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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

West Asia crisis could threaten 12 million Indian livelihoods, says new study — but green transition may create 35 million jobs

West Asia crisis could threaten 12 million Indian livelihoods, but a green transition may create 35 million jobs in India by 2047, says study.

Dipin Damodharan

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West Asia Crisis Could Put 12 Million Indian Livelihoods at Risk, Study Sees 35 Million Green Jobs Ahead
Image credit: Aziz Özden/Pexels

A new policy brief released by IPE Global has warned that the ongoing geopolitical instability in West Asia could place nearly 10–12 million Indian livelihoods at risk, particularly in sectors linked to energy, agriculture and migration-dependent economies. But the report also argues that the same crisis could accelerate India’s transition toward a green economy capable of generating nearly 35 million jobs by 2047.

The peer-reviewed study, “Paving a Green Transition: A New Social Contract Amid West Asia Crisis,” released in New Delhi on June 18, outlines 30 policy recommendations aimed at aligning India’s existing climate, agriculture and industrial schemes into a coordinated transition strategy.

According to the report, India already has the institutional architecture needed for a large-scale green transition through programmes such as PM-KUSUM, the National Green Hydrogen Mission, Production Linked Incentive (PLI) schemes, the Carbon Credit Trading Scheme (CCTS), PM-Pranam and RDSS. However, these schemes currently operate in silos, limiting their impact.

“The West Asia crisis has exposed how closely energy security, food security, livelihoods and climate resilience are tied together,” said Ashwajit Singh, Founder and Managing Director of IPE Global. “When 10 to 12 million livelihoods sit at the intersection of SDG 2, SDG 7, SDG 8 and SDG 13, the only meaningful response is convergence.”

India’s energy dependence under scrutiny

The report notes that India imports nearly 85% of its crude oil requirements and continues to depend heavily on imported fertilisers and fossil fuel-linked industrial inputs.

This dependence, researchers argue, leaves the country vulnerable to geopolitical shocks originating in West Asia. Rising fuel prices, supply chain disruptions and inflationary pressures have already begun affecting key sectors.

“The numbers tell a story India cannot afford to ignore,” said Abinash Mohanty, Head of Climate Change and Sustainability Practice at IPE Global and lead author of the study. “With 85 per cent of our crude oil imported, and 10 to 12 million livelihoods exposed to a single geopolitical shock from West Asia, the fragility is real. But so is the opportunity.”

The report estimates that India could mobilise a funding cushion of nearly USD 42–53 billion from existing schemes without requiring substantial new financing. It further projects that a coordinated green transition could contribute to a USD 15 trillion green economy by 2070.

Kerala among states most vulnerable

The study identifies Kerala, Uttar Pradesh and Bihar as among the states most exposed to job losses linked to the West Asia crisis because of their high dependence on Gulf migration and remittances.

Kerala alone could see between 1.5 and 2 million livelihoods at risk, according to the estimates. However, the report projects that the state’s green jobs absorption potential may remain relatively limited at around one million jobs by 2047.

In contrast, states such as Rajasthan and Gujarat — with stronger renewable energy infrastructure and industrial corridors — are expected to generate significantly larger green employment opportunities. Rajasthan alone could create nearly five million green jobs, while Gujarat may generate around 4.5 million.

The report describes this as a “geographic mismatch problem,” where workers most vulnerable to job losses are not necessarily located in regions where new green jobs are emerging. Researchers say this has implications for migration policy, skilling programmes and regional investment planning.

West Asia crisis could threaten 12 million Indian livelihoods, but a green transition may create 35 million jobs in India by 2047, says study.
Source: IPE Global

Farmers as energy producers

One of the central recommendations in the study is to reframe PM-KUSUM into a “Farmer-as-Energy-Producer” programme. The proposal aims to enable farmers to generate and sell surplus solar power to distribution companies through decentralised solar infrastructure.

According to the report, this intervention alone could create 15 lakh green jobs, generate 50,000 MW of agri-solar capacity and increase annual farmer incomes by ₹25,000–40,000. It could also reduce nearly 70 million tonnes of carbon dioxide equivalent emissions annually.

The agriculture sector recommendations also include scaling natural farming to 50 million hectares, integrating carbon markets into agriculture and strengthening climate-resilient farming systems through digital platforms and weather-linked advisory services.

Green hydrogen and industrial transition

The report argues that India’s clean energy transition must move beyond renewable energy generation and focus equally on storage, grid infrastructure and industrial demand creation.

It proposes an Emergency Grid Acceleration Programme to support India’s target of 500 GW renewable energy capacity. According to the study, achieving this target could generate 3.4 million jobs and avoid nearly 700 million tonnes of carbon emissions annually.

The National Green Hydrogen Mission is also positioned as a major employment driver, with the report estimating 1.5–2 million jobs across the hydrogen value chain.

On the industrial front, the study recommends establishing a National Green Steel Mission to protect India’s export competitiveness amid tightening carbon regulations such as the European Union’s Carbon Border Adjustment Mechanism (CBAM).

Researchers estimate that industrial decarbonisation, EV manufacturing and green supply chains together could generate over 20 million green jobs.

‘Cost of delay is now higher than transition’

The report concludes that India’s challenge is no longer technological but institutional. Most of the necessary policies, financing structures and sectoral schemes already exist, it argues. What remains missing is coordination across sectors and ministries.

“This crisis isn’t asking India to choose between resilience and growth,” Mohanty said. “It’s showing us they were always the same investment.”

The study ultimately frames India’s green transition not merely as a climate obligation, but as a strategic response to energy insecurity, geopolitical instability and long-term economic resilience.

“The cost of delays in action now exceeds the cost of transition,” the report states.

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Climate

Japan’s US LNG Trade Leaves Asia With Emissions Equal to 17 Coal Plants

Japan US LNG trade generated lifecycle emissions equal to about 17 coal plants in a year, according to a new analysis, raising concerns about Asia’s growing dependence on imported gas.

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Japan US LNG trade generated lifecycle emissions equal to about 17 coal plants in a year, according to a new analysis
Image credit/Tom Fisk/Pexels

As Japan expands its role as a global gas trader, a new analysis raises questions about whether Asia is importing energy security—or future climate liabilities. Japan US LNG trade generated lifecycle emissions equal to about 17 coal plants in a year, raising concerns about Asia’s growing dependence on imported gas.

The liquefied natural gas (LNG) cargoes that Japan resold across Asia over the past five years generated greenhouse gas emissions equivalent to running about 17 coal-fired power plants for a year, according to a new analysis by Zero Carbon Analytics.

The finding comes at a time when several Asian economies are turning to LNG as a bridge fuel in their energy transition strategies, while governments simultaneously pledge to cut emissions and expand renewable energy.

According to the analysis, Japan resold 16.5 billion kilograms of US-produced LNG to nine Asian countries between 2020 and 2025. Across the fuel’s lifecycle—from extraction and liquefaction in the United States to shipping, regasification and combustion in Asia—those sales generated an estimated 63.5 billion kilograms of carbon dioxide emissions.

The report highlights a little-discussed aspect of Asia’s gas trade: Japan is increasingly acting as a middleman in the global LNG market.

Japan’s US LNG Trade–Japan Now Resells More US LNG Than It Uses

Japan remains one of the world’s largest LNG importers, but its domestic demand for gas has been declining.

The analysis found that between 2021 and 2025, Japan sold 77 percent more US LNG to other countries than it imported for its own domestic consumption.

In 2024, Japan ranked as the world’s second-largest LNG trader. While Europe remained the largest destination for Japanese LNG resales, nearly one-third of those transactions were directed to Asian markets, including South Korea, China, India, Taiwan, Thailand, Singapore, Bangladesh, Pakistan and Malaysia.

Three of Japan’s top ten LNG resale destinations were Asian economies: South Korea, China and India.

The numbers reflect a broader shift in regional energy markets. Countries seeking alternatives to coal have increasingly turned to LNG, often presenting gas as a cleaner transition fuel. Yet critics argue that this framing overlooks emissions generated throughout the fuel supply chain.

The Methane Problem

Natural gas is composed primarily of methane, a greenhouse gas that has far greater warming potential than carbon dioxide in the short term.

According to the International Energy Agency’s 2026 Global Methane Tracker, methane emissions from fossil fuel operations remain near record levels globally.

The Zero Carbon Analytics analysis estimates that roughly 30 percent of total LNG lifecycle emissions arise from methane released during extraction, processing and transportation.

Methane can trap around 80 times more heat than carbon dioxide during the first two decades after it enters the atmosphere, making leakage a critical concern for climate scientists.

The report’s emissions calculations include every stage of the LNG supply chain rather than focusing solely on combustion emissions at power plants.

Energy Security or Fossil Fuel Lock-In?

The findings arrive amid renewed concerns over energy security following instability in the Middle East and uncertainty surrounding global gas supplies.

Several Asian economies, including Thailand, Vietnam and the Philippines, have expanded LNG imports in recent years to diversify their energy systems. However, the same dependence has exposed them to volatile international fuel prices.

Yu Sun Chin, Asia Regional Researcher at Zero Carbon Analytics, said the growing trade has implications beyond emissions.

“Japan’s growing role as an LNG trader has significant implications for Asia, which is absorbing close to a third of Japan’s excess supplies. Our calculations of the full lifecycle emissions of these LNG resales highlight the risk they pose to a region already vulnerable to extreme weather and other climate impacts. Rather than increasing reliance on gas as a ‘transition fuel’, transitioning to renewables offers Asia a clearer route to a clean and secure energy future.”

The concern is not merely about current emissions. Energy analysts warn that investments in LNG terminals, pipelines and related infrastructure could lock countries into fossil fuel consumption for decades.

Sam Reynolds, LNG and Gas Research Lead for Asia at the Institute for Energy Economics and Financial Analysis (IEEFA), noted that Japanese companies are increasingly looking abroad as domestic demand declines.

“As Japan’s own LNG demand continues to decline, Japanese companies are becoming increasingly active traders of the fuel to other countries. At the same time, public and private financiers in Japan are investing in downstream infrastructure to stimulate demand and secure long-term customers.”

He added that such investments could leave emerging economies dependent on “a volatile, expensive fuel source for decades” while delaying renewable energy deployment.

Asia’s Climate Challenge

Asia is simultaneously one of the world’s fastest-growing energy markets and one of the regions most vulnerable to climate impacts.

From deadly heatwaves in South Asia to flooding in China and stronger tropical cyclones across Southeast Asia, the region is already experiencing the consequences of rising temperatures.

Climate scientists estimate that global emissions must nearly halve within this decade to keep the Paris Agreement’s 1.5°C goal within reach.

Against that backdrop, environmental groups argue that expanding LNG infrastructure risks undermining climate commitments.

Shruti Shukla, Senior Advocate for International Energy at the Natural Resources Defense Council (NRDC), said the region faces a strategic choice.

“Japan has long positioned itself as a regional energy and economic leader in Asia. That leadership should help accelerate a resilient clean energy transition across the region, not deepen dependence on another generation of imported fossil fuels.”

She warned that growing LNG imports expose countries to methane emissions, volatile fuel markets and costly infrastructure that could become obsolete as renewable technologies become cheaper.

The Economic Risks

The debate extends beyond climate concerns.

Researchers increasingly point to the possibility that LNG infrastructure built today may become stranded assets before the end of its expected lifespan.

Nawaphat Junkrajang, senior researcher at Climate Finance Network Thailand, cited research suggesting that nearly half of Thailand’s operating and proposed LNG terminal capacity could become economically unviable under the country’s climate commitments.

“Each additional resale cargo is not energy security. It is one more step into a lock-in the transition will eventually have to unwind,” he said.

Bangladesh faces similar concerns.

Dr Khondaker Golam Moazzem, Research Director at the Centre for Policy Dialogue, said new energy agreements and infrastructure investments could deepen dependence on imported LNG while narrowing opportunities for renewable energy investment.

A Growing Regional Debate

The analysis arrives as governments across Asia reassess their energy pathways.

Supporters of LNG argue that gas provides reliable electricity generation and can complement intermittent renewable sources. Critics counter that falling costs of solar, wind and battery storage are weakening the economic rationale for large-scale LNG expansion.

What is clear from the data is that Japan’s role in regional gas markets is evolving rapidly. The country is no longer simply a major LNG consumer; it has become a significant intermediary connecting US gas producers with Asian buyers.

As Asia balances energy security, affordability and climate goals, that role is likely to attract increasing scrutiny.

For policymakers, the question may no longer be whether LNG emits less carbon than coal at the point of combustion. Instead, it is whether a region racing to build a low-carbon future can afford to lock itself into another generation of fossil fuel infrastructure.

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Society

From Bell Labs to the Classroom: A Second Career in Teaching

In this edition of Second Act, Sudhir Ambekar reflects on a journey that spans engineering, cutting-edge research, and an unexpected second career in teaching—revealing how purpose can evolve long after retirement

Sudhir M. Ambekar

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About Second Act
Second Act is an EdPublica series that explores the lives of individuals who have embraced a new calling after a successful first career. Through their journeys, the series examines purpose, reinvention, lifelong learning, and the impact of experience in shaping meaningful contributions to society.

This edition explores how former Bell Labs researcher Sudhir Ambekar transitioned from engineering and telecommunications research to the classroom. His journey from IIT Bombay and UC Berkeley to teaching mathematics reveals how purpose, curiosity and lifelong learning can shape a meaningful second act.

I was born in Mumbai (formerly Bombay), but my formative years were shaped in Kolkata (formerly Calcutta), where I completed my high school education. From there, I entered IIT Bombay to study mechanical engineering, graduating in 1965. After a brief stint at a small company in Thane, I left for the University of California, Berkeley—an experience that would shape the trajectory of my professional life.

At Berkeley, I chose to pursue a Doctor of Engineering rather than a traditional PhD. The distinction mattered to me. While a PhD was more research-oriented, the Doctor of Engineering emphasised applied work—something I was drawn to because I preferred seeing tangible results sooner rather than later.

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Bell Laboratories at Murray Hill, New Providence, New Jersey. Image credit: Ken Lund/Wikimedia Commons

My research focused on joining TRIP (Transformation Induced Plasticity) steel, a specialised material being developed at the Lawrence Radiation Laboratory. TRIP steel has the remarkable ability to retain the sharpness of a cutting edge even after repeated use. Under stress, its internal structure transforms in a way that preserves strength. Welding, however, typically weakens metal at the joint. My work aimed to solve precisely that problem: how to retain strength even after welding.

After completing my graduate work, I joined Bell Labs, then the research and development arm of AT&T. Bell Labs was an extraordinary place—not because it assigned people to narrowly defined roles, but because it brought together individuals who could contribute across a wide range of problems.

During my time there, I worked on developing micro gold crossovers on ceramic substrates, a technology used in high-density electronic components for advanced telecommunications systems. Over the years, I participated in both development and research projects. Development projects were implemented in real-world systems, while research projects explored possibilities that often pushed the boundaries of what seemed feasible at the time.

In one such project, I was part of a team that demonstrated the feasibility of transmitting voice, data, and video simultaneously over household electrical wiring—an idea that anticipated a future where any data device could simply be plugged into a wall, much like an electrical appliance. In another, I worked with a colleague who built a prototype computer, roughly the size of a desktop, capable of supercomputer-level performance using commercially available components. Although the technology was not adopted due to the scale of software changes required, it reflected the kind of forward-thinking work that defined Bell Labs in the early 1980s.

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Alongside this professional work, I found myself drawn to teaching in an unexpected way. Within the Indian community, we started a small Marathi school as a voluntary initiative. Despite having no formal training as a teacher—and limited formal grounding in Marathi myself, having grown up outside Maharashtra—I decided to teach.

That decision changed something fundamental for me.

I realised that one of the best ways to truly learn a subject is to teach it. My own command of Marathi improved significantly, but more importantly, I discovered that I enjoyed teaching deeply. It offered a kind of immediacy and human connection that was different from research.

Circumstances eventually led me to retire earlier than I had expected. But rather than seeing retirement as an end, I began to think of it as an opportunity.

Teaching, I realised, was something I could carry into my later years—not just as an occupation, but as a source of purpose.

I had already helped my children with mathematics during their high school years, and I had noticed that the way mathematics was taught in the United States differed significantly from how I had learned it in India. Curious and motivated, I decided to pursue teaching more seriously.

To do so, I enrolled in a year-long certification programme to become a high school mathematics teacher. It was a humbling experience—returning to the classroom, this time as a learner preparing to teach.

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After certification, I began teaching full-time. This marked the beginning of my second career.

It was, in many ways, a completely new world.

This is the first part of a two-part series. The concluding part will appear in the next issue of Education Publica.

Sudhir M. Ambekar is a mechanical engineer trained at IIT Bombay and the University of California, Berkeley. He spent nearly three decades at Bell Labs working in telecommunications research and development. After retirement, he became a certified mathematics teacher and now tutors students for SAT and ACT college entrance examinations.

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