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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.

Society

How a South Indian Startup Is Reimagining Agriculture From the Sky

From flood-ravaged fields in Kerala to precision farming systems powered by drones, Fuselage Innovations is rethinking agriculture through data, efficiency, and real-time intelligence.

Rishika Nair

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How Drone Technology In Agriculture Is Helping a South Indian Startup Reimagine farming
Image credits: Fuselage Innovations

Drone technology in agriculture is rapidly changing how farmers monitor crops, manage resources and improve productivity. A South Indian startup is now using aerial innovation and precision farming tools to reshape agriculture from the sky

In 2018, catastrophic floods swept across South Indian state of Kerala, submerging farmland and leaving behind more than visible damage. When the waters receded, they revealed a deeper crisis—soil chemistry had changed, salinity had increased, and farming systems that had sustained communities for generations no longer behaved the same way.

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For many farmers, the land had become unfamiliar.

For Devan Chandrasekharan, an aeronautical engineer with roots in farming, this moment marked a turning point.

“That moment made it clear that agriculture needed more than incremental change,” he says. “It needed a different way of understanding what’s happening in the field.”

Today, as co-founder of Fuselage Innovations, a Kerala-headquartered agritech company with operations expanding across southern India and early international pilots, Devan is part of a new wave of innovators rethinking agriculture through technology.

Drone technology in agriculture being used above farmland for crop monitoring and precision spraying in modern farming.
Image credits: Fuselage Innovations

Drone Technology in Agriculture: From Fields to Flight Paths

Modern agriculture is increasingly shaped by data. But while satellite systems offer scale, they often lack immediacy. Cloud cover, delays, and low resolution limit their usefulness in time-sensitive decisions.

“In farming, timing is everything,” Devan notes. “If you cannot act at the right moment, even the best data loses its value.”

Fuselage Innovations addresses this gap using drones equipped with multispectral sensors, capable of capturing real-time, high-resolution data directly from the field. These systems detect early signs of stress—nutrient deficiencies, pest risks, or water imbalances—long before they become visible.

Farming as a Predictive System

The company’s approach goes beyond aerial imaging. It is built around a stage-wise model that tracks crop growth from early development to harvest, linking each phase to targeted interventions.

This transforms farming from a reactive process into a predictive one.

“Instead of responding to visible damage, we can identify stress signals early and intervene precisely,” Devan says. “That changes the entire economics of farming.”

The results are significant. Field applications have shown yield increases of up to 35 percent, alongside a reduction of nearly 50 percent in pesticide and fertiliser use. Precision spraying has also cut input volumes dramatically—from 150–200 litres per acre to just 10–15 litres—reducing both costs and environmental impact.

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Scaling Beyond Boundaries

While the company’s early work was rooted in Kerala, its reach has expanded into Tamil Nadu and other parts of India, with pilot projects now extending to international markets such as Canada.

“Farming challenges may vary across regions, but the need for efficiency, sustainability, and better decision-making is universal,” Devan says.

Yet adoption remains a challenge. Farming is inherently risk-sensitive, and new technologies are often met with caution. To address this, the company initially offered its services free of cost, allowing farmers to see results before committing.

“Trust is the biggest barrier,” Devan says. “Farmers need to see the impact on their own fields before they adopt something new.”

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Devika Chandrasekharan, Devan Chandrasekharan

The Future from Above

As climate pressures intensify and resource constraints deepen, agriculture is entering a new phase—one where data and precision will define productivity.

“Technology alone cannot solve agriculture,” Devan emphasises. “But when it is aligned with the realities of farmers and ecosystems, it can become a powerful tool for transformation.”

What began in the aftermath of a flood has now evolved into a model for the future—where farming is not just guided by tradition, but informed by intelligence.

Because the future of agriculture may not lie only in the soil—but in how we see it from above.

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The Coal Paradox: More Coal Plants, Less Coal Power

A new Global Energy Monitor report shows global coal capacity rising in 2025 even as coal-fired electricity generation declines amid rapid renewable energy growth.

Rishika Nair

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Image credit: Dapur Melodi /Pexels

The world is building more coal plants, but using less coal than before. That contradiction lies at the centre of a new report by Global Energy Monitor (GEM), an international organisation that tracks energy infrastructure and the global shift toward cleaner power.

According to GEM, whose databases and research are widely used by institutions including the IPCC, IEA, UNEP and the World Bank, countries are continuing to expand coal power infrastructure even as coal’s role in electricity generation weakens globally.

The latest edition of GEM’s Boom and Bust 2026 report found that global coal power capacity grew by 3.5% in 2025, while coal-fired electricity generation declined by 0.6%. The report describes the trend as a major structural shift in the global energy system, where coal remains politically important in several countries even as renewable energy increasingly replaces it in practice.

China and India Drive Coal Growth

The contradiction is most visible in China and India, the world’s two largest coal consumers. Both countries commissioned large amounts of new coal capacity in 2025, even as coal generation declined because of record additions in solar and wind power.

China expanded coal capacity by 6% in 2025, while coal-fired generation fell by 1.2%. India recorded a similar pattern, with coal capacity increasing by 3.8% even as coal generation dropped by 2.9%.

The report suggests that coal’s decline is becoming increasingly durable despite global energy uncertainties, including geopolitical tensions affecting fuel supply routes such as the Strait of Hormuz. Renewable energy expansion has continued rapidly enough to reduce coal’s role in meeting new electricity demand.

Christine Shearer, Project Manager of GEM’s Global Coal Plant Tracker, described the trend as a defining paradox of the global energy transition.

“In 2025, the world built more coal and used it less,” she said. She added that 95% of all coal plant construction is now concentrated in China and India, even as both countries expand renewable energy fast enough to displace coal generation.

China’s Coal Pipeline Continues to Surge

China remained the dominant force in global coal expansion during 2025. The country recorded a record 161.7 GW of new and revived coal projects, while more than 500 GW of coal-fired capacity is currently under development.

The report warned that if these projects move ahead, China could remain locked into years of additional coal use throughout its 15th Five-Year Plan period from 2026 to 2030, despite official commitments to reduce coal consumption during the same timeframe.

India Expands Coal While Renewables Accelerate

India is also continuing major coal expansion plans. The country recorded 27.9 GW of new and revived coal proposals in 2025. Overall, India now has more than 107 GW of coal capacity in pre-construction planning and another 23.5 GW already under construction.

The Indian government has announced plans to add 100 GW of new coal capacity over the next seven years, even as renewable energy growth continues at record pace. In 2025, non-fossil fuel sources crossed the milestone of accounting for more than half of India’s installed electricity capacity.

Coal Development Shrinks Outside Asia

Outside China and India, coal development is shrinking rapidly. Only 32 countries were proposing or building new coal plants in 2025, down from 38 countries the previous year and less than half the 75 countries pursuing coal expansion in 2014.

Coal construction activity outside China and India accounted for just 5% of global coal construction capacity in 2025, marking a record low and highlighting how geographically concentrated coal development has become.

Several regions also made notable progress away from coal. Latin America achieved “No New Coal” status in 2025, while South Korea committed to a complete coal phaseout.

Türkiye, which is preparing to host COP31, now has only one active coal plant proposal remaining, compared with more than 70 proposed projects in 2015.

Delayed Coal Retirements Raise Concerns

The report also found that retirement plans for existing coal plants are slowing in several regions. Nearly 70% of coal-fired units scheduled for retirement globally in 2025 failed to retire as planned.

In the European Union, many delays were linked to energy security concerns that emerged during the 2022–23 energy crisis. In the United States, several ageing coal plants remained operational because of direct government interventions aimed at maintaining grid reliability.

Indonesia continued expanding its coal fleet, which grew by 7% in 2025, largely driven by captive coal plants supporting nickel and aluminium processing industries.

South Asia and Southeast Asia Show Mixed Trends

Elsewhere in South Asia, Pakistan rapidly expanded distributed solar energy, helping stabilise its electricity system against volatile fossil fuel markets. Bangladesh, meanwhile, continues to face fuel supply and technical challenges linked to its fossil-fuel-based power sector.

Across Southeast Asia outside Indonesia, coal commissioning declined for the third consecutive year. However, disruptions in regional gas supplies during 2026 led some countries to rely more heavily on existing coal infrastructure as a temporary backup source.

In Africa, new coal proposals remain limited and are mainly concentrated in Zimbabwe and Zambia.

Renewable Energy Reshapes the Global Energy Transition

The report concludes that coal is no longer expanding as a universally accepted solution for rising electricity demand. Instead, coal development is increasingly concentrated in a small number of countries, even as renewable energy demonstrates its ability to meet growing demand more efficiently and sustainably.

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India Built the Pipes. Now It Needs Better Water Data

JalSoochak is helping strengthen rural water delivery in India by turning paper-based records into real-time data for faster monitoring and response.

Rishika Nair

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Digital monitoring system used to improve rural water delivery under India’s Jal Jeevan Mission.
Jalsoochak is a platform designed to make frontline water delivery measurable, verifiable, and useful, all the way up the system. Image credit: By Special Arrangement

>> Rural water delivery in India has expanded rapidly under the Jal Jeevan Mission. But ensuring that water actually reaches homes every day now depends on better data, real-time monitoring, and systems like JalSoochak.

India built the pipes. Now comes the harder part.

Under the Jal Jeevan Mission (JJM), more than 1.5 crore rural households have been connected to piped water supply — a number that would have been unthinkable a decade ago. But connection is not the same as service. The pipe in the ground tells you nothing about whether water came out of the tap this morning, in what quantity, or whether the source feeding it is under stress.

That gap — between infrastructure built and service delivered — is where India’s rural water systems are now being tested. And it is a gap that turns, fundamentally, on data.

Why Rural Water Delivery Depends on Better Data

Pump operators and Jal Mitras are the ones who know. They manage supply cycles, monitor pumps, and record water delivery across thousands of villages every day. But in most states, those records live in paper registers. They cannot be verified, compared across districts, or acted on quickly. By the time a problem surfaces through the usual channels, it has often been festering for weeks. Engineers and administrators are left reconciling inconsistent figures instead of responding to the thing that actually went wrong.

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Arghyam, a Bengaluru-based philanthropic organisation founded by Rohini Nilekani, has been working on this problem. In partnership with Assam’s Public Health Engineering Department (PHED), it developed JalSoochak (Water indicator) — a platform designed to make frontline water delivery measurable, verifiable, and useful, all the way up the system.

How JalSoochak Is Transforming Rural Water Delivery

“Since the expansion of rural water infrastructure, understanding what is actually happening on the ground at scale has remained difficult. JalSoochak addresses this by enabling frontline workers to capture a simple image as evidence of water supply, while also giving Jal Mitras a verifiable record of their service delivery and attendance,” said Kailash Karthik, Secretary, Public Health Engineering Department, Government of Assam and Mission Director, Jal Jeevan Mission Assam.

The tool itself is straightforward. A frontline worker photographs a meter reading on their mobile phone. The image is processed using AI, the user verifies the reading, and it is logged as a daily record. What used to be a handwritten entry in a register — easily disputed, easily lost — becomes a time-stamped, verifiable data point that engineers, block-level officers, and state administrators can all see and act on.

JalSoochak platform supporting rural water delivery monitoring in Indian villages.
JalSoochak platform supporting rural water delivery monitoring in Indian villages. Image credit: By special arrangement

Accumulated over months, those daily records start to show things that no single entry would. A supply dip that recurs every fortnight. A pump whose readings are quietly declining. A source under pressure before anyone has formally flagged it. Problems get caught earlier, and the people responsible for fixing them have the evidence they need to act.

How Assam Is Digitising Rural Water Delivery

The numbers from Assam are substantial. More than 16,500 pump operators now use JalSoochak, collectively logging over 20 lakh readings. Together, those entries account for more than 37,600 million litres of water supply recorded.

Assam also made something else clear: what works in one state will not simply work everywhere. Each state has its own administrative logic, its own infrastructure, its own ways of capturing supply data. JalSoochak had to be rebuilt to absorb that variation rather than ignore it.

The platform now supports multiple modes of input — bulk flow meters, electric meter readings, pump operation duration, IoT devices, and manual entries. It works in local languages. Rather than running parallel to existing government systems, it is built to plug into them, so the data flows to where decisions are actually made, without creating extra work for anyone in the chain.

“JalSoochak is not just a technology platform. It is an attempt to strengthen service delivery to ensure that the investments made in rural water systems translate into reliable services for people. The journey from Assam to a national scale Digital Public Good has been about one core idea: making data useful for action, where it matters most,” said Deepak Gupta, Director of Digital Infrastructure and Government Partnerships, Arghyam.

JalSoochak is part of a broader effort to build a Digital Public Infrastructure for India’s water sector — a set of open, interoperable systems through which data can move across programmes and institutions, enabling governments to respond to problems where and when they actually occur, rather than when they finally show up in a report.

Crores of households now have a connection. The question that follows is simpler, and harder: is the water actually there? Getting a reliable answer to that question, consistently, across every village and every state, is what the next phase of rural water delivery will depend on.

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