Рубрика: AI

As AI becomes part of everyday life, understanding it has never been more important. The books listed in this article will help you better understand the ecosystem.

From deep investigations into companies like OpenAI, Microsoft, Google and NVIDIA to personal stories of the leaders shaping the field, these books help readers understand how AI is being built and where it is taking us. They cover everything from the risks of superintelligence to the breakthroughs that made large language models possible, and the fierce competition among tech giants to control the future.

This list brings together the most helpful and interesting titles published over the last year.

Empire of AI: Dreams and Nightmares in Sam Altman’s OpenAI by Karen Hao

This influential 2025 release offers an in-depth look at OpenAI’s rapid ascent, examining its goals, inner workings, and the impact of its pursuit of artificial general intelligence (AGI). The author, a former AI journalist at a major tech publication, builds the narrative through conversations with more than 260 people connected to the company, along with private emails and internal files.

If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All by Eliezer Yudkowsky & Nate Soares

If Anyone Builds It, Everyone Dies delivers a clear and unsettling look at the risks that superintelligent AI could create. The authors, Eliezer Yudkowsky and Nate Soares, warn that today’s large-scale AI models built from massive networks of learned parameters rather than readable code operate in ways that are difficult to interpret or predict. Because their behaviour emerges from statistical patterns rather than explicit instructions, these systems can behave unpredictably when placed in new situations.

Yudkowsky and Soares argue that once AI surpasses human intelligence, it may pursue goals that don’t match human intentions, and we may not be able to correct or restrain it.

The Thinking Machine: Jensen Huang, Nvidia, and the World’s Most Coveted Microchip by Stephen Witt

Written by journalist Stephen Witt, The Thinking Machine charts NVIDIA’s transformation from a small company focused on gaming graphics to one of the most influential players in the global AI industry.

The book shows how, under Jensen Huang’s leadership, NVIDIA made bold bets on parallel computing and reimagined what chips could do. This shift turned the GPU from a gaming accessory into a core engine driving the training of modern AI systems.

Witt builds the narrative through extensive reporting, drawing on insights from engineers, executives, investors, and people close to the company. He captures not only the technical breakthroughs but also the intense competition, strategic risks, and sheer persistence that shaped NVIDIA’s rise.

AI Valley: Microsoft, Google and the Trillion‑Dollar Race to Cash In on Artificial Intelligence by Gary Rivlin

Published in 2025, Gary Rivlin’s AI Valley offers an inside look at the intense competition among tech giants and investors as they chase dominance in the booming AI industry.

Rivlin, a seasoned journalist, follows founders, executives and influential venture capitalists over the course of a year to capture how they navigate what many believe is the most transformative tech wave in a generation.

The book brings together stories from across the ecosystem—from young startups trying to break through to big players like Microsoft, Google and Meta, all fighting to secure their share of the AI future. By weaving together personal narratives, boardroom decisions and market pressure, the book paints a vivid picture of an industry in rapid motion.

The Scaling Era: An Oral History of AI, 2019–2025 by Dwarkesh Patel & Gavin Leech

The Scaling Era offers a unique, interview-driven account of how AI has developed during these pivotal years. Patel draws on his long-form conversations with leading researchers, entrepreneurs and engineers to build a detailed picture of the breakthroughs, concerns and debates that shaped modern AI.

The book captures an extraordinary moment in tech history—the period when scaling models with more compute, more data and more parameters became the dominant strategy for progress. Through first-hand stories, readers see how large language models were built, why emergent behaviours surprised even their creators, and the challenges teams faced around safety, interpretability and deployment.

The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future by Keach Hagey

Authored by journalist Keach Hagey, The Optimist provides the first detailed biography of OpenAI CEO Sam Altman, a charismatic and sometimes controversial figure who has become central to the modern AI movement. The book explores how Altman rose to prominence and why he is considered one of the most influential leaders in technology today.

Hagey relies on more than 250 interviews with people connected to Altman, including family members, close friends, colleagues and major investors. These conversations help her build a rich, layered picture of his personality, ambitions and worldview.

The story follows Altman from his early years in St Louis to his rapid ascent at Y Combinator, where he helped shape dozens of successful startups, and eventually to his role as the head of OpenAI, one of the most powerful and closely watched AI labs in the world.

The Nvidia Way: Jensen Huang and the Making of a Tech Giant by Tae Kim

Written by technology journalist Tae Kim, The Nvidia Way tells the story of how NVIDIA grew from a modest graphics-chip startup in the early 1990s into one of the most important companies powering today’s AI boom. Kim traces the company’s evolution across three decades and explains how it became a key force behind modern computing and machine learning.

The book draws on more than 100 interviews with founders, early team members, investors and senior leaders. These accounts help Kim construct a detailed picture of the company’s internal challenges, bold bets and significant turning points.

The post 7 Must-Read Books on AI in 2025 appeared first on Analytics India Magazine.

MapmyIndia Mappls and Zoho have unveiled a new integration that brings indigenous mapping tools into Zoho CRM, giving businesses in India access to location-aware features inside Zoho’s platform.

The two companies announced the partnership on November 26 in New Delhi.

The move plugs MapmyIndia’s Address Capture and Nearby Lead Finder into Zoho CRM, allowing teams to record verified addresses, view customer locations, discover leads around them and plan sales routes. The features run on MapmyIndia’s mapping stack that has been built and refined in India for three decades.

“This partnership between MapmyIndia and Zoho is a true blue Swadeshi celebration — two Indian innovators and leaders in their respective fields – coming together to deliver cutting-edge, homegrown technology that is world-class,” Rakesh Verma, co-founder, managing director and group chairman, MapmyIndia Mappls, said.

“I am confident that this partnership will boost collaborations amongst Indian tech companies creating a sympathetic ecosystem towards the realization of an Atmanirbhar, Viksit Bharat,” he added.

The integration aims to strengthen India’s push toward technology built within the country. Both companies said the effort will help businesses improve field operations, speed up customer service and make informed decisions using mapping data that never leaves India.

“At Zoho, we believe that true technological progress begins with self-reliance. Building deep-tech R&D from India has always been one of our foundational pursuits, driven by the immense talent and creativity that thrive in the country,” Mani Vembu, CEO, Zoho, said.

MapmyIndia, known for developing India’s digital maps since 1995, supplies mapping data, navigation tools, geospatial analytics and a wide ecosystem of APIs and platforms. Zoho, a global software firm headquartered in India, provides cloud-based business applications used by enterprises worldwide.

MapmyIndia’s mapping system covers towns, villages and the full road network of India with detailed layers that include 2D, 3D, HD, real-time and hyper-local visual data. The company also maintains digital maps for more than 200 countries through its Mappls platform.

The companies said their collaboration reflects the rising confidence of India’s tech ecosystem and its ability to build high-quality products for global and domestic use.

The post MapmyIndia Joins Zoho CRM for Location Intelligence appeared first on Analytics India Magazine.

There’s a quiet shift underway inside applied AI teams. As companies move past demo culture and start wiring AI agents into real business processes, a different kind of role is rising to prominence—part engineer, part field operator, part architect, part empath.

For years, the function of a forward-deployed engineer (FDE) existed in different forms, useful mostly in tough government or industrial environments. But as AI agents begin speaking to customers, handling decisions and shaping frontline workflows, the need for engineers who understand the world outside the codebase has exploded.

Now, across AI companies, this once-niche model is making a strong comeback. Practitioners who’ve spent time in the field say this role is becoming critical to building AI systems that are safe in practice, useful in context, and tailored to the understated realities of each industry.

Applied AI’s Two Worlds

Sumanyu Ghoshal, product manager at Prodigal, says applied AI now operates across two parallel tracks. The first is product development—building the core system through orchestration, secure hosting and continuous fine-tuning. The second is deployment—getting these evolving AI systems to work reliably in real customer environments.

“The second piece is how you deploy the solution while you keep building the product,” he said. “That’s why FDEs, what we call agent engineers, matter.”

The title is still taking shape at Prodigal, but the role resembles the classic FDE: part solution builder, part product contributor.

Ghoshal experienced this firsthand as a forward-deployed engineering intern at Palantir, where the work blurred the line between customer-specific builds and core product development. “Whatever you build, a good chunk of it ends up influencing the product,” he said. That tight loop is especially valuable in AI, where systems must adapt to domain-specific needs.

It’s also essential for risk control. As Ghoshal noted, an AI agent can’t afford casual errors. “If an AI agent talking to a consumer gives a 90% discount or says something offensive, that’s a big problem for the business.” The stakes far exceed those of traditional software, making careful deployment and on-ground engineering oversight indispensable.

Companies look for specific qualifications and skills in FDEs. Neeti Sharma, CEO of TeamLease Digital, said, “Most companies look for a strong engineering background and three to eight years of real experience in software, data or applied ML.”

“They must know Python plus one backend language, and should be comfortable deploying systems on AWS, GCP or Azure, and familiar with APIs, microservices and DevOps practices.”

FDEs now also require practical expertise in AI technologies like LLMs, RAG and ML to successfully deliver working solutions. Beyond technical skills, top FDEs are distinguished by clear communication, strong product thinking and the ability to handle ambiguity.

Context may make or break AI Agents

Alex Hill, director of applied AI at Celonis, believes meaningful AI systems can’t be built from a distance. “In the state AI is in today, you cannot build solutions that move the needle from your headquarters,” he said. The gap is simple: no internal discussion can replicate the reality of someone working on a manufacturing line or inside a plant.

Celonis didn’t plan for a forward-deployed engineering model; it arrived there by necessity. Its most effective deployments came from sending strong technical builders on-site to shadow end users, observe workflows, build quick prototypes and iterate immediately. “They build a solution in one or two days,” Hill said. Some attempts fail, but rapid, real-world feedback tightens the loop—eventually resembling an FDE model without the label.

For Hill, the key is context. The industry has moved past prompt engineering into what he calls context engineering: giving AI agents the depth of historical, operational and relational information that humans rely on. Without this foundation, AI cannot meet enterprise standards or operate safely. Context spans vendor relationships, past decisions and subtle process cues—“at least what the human knows, and often more.”

Where the Role Goes Next

FDEs are emerging as the link between product teams and real-world workflows. They bring empirical grounding to product decisions and expose model limitations early.

Omkar Pandharkame, chief strategy officer at Supervity AI, describes them as hybrids with customer-facing instincts and AI-first development skills. “An FDE is not a rebranded solutions engineer; they need a deep understanding of workflows and the agility to build agents in days, not months.”

Highlighting the rise in the demand for the role, Teamlease’s Sharma said, “Demand for FDEs has risen sharply as enterprises move from AI pilots to real, scalable deployments. The challenge today isn’t accessing AI models, but integrating AI into complex workflows, legacy systems and real user environments. FDEs are the bridge that makes this possible.”

She noted that the demand for FDEs is experiencing substantial growth, with high double-digit increases in job demand during the first three quarters of 2025 alone.

Unlike traditional developers, FDEs work in short, high-impact cycles, building fast prototypes that prove value on the ground. As Pandharkame put it, the role “sits at the intersection, one part consultant, one part generative AI engineer.” Companies like Supervity AI are hiring aggressively, viewing the role as the next evolution of pre-sales engineering.

Experts broadly agree as AI agents integrate deeper into business processes, the demand for engineers who can work directly with customers will only rise. Far from being automated away, the FDE may become one of the defining roles of enterprise AI.

The post The Rise of Forward Deployed Engineers in Applied AI appeared first on Analytics India Magazine.

China’s UBTech Robotics has signed a $37 million deal to deploy humanoid robots at border crossings in China’s Guangxi region, as per a report by South China Morning Post (SCMP). The agreement involves the Fangchenggang humanoid robot centre, which will use the robots for traveller guidance, inspections, patrols and logistics.

The company said deliveries will start in December, as reported by SCMP.

This huge order was also announced by UBTech in an X post on November 25. It said, “UBTECH has been added to the MSCI China Index and secured a massive new order: $37.2M!”

The post also said that the Walker humanoid robot series has accumulated over $153 million in orders for 2025.

This particular project will use UBTech’s Walker S2, which will also conduct inspections at steel, copper and aluminium manufacturing sites as part of the initiative.

UBTech said cumulative orders for its Walker series have reached ¥1.1 billion since shipments began this month. Michael Tam, the company’s chief branding officer, said UBTech aims to deliver 500 industrial humanoids this year and increase the figure tenfold next year.

“We plan to reach 10,000 units by 2027,” he told SCMP. He added that the company seeks to lower production costs.

The deal aligns with China’s broader push to integrate embodied AI into real-world operations. Government agencies across provinces are now using humanoids and quadruped robots at airports, immigration checkpoints, and in security work.

A similar concept has been deployed at Hangzhou Xiaoshan International Airport, where a robot is handling passenger queries. Shenzhen Customs has also integrated DeepSeek’s large language model into an inspection robot for cargo checks.

The post UBTech to Deploy Humanoids on China-Vietnam Border appeared first on Analytics India Magazine.

DHL Supply Chain has partnered with San Francisco-based HappyRobot to deploy AI agents that automate routine communication tasks across its global operations. The partnership aims to improve operational efficiency by handling high-volume phone and email interactions. DHL is using the technology for appointment scheduling, driver follow-up calls, and warehouse coordination.

Pablo Palafox, CEO of HappyRobot, said, “Too often, people are stuck maintaining systems and inboxes, with little time to solve exceptions or improve processes. DHL recognised early on the potential of AI agents as a new operating layer.”

The collaboration builds on DHL’s enterprise-wide AI strategy and supports its goal of improving customer communication and employee experience. The company said the AI agents help teams manage operational workflows at scale and free staff to focus on strategic work.

DHL said it has been identifying and validating AI use cases for more than 18 months.

Sally Miller, CIO at DHL Supply Chain, said the company is now integrating AI agents to “drive greater process efficiency for customers while making operational roles more engaging and rewarding for employees”. Current deployments handle hundreds of thousands of emails and millions of voice minutes each year.

These agents are said to be improving consistency in scheduling, transport status updates, and warehouse coordination.

Yamil Mateo, HappyRobot’s head of product, said the collaboration helped design capabilities suited to DHL’s operational needs. “The DHL team understood very early the scale of enablement our platform brings to their organisation,” he said.

HappyRobot engineers have built a unified system that works across email, WhatsApp, and SMS. Senior engineer Danny Luo said it includes “fault tolerance and recovery” to support DHL’s scale.

DHL also reported that the AI agents have reduced manual effort and increased responsiveness in communication-heavy tasks. The company said this shift supports employee retention by reducing repetitive work.

“AI agents help us relieve our teams from repetitive, time-consuming tasks and give them space to focus on meaningful, high-value work,” said Lindsay Bridges, EVP human resources.

The post DHL Rolls Out AI Agents with HappyRobot to Automate Global Operations appeared first on Analytics India Magazine.

Digital Connexion will invest about $11 billion by 2030 to build 1 gigawatt of AI-native data centres in Visakhapatnam, Andhra Pradesh, as per the company’s release. The company signed an MoU with the Andhra Pradesh economic development board to develop the project across 400 acres.

The planned facilities will be designed to handle high-performance computing and AI workloads. According to the company, the centres will feature “future-ready systems,” strong power infrastructure, and high-density racks to support the next wave of digital growth.

Digital Connexion said the expansion aligns with India’s Viksit Bharat 2047 vision and aims to strengthen the country’s digital foundation. The company emphasised that the upcoming sites will use renewable energy, efficient building designs, and advanced cooling to reduce environmental impact.

The firm currently operates a campus in Chennai and is building another in Mumbai’s Chandivali area, both located to offer low-latency and carrier-neutral connectivity.

The release stated that for Digital Connexion Andhra Pradesh investment marks a significant step toward becoming “India’s trusted digital infrastructure provider” as demand for AI-ready data capacity accelerates.

Previously this year, the government of Andhra Pradesh has signed an MoU with US-based Tillman Global Holdings to develop a ₹15,000-crore, 300 MW hyperscale data centre campus in Visakhapatnam, positioning the state as a key digital infrastructure hub in India and the Indo-Pacific region.

The agreement, executed through the Andhra Pradesh Economic Development Board (APEDB), outlines a facilitation framework for time-bound development of the project codenamed TDGAP1 over the next 12 months.

Additionally, Andhra Pradesh will also host India’s first indigenously built eight-qubit quantum computer this November in Amaravati. The installation, developed by Bengaluru-based quantum startup QpiAI, is supported by the National Quantum Mission (NQM).

The post Digital Connexion to Invest $11 Billion in Andhra Pradesh for AI Data Centres appeared first on Analytics India Magazine.

Expedia is doubling down on AI-powered travel, and CTO Ramana Thumu says the timing couldn’t be better. After stints at Fanatics and eBay, he joined the company to tap what he calls a rare intersection of technology, data, commerce and global-scale travel.

In an exclusive interview with AIM, he said, “There are very few global-scale companies with this depth of data.”

Expedia Group, an online travel agency (OTA), which operates major brands such as Expedia, Hotels.com, Vrbo, Orbitz, Travelocity and Hotwire, reported a 9% YoY revenue increase in Q3 2025. Thumu said the priority now was using AI to elevate customer experience across its massive marketplace of airlines, hotels and car rentals—a moat powered by a unified data platform.

3-Bucket AI strategy

The company has a three-part AI strategy. First is traveller-facing innovation like Trip Matching—a feature that allows users to turn Instagram Reels into real, bookable travel itineraries, along with a conversational booking feature in Hotels.com. Next comes powering hotel and advertiser partners with smarter insights, followed by boosting internal productivity through a GenAI playground used by 6,000 employees who have built 1,500 agents.

Expedia uses models from OpenAI, Anthropic and Google and is also integrated with ChatGPT, allowing users to discover trip options and refine preferences.

According to Thumu, coding assistants are translating into real productivity gains across teams. “85-88% of engineers use coding assistants. We are seeing 15-30% improvement in cycle time. That’s a real unlock,” he added.

Thumu mentioned that roughly 100 engineers across Expedia Group contributed to agentic AI initiatives worldwide, working with global product leads. Expedia also has AI squads inside departments like finance, legal and market management to automate routine tasks and make everyday work easier and faster for teams.

In customer support, more than half the issues are handled by Expedia Group’s virtual agents, CFO Scott Schenkel had said previously. The company is also using AI to generate quick summaries for human agents, helping lower the cost of each service interaction.

Not Afraid of Google

Google is introducing new agentic features in its AI Search mode, allowing it to book restaurants, events, etc., which sent Expedia’s shares down. Users simply have to state their preferences, and the AI scans multiple reservation platforms to present real-time options.

Thumu, however, remains unaffected. “Inspiration can happen anywhere,” he said. “Our job is to be at the forefront of integration. And once travellers come to us, loyalty and personalisation keep them in our ecosystem.”

He explained that Expedia’s approach is to deliver shared platform capabilities for both B2C and B2B, and then add partner-specific integrations wherever required. “We have different offerings for our B2B players—white-label, templates, Rapid API and various products,” he said.

“The beauty is that the same platform that powers B2C also powers B2B for the large part, and then we build specialised integrations so business development can move much faster and independently.”

Expedia Group competes with a wide range of major online travel platforms, including Booking.com under Booking Holdings, Airbnb, Tripadvisor, Priceline, Kayak, Trivago, Hotels.com, Skyscanner and Travelocity, among others. Many of them are already adopting AI to improve user experiences and smoothen operations.

When asked what differentiates Expedia from the others, Thumu said it was the travel data. “We flow all the data of different brands into the same data lake.”

Expedia’s Bengaluru and Gurugram hubs house 1,800 engineers and scientists driving its insurance tech, ad tech and AI platforms, with active hiring across IITs and NITs.

On skills, Thumu said, Expedia is hiring aggressively in India for mobile engineering, cloud engineering and, most importantly, deep AI skills. “AI, mobile engineering, cloud and core platform application engineering is always going to be there, but we are doubling down on AI machine learning footprint,” he said.

Expedia runs its entire insurance technology operation, including product engineering, AI and machine learning, out of its Indian offices. A large share of the company’s multibillion-dollar advertising technology stack is also developed in the country.

The post Expedia Isn’t Losing Sleep Over Google’s AI Push appeared first on Analytics India Magazine.

Two years ago, no one could have imagined that Google would suddenly leap ahead of OpenAI in the AI race.

The search giant has come a long way since Bard’s rocky debut in 2023. In its inaugural demo, the chatbot incorrectly stated that the James Webb Space Telescope had taken the first-ever image of an exoplanet, even though the first such image was captured in 2004. This mistake proved embarrassing for Google.

Following that, in 2024, Google’s image generation model, Gemini, faced criticism for producing historically inaccurate and racially biased visuals.

Between 2022 and 2024, OpenAI surged ahead as ChatGPT became a household name. Feeling the pressure, Google launched Gemini in December 2023 and tweaked its benchmark methodology to assert an edge over GPT-4.

However, with the release of Gemini 3 and Nano Banana Pro, the search giant has finally demonstrated its technical strength—so much so that its market cap is now edging towards $4 trillion, joining NVIDIA, Microsoft and Apple in the club.

Google’s stock price has been up nearly 20% since last month, compared to NVIDIA and SoftBank, which were down 7% and 35% respectively over the same period.

Google said Gemini 3 Pro outperforms OpenAI GPT-5.1 and Claude Sonnet 4.5 across significant independent AI benchmarks, including LMArena, Humanity’s Last Exam, GPQA Diamond and MathArena Apex. These benchmarks measure how effectively LLMs handle complex, human-level tasks that test their reasoning, problem-solving and real-world capability.

Since the launch of these models, social platforms have been filled with infographics and images generated by Nano Banana Pro, with users experimenting across artistic styles.

For instance, OpenAI co-founder Andrej Karpathy said he asked Gemini 3 to design a personalised workout schedule along with accompanying posters he could print and hang on the wall as reminders.

Meanwhile, Google DeepMind CEO Demis Hassabis credits the company’s strength to how research, engineering, and infrastructure teams work together. In a post on X he asserted that the company’s “real secret” is this deep integration, driven by relentless focus and intensity.

But Google isn’t alone in the race. Anthropic remains close beside, most recently launching Claude Opus 4.5, which the company claims beats Gemini 3 Pro in coding and agentic tasks in key benchmarks.

While Anthropic is doubling down on coding excellence, Google is pushing ahead on multimodality and ecosystem integration.

Soumith Chintala, co-creator of PyTorch, said on X that the launch of Gemini 3 feels “closer to the GPT-4 moment than any other in recent times”, describing the sudden burst of progress, especially with Nano Banana, as “overwhelming.”

He added that although Google now looks invulnerable with Gemini 3 backed by Tensor Processing Units (TPUs), Android and Chrome, the race is far from over.

Chintala also pointed out that Anthropic continues to dominate coding tasks, suggesting that real-world usage will ultimately determine the winner.

His comments capture a divide among users. While some prioritise strong coding performance, where Anthropic leads, others are drawn to Google’s multimodal features.

Vikrant Patankar, founding filmmaker at Composio, told AIM that Gemini 3 feels like the first time Google shipped a model family that is both powerful and practical. “The quality is noticeably stable across text, images and real-time tasks, and the Nano Banana efficiency jump makes on-device multimodal feel real instead of theoretical,” he said.

That reaction sets the tone for how the broader industry is responding. For several leaders, Gemini 3 stands out not just for performance but for how usable it feels. Salesforce CEO Marc Benioff said he was stunned by Gemini 3’s capabilities, calling the leap in reasoning, speed, images and video “insane”. Having used ChatGPT “every day for three years”, he revealed that after spending just two hours with Gemini 3, he decided he’s “not going back”.

OpenAI in Crisis?

The positive response of Gemini 3 even prompted OpenAI CEO Sam Altman to congratulate Google publicly. “Looks like a great model,” he posted on X.

However, in a recent internal memo accessed by The Information, Altman acknowledged that Google’s recent AI progress could create some temporary economic headwinds for OpenAI. “Google has been doing excellent work recently in every aspect,” he said in a compliment to the tech giant, mentioning that OpenAI is catching up fast.

“It sucks that we have to do so many hard things at the same time—the best research lab, the best AI infrastructure company, and the best AI platform/product company—but such is our lot in life. And I wouldn’t trade positions with any other company,” Altman wrote.
Despite these challenges, OpenAI continues to lead in user adoption. ChatGPT has around 800 million weekly active users as of late 2025, Altman revealed during his keynote address at OpenAI DevDay 2025.

Meanwhile, Google CEO Sundar Pichai announced during the earnings call that the company reported over 650 million monthly active users of Gemini by Q3 2025.

In a blog post, Google said that 65% of its Cloud customers are already using its AI products, a category that includes the Gemini Enterprise offering. Meanwhile, OpenAI has announced that it has surpassed one million business customers globally.

According to Patankar, Google’s new stack finally feels like a unified AI layer. Search, Android and Workspace all gain tightly integrated capabilities, from richer reasoning to real-time multimodal understanding.

He said that while OpenAI still dominates the story around a single powerful app,

Google is trying to build AI into the way people use their devices every day. “If they can keep this stable at scale, it changes the battlefield completely.”

Shrivastava said many people believed “Google Search was finished”, but AI Mode shows it is “far from over and has actually got better.” He also pointed out that more than 13 million developers are now using Google’s generative AI models.

Notably, AI Search Mode is now the default when users type into the Google search bar, with AI Overviews also surfacing automatically as results.

Sergey is Back

Part of Google’s resurgence can be linked to co-founder Sergey Brin’s return. In an interview earlier this year, Brin recalled how, at a party, an OpenAI employee named Dan encouraged him to rejoin the company. “What are you doing? This is the greatest transformative moment in computer science,” Dan had asked him. Brin returned to active work at Google in 2023 to focus on developing AI products, particularly Gemini.

Meanwhile, Pichai, in May this year, said, “I think Sergey is definitely spending time with the Gemini team in a pretty hardcore way, setting and coding and spending time with the engineers.” He added that this involvement brings unmatched momentum to the group.

Google’s NVIDIA Alternative

Besides LLMs and the software ecosystem, Google also holds an advantage in hardware. Gemini 3 was trained on Google TPUs, while OpenAI is currently building compute partnerships with Oracle, Amazon, and NVIDIA. Google’s TPUs are engineered to excel at inference workloads by offering high throughput, low latency and power-efficient compute.

According to Paras Chopra of Lossfunk, Google could “eat NVIDIA’s lunch”. He explained that Google should capitalise on the fact that Gemini 3 was trained entirely on TPUs and build on that advantage by expanding JAX and cutting TPU costs on its cloud platform.

Notably, Anthropic recently announced plans to expand its use of Google Cloud services, including the deployment of one million TPUs. Recent reports also suggest that Meta is considering adopting them.

This shift prompted a slide in NVIDIA’s stock, leading the company to issue a statement saying, “NVIDIA is a generation ahead of the industry—it’s the only platform that runs every AI model and does it everywhere computing is done.”

Currently, Google’s specialised chips (TPUs) work very well with JAX, the newer software framework that has largely taken over from TensorFlow. However, they don’t work as well with PyTorch, the most popular framework in the tech industry.

At the same time, OpenAI has partnered with Broadcom and Foxconn to build AI chips and accelerators, as well as develop data-centre networking technologies.

Tech analyst Beth Kindig captured the market’s shift in sentiment towards Google. In a post on X, she said that just nine months ago, investors believed Google was “toast” because ChatGPT’s rise threatened its dominance in search. Today, she said, sentiment has flipped so dramatically that the market now thinks Google is strong enough to challenge NVIDIA in custom silicon.

What’s Next for OpenAI?

In response to Google’s rapid advancements, Altman has reportedly told employees that OpenAI will close the gap. However, the company’s latest model, GPT-5.1, has so far struggled to capture users’ interest.

Reports further stated that the company is working on a new language model internally referred to as ‘Shallotpeat’. The model attempts to tackle the issues that surfaced during pre-training.
Besides that, OpenAI recently launched GPT-5.1-Codex-Max, a new agentic coding model that can run for over 24 hours.

The post Google Went After OpenAI But Ended up Rattling NVIDIA appeared first on Analytics India Magazine.

SoftBank Group has completed the acquisition of all equity interests in Ampere Computing. The deal makes Ampere a wholly owned subsidiary of SoftBank and brings the semiconductor firm into SoftBank’s consolidated financial statements.

SoftBank carried out the takeover through its subsidiary Silver Bands 6 (US) Corp. The company said it is reviewing the financial impact of the transaction and will disclose details if required.

Ampere, a Santa Clara based semiconductor design company, focuses on AI compute built on the ARM platform. This comes just after the Japanese giant sold all its NVIDIA shares earlier this month.

SoftBank had first announced its plan to acquire Ampere on March 20 for $6.5 billion. In that statement, SoftBank said the transaction would make Ampere “an indirect, wholly owned subsidiary” once closed and would support its broader AI strategy.

SoftBank had also noted that Ampere could complement the chip design work of Arm.

The deal was subject to US antitrust clearance, approval by the foreign investment committee and other closing conditions.

Ampere was founded in 2017 by Renée J James, its chairman and CEO. The company designs processors for cloud computing and AI workloads. Its earlier financials in the attached document show revenue of $151.8 million in 2022, $46.7 million in 2023 and a further decline the following year, along with continued losses.

SoftBank said in the new update that Ampere’s results will now be consolidated and that it will provide further disclosures if any arise from the review of the financial impact. The company also said, “Should any matters requiring disclosure arise in the future, SBG will announce them promptly.”

The post SoftBank Completes Ampere Acquisition Amid Stock Decline Tensions appeared first on Analytics India Magazine.

According to global talent firm Randstad’s Workmonitor report on AI & Equity, the vast majority of workers who say they’re skilled in AI are men at 71%, while women with AI skills stand at 29%, indicating a 42 percentage point gender gap.

Upskilling women, especially those from non-metro regions, does more than diversify the talent pool. It broadens the innovation lens, bringing in perspectives that tech teams often overlook.

Addressing this imbalance is Nimaya, a non-profit organisation quietly equipping women with the skills, confidence, and leadership capabilities required to excel in AI-driven industries.

In a podcast with AIM, Nimaya co-founders Navya Nanda and Samyak Chakrabarty talked about their vision to bridge the gender gap in technology through structured programs that combine technical training, mentorship, and real-world exposure.

“Our goal is to create an environment where women are not just participants in AI but are leading innovation and decision-making,” said Nanda. “We believe that diverse perspectives are essential to designing AI solutions that are ethical, inclusive, and impactful,” she added.

Building Skills Beyond Coding

While many tech programs focus solely on coding and algorithmic skills, Nimaya takes a broader approach. Its curriculum integrates AI fundamentals with hands-on projects, data analytics, machine learning, and ethical AI practices.

Women enrolled in the program work on real-life AI applications, ranging from predictive analytics to natural language processing, allowing them to experience the end-to-end process of AI development.

“We wanted to go beyond just teaching technical skills. Leadership, problem-solving, and strategic thinking are equally important,” said Chakrabarty.

One of Nimaya’s distinguishing features is its mentorship program. Participants are paired with industry leaders, including AI researchers, data scientists, and executives, who provide guidance on career paths, technical challenges, and personal development. This mentorship helps participants envision themselves in high-impact roles, and gain the confidence to pursue ambitious goals.

“Having a mentor who believes in you can completely change the trajectory of your career,” said Chakrabarty, adding that their focus is on inculcating leadership, beyond imparting AI training.

Mentorship also extends to collaborative projects where women work in teams to solve real-world AI problems.

Creating Opportunities in the AI Ecosystem

Nimaya actively partners with tech companies, startups, and research institutions to create internship and placement opportunities for its participants. These collaborations ensure that women not only acquire knowledge, but also gain meaningful industry experience. By integrating classroom learning with professional exposure, Nimaya helps participants transition smoothly into AI careers.

“We want our participants to leave the program with not just skills, but opportunities to lead,” Nanda said. “This approach ensures that women can make tangible contributions to AI innovation from day one.”

The program has already seen success stories across various sectors. From fintech to healthcare AI, Nimaya-trained women have contributed to building algorithms, designing ethical AI frameworks, and leading AI-powered product initiatives.

Nimaya’s work is not limited to training; it also actively advocates for gender diversity in AI. Through workshops, webinars, and conferences, the initiative highlights the importance of including women in AI decision-making roles. It also engages with policymakers and industry leaders to promote inclusive hiring practices.

“Diversity is not just a moral imperative, it’s an innovation imperative,” Nanda emphasised.

She further added that studies have consistently shown that diverse teams outperform homogeneous ones, particularly in complex problem-solving scenarios such as AI design and implementation.

Talking to AIM on the subject, Arppna Mehra, VP human resource, Honeywell India, mentioned, “India’s next big leap in AI won’t come only from flagship tech hubs. It’s already taking shape in smaller cities where young women are picking up real, industry-ready skills.”

For Nimaya, plans are underway to expand its programs across multiple cities, and to partner with global tech firms to offer specialised tracks in emerging AI domains such as generative AI, autonomous systems, and ethical AI governance.

The initiative aims to create a pipeline of women who are ready to take on high-impact roles and drive innovation in AI on a global scale.

With the right partnerships to scale this momentum, India has the potential to unlock a new generation of AI builders who won’t merely fill roles, but redefine the boundaries of what the industry believes is possible.

“The future of AI must be inclusive. Women should not be the exception; they should be the norm,” Nanda concluded. “Through Nimaya, we are committed to ensuring that women are at the forefront of AI leadership.”

The post How Nimaya is Preparing Women to Lead in AI appeared first on Analytics India Magazine.