Tag: cloud computing

  • AI, Cloud, and Cybersecurity: The Three Forces Reshaping Tech Stocks in 2026

    AI, Cloud, and Cybersecurity: The Three Forces Reshaping Tech Stocks in 2026

    Technology stocks are entering a pivotal period as artificial intelligence, cloud computing, cybersecurity, and shifting enterprise spending patterns reshape the IT sector. For investors tracking technology companies, the focus is moving from traditional outsourcing growth to how businesses adapt to newer technology cycles.

    In India, this transition is already influencing the outlook for IT services companies. Recent industry assessments point to muted near-term growth as clients remain cautious about discretionary technology spending, while AI simultaneously creates new demand for cloud, data engineering, cybersecurity, and digital transformation.

    AI Is Changing the IT Services Model

    AI will be a major factor influencing technology stocks in the coming years. But AI will not only open new avenues for companies—AI-driven productivity gains are also pressuring IT service providers’ business models.

    According to Crisil Ratings, the IT services industry in India would see subdued revenue growth of 1-3% this fiscal year and next, driven by disruption from artificial intelligence, subdued discretionary spending, and geopolitical uncertainties. Companies will have to reimagine their models and move into newer services, the rating agency said.

    Meanwhile, ICRA forecasts an annualized revenue growth rate of 3-5% for its basket of Indian IT services firms in FY2027 from 3.5% in FY2026. It says demand for traditional services will continue to be affected by delays in discretionary spending tied to generative AI.

    Cloud Spending Remains Key Growth Area

    Cloud computing remains an important part of the technology investment cycle as businesses move more applications and workloads to scalable infrastructure.

    Gartner forecasts that end-user spending on public cloud services in India will grow 28.1% in 2026 to USD 17.5 billion, up from USD 13.7 billion in 2025. The research firm said demand for AI-ready infrastructure and application modernization is driving this increase.

    For technology companies, this creates opportunities beyond traditional application development. Cloud migration, modernization, and consumption-based technology models are becoming increasingly connected to enterprise AI strategies.

    Data Centers and Semiconductors Gain Importance

    The AI revolution is creating investment opportunities not only for software companies but also for the technologies needed to build and sustain AI infrastructure.

    According to Deloitte, India’s data center power capacity will increase from around 1.5 GW in 2025 to around 10 GW by 2030. Moreover, the sector’s electricity consumption would be around 57 TWh in FY2030.

    This indicates that tech investors are increasingly interested in firms that build infrastructure for AI and cloud-based computing, rather than focusing only on software firms.

    Importance of Cybersecurity Grows

    As more companies adopt cloud computing and AI applications, cybersecurity is becoming a critical area of technology investment. As per CRISIL, Indian IT firms will invest selectively in AI, cloud, cybersecurity, and digital engineering in the future. Healthy balance sheets and cash flow will enable these investments.

    What It Means for IT Stocks

    The evolving technology ecosystem is adding complexity for IT stocks. Firms heavily dependent on legacy services face pressure from subdued discretionary spending and productivity gains from artificial intelligence. On the other hand, AI deployment, cloud adoption, cybersecurity, and modernization create business opportunities.

    Recent market trends stem from this uncertainty. As reported by The Economic Times in September, the Nifty IT index surged 5% in a single day of trading, adding Rs 1.13 lakh crore to market cap.

    The Next Phase of Technology Growth

    The future performance of tech stocks is likely to depend not on AI use alone but on companies’ ability to turn AI demand into revenue and preserve margins despite improved productivity. For the Indian IT industry, this transition will also mean shifting its focus toward offering higher value-added services like AI-driven transformation, application modernization, data engineering, cloud, and cybersecurity solutions.

    According to ICRA, these verticals are expected to grow as organizations move generative AI initiatives from pilots to broader implementations across the company. Overall, the technology industry is currently experiencing a structural change cycle, not just a boom cycle, driven by AI changing how technology is developed and used, growing cloud infrastructure demand, and cybersecurity becoming crucial to operations.

  • Anthropic Inks $11.6 Billion Deal with Akamai to Boost AI Computing Infrastructure

    Anthropic Inks $11.6 Billion Deal with Akamai to Boost AI Computing Infrastructure

    Anthropic has signed a massive $11.6 billion, seven-year contract with Akamai Technologies to secure computing power for its artificial intelligence operations. The deal expands upon an earlier $1.8 billion computing agreement between the two companies.

    Under the terms, Akamai will provide Anthropic with access to central processing units (CPUs), as demand for general-purpose chips continues to rise in data centers supporting AI services. The surge in usage of Anthropic’s Claude software, particularly for coding and other tasks, has driven the need for additional computing resources. The AI developer is also purchasing computing capacity from other major tech firms, including Alphabet’s Google and Elon Musk’s SpaceX.

    As part of the agreement, Akamai issued Anthropic a warrant allowing the AI company to purchase Series B shares at $111.33 each. The warrant converts into 7.7 million common shares. A portion of the warrant, representing about 2% of Akamai’s common stock, will vest alongside the computing agreement, which is expected to start in the second half of next year. The remaining portion will become available over the seven-year contract.

    Akamai estimates that capital spending tied to this deal will total about $5.5 billion, more than six times last year’s spending. Most of the investment will go toward servers, chips, and networking equipment, with the bulk expected next year. Akamai CEO Tom Leighton noted that the company’s cloud business is “growing obviously extremely fast” and that revenue from cloud contracts could soon eclipse sales from other segments.

    Revenue from the Anthropic business is expected to reach $150 million to $300 million next year, with an annual run rate of about $1.7 billion by 2028. Leighton described the warrant as a “serious step” that could bring the two companies closer together, marking the first time Akamai has agreed to such a warrant in a cloud deal.

    The deal comes amid investor concerns over circular AI deals, where cloud, hardware, and AI companies buy each other’s products while also making direct investments. Nevertheless, Anthropic’s commitment underscores the intense competition for computing resources in the AI sector.

  • Amazon Commits $48 Billion to India’s Digital Future with AI, Cloud, and Quick Commerce Expansion

    Amazon Commits $48 Billion to India’s Digital Future with AI, Cloud, and Quick Commerce Expansion

    Amazon has announced a massive $48 billion investment plan for India through 2030, focusing on artificial intelligence, cloud infrastructure, e-commerce, and quick commerce. The commitment follows a June 2026 meeting between Amazon CEO Andy Jassy and Indian Prime Minister Narendra Modi in New Delhi.

    More than $21 billion of the total will be directed toward AI and cloud infrastructure, with AWS expanding data center capacity in Mumbai and Hyderabad. Amazon plans to add fulfillment centers and last-mile delivery stations nationwide, targeting India’s rapidly growing digital economy. The company also intends to invest an additional $3 billion specifically in quick commerce by 2030, scaling its Amazon Now service to compete in the ultra-fast delivery segment.

    The latest pledge includes a $13 billion top-up for AI and cloud announced in June, building on a previous $35 billion commitment across Indian businesses in 2025. AWS remains central to the strategy as Indian enterprises, startups, and government entities increase adoption of cloud computing and AI tools. Amazon will provide custom AI chips, managed AI services, and developer tools, along with support for local data storage to help organizations build AI applications within India’s cloud ecosystem.

    Amazon’s cloud business is already showing strong momentum: AWS sales reached $42.2 billion in the second quarter of 2026, up 37% year over year, and the company’s AI business annual revenue run rate exceeded $25 billion. Jassy said the investment addresses strong demand across Amazon’s Indian operations and aligns with goals to digitize small businesses, create jobs, and boost exports.

    On the retail front, Amazon plans to open more than 20 new fulfillment centers and over 100 last-mile delivery stations, with expanded capacity in smaller Indian cities. The quick-commerce push, reported by Reuters on September 24, 2026, intensifies competition in the minutes-delivery space. Amazon also targets bringing AI benefits to 15 million small businesses and providing AI education to 4 million government-school students by 2030. Cumulative investment in India is expected to exceed $88 billion through the end of the decade.

  • Amazon Recruits Former Employees for AI and Cloud Roles After Major Layoffs

    Amazon Recruits Former Employees for AI and Cloud Roles After Major Layoffs

    Amazon is reaching out to former employees, including those affected by recent layoffs, to fill positions in artificial intelligence, machine learning, and cloud computing. According to a report by Business Insider, the company has initiated a “Boomerang Reengagement Initiative” to bring back former staff.

    Amazon Launches Former Employee Outreach

    Recruiter emails seen by Business Insider reveal that Amazon’s AI agent organization, under AWS Vice President Swami Sivasubramanian, contacted former employees last month about available AI and machine learning positions. The outreach was described as “Swami’s Boomerang Reengagement Initiative.”

    In another instance, an AWS Finance recruiter contacted a former employee and offered an expedited interview process that could potentially lead to a job offer. One email read: “A lot has changed since you were here, and there’s some really compelling work happening across AI/ML right now.”

    Outreach Follows More Than 30,000 Job Cuts

    This outreach comes after Amazon eliminated more than 30,000 positions through multiple rounds of job cuts. Despite these reductions, the company continues to recruit for AI-related roles as it expands its focus on the technology.

    Amazon Says Rehiring is Longstanding Practice

    Amazon told Business Insider that rehiring former employees is a longstanding companywide practice. Company spokeswoman Haley Silva said that former employees, including those laid off, could be considered for open positions based on current needs. She clarified that the outreach was not specifically aimed at people who had left because of Amazon’s return-to-office policy, and that workplace attendance requirements remain unchanged.

    “We’re always interested in connecting with talented people, both new candidates and former Amazonians, for open roles,” Silva was quoted as saying.

  • How Generative AI Is Fueling a Boom in Data Storage Infrastructure

    How Generative AI Is Fueling a Boom in Data Storage Infrastructure

    Generative AI is not only creating new possibilities for businesses but also generating an unprecedented demand for data storage infrastructure. As organizations deploy AI at scale, storage systems are becoming a critical component of technology planning, handling everything from training datasets to model outputs and archived information.

    Rising Data Volumes

    A study by Western Digital and IDC revealed that 94.7% of organizations reported increased data volumes due to AI adoption. More than 61% saw data growth of at least 25% over the past year, highlighting the immense pressure on storage systems.

    Massive Training Datasets

    Businesses must store vast amounts of text, images, video, code, and other data used to develop increasingly capable AI models. This creates sustained demand for high-capacity storage systems that can handle the scale of modern AI training.

    AI Inference Generates Continuous Data

    Every AI interaction produces prompts, outputs, logs, embeddings, and operational information, creating a continuous stream of data that organizations need to store, analyze, and secure. This adds another layer to storage requirements.

    Reviving Cold Data

    The Western Digital study found that 75.9% of respondents were reactivating cold-tier data for AI workloads. Previously dormant information is being turned into valuable training and analysis material, further increasing storage demands.

    The Need for Speed

    AI workloads require not just capacity but also fast access, high throughput, and low latency. This is especially critical when GPUs and other accelerators process enormous datasets, making storage performance a key factor.

    Cloud Spending on the Rise

    Gartner forecasts that India’s public-cloud spending will reach $17.5 billion in 2026, driven by demand for GPUs, networking, scalable storage, and continuous AI inference. Infrastructure growth is set to accelerate.

    Storage Costs Under Pressure

    With more data comes the need for careful economic planning. In the Western Digital study, 98.2% of respondents considered total cost of ownership per terabyte important when making storage decisions for expanding AI workloads.

    Smarter Infrastructure Required

    AI infrastructure demands a combination of fast storage, scalable capacity, efficient data movement, and intelligent data management. Businesses are rethinking storage architecture rather than simply adding more disks as data volumes grow.

  • Quanome Inks $18.8 Million GPU Deal to Expand AI Cloud Infrastructure

    Quanome Inks $18.8 Million GPU Deal to Expand AI Cloud Infrastructure

    Quanome Technologies has signed an infrastructure purchase agreement valued at approximately USD 18.8 million for 32 GPU server units, powered by NVIDIA technology, to advance its AI cloud business through subsidiary XDT Infrastructure I, LLC (XDT). The deal, finalized on September 16, 2026, marks the first phase of the company’s planned development of XDT and its broader Quantum and AI Systems strategy.

    XDT to Offer Managed AI Computing Access

    Through XDT, Quanome aims to provide businesses with managed access to AI computing capacity for development and scaling. Once operational, clients will be able to develop and run AI applications, process complex data, and scale computing resources without building and managing their own infrastructure. According to the company, these capabilities are designed to accelerate product development, automate processes, and improve business productivity. XDT also opens a recurring revenue stream for Quanome from computing services, with further investments planned based on customer demand, available funding, and operational readiness.

    The purchase agreement covers 32 GPU server units for an aggregate purchase price of approximately USD 18.8 million, as disclosed in a regulatory filing. Quanome is required to make an initial payment of 20% of the purchase price upon acceptance of the purchase order, with the remaining 80% due before shipment, subject to applicable conditions.

    First US AI Infrastructure Hub Targeted for Late 2026

    Quanome expects its first US AI infrastructure hub to become operational in Q4 2026, subject to delivery and deployment. The GPU servers are scheduled for delivery to a designated data center location in the United States. The company cited growing business adoption of artificial intelligence as a key driver for scalable computing infrastructure. Data from the US Census Bureau shows that 17% to 20% of US businesses used AI between December 2025 and May 2026, while 20% to 23% expected to adopt it in the following six months.

    Part of Quanome’s Broader Quantum and AI Strategy

    Yang Li, Chief Executive Officer of Quanome Technologies, stated that this investment translates the company’s commitment to quantum and AI into concrete action. He emphasized the priorities of launching the first hub, building a strong client base, and developing recurring revenue streams. XDT is part of Quanome’s Quantum and AI Systems strategic stream, which also includes Quantum Life Sciences – Molecular Discovery, Advanced Nuclear, and Quantum-Safe Cybersecurity initiatives.

  • Tech Giants Amazon, Google, and Microsoft Commit $80.5 Billion to India’s AI Infrastructure

    Tech Giants Amazon, Google, and Microsoft Commit $80.5 Billion to India’s AI Infrastructure

    Amazon, Google, and Microsoft have announced a combined investment of $80.5 billion in India’s AI and cloud infrastructure through 2030, signaling the country’s growing importance as a global technology hub. Meta and Nvidia are also expanding their presence through strategic partnerships and hardware supply deals.

    Amazon leads with a $48 billion commitment through 2030, including $13 billion specifically for AI and cloud infrastructure. The company plans to expand AWS capacity in Mumbai and Hyderabad, and support over 20 fulfillment centers and 100 delivery stations. Amazon also aims to provide AI tools for millions of small businesses in India.

    Microsoft has pledged $17.5 billion between 2026 and 2029 for cloud and AI infrastructure, including a fourth cloud region in Hyderabad. The company also launched a nationwide AI skilling program targeting 20 million people by 2030.

    Google is investing approximately $15 billion from 2026 to 2030 to build its first Indian AI hub in Visakhapatnam. The project includes gigawatt-scale computing, data centers, and new subsea connectivity, developed in partnership with AdaniConneX and Airtel.

    Meta is leasing capacity at Reliance’s 168 MW AI-enabled data center in Jamnagar, Gujarat, and has contracted nearly one gigawatt of renewable energy. While Meta did not disclose a specific financial commitment, the projects mark a significant step in its Indian infrastructure expansion.

    Nvidia is supplying advanced GPUs for India’s AI computing ecosystem. L&T is building an AI factory in Chennai with 10,000 Nvidia B300 GPUs to support Together AI workloads, demonstrating Nvidia’s role in powering India’s AI growth without a direct investment figure.

    The combined $80.5 billion from Amazon, Microsoft, and Google excludes Meta’s undisclosed deals and Nvidia’s hardware partnerships, reflecting different investment models. India’s growing AI demand, developer talent, and infrastructure plans continue to attract major technology investments, marking a shift toward building AI compute capacity inside the country.

  • Accenture and AWS Unveil AI and Cloud Solutions for Mid-Market Businesses

    Accenture and AWS Unveil AI and Cloud Solutions for Mid-Market Businesses

    Accenture has partnered with Amazon Web Services (AWS) to introduce a suite of AI and cloud tools specifically designed for mid-sized companies. The offerings, available through AWS Marketplace, cover cloud migration, security, data management, customer service, and AI testing.

    Key solutions include tools for moving virtual machines to AWS, optimizing cloud spending, securing cloud environments, and testing AI systems for potential risks. The collaboration is part of Accenture Edge, a business unit focused on organizations with annual revenues between USD 300 million and USD 3 billion. Accenture Edge aims to deliver tailored technology solutions for mid-market needs.

    Customer service technologies, such as conversational AI and virtual agents, are also included. Additional tools help companies identify data sources and dependencies during cloud migrations.

    Accenture highlighted its work with 407 ETR, an electronic toll highway operator in Toronto. The company used Accenture’s NeuraFlash unit and AWS to build a new customer service system without disrupting existing operations—demonstrating how the collaboration can modernize customer-facing technology smoothly.

    Accenture shares rose 1.6% before the opening bell on Tuesday, extending Monday’s gains. This move follows Anthropic’s partnership with Accenture to test the safety of advanced AI models.

  • Alibaba Stock Rallies on New AI Chip and Flagship Model as China’s AI Ambitions Intensify

    Alibaba Stock Rallies on New AI Chip and Flagship Model as China’s AI Ambitions Intensify

    Alibaba shares rose in premarket trading on Tuesday after the Chinese technology giant unveiled its latest artificial intelligence chip and its most powerful flagship AI model to date. The announcements mark another step in China’s efforts to close the gap with US tech leaders, though significant challenges remain.

    Alibaba Unveils Its Most Powerful AI Chip

    The company introduced the Zhenwu V900, which it described as its most powerful AI chip to date. Alibaba said the new chip delivers three times the performance of its predecessor, which was released just four months ago. Alongside the chip, Alibaba also launched Qen3.8-Max, its most powerful flagship AI model so far.

    Data-Center Expansion Plans

    Alibaba CEO Eddie Wu said the company expects significant growth in annual AI chip shipment volumes. He also pointed to shortages across the global AI data-center supply chain as a constraint on the pace at which Alibaba can expand its computing infrastructure. Wu said Alibaba would work with partners to address these challenges.

    Alibaba Cloud is targeting more than 20 gigawatts of global data-center capacity by 2032. The company sees expanding computing infrastructure as a core part of its broader AI strategy as demand for AI services continues to grow.

    Alibaba Still Trails US AI Leaders

    Despite the latest announcements, Alibaba’s AI infrastructure still lags behind leading US companies. The Zhenwu V900 is considered competitive with NVIDIA’s Hopper hardware, which is now two generations old. The data-center gap is also significant: one major US player has said it intends to have enough power and cooling to support 20 gigawatts of computing capacity by the end of next year.

    Parv Sharma, senior analyst at Counterpoint Research, said China’s AI infrastructure is more supply-constrained because AI has become a strategic priority. He noted that Alibaba’s 20-gigawatt target by 2032 should be viewed against the scale already operated by US hyperscalers.

    US-China Talks Could Shape AI Competition

    Investors are also watching talks between US President Donald Trump and Chinese President Xi Jinping, particularly for any developments affecting AI technology and access to advanced chips. According to Barron’s, China’s ability to close the gap with the US could remain constrained unless access to cutting-edge chips improves.

    Before Tuesday’s trading session, Alibaba’s American depositary receipts had fallen 21% so far this year.

  • The Modern Computer Science Skill Set: What Students Need for a Tech Career

    The Modern Computer Science Skill Set: What Students Need for a Tech Career

    A computer science degree can open many technology career paths, but employers are looking for more than transcripts and basic coding exercises. Today’s tech roles demand strong fundamentals in programming and algorithms, plus practical experience with AI, cloud systems, databases, cybersecurity, and real-world software development. The World Economic Forum ranks AI and big data among the fastest-rising skill areas, followed closely by networks, cybersecurity, and technology literacy.

    Build Strong Programming and Problem-Solving Skills

    A successful developer needs more than syntax knowledge. Languages like Python, TypeScript, Java, C++, Go, and Rust serve different technology areas. Python plays a major role in AI, data science, automation, and back-end development. GitHub reports that TypeScript became its most-used language in August 2025, ahead of Python and JavaScript, while Python remains essential for AI and data science. Developing in-depth skill in one primary language and basic comfort with a second creates a solid foundation.

    Data structures and algorithms still matter. Arrays, trees, graphs, stacks, queues, recursion, dynamic programming, and Big-O analysis are valuable for technical interviews and system design. These skills also support code review, bug fixes, and architecture decisions. Algorithm knowledge should connect to real software work rather than stop at interview puzzles.

    Develop AI Literacy With Human Judgment

    AI now forms a major part of software development. The 2025 Stack Overflow Developer Survey found that 84% of respondents use or plan to use AI tools in development. However, 46% distrust AI output, while only 33% trust it. These numbers show why AI skills must be paired with human judgment.

    Useful AI knowledge includes large language models, AI APIs, embeddings, vector databases, retrieval-augmented generation, tool calling, AI agents, model evaluation, and foundational machine learning concepts. GitHub reports more than 1.1 million public repositories with an LLM software development kit, with more than 693,000 such projects created in the past 12 months. AI skills go beyond prompt writing. Code review, testing, security checks, and clear model limitations remain essential. AI can accelerate tasks, but technical knowledge determines whether the final result actually works.

    Master Software Engineering, Data, Cloud, and Security

    Real technology work requires professional software development skills. Git, GitHub, testing, debugging, APIs, code review, documentation, and continuous integration should all be part of a computer science education.

    Database knowledge also matters. SQL, PostgreSQL or MySQL, indexes, joins, transactions, query design, and data modeling provide a strong base. MongoDB and Redis add useful NoSQL options for specific system requirements.

    Cloud skills add another important layer. AWS, Microsoft Azure, and Google Cloud offer services for compute, storage, databases, networking, identity, serverless systems, and monitoring. Linux, Docker, CI/CD, basic Kubernetes, cloud deployment, and infrastructure tools help turn a local project into a reliable service.

    Cybersecurity needs daily attention. Authentication, authorization, HTTPS, encryption, secure passwords, API security, secrets management, dependency checks, and awareness of common web risks should be part of normal software work. The World Economic Forum places networks and cybersecurity among the fastest-rising skill areas for good reason.

    Understand Systems and Choose a Specialty

    Computer science fundamentals still form the technical foundation. Operating system concepts, computer networks, databases, computer architecture, concurrency, memory management, distributed systems, and system design explain what happens inside a real application. System design can begin with clients, servers, caches, queues, replicas, and databases, then expand to scaling, fault tolerance, and service architecture.

    A strong career path does not require mastery of every technology. Core computer science knowledge should be paired with one clear area of depth:

    • AI and machine learning: Python, statistics, neural networks, LLM systems, and model operations.
    • Software engineering: Data structures and algorithms, back-end or front-end development, databases, cloud, and system design.
    • Cloud and DevOps: Linux, networking, Docker, Kubernetes, CI/CD, and cloud platforms.
    • Cybersecurity: Network security, application security, cryptography, and incident response.

    Current data points to one clear career lesson: technical depth matters, but flexibility matters too. Stack Overflow reports that 69% of developers spent time in the past year learning a new coding technique or software language, while more than 36% studied AI-enabled tools for work or career growth. The World Economic Forum also identifies analytical thinking, creative thinking, resilience, flexibility, curiosity, and continuous learning as high-value skills.

    Why This Matters

    A computer science degree alone may not fully prepare students for modern technology roles. Employers need professionals with strong programming, problem-solving, AI, cloud, data, security, and software engineering skills. Learning these areas early helps students build meaningful projects, understand workplace expectations, adapt to new tools, and create a stronger foundation for long-term career growth.

    Frequently Asked Questions

    1. Which programming languages should computer science students learn?

    Python, TypeScript, Java, C++, Go, and Rust are all useful options across different technology fields.

    2. Does DSA still matter for tech careers?

    Yes. Data structures and algorithms support problem-solving, technical interviews, software design, code quality, and system architecture.

    3. Should every computer science student learn AI?

    Basic AI literacy has become valuable across many technology roles, even for students who do not plan to enter AI research.

    4. Why should students learn cloud computing?

    Cloud knowledge helps students understand how modern applications use computing, storage, databases, networking, and deployment platforms.

    5. Which skill should a student specialize in?

    Students should build core computer science knowledge first, then develop deeper expertise in an area such as AI, software engineering, cybersecurity, cloud, or data engineering.