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Air India has scrapped all its flights to and from New York City and Newark Liberty International Airport for both Monday, 23 February and Tuesday, 24 February, citing a powerful winter storm bearing down on the US East Coast that has forced widespread travel disruption and grounding of services. The airline said heavy snow, strong winds and hazardous conditions forecast across New York, New Jersey and adjoining states would significantly disrupt airport operations, prompting a precautionary halt to services to prioritise passenger and crew safety.

The cancellations form part of a wider aviation standstill triggered by one of the most intense nor’easter systems to sweep the Northeast this season. The National Weather Service has issued blizzard warnings stretching from Delaware through to Massachusetts, including the New York metropolitan area, where forecasters expect between 30 cm and 61 cm of snow, strong gusts and white-out conditions that threaten to make travel perilous. Municipal authorities have imposed travel bans on non-essential movement and activated emergency measures to cope with the storm’s impact.

Air India’s travel advisory advised travellers booked on affected services to contact the carrier’s support teams for rebooking and assistance, underscoring the airline’s efforts to manage disruption for customers despite the operational freeze. Dedicated ground teams have been deployed at airports to help passengers with alternative arrangements and support. The move mirrors actions by other international carriers, with Emirates and Etihad also cancelling flights to US destinations as the storm hobbles aviation corridors between the Middle East and the United States.

The storm system, dubbed a blizzard by weather authorities, has shut down road travel across much of the Northeast. New York City’s mayor has ordered a citywide ban on non-essential travel during peak weather, while neighbouring New Jersey and several states along the Eastern Seaboard have declared states of emergency, mobilising snow-clearing crews and National Guard units. Public transit services, including buses and commuter rail, face severe curtailments as snowfall rates surge.

Across the region, airports have reported thousands of flight cancellations and delays as shuddered operations ripple outward from the hardest-hit hubs. Alongside Air India’s transatlantic cancellations, major carriers have pulled back services at John F. Kennedy, LaGuardia and Boston Logan International Airports, where airlines have warned passengers to expect prolonged disruption as crews and infrastructure teams work to clear runways and de-ice aircraft.

The National Weather Service’s warnings reflect the storm’s scale. Blizzard alerts encompassing New York City mark the first of their kind in several years for the area, with forecasters noting that coastal flooding, damaging winds and snow loads that can down power lines are all present within the storm’s profile. Some jurisdictions are anticipating snow accumulations that could eclipse seasonal norms, heightening concerns over transportation and emergency response capabilities as attention turns to post-storm recovery.

The pattern of disruption emerging from this event follows a string of significant winter weather episodes across North America in recent weeks, including powerful storms earlier in the winter season that had already stressed transport networks and emergency services. This latest system’s timing and intensity compound logistical challenges for travellers and carriers alike, with airlines adjusting timetables, offering refunds and reshaping schedules as conditions evolve.

Global technology groups including Microsoft, Amazon and Google have pledged billions of dollars to expand cloud and AI infrastructure across the country, betting on strong digital adoption, a vast developer base and policy support from New Delhi. At the same time, domestic conglomerates such as Reliance Industries and the Adani Group have unveiled plans to scale up capacity, signalling an arms race to capture the next phase of AI growth.

The surge is centred on established data centre hubs such as Mumbai, Hyderabad, Chennai and the National Capital Region. Industry estimates place India’s operational data centre capacity at more than 900 megawatts, with projections that this could double within a few years as hyperscale facilities come online. Much of the new supply is being designed for AI workloads, which require more powerful processors and higher rack densities than traditional cloud computing.

Microsoft has committed to expanding its Azure footprint in multiple regions, while Amazon Web Services continues to invest in its Mumbai and Hyderabad zones. Google Cloud has also deepened its presence, citing demand from financial services, healthcare and government clients. Nvidia, whose graphics processing units underpin many AI systems, has strengthened partnerships with Indian firms to support sovereign AI initiatives and localised computing.

Officials argue that this wave of investment will support economic growth, digital inclusion and advanced research. India’s data protection framework and semiconductor incentives have been positioned as part of a broader strategy to create a resilient digital ecosystem. The government has also outlined ambitions to develop domestic AI models trained on local languages and datasets, increasing the need for high-performance computing infrastructure within national borders.

Yet environmental experts warn that the geography of expansion raises difficult trade-offs. Mumbai and Chennai, two of the country’s largest data centre clusters, face periodic water shortages and are vulnerable to climate-related risks, including extreme heat and flooding. Hyderabad, another key hub, has experienced mounting pressure on groundwater resources amid rapid urbanisation.

AI data centres are particularly resource-intensive. Training large language models can consume vast amounts of electricity, and the cooling systems required to maintain optimal temperatures often rely on significant volumes of water. While operators increasingly use advanced cooling technologies and pursue renewable energy procurement, critics question whether efficiency gains can keep pace with the scale of expansion.

According to industry analysts, AI-optimised facilities can draw substantially more power per rack than conventional data centres. India’s peak power demand has been rising steadily, touching record highs during heatwaves. Coal continues to account for a large share of electricity generation, although solar and wind capacity have expanded rapidly. The tension between decarbonisation goals and surging digital energy needs is becoming more pronounced.

Water usage is another flashpoint. Traditional evaporative cooling systems can require millions of litres annually for large facilities, though some operators are shifting to air-cooled or liquid immersion systems to reduce dependence on freshwater. Companies state that they are investing in water recycling, rainwater harvesting and wastewater treatment, particularly in regions classified as water-stressed.

Environmental groups argue that even with mitigation measures, cumulative impacts could strain local communities. Urban residents in Chennai and parts of Maharashtra have already experienced water rationing during drought periods. The prospect of energy-hungry AI campuses expanding in these same areas has prompted calls for stricter environmental assessments and greater transparency around resource use.

Corporate leaders counter that hyperscale operators often have stronger sustainability commitments than smaller, on-premise data centres. Many global firms have pledged to run on 100 per cent renewable energy and to become water positive within the decade. In India, power purchase agreements for solar and wind farms are increasingly tied to data centre projects, supporting the build-out of clean energy capacity.

Policy makers face a delicate balancing act. Digital infrastructure is viewed as critical to competitiveness, especially as countries race to develop generative AI capabilities. India’s large pool of engineers and its expanding start-up ecosystem make it an attractive base for AI research and deployment. Delaying capacity expansion could risk ceding ground to regional rivals in Southeast Asia or the Middle East, where governments are also courting hyperscale investment.

At the same time, climate resilience is rising on the national agenda. The country has set ambitious renewable energy targets and emphasised sustainable development in international forums. Integrating AI growth with environmental safeguards will test regulatory frameworks at both central and state levels.

Agentic artificial intelligence is forcing a fundamental reassessment of how enterprise software is sold, bought and priced, with implications for licensing models, cloud economics and the broader SaaS market worldwide. As autonomous AI agents that can plan, execute and optimise complex workflows proliferate across enterprises, traditional per-seat software licensing is coming under pressure, major vendors are redesigning pricing frameworks and investors are recalibrating valuations around AI’s transformative potential.

Industry executives and analysts say that agentic AI – autonomous systems that operate beyond simple generative tasks to perform multi-step functions without constant human direction – is altering the value proposition of enterprise software. Organisations are increasingly demanding pricing that reflects outcome-based performance and consumption rather than fixed seat counts, pushing large software providers to experiment with enterprise licence agreements that bundle AI capacity with predictable costs and shared risk. This shift is expected to reshape cloud economics by altering how revenue and costs are aligned in the era of pervasive AI automation.

The software industry’s long reliance on per-seat or subscription models is facing disruption as AI agents demonstrate the ability to automate work historically done by humans. Gartner forecasts that around 40 per cent of enterprise software applications will feature embedded task-specific AI agents by the end of this year, exponentially up from a fraction only a short time ago. Enterprises are seeking simpler, more transparent pricing arrangements that reflect AI usage and business outcomes, rather than paying for seats that may no longer correspond to human users in traditional roles.

Salesforce and other leading software vendors have moved to reframe their pricing strategies for an agent-centric world. Executives have described new Agentic Enterprise Licence Agreements that offer “all you can eat” access to AI-powered tools and platforms at agreed-upon costs, designed to give customers confidence to scale AI deployments without unpredictable usage charges. These agreements represent a departure from legacy licence structures and a bet that long-term customer engagement will outweigh short-term licence profitability concerns.

Investors are already pricing in uncertainty about how this transition will unfold. Episodes of sharp stock sell-offs in global software equities have highlighted market anxiety about whether traditional enterprise software will maintain its relevance as agentic AI systems gain traction. While some market observers argue that fears of widespread obsolescence are overstated, the volatility reflects genuine debate about how software demand will evolve as autonomous AI agents become more capable of developing, integrating and executing workflows.

The transformation extends beyond pricing into cloud infrastructure economics. AI workloads, particularly those driven by agentic systems, impose heavier and more variable demands on cloud compute and data storage, challenging conventional cost forecasts. Infrastructure expenses tied to training and operating autonomous agents can grow faster than top-line revenue if not carefully managed. Finance teams are rethinking cost allocation, forecasting and real-time billing to provide clearer visibility into consumption by AI agents, a trend that underscores the complexity of cloud economics in an AI era.

Enterprise readiness for agentic AI varies. While adoption continues to expand rapidly, many organisations are cautious about the risks of autonomous decision-making, prioritising data trust, governance and integration challenges. A significant number of leaders report that scaling fully autonomous agents remains an enterprise priority, but that confidence in data integrity and risk controls are the biggest bottlenecks for broad deployment. The interplay between promise and practical reality is shaping cautious but forward-looking strategies.

For software providers, the transition to AI-centric offerings is pushing a broader rethinking of product strategy and business model evolution. Traditional vendors are integrating agentic capabilities into existing platforms, expanding from assistive features to autonomous operations, while newer AI-native firms are building products around AI agents from the ground up. Software incumbents face a dual challenge: reinventing core products to leverage agentic capabilities while maintaining service quality, compliance and continuity for enterprise customers.

Beyond commercial implications, the rise of agentic AI is prompting discussions about its broader impacts on organisational structures, work roles and human-AI collaboration. Proponents argue that autonomous agents can dissolve rigid organisational silos, enhance productivity by taking over repetitive and complex tasks, and free human workers to focus on higher-order decision-making. Skeptics warn of ethical, legal and accountability challenges associated with handing over greater autonomy to AI systems, including liability, bias and governance risks.