyers.”

Additionally, Doqfy’s hybrid experience with Turbostart and Google for Startups Accelerator demonstrates this complementary model, where Turbostart shaped their “enterprise go-to-market strategy and compliance-first scaling approach,” while Google provided cloud architecture and secure API mentorship.

This momentum signals a maturing ecosystem where accelerators are no longer optional, they are indispensable infrastructure for AI innovation.

Startup India has made significant progress in funding, yet access to capital remains a major hurdle, especially for early-stage entrepreneurs in tier 2 and 3 cities. Despite the recent 14% funding increase in 2024, startups still rely on private capital, with venture capitalists hesitant to invest outside major metropolitan areas.

As the public ecosystem scales its own initiatives, the hybrid model emerging between private agility and public legitimacy may well become the backbone of India’s AI transformation.

The post As Public Support for AI Startups Lags, Private Accelerators Take the Lead appeared first on Analytics India Magazine.

Pharmaceutical companies often find themselves at a crossroads with every new technology—eager to lead, yet cautious about its reliability.

AI promises faster drug discovery, smarter clinical trials and more personalised patient care. But as regulators like the FDA roll out detailed guidance on AI and machine-learning-based medical devices, the industry faces a sharper question: how to innovate at speed without tripping over compliance?

According to Manish Mittal, managing principal and India business head at Axtria, the answer lies in embedding compliance into the DNA of AI programmes rather than treating it as an afterthought.

“Compliance should be baked in, not bolted on at the end,” he said. “Doing so not only reduces duplicated work and accelerates approvals, but it also safeguards patient safety and trust—two assets regulators now treat as non-negotiable.”

The FDA, along with its global counterparts, has moved from broad principles to lifecycle expectations. Frameworks like Good Machine Learning Practices, predetermined change control plans and laws such as the EU AI Act have raised the bar for everyone. “AI projects can no longer be run like R&D experiments,” Mittal emphasised.

Building Compliance and Trust Together

AI in pharma needs enterprise-grade governance, not lab-style exploration. From day one, regulatory, clinical, data science, quality and legal experts must work as one team. “Cross-functional AI product teams—bringing together regulatory, clinical, data science, quality and legal expertise—help ensure compliance and innovation evolve together,” Mittal said.

Automation plays a significant role. Version-controlled datasets, continuous integration pipelines, and secure audit trails make regulatory reviews faster and cleaner. Early regulator engagement, through pre-submission meetings or sandboxes, helps prevent last-minute obstacles. Post-market analytics close the loop by tracking data drift and safety issues, keeping systems accountable long after deployment.

Still, technology alone doesn’t create trust. As Mittal pointed out, trust in AI rests on three pillars: privacy, fairness and accountability. For pharma, these are not optional values—they’re the foundation of adoption.

Privacy begins with clarity on data rights and obligations. Companies must map datasets to consent requirements, conduct impact assessments, and design privacy-first architectures using pseudonymisation, encryption, and federated learning to limit exposure. Accountability is built through transparent governance, independent audits and clear consent protocols that make patients partners, not subjects.

“If you want people to trust a new medicine, you show them it’s safe. AI is no different. We have to show, in plain sight, that it’s fair, protects privacy and helps doctors make better choices, not mysterious ones,” Mittal said.

Firms that do this well can even turn trust into an advantage—by publishing transparency metrics or offering privacy-preserving deployment options, they can signal leadership in ethical AI.

Fairness is Non-Negotiable

Bias is the biggest ethical fault line in AI-driven healthcare. Models trained on skewed data can harm underrepresented patient groups. Mittal insisted on diverse datasets that reflect real-world populations across ethnicity, gender, age and income. Validation must go beyond accuracy—testing performance across subgroups, monitoring algorithmic drift and conducting fairness audits regularly.

“AI learns from what we feed it, so if we only show it one kind of person, it won’t know how to help everyone. The goal isn’t just to make things faster; it’s to make them fair for every patient,” Mittal added.

Fairness also depends on openness. Publishing methodologies, validation processes and data sources allow scrutiny and correction. Involving underrepresented communities in development makes systems more relevant and inclusive.

Human Oversight Anchors It All

Generative AI is now being used in clinical trial design—optimising protocols, selecting sites and identifying patient cohorts. It can cut costs and timelines but raises new ethical challenges. AI must remain under human supervision. Ethics committees and investigators should assess every AI-driven recommendation to protect patient safety and scientific validity.

Mittal stressed that efficiency should never outrun ethics. “AI outputs need human oversight,” he said. “Patient-centricity must guide every step, safeguarding autonomy and informed consent.”

Independent oversight from review boards and safety monitoring panels ensures AI supports, not replaces, human judgement. Training clinicians to interpret AI outputs and understand system limits is equally vital.

One Compliance Standard, Many Jurisdictions

The lack of global alignment adds complexity. The EU AI Act, US FDA guidance, and other national rules differ, creating uncertainty for cross-border trials and devices. Mittal advises aligning with the toughest standards available.

“Every country has its own rulebook for AI, but patients everywhere deserve the same care. If we build AI safely enough for the toughest rules, we build it safely enough for everyone. Good governance is the confidence that your AI can stand up to scrutiny in any market,” he said.

By embedding compliance, fairness and transparency throughout the AI lifecycle, pharma companies can stay ahead of shifting rules instead of scrambling to meet them.

The Human Bridge

Perhaps the most important lesson, Mittal said, is that AI should never function in isolation. “Think of responsible AI as building a suspension bridge. Data scientists lay the cables, regulators inspect the beams, and clinicians test the path. The bridge only holds if everyone builds together, with transparency and trust as the anchors.”

AI in pharma is no longer a concept—it’s a reality. But its promise will only hold if innovation moves hand in hand with compliance, fairness and human oversight. Embedding responsibility into every layer of AI is not a choice. It’s the difference between progress that heals and innovation that risks losing public trust.

The post AI Projects Can’t Run like R&D Experiments in Pharma appeared first on Analytics India Magazine.

Reliance Jio has expanded its partnership with Google by rolling out 18 months of free access to Google AI Pro for all Jio 5G users, removing the earlier age restriction that limited the offer to users aged 18–25.

The move comes a week after Jio and Google announced their collaboration to bring Gemini’s premium AI tools to Indian users at no additional cost, signalling a major push to make advanced AI utilities mainstream.

Initially marketed as a youth-focused offer, Jio’s free Google AI Pro subscription is now available to users of all age brackets. The telecom giant confirmed that anyone with a Jio 5G connection and an active unlimited plan can now activate the 18-month complimentary subscription through the MyJio app.

How to Activate the Free Gemini Pro Plan

Activating the offer takes just a few steps:

1. Open or install the MyJio app on your device.
2. Tap on the Early access banner that appears on the home screen.
3. Select Claim now, and a browser window will open with offer details.
4. Scroll down, hit Agree, and confirm your activation.

Once completed, users can check their Gemini app to verify that their Pro plan has been successfully enabled.

Normally priced at ₹1,950 per month, the Google AI Pro subscription includes access to the Gemini 2.5 Pro model, along with higher limits for generating images and videos using the Nano Banana and Veo 3.1 models.

The plan also offers expanded access to NotebookLM for study and research, plus 2TB of cloud storage across Google Photos, Gmail, and Drive, along with WhatsApp chat backup for Android users. Together, these benefits are valued at around ₹35,100.

Jio said the collaboration aims to bring powerful benefits to people’s everyday lives by expanding access to Google’s most advanced AI tools.

The post Reliance Jio Expands Free Google AI Pro Access to Users of All Ages appeared first on Analytics India Magazine.

Indian automotive giant Mahindra & Mahindra revealed how it handled one of its largest booking events using a cloud-based architecture built on Google Cloud and AMD’s EPYC processors.

When the company opened reservations for the Thar ROXX, its new five-door SUV, the website processed around 2 lakh bookings in roughly an hour and a half, without downtime or performance issues.

The system ran entirely on Kubernetes clusters hosted on AMD EPYC processor-based Google Cloud virtual machines.

According to Abhishek Sukhwal, head of infrastructure at Mahindra Group, this setup reduced compute costs by about 40% compared to alternative configurations.

“This is an experience where you can justify new technologies to your CFO,” he said.

“You can never tell your CFO that going to cloud will save money. Instead, you must say we will earn money by going to cloud,” he said, stating that 2 lakh bookings of a single vehicle cannot be handled in physical showrooms, but only on such high-performance digital infrastructures.

The automotive giant has also migrated its design-oriented HPC, or high computing workloads to Google Cloud virtual machines, powered by AMD’s EPYC CPUs.

Sukhwal claimed that the company is among the few to have achieved this profitably, attributing the cost efficiencies directly to AMD’s hardware.

For context, AMD’s EPYC processors are server-grade CPUs designed for data centres and cloud environments.

Kubernetes, the container orchestration platform originally developed by Google, manages how software components, packaged in containers, are deployed and scaled across servers.

In this case, Kubernetes automatically balanced incoming traffic across clusters of AMD-powered cloud instances, keeping the booking system responsive even under peak load.

Google Cloud provided the backbone for this setup, integrating compute, networking, and storage resources under a managed service layer.

Together, the three technologies, AMD’s processors for raw compute efficiency, Kubernetes for orchestration, and Google Cloud for elasticity formed a unified system that let Mahindra’s digital platform handle unprecedented traffic volumes securely and efficiently.

The company also revealed that plans to extend this model to other business units, promoting the same architecture for several other applications.

The post Mahindra Used AMD’s EPYC & Kubernetes to Handle 2 Lakh Thar Bookings Online appeared first on Analytics India Magazine.

KPIT Technologies Ltd reported its 21st consecutive quarter of growth in Q2 FY26, with revenues of $181 million and an EBITDA margin of 21.1%.

Headquartered in Pune, KPIT is a global software and engineering company specialising in mobility solutions, focused on developing software- and AI-defined vehicle technologies.

The company posted a 7.9% year-on-year (YoY) revenue growth in rupee terms and 4.4% in dollar terms, alongside a 1.8% sequential increase.
Kishor Patil, co-founder, CEO and MD of KPIT Technologies, said that the company’s strategic investments, including the closure of the Caresoft Engineering Solutions Business acquisition in Q2, the stake increase in NDream, and the investment in helm.ai in Q3, were strengthening its foundation and expanding its capabilities.

He said that a commitment to delivering value to clients globally is being maintained, with significant investments made in AI-led technologies, adjacencies in mobility, and new markets to ensure sustainable growth in the medium term.

During the quarter, KPIT closed new engagements worth $232 million in total contract value (TCV), underscoring sustained client confidence and continued expansion in software-defined vehicle (SDV) programs.

The company also announced a long-term, multi-million-dollar strategic partnership with a leading European OEM group to accelerate the rollout of next-generation mobility technologies.

The engagement spans key vehicle domains including infotainment, propulsion, vehicle engineering, body and chassis, middleware, and cloud systems.

KPIT’s proprietary platforms, tools, accelerators, and AI-powered enhancements will play a central role in driving scale, speed, and efficiency across the program.

Sachin Tikekar, co-founder and joint MD, added that KPIT’s trusted partnerships with clients are continuing to deepen as they are being helped to navigate an evolving business landscape.

He said that the consolidation of recent acquisitions and investments in talent and AI upskilling are enabling quicker responses to client needs and the delivery of innovative solutions at scale.

He said that investments are being made to transform the business from services to solutions aimed at solving client problems faster, cheaper, and better.

The company said that returns from investments in adjacencies have begun to materialise, resulting in the winning of strategic engagements and the creation of reliable partnerships.

The post KPIT Reports 21st Consecutive Quarterly Growth with $232 Mn in New Deals appeared first on Analytics India Magazine.