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Understanding AI: The Science, Systems, and Industries Powering a $3.6 Trillion Future

Explore how artificial intelligence is transforming finance, automation, and industry — and what the $3.6 trillion AI boom means for our future

Jay Magiya

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The global artificial intelligence market growing from $538 billion in 2023 to $3.68 trillion by 2034, with steady year-on-year increases and a projected 18.6% CAGR. Image credit: Satheesh Sankaran/Pixabay

Artificial Intelligence (AI) has become a major point of discussion and a defining technological frontier of our time. Experiencing remarkable growth in recent years, AI refers to computer systems capable of mimicking intelligent human cognitive abilities such as learning, problem-solving, critical decision-making, and creativity. Its ability to identify objects, understand human language, and act autonomously makes it increasingly feasible for industries like automotive manufacturing, financial services, and fraud detection.

As of 2024, the global AI market is valued at approximately USD 638.23 billion, marking an 18.6% increase from 2023, and is projected to reach USD 3,680.47 billion by 2034 (Precedence Research). North America leads this global growth with a 36.9% market share, dominated by the United States and Canada. The U.S. AI market alone is valued at around USD 146.09 billion in 2024, representing nearly 22% of global AI investments.

The Early Evolution of AI: From Reactive Machines to Learning Systems

Our understanding of AI has evolved through different models, each representing a step closer to mimicking human intelligence.

Reactive Machines: The First Generation

One of the earliest and most famous AI systems was IBM’s Deep Blue, the chess-playing computer that defeated world champion Garry Kasparov in 1997. Deep Blue was a reactive machine model, relying on brute-force algorithms that evaluated millions of possible moves per second. It could process current data and generate responses, but lacked memory or the ability to learn from past experiences.

Reactive machines are task-specific and cannot adapt to new or unexpected conditions. Despite these limitations, they remain integral to automation, where precision and repeatability are more important than learning—such as in manufacturing or assembly-line robotics.

Artificial intelligence is evolving from a niche technology to a global economic powerhouse. According to Precedence Research, the global AI market is expected to expand nearly sevenfold between 2023 and 2034, driven by applications in finance, healthcare, and manufacturing.

Limited Memory AI: Learning from Experience

To overcome the rigidity of reactive machines, researchers developed Limited Memory AI, a model that can store and recall past data to make more informed decisions. This model powers technologies such as self-driving cars, which constantly analyze road conditions, objects, and obstacles, using stored data to adjust their behaviour.

Limited Memory AI is also valuable in financial forecasting, where it uses historical market data to predict trends. However, its memory capacity is still finite, making it less suited for complex reasoning or tasks like Natural Language Processing (NLP) that require deeper contextual understanding.

Theoretical Models: Towards Human-Like Intelligence

Theory of Mind AI

The next conceptual step is Theory of Mind AI, a model designed to understand human emotions, beliefs, and intentions. This approach aims to enable AI systems to interact socially with humans, interpreting emotional cues and behavioral patterns.

Researchers like Neil Rabinowitz from Google DeepMind have developed early prototypes such as ToMnet, which attempts to simulate aspects of human reasoning. ToMnet uses artificial neural networks modeled after brain function to predict and interpret behavior. However, replicating the complexity of human mental states remains a distant goal, and these systems are still largely experimental.

Self-Aware AI: The Future Frontier

The ultimate ambition of AI research is self-aware AI — systems that possess conscious awareness and a sense of identity. While this remains speculative, the potential applications are vast. Self-aware AI could revolutionize fields like environmental management, creating bots capable of predicting ecosystem changes and implementing conservation strategies autonomously.

In education, self-aware systems could understand a student’s cognitive style and deliver personalized learning experiences, adapting dynamically to each learner.

However, replicating human self-awareness is extraordinarily complex. The human brain’s intricate memory, emotion, and decision-making systems remain only partially understood. Additionally, self-aware AI raises profound ethical and privacy concerns, as such systems would require massive amounts of sensitive data. Strict guidelines for data collection, storage, and usage would be essential before such systems could be deployed responsibly.

Artificial Intelligence in the Financial Services Industry

The financial sector has undergone a massive transformation powered by AI-driven analytics, automation, and predictive intelligence. AI enhances Corporate Performance Management (CPM) by improving speed and precision in financial planning, investment analysis, and risk management.

From financial services (38%) to healthcare (35%), sectors are integrating AI to improve efficiency, prediction, and decision-making. Manufacturing, retail, and education are close behind, showing that automation and data intelligence are reshaping every industry

Natural Language Processing and Automation

Leading financial firms such as JPMorgan Chase and Goldman Sachs employ Natural Language Processing (NLP) — AI systems that understand human language — to streamline customer interaction and analyze market information. NLP tools like chatbots handle millions of customer queries efficiently, while advanced systems process unstructured text data from financial reports and news sources to inform investment decisions.

Paired with Optical Character Recognition (OCR) and document parsing, NLP systems can convert scanned or image-based documents into machine-readable text, accelerating compliance checks, fraud detection, and financial forecasting.

However, the accuracy of NLP models depends on the quality and diversity of training data. Biased or incomplete data can lead to errors in analysis, potentially influencing high-stakes financial decisions.

Generative AI in Finance

Another major shift in finance comes from Generative AI, a branch of AI that creates new content — including text, images, videos, and even financial models — based on learned patterns. Using Large Language Models (LLMs) and Generative Adversarial Networks (GANs), these systems simulate complex financial scenarios, improving fraud detection and stress testing.

For instance, PayPal and American Express use generative AI to simulate fraudulent transaction patterns, strengthening their security systems. Transformers — deep learning architectures behind tools like OpenAI’s GPT — enable these models to understand and generate human-like language, allowing them to summarize extensive reports, produce research briefs, and assist analysts in decision-making.

Yet, Generative AI also presents challenges. It can be manipulated through adversarial attacks, producing misleading or biased outputs if trained on flawed data. Ensuring transparency and fairness in training datasets remains critical to prevent discriminatory outcomes, especially in credit scoring and loan assessment.

AI and Automation: Revolutionizing Industry Operations

Artificial Intelligence has become a cornerstone of intelligent automation (IA), reshaping business process management (BPM) and robotic process automation (RPA). Traditional RPA handled repetitive, rule-based tasks, but with AI integration, these systems can now manage complex workflows that require contextual decision-making.

AI-driven automation enhances productivity, reduces operational costs, and increases accuracy. For example, in manufacturing, AI-enabled systems perform predictive maintenance by analyzing sensor data to detect machinery issues before failures occur, minimizing downtime and extending equipment lifespan.

In the automotive sector, AI-powered machine vision systems inspect car components with higher accuracy than human inspectors, ensuring consistent quality and safety. These innovations make automation not only efficient but also economically advantageous for large-scale industries.

Machine Learning: The Engine of Artificial Intelligence

At the heart of AI lies Machine Learning (ML) — algorithms that allow computers to learn from data and improve over time without explicit programming. Three fundamental ML models underpin most modern AI applications: Decision Trees, Linear Regression, and Logistic Regression.

Decision Trees

Decision trees simplify complex decision-making processes into intuitive, branching structures. Each branch represents a decision rule, and each leaf node gives an outcome or prediction. This makes them powerful tools for disease diagnosis in healthcare and credit risk assessment in finance. They handle both numerical and categorical data, offering transparency and interpretability.

Linear Regression

Linear regression models relationships between one dependent and one or more independent variables, making it useful for predictive analytics such as stock price forecasting. It applies mathematical techniques like Ordinary Least Squares (OLS) or Gradient Descent to optimize prediction accuracy. Its simplicity, efficiency, and scalability make it ideal for large datasets.

Logistic Regression

While linear regression predicts continuous outcomes, logistic regression is used for classification problems — determining whether an instance belongs to a particular category (e.g., yes/no, fraud/genuine). It calculates probabilities between 0 and 1 using a sigmoid function, providing fast and interpretable results. Logistic regression is widely used in healthcare (disease prediction) and finance (loan default assessment).

Types of Machine Learning Algorithms

Machine Learning can be broadly classified into Supervised, Unsupervised, and Reinforcement Learning — each suited for different problem types.

Supervised Learning

In supervised learning, algorithms train on labelled datasets to identify patterns and make predictions. Once trained, they can generalize to new, unseen data. Applications include spam filtering, voice recognition, and image classification.

Supervised models handle both classification (categorical predictions) and regression (continuous predictions). Their strength lies in high accuracy and reliability when trained on quality data.

Unsupervised Learning

Unsupervised learning, in contrast, deals with unlabelled data. It identifies hidden patterns or groupings within datasets, commonly used in customer segmentation, market basket analysis, and anomaly detection.

By autonomously discovering relationships, unsupervised learning reduces human bias and is valuable in exploratory data analysis.

Reinforcement Learning (Optional Expansion)

While not yet as mainstream, reinforcement learning trains algorithms through trial and error, rewarding desired outcomes. It is foundational in robotics, autonomous systems, and game AI — including the systems that now outperform humans in complex strategic games like Go or StarCraft.

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Image credit: Gerd Altmann/Pixabay

Ethical and Societal Considerations of AI

Despite its transformative potential, AI raises significant ethical and privacy challenges. Issues such as algorithmic bias, data exploitation, and job displacement are increasingly at the forefront of public discourse.

Ethical AI demands transparent data practices, accountability in algorithm design, and equitable access to technology. Governments and academic institutions, including Capitol Technology University (captechu.edu), emphasize developing AI systems that align with social good, human rights, and sustainability.

Furthermore, the rise of generative AI has intensified debates about content authenticity, intellectual property, and deepfake misuse, underscoring the urgent need for comprehensive AI regulation.

A Technology Still in Transition

Artificial Intelligence stands at the intersection of opportunity and uncertainty. From Deep Blue’s deterministic algorithms to generative AI’s creative engines, the technology has redefined industries and continues to evolve at an unprecedented pace.

While self-aware AI and full cognitive autonomy remain theoretical, the rapid integration of AI across industries signals an irreversible shift toward machine-augmented intelligence. The challenge ahead is ensuring that this evolution remains ethical, inclusive, and sustainable — using AI to enhance human potential, not replace it.

References

  • Artificial Intelligence (AI) Market Size to Reach USD 3,680.47 Bn by 2034 – Precedence Research
  • What is AI, how does it work and what can it be used for? – BBC
  • 10 Ways Companies Are Using AI in the Financial Services Industry – OneStream
  • The Transformative Power of AI: Impact on 18 Vital Industries – LinkedIn
  • What Is Artificial Intelligence (AI)? – IBM
  • The Ethical Considerations of Artificial Intelligence – Capitol Technology University
  • What is Generative AI? – Examples, Definition & Models – GeeksforGeeks

Jay Magiya is a contributor for EdPublica, based in Abu Dhabi, and an A-Level student of Mathematics, Further Mathematics, Economics, and Physics with a deep interest in Artificial Intelligence and its applications in finance. His research explores how machine learning and generative AI models — including decision trees, regression, and GANs — are transforming financial systems and automation. He is keen to continue researching the intersection between the financial industry with theoretical models and their practical implementations of AI in the real world.

Technology

From Tehran Rooftops To Orbit: How Elon Musk Is Reshaping Who Controls The Internet

How Starlink turned the sky into a battleground for digital power — and why one private network now challenges the sovereignty of states

Dipin Damodharan

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From Tehran Rooftops To Orbit: How Starlink Is Reshaping Who Controls The Internet
AI-assisted illustration | S James / EdPublica

On a rooftop in northern Tehran, long after midnight, a young engineering student adjusts a flat white dish toward the sky. The city around him is digitally dark—mobile data throttled, social media blocked, foreign websites unreachable. Yet inside his apartment, a laptop screen glows with Telegram messages, BBC livestreams, and uncensored access to the outside world.

Scenes like this have appeared repeatedly in footage from Iran’s unrest broadcast by international news channels.

But there’s a catch. The connection does not travel through Iranian cables or telecom towers. It comes from space.

Above him, hundreds of kilometres overhead, a small cluster of satellites belonging to Elon Musk’s Starlink network relays his data through the vacuum of orbit, bypassing the state entirely.

For governments built on control of information, this is no longer a technical inconvenience. It is a political nightmare. The image is quietly extraordinary. Not because of the technology — that story is already familiar — but because of what it represents: a private satellite network, owned by a US billionaire, now functioning as a parallel communications system inside a sovereign state that has deliberately tried to shut its citizens offline.

The Rise of an Unstoppable Network

Starlink, operated by Musk’s aerospace company SpaceX, has quietly become the most ambitious communications infrastructure ever built by a private individual.

As of late 2025, more than 9,000 Starlink satellites orbit Earth in low Earth orbit (LEO) (SpaceX / industry trackers, 2025). According to a report in Business Insider, the network serves over 9 million active users globally, and Starlink now operates in more than 155 countries and territories (Starlink coverage data, 2025).

It is the largest satellite constellation in human history, dwarfing every government system combined.

This is not merely a technology story. It is a power story.

Unlike traditional internet infrastructure — fibre cables, mobile towers, undersea routes — Starlink’s backbone exists in space. It does not cross borders. It does not require landing rights in the conventional sense. And, increasingly, it does not ask permission.

Iran: When the Sky Replaced the State

During successive waves of anti-government protests in Iran, authorities imposed sweeping internet shutdowns: mobile networks crippled, platforms blocked, bandwidth throttled to near zero. These tactics, used repeatedly since 2019, were designed to isolate protesters from each other and from the outside world.

They did not fully anticipate space-based internet.

By late 2024 and 2025, Starlink terminals had begun appearing clandestinely across Iranian cities, smuggled through borders or carried in by diaspora networks. Possession is illegal. Penalties are severe. Yet the demand has grown.

Because the network operates without local infrastructure, users can communicate with foreign media, upload protest footage in real time, coordinate securely beyond state surveillance, and maintain access even during nationwide blackouts.

The numbers are necessarily imprecise, but multiple independent estimates provide a sense of scale. Analysts at BNE IntelliNews estimated over 30,000 active Starlink users inside Iran by 2025.

Iranian activist networks suggest the number of physical terminals may be between 50,000 and 100,000, many shared across neighbourhoods. Earlier acknowledgements from Elon Musk confirmed that SpaceX had activated service coverage over Iran despite the lack of formal licensing.

This is what alarms governments most: the state no longer controls the kill switch.

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Infographics: created using NotebookLM. Concept & Analysis: EdPublica. Sources: International Telecommunication Union (ITU) reports; telecom shutdown analyses; SpaceX technical documentation; industry studies

Ukraine: When One Man Could Switch It Off

The power — and danger — of this new infrastructure became even clearer in Ukraine.

After Russia’s 2022 invasion, Starlink terminals were shipped in by the thousands to keep Ukrainian communications alive. Hospitals, emergency services, journalists, and frontline military units all relied on it. For a time, Starlink was celebrated as a technological shield for democracy.

Then came the uncomfortable reality.

Investigative reporting later revealed that Elon Musk personally intervened in decisions about where Starlink would and would not operate. In at least one documented case, coverage was restricted near Crimea, reportedly to prevent Ukrainian drone operations against Russian naval assets.

The implications were stark: A private individual, accountable to no electorate, had the power to influence the operational battlefield of a sovereign war. Governments noticed.

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Infographics: created using NotebookLM. Concept & Analysis: EdPublica. Sources: SpaceX disclosures, Business Insider, satellite trackers, Starlink coverage data

Digital Sovereignty in the Age of Orbit

For decades, states have understood sovereignty to include control of national telecom infrastructure, regulation of internet providers, the legal authority to impose shutdowns, the power to filter, censor, and surveil.

Starlink disrupts all of it.

Because, the satellites are in space, outside national jurisdiction. Access can be activated remotely by SpaceX, and the terminals can be smuggled like USB devices. Traffic can bypass domestic data laws entirely.

In effect, Starlink represents a parallel internet — one that states cannot fully regulate, inspect, or disable without extraordinary countermeasures such as satellite jamming or physical raids.

Authoritarian regimes view this as foreign interference. Democratic governments increasingly see it as a strategic vulnerability. Either way, the monopoly problem is the same: A single corporate network, controlled by one individual, increasingly functions as critical global infrastructure.

How the Technology Actually Works

The power of Starlink lies in its architecture. Traditional internet depends on fibre-optic cables across cities and oceans, local internet exchanges, mobile towers and ground stations, and centralised chokepoints.

Starlink bypasses most of this. Instead, it uses thousands of LEO satellites orbiting at ~550 km altitude, user terminals (“dishes”) that automatically track satellites overhead, inter-satellite laser links, allowing data to travel from satellite to satellite in space, and a limited number of ground gateways connecting the system to the wider internet.

This design creates resilience: No single tower to shut down, no local ISP to regulate, and no fibre line to cut.

For protesters, journalists, and dissidents, this is transformative. For governments, it is destabilising.

A Private Citizen vs the Rules of the Internet

The global internet was built around multistakeholder governance: National regulators, international bodies like the ITU, treaties governing spectrum use, and complex norms around cross-border infrastructure.

Starlink bypasses much of this through sheer technical dominance, and it has become a company that: owns the rockets, owns the satellites, owns the terminals, controls activation, controls pricing, controls coverage zones… effectively controls a layer of global communication.

This is why policymakers now speak openly of “digital sovereignty at risk”. It is no longer only China’s Great Firewall or Iran’s censorship model under scrutiny. It is the idea that global connectivity itself might be increasingly privatised, personalised, and politically unpredictable.

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Infographics: created using NotebookLM. Concept & Analysis: EdPublica. Sources: BNE IntelliNews, Reuters, investigative journalism, activist networks, policy reports

The Unanswered Question

Starlink undeniably delivers real benefits, it offers connectivity in disaster zones, internet access in rural Africa, emergency communications in war, educational access where infrastructure never existed.

But it also raises an uncomfortable, unresolved question: Should any individual — however visionary, however innovative — hold this much power over who gets access to the global flow of information?

Today, a protester in Tehran can speak to the world because Elon Musk chooses to allow it.

Tomorrow, that access could disappear just as easily — with a policy change, a commercial decision, or a geopolitical calculation.The sky has become infrastructure. Infrastructure has become power. And power, increasingly, belongs not to states — but to a handful of corporations.

There is another layer to this power calculus — and it is economic. While Starlink has been quietly enabled over countries such as Iran without formal approval, China remains a conspicuous exception. The reason is less technical than commercial. Elon Musk’s wider business empire, particularly Tesla, is deeply entangled with China’s economy. Shanghai hosts Tesla’s largest manufacturing facility in the world, responsible for more than half of the company’s global vehicle output, and Chinese consumers form one of Tesla’s most critical markets.

Chinese authorities, in turn, have made clear their hostility to uncontrolled foreign satellite internet, viewing it as a threat to state censorship and information control. Beijing has banned Starlink terminals, restricted their military use, and invested heavily in its own rival satellite constellation. For Musk, activating Starlink over China would almost certainly provoke regulatory retaliation that could jeopardise Tesla’s operations, supply chains, and market access. The result is an uncomfortable contradiction: the same technology framed as a tool of freedom in Iran or Ukraine is conspicuously absent over China — a reminder that even a supposedly borderless internet still bends to the gravitational pull of corporate interests and geopolitical power.

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Climate

Ancient lake sediments suggest India’s monsoon was far stronger during medieval warm period

New palaeoclimate evidence from central India suggests that the Indian Summer Monsoon was significantly stronger during the medieval warm period than previously believed

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Ancient lake sediments suggest India’s monsoon was far stronger during medieval warm period
Image credit: Ankit Rainloure/Pexels

India’s monsoon history may be more intense than previously assumed, according to new palaeoclimate evidence recovered from lake sediments in central India. Scientists analysing microscopic pollen preserved in Raja Rani Lake, in present-day Korba district of Chhattisgarh, have found signs of unusually strong and sustained Indian Summer Monsoon rainfall between about 1,060 and 1,725 CE.

The findings come from researchers at the Birbal Sahni Institute of Palaeosciences (BSIP), an autonomous institute under the Department of Science and Technology, and are based on a detailed reconstruction of vegetation and climate in India’s Core Monsoon Zone (CMZ)—the region that receives nearly 90 percent of the country’s annual rainfall from the Indian Summer Monsoon.

Reading climate history from pollen

Researchers extracted a 40-centimetre-long sediment core from Raja Rani Lake. These layers of mud record environmental changes spanning roughly the last 2,500 years. Embedded within them are fossil pollen grains released by plants that once grew around the lake.

By identifying and counting these grains—a method known as palynology—the team reconstructed past vegetation patterns and inferred climate conditions. Forest species that thrive in warm, humid environments point to periods of strong rainfall, while grasses and herbs are indicators of relatively drier phases.

According to the scientists, the pollen record from the medieval period shows a clear dominance of moist and dry tropical deciduous forest taxa. This points to a persistently warm and humid climate in central India, driven by a strong monsoon system, with no evidence of prolonged dry spells within the CMZ during that time.

Medieval Climate Anomaly linked to stronger monsoon

The period of intensified rainfall coincides with the Medieval Climate Anomaly (MCA), a globally recognised warm phase dated to roughly 1,060–1,725 CE. The study suggests that the strengthened Indian Summer Monsoon during this interval was shaped by a combination of global and regional drivers.

In a media statement, the researchers noted that La Niña–like conditions—typically associated with stronger Indian monsoons—may have prevailed during the MCA. Other contributing factors likely included a northward shift of the Inter Tropical Convergence Zone, positive temperature anomalies, higher sunspot numbers and increased solar activity.

Why this matters today

The Core Monsoon Zone is particularly sensitive to fluctuations in the Indian Summer Monsoon, making it a key region for understanding long-term hydroclimatic variability during the Late Holocene (also known as the Meghalayan Age). Scientists say insights from this period are crucial for contextualising present-day monsoon behaviour under ongoing climate change.

The BSIP team said high-resolution palaeoclimate records such as these can strengthen climate models used to simulate future rainfall patterns. Beyond academic interest, the findings have implications for water management, agriculture and climate-resilient policy planning in monsoon-dependent regions.

By revealing that central India once experienced a more intense and sustained monsoon than previously recognised, the study adds a deeper historical perspective to debates on how the Indian monsoon may respond to current and future warming.

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Society

Reliance to build India’s largest AI-ready data centre, positions Gujarat as global AI hub

As part of making Gujarat India’s artificial intelligence pioneer, in Jamnagar we are building India’s largest AI-ready data centre: Mukesh Ambani

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Reliance Industries Limited, India’s largest business group, has announced plans to build the country’s largest artificial intelligence–ready data centre in Jamnagar, a coastal industrial city in the western Indian state of Gujarat, as part of a broader push to expand access to AI technologies at population scale.

The announcement was made by Mukesh Ambani, chairman and managing director of Reliance Industries, during the Vibrant Gujarat Regional Conference for the Kutch and Saurashtra region, a government-led investment and development forum focused on regional economic growth.

Ambani said the Jamnagar facility is being developed with a single objective: “Affordable AI for every Indian.” He positioned the project as a foundational investment in India’s digital infrastructure, aimed at enabling large-scale adoption of artificial intelligence across sectors including industry, services, education and public administration.

“As part of making Gujarat India’s artificial intelligence pioneer, in Jamnagar we are building India’s largest AI-ready data centre,” Ambani said, adding that the facility is intended to support widespread access to AI tools for individuals, enterprises and institutions.

Reliance also announced that its digital arm, Jio, will launch a “people-first intelligence platform,” designed to deliver AI services in multiple languages and across consumer devices. According to Ambani, the platform is being built in India for both domestic and international users, with a focus on everyday productivity and digital inclusion.

The AI initiative forms part of Reliance’s broader commitment to invest approximately Rs 7 trillion (about USD 85 billion) in Gujarat over the next five years. The company said the investments are expected to generate large-scale employment while positioning the region as a hub for emerging technologies.

The Jamnagar AI data centre is being developed alongside what Reliance describes as the world’s largest integrated clean energy manufacturing ecosystem, encompassing solar power, battery storage, green hydrogen and advanced materials. Ambani said the city, historically known as a major hub for oil refining and petrochemicals, is being re-engineered as a centre for next-generation energy and digital technologies.

The announcements were made in the presence of Indian Prime Minister Narendra Modi and Gujarat Chief Minister Bhupendra Patel, underscoring the alignment between public policy and private investment in India’s long-term technology and infrastructure strategy.

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