Society
Why AI will be the Catalyst for a new era of productivity growth

The dawn of the artificial intelligence (AI) era is often compared to transformative technological advancements such as the steam engine, electricity, and the personal computer. These innovations reshaped industries and daily life, and AI is poised to make an equally revolutionary impact, particularly on global productivity. While the effects of AI are still unfolding, experts believe that its ability to significantly boost productivity could happen in record time—just seven years, compared to decades for earlier technological revolutions.
This optimism comes at a critical juncture in the global economy. Post-pandemic, many countries are grappling with stagnating growth, rising inflation, and mounting debt, alongside the fundamental issue of declining productivity. In fact, several international agencies have noted that the productivity decline following the global economic downturn is unprecedented in recent history. Yet, AI is emerging as a way of hope, offering the potential not only to reverse this trend but to propel productivity to unprecedented heights.

The Economic Impact of AI: A Long-Awaited Leap
The global economy has struggled with low productivity growth for over a decade. For example, U.S. labour productivity growth averaged just 1.68% from 1998 to 2007, a period during which significant technological innovations like the internet and personal computers began to take root. But since 2010, productivity growth has fallen further, dipping to 0.38% between 2010 and 2019.
Some forecasts suggest that generative AI alone could add between $2.6 trillion and $4.4 trillion to the global economy
In this environment, AI is seen as the key to unlocking a new wave of economic efficiency. According to recent reports from the International Monetary Fund (IMF), AI technologies are expected to drive a substantial increase in global productivity. Some forecasts suggest that generative AI alone could add between $2.6 trillion and $4.4 trillion to the global economy.
To understand the potential of AI in the context of productivity growth, it’s useful to compare it to previous technological breakthroughs. The steam engine, for example, took about 60 years to fully transform productivity in manufacturing. Personal computers accelerated productivity growth over 15 years. By contrast, AI is expected to have a profound impact on productivity within just seven years.
Generative AI and Its Promising Future
Generative AI is a form of artificial intelligence that creates new content—whether it’s text, images, or even software code—based on patterns learned from large datasets. The speed with which generative AI is advancing is extraordinary. ChatGPT, released in November 2022, was quickly followed by a more advanced version, GPT-4, and other breakthroughs have appeared throughout 2023. This technology is expanding rapidly, with the capability to process tens of thousands of words in a minute, creating a powerful tool for automating complex tasks.
The applications of generative AI are vast and varied. In the business world, AI systems are already transforming industries like customer operations, marketing, software engineering, and research and development. The banking sector, for example, is projected to see an annual revenue increase of $200 billion to $340 billion through the adoption of AI. The retail and consumer goods sectors could see similar gains, potentially adding up to $600 billion annually.
AI’s potential to automate routine tasks could also free up significant amounts of time for human workers. Studies indicate that generative AI could automate between 60% and 70% of the tasks currently performed by employees, dramatically increasing efficiency. For knowledge-based workers, particularly in high-wage and high-skill sectors, AI is poised to amplify productivity by reducing time spent on routine tasks, such as data analysis, customer service, and administrative work.
Transforming Labour Markets: A Double-Edged Sword
However, the rapid rise of AI is not without its challenges, particularly when it comes to labor markets. Many fear that the widespread adoption of AI could lead to massive job displacement, especially in developed countries where white-collar jobs are more susceptible to automation. According to the IMF, while 30% of U.S. jobs may be at risk of automation by AI, only 13% of jobs in India are likely to be affected, reflecting the differing technological capabilities and labor market structures across the globe.
At the same time, AI’s integration into the economy is expected to create new job opportunities, especially in fields that require advanced technical skills, such as AI development, data science, and cybersecurity. This pattern mirrors historical trends: when previous technological revolutions disrupted the labor market, they also created entirely new industries and job categories. A recent study by MIT found that 60% of the jobs in America today did not exist in 1940, highlighting the constant evolution of the labor market in response to technological innovation.
AI’s Role in Healthcare: Beyond Productivity
AI’s potential extends far beyond traditional sectors like manufacturing or finance. The healthcare industry stands to benefit greatly from AI’s ability to analyze vast amounts of medical data quickly and accurately. For example, AI systems can assist doctors by analyzing scan reports, identifying patterns, and recommending treatment protocols. AI can also reduce the burden of administrative tasks, such as summarizing doctors’ notes and processing insurance claims, thereby improving productivity in healthcare settings while also reducing costs.
Generative AI is now widely recognized as a general-purpose technology (GPT), similar to electricity or the personal computer
Such advancements could lead to significant improvements in healthcare delivery, making it more efficient and cost-effective. This would not only improve outcomes for patients but also contribute to economic growth by lowering healthcare costs for both consumers and governments.
The Path Forward
Generative AI is now widely recognized as a general-purpose technology (GPT), similar to electricity or the personal computer. These technologies have historically contributed to broad-based productivity growth across multiple sectors. The key to AI’s success as a GPT lies in its ability to integrate seamlessly with existing technologies and applications across various industries, driving continuous innovation and productivity gains.
The widespread adoption of AI in industries like logistics, manufacturing, education, and even creative arts has the potential to revolutionize how businesses operate and how workers contribute. As businesses continue to integrate AI into their processes, the resulting efficiencies will likely lead to increased competition, lower prices, and higher wages for workers in industries that embrace these changes.
AI’s transformative potential for global productivity cannot be overstated. Just as the steam engine and personal computers reshaped industries and economies, AI is positioned to trigger an unprecedented leap in productivity across nearly every sector. While challenges related to job displacement and economic inequality remain, the promise of a future in which AI drives substantial economic growth is undeniably exciting.
As AI continues to evolve, it is crucial for businesses, policymakers, and workers to embrace this change, adapting to new technologies and fostering an environment that allows AI to reach its full potential. The future of productivity is unfolding before us, and AI will be at the centre of this revolution.
Earth
In ancient India, mushy earth made for perfume scent
Kannauj, a city in the Indian state of Uttar Pradesh, offers a sustainable alternative in producing perfumes using traditional modes of distillation.

A sweet scent typically lingers around in the air at Kannauj, an ancient city in India’s most populous state of Uttar Pradesh. It’s an imprint of the countless occasions when it had rained, of roses that bloomed at dawn, and of sandalwood trees that once breathed centuries of calm.. Though mushy smells are not unique to Kannauj, the city utilized traditional distillation methods to make perfume out of these earthly scents.
Kannauj has had a longstanding tradition in perfume-making since four centuries ago. The city, colloquially known as the country’s ancient perfume capital, still uses rustic copper stills, wood-fired ovens, and bamboo pipes leading to sandalwood oil-filled vessels, or attar as it is colloquially known, to make their perfume. Though it gives a pre-industrial look, a closer peek would reveal an ecosystem of complex thermal regulation, plant chemistry, sustainability science, and hydro-distillation chemistry at work.
When synthetically-made but sustainable perfumes, and AI-generated ones share the spotlight today, Kannauj’s tryst with perfumes offer an alternative, sustainable model in traditional distillation, which is inherently low-carbon, zero-waste, and follow principles of a circular economy; all in alignment with sustainable development goals.
Traditional perfume-making is naturally sustainable
In industrial processing, hydro-distillation is a commonly done to separate substances with different boiling points. Heating the liquids produce vapors, which can later be liquefied in a separate chamber. Perfumers in Kannauj follow the same practice, except it promises to be more sustainable with the copper stills, a process colloquially known as dheg-bhakpa hydro-distillation.
There’s no alcohol or synthetic agents in use. Instead, they heat up raw botanicals – such as roses, vetiver roots, jasmine, or even sunbaked clay – to precise temperatures well short of burning, thereby producing fragrant vapor. The vapors are then guided into cooling chambers, where they condense and bond with a natural fixative, often sandalwood oil. Plant residue is the only byproduct, which finds use as organic compost to cultivate another generation of crops.

Trapping earthly scent to make perfume
In the past five years, Kannauj’s veteran perfumers noticed a quiet, but steady shift in their timely harvest and produce. Rose harvests have moved earlier by weeks. Vetiver roots grow shallower due to erratic rainfall. Jasmine yields are fluctuating wildly. The local Ganges river, which influences humidity levels essential for distillation timing, is no longer as predictable. For an entire natural aromatic economy built on seasonal synchrony, this uncertainty has rung alarm bells.
“The scent of a flower depends not just on the flower itself,” Vipin Dixit, a third-generation attar-maker whose family has distilled fragrance for decades, said to EdPublica.
“It depends on the weather the night before, on the heat at sunrise, on the moisture in the air. Even the soil has a scent-memory.”

As a result, perfumers in Kannauj have begun to adapt, applying traditional wisdom through a modern scientific lens. Local distillers are now working with botanists and environmental scientists to study soil microbiomes, measure scent compounds using chromatography, and develop community-based rainwater harvesting to ensure sustainable crop health.
One of the most surprising innovations is trapping petrichor — the scent of first rain — through earth attars. Clay is baked during extreme heat waves, mimicking summer conditions, then distilled to trap the scent of rain hitting dry soil. This aroma, called mitti attar, is one of the few scents in the world created from an environmental phenomenon; and not a flower.
At a time when the world is scrambling to save biodiversity, the humble attar may become a template for green chemistry — one that doesn’t just preserve scent, but also restores the relationship between science, nature, and soul.
Society
How Scientists and Investigators Decode Air Crashes — The Black Box and Beyond
The final report may take months, but it will be critical in issuing safety directives or revising standard procedures.

As rescue and recovery operations continue following the June 12, 2025, plane crash in Ahmedabad, aviation safety experts are now focusing on the technical investigation phase. With 241 lives lost, the search for the cause isn’t just about accountability—it’s about prevention.
The Black Box: Aviation’s Memory Keeper
1. What Is the Black Box?
Despite the name, the black box is actually orange — for visibility. It consists of two components:
- Cockpit Voice Recorder (CVR): Captures conversations and audio from the flight deck.
- Flight Data Recorder (FDR): Logs dozens to hundreds of parameters — speed, altitude, engine status, control inputs.
These devices are housed in titanium or steel and can withstand:
- Temperatures above 1,000°C
- Underwater pressures up to 20,000 feet
- Crashes with up to 3,600 G-force
They also emit underwater locator beacons for up to 30 days.
2. Forensic Engineering & Flight Reconstruction
Beyond black boxes, investigators use:
- Radar data and air traffic control logs
- Wreckage analysis for structural failure clues
- Satellite-based tracking systems like ADS-B
- Weather data for turbulence or wind shear insights
Forensic teams often reconstruct the flight path virtually or even physically using recovered debris to determine failure points.
3. Human Factors & AI in Modern Investigation
New tools like machine learning and human factors analysis are used to identify procedural errors or lapses in judgement.
In many modern investigations, AI helps:
- Filter large datasets (e.g., over 1,000 flight parameters per second)
- Detect patterns missed by the human eye
- Predict similar risk scenarios in future flights
What Happens Next in the Ahmedabad Crash?
Authorities, in coordination with the Directorate General of Civil Aviation (DGCA), are likely:
- Retrieving and analyzing the black box
- Interviewing air traffic controllers
- Reconstructing the aircraft’s final seconds using both data and simulation
The final report may take months, but it will be critical in issuing safety directives or revising standard procedures.
Society
Researchers Unveil Light-Speed AI Chip to Power Next-Gen Wireless and Edge Devices
This could transform the future of wireless communication and edge computing

In a breakthrough that could transform the future of wireless communication and edge computing, engineers at MIT have developed a novel AI hardware accelerator capable of processing wireless signals at the speed of light. The new optical chip, built for signal classification, achieves nanosecond-level performance—up to 100 times faster than conventional digital processors—while consuming dramatically less energy.
With wireless spectrum under growing strain from billions of connected devices, from teleworking laptops to smart sensors, managing bandwidth has become a critical challenge. Artificial intelligence offers a path forward, but most existing AI models are too slow and power-hungry to operate in real time on wireless devices.
The MIT solution, known as MAFT-ONN (Multiplicative Analog Frequency Transform Optical Neural Network), could be a game-changer.
“There are many applications that would be enabled by edge devices that are capable of analyzing wireless signals,” said Prof. Dirk Englund, senior author of the study, in a media statement. “What we’ve presented in our paper could open up many possibilities for real-time and reliable AI inference. This work is the beginning of something that could be quite impactful.”
Published in Science Advances, the research describes how MAFT-ONN classifies signals in just 120 nanoseconds, using a compact optical chip that performs deep-learning tasks using light rather than electricity. Unlike traditional systems that convert signals to images before processing, the MIT design processes raw wireless data directly in the frequency domain—eliminating delays and reducing energy usage.
“We can fit 10,000 neurons onto a single device and compute the necessary multiplications in a single shot,” said Ronald Davis III, lead author and recent MIT PhD graduate.
The device achieved over 85% accuracy in a single shot, and with multiple measurements, it converges to above 99% accuracy, making it both fast and reliable.
Beyond wireless communications, the technology holds promise for edge AI in autonomous vehicles, smart medical devices, and future 6G networks, where real-time response is critical. By embedding ultra-fast AI directly into devices, this innovation could help cars react to hazards instantly or allow pacemakers to adapt to a patient’s heart rhythm in real-time.
Future work will focus on scaling the chip with multiplexing schemes and expanding its ability to handle more complex AI tasks, including transformer models and large language models (LLMs).
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