Society
New Report Highlights Pathways to Inclusive Economic Growth through AI
The WEF’s report offers key strategies for addressing equity concerns, tailoring AI solutions to local needs, and driving sustainable, long-term economic growth.

The World Economic Forum (WEF) has released a comprehensive new report that explores how artificial intelligence (AI) can foster inclusive economic growth and societal progress. While AI has the potential to reshape economies and societies, the report underscores the significant challenge of ensuring that its benefits are shared equitably across the globe. The WEF’s report offers key strategies for addressing equity concerns, tailoring AI solutions to local needs, and driving sustainable, long-term economic growth.
Titled Blueprint for Intelligent Economies, the report was developed in collaboration with KPMG and provides a roadmap for governments, businesses, and other stakeholders to advance AI adoption at the national, regional, and global levels. Part of the Forum’s AI Competitiveness through Regional Collaboration Initiative, the report aims to tackle disparities in AI access, infrastructure, computing capabilities, and skills.
“We must recognize that while leveraging AI for economic growth and societal progress is a shared goal, countries and regions start from very different positions,” said Cathy Li, Head of AI, Data, and the Metaverse at the World Economic Forum. “This blueprint serves as a compass, guiding decision-makers toward impact-oriented collaboration and practical solutions that can unlock AI’s full potential.”
National and Regional Collaboration Key to Success
Central to the report’s findings is the emphasis on designing AI strategies that involve a wide range of stakeholders—including governments, businesses, entrepreneurs, civil society, and end-users. Such strategies must be locally driven, supported by high-level leadership, and developed in close consultation with communities to address pressing issues like governance, data privacy, and the impact of AI policies on innovation and investment.
“The significant potential of AI remains largely untapped in many regions worldwide. Establishing an inclusive and competitive AI ecosystem will become a crucial priority for all nations,” remarked Solly Malatsi, Minister of Communications and Digital Technologies of South Africa. “Collaboration among multiple stakeholders at the national, regional, and global levels will be essential in fostering growth and prosperity through AI for everyone.”
Tailored Frameworks for AI Development
The Blueprint for Intelligent Economies draws on global expertise to offer tailored frameworks for nations at different stages of AI development. The report highlights how successful solutions from other regions can be adapted to overcome local challenges. For instance, sharing AI infrastructure and energy resources across regions can alleviate national resource limitations, while centralized data banks can ensure local datasets reflect the diverse needs of communities. Public-private partnerships can also make AI-ready devices more affordable, allowing local innovators to scale their operations.
“All nations have a unique opportunity to advance their economic and societal progress through AI,” said Hatem Dowidar, CEO of E&. “This requires a collaborative approach with intentional leadership from governments, supported by active engagement from all stakeholders at all stages of the AI journey. Regional and global collaborations remain fundamental to addressing shared challenges and ensuring equitable access to key AI capabilities.”
Top Strategic Objectives for AI Development
The report outlines nine strategic objectives to guide AI strategies globally, focusing on three top priorities:
- Building Sustainable AI Infrastructure: Developing secure, scalable, and environmentally responsible AI systems is essential for unlocking growth. However, this requires significant investment and cross-sector collaboration.
- Curating Diverse and High-Quality Datasets: Data is critical to developing fair, accurate, and equitable AI models. Overcoming challenges like data accessibility, imbalance, and ownership is key to creating datasets that reflect the diversity of populations.
- Establishing Robust Ethical and Safety Guardrails: Ethical frameworks and safety standards are necessary to ensure that AI benefits society while minimizing risks. Preventing misuse and promoting responsible development will help build public trust in AI.
The report advocates for a multi-layered approach to advancing these objectives, starting with a focus on sustainable infrastructure and energy use, followed by embedding AI across sectors to drive innovation, and ending with a people-centered approach that prioritizes workforce empowerment and ethical governance.
Public-Private Partnerships Critical for Global AI Adoption
The WEF report also underscores the importance of collaboration between the public and private sectors to accelerate AI adoption. Governments, by implementing supportive policies and incentivizing continuous learning, can unlock AI’s potential as a growth engine and ensure that workers thrive in an AI-powered world.
In support of this vision, the AI Governance Alliance is launching Regional AI Activation Networks. These initiatives, set to roll out across the Middle East, Africa, and Southeast Asia throughout 2025, aim to deliver tailored programs that enhance AI capabilities, promote local data governance, and foster resilient AI value chains in regional ecosystems.
With this new report, the World Economic Forum continues to drive the conversation on how to harness AI for equitable growth, ensuring that no region or community is left behind as the world moves into an increasingly AI-powered future.
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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