Artificial intelligence is increasingly being seen as a powerful tool in India’s battle against air pollution, but technology alone is unlikely to deliver cleaner air. AI can help authorities predict pollution spikes, identify hotspots and improve decision-making, yet its effectiveness will ultimately depend on reliable data, strong enforcement and action against the sources of emissions. Air pollution in India is influenced by a complicated combination of traffic, industrial activity, construction dust, waste burning, agricultural emissions and changing weather conditions. Because these factors interact constantly, predicting air quality is not always straightforward. AI and machine-learning systems can analyse large amounts of information from monitoring stations, weather models and satellites to identify patterns and provide earlier warnings.

In Delhi, authorities are working with IIT Kanpur on an AI-based decision-support system that is designed to forecast air pollution 48 to 72 hours in advance and identify areas where pollution could become particularly severe. The aim is to give government agencies enough time to take targeted action rather than waiting until air quality has already deteriorated. Similar efforts are also emerging elsewhere. Maharashtra has been developing an AI-powered air-quality forecasting system for Mumbai and surrounding areas, with the broader objective of anticipating deteriorating conditions and helping authorities respond more quickly.

The technology could be particularly useful during periods when weather conditions trap pollutants close to the ground. An AI model could combine information about wind speed, temperature, humidity, traffic and emissions to estimate where pollution is likely to increase. If a specific area is identified as a potential hotspot, officials could focus inspections and pollution-control measures there. Researchers are also exploring the use of satellite observations alongside ground-based monitoring. Recent studies have shown that machine-learning models can use these different sources of information to improve forecasts of particulate pollution in several Indian cities. Such systems could become especially valuable in places where conventional monitoring stations are limited.

The growing interest in AI also highlights a basic problem: sophisticated technology is only as good as the information it receives. If monitoring stations are poorly distributed, sensors are not properly maintained or important sources of pollution are missing from the data, an AI system may struggle to provide an accurate picture. This is why expanding and maintaining India's air-quality monitoring network remains critical. More sensors and better-quality measurements can give AI systems a stronger foundation, allowing them to distinguish between local pollution and pollution transported from other parts of a region.

That distinction is particularly important in northern India, where air pollution often crosses administrative boundaries. Delhi cannot treat its air-quality problem entirely as a Delhi problem. Pollution can move across neighbouring districts and states, making regional coordination essential. AI can help reveal these connections, but it cannot enforce environmental regulations. If an algorithm identifies a construction site as a major dust source, someone still has to inspect the site and ensure that pollution-control measures are followed. If it predicts a dangerous pollution episode, authorities still need to decide whether traffic, construction or industrial activity should be restricted.

This is where the difference between forecasting pollution and reducing pollution becomes important. A highly accurate prediction does not automatically translate into cleaner air. There is also a risk that the excitement around artificial intelligence could distract from measures that are less glamorous but far more fundamental. Cleaner public transport, better waste management, dust suppression, industrial emission controls, cleaner fuels and stronger enforcement remain central to reducing pollution.

The real test for AI-based air-quality systems should therefore be measured in outcomes. Authorities need to know whether an AI warning was accurate, what action followed the warning and whether that action actually reduced pollution. Without such a feedback system, AI could become another sophisticated dashboard without delivering meaningful environmental improvements.

Still, the potential is considerable. If AI can provide reliable forecasts several days in advance, governments may be able to move from a largely reactive approach to a more preventive one. Instead of responding only after pollution reaches hazardous levels, agencies could prepare for high-risk conditions and concentrate resources where they are most needed. For India, that could make pollution-control efforts more efficient and targeted. But the technology should be treated as an additional tool rather than a magic solution. The bigger challenge remains the same: reducing the amount of pollution entering the atmosphere in the first place.

AI can help India understand its air better, predict what is coming and identify where intervention may have the greatest impact. But cleaner air will ultimately depend on whether governments, businesses and citizens act on that information. Artificial intelligence may become an important part of India's clean-air strategy, but the smartest algorithm in the world cannot compensate for weak monitoring, poor enforcement or failure to tackle pollution at its source. The technology can provide the intelligence; the real work still has to happen on the ground.

Disclaimer: This image is taken from Hindustan Times.

Apple is expected to launch the iPhone 18 Pro and iPhone 18 Pro Max in September, along with its first foldable iPhone. However, the new models could arrive at a time when Apple is facing rising component costs, particularly for memory, storage and advanced processors. Memory prices have surged sharply over the past year, with DRAM costs reportedly increasing by around 400% and NAND prices by more than 300%. At the same time, Apple is expected to use a new 2nm-based A-series chip for the iPhone 18 lineup, which could further increase production expenses.

Apple CEO Tim Cook had previously warned that higher prices were becoming unavoidable. The company has already increased prices for several Macs, iPads and other products, including some significant hikes in India. The iPhone lineup has so far avoided similar increases, helping Apple maintain strong demand even as the broader smartphone market struggles.

Counterpoint Research estimates that global smartphone shipments declined 11% year-on-year in the second quarter of 2026, the weakest second quarter since 2013. Apple, however, recorded 3% shipment growth and captured a 20% market share, its strongest Q2 performance on record. The company's tablet business tells a different story. Global tablet shipments dropped 9.9% during the same period, according to Omdia, while Apple's iPad shipments declined by roughly 7.5%.

The rising cost of components could eventually force Apple to increase iPhone prices. Counterpoint estimates that the bill of materials for a high-end iPhone 18 Pro Max with 12GB RAM and 1TB storage could rise by nearly $300 compared with the previous generation. Costs for 256GB and 512GB versions could also increase by around $200-$250.

Apple may not pass the entire increase directly to customers. Instead, the company could adopt different pricing across storage configurations, with smaller increases for entry-level models and larger increases for higher-capacity versions. This would allow Apple to protect its margins while keeping the starting price relatively competitive. Apple's strong position in the premium smartphone market could also help it absorb higher prices. Counterpoint says smartphones priced at $600 or above accounted for 29% of global smartphone shipments in the first half of 2026, up from 25% a year earlier. Apple and Samsung together controlled 84% of this premium segment.

India is another example of Apple's pricing strength. In 2025, Apple accounted for 9% of India's smartphone shipments and 28% of the market by value. The iPhone 17 also became India's best-selling smartphone by volume during the first quarter of 2026, despite starting at Rs 82,900. Financing and EMI options could further reduce the impact of a higher price tag. Counterpoint expects financed purchases to account for 42% of India's smartphone sales in 2026, compared with 35% in 2025. For consumers, spreading a price increase over several years can make a more expensive phone appear more affordable on a monthly basis.

Apple can also rely on its services business to offset some hardware-related costs. Counterpoint estimates Apple's services business at around $120 billion and believes artificial intelligence services could eventually create another major source of recurring revenue. Any potential AI subscription pricing, however, remains a research scenario rather than an announced Apple plan.

Apple may also attempt to negotiate better terms with suppliers and reduce costs in areas such as displays, cameras, materials and packaging. However, increasing demand from data centres and other industries is putting additional pressure on chip and memory supplies, limiting Apple's ability to control costs. As a result, the iPhone 18 Pro series could become more expensive, but Apple has several ways to limit the impact on consumers. The company may keep lower-end configurations relatively close to current prices while placing larger increases on premium storage variants. Whether higher prices hurt demand will ultimately depend on how aggressively Apple passes its increased production costs to consumers. Given the company's strong premium-market position and continued demand in markets such as India, a moderate price increase may not significantly affect sales.

Disclaimer: This image is taken from Bloomberg.

Few institutions embody the ambition of independent India as powerfully as IIT Kharagpur. The campus stands on the site of the former Hijli Detention Camp, where British authorities imprisoned freedom fighters and killed two unarmed detainees in 1931. Less than twenty years later, the same site was transformed into India’s first Indian Institute of Technology. A place once used to suppress Indian thought became a centre for producing scientists and engineers who would contribute to the country and the wider world.

As IIT Kharagpur marks its platinum jubilee, its history offers a broader message: India achieves its greatest progress when it trusts its own intellectual talent instead of relying on the West to define excellence. The IIT system remains one of independent India’s most successful institutional creations. Its graduates have strengthened Indian industry, contributed to Silicon Valley and gone on to lead major global technology companies. Yet India still does not fully appreciate the scientific and engineering capabilities that exist within the IITs and other leading institutions.

For years, Indian universities have been encouraged to model themselves on institutions such as MIT, Stanford and Harvard. The usual recommendations are familiar: increase research spending, publish more papers, expand tenure systems, improve citation records and rise in global rankings. American universities undoubtedly possess major strengths, and I have spent much of my career working within that system. But India should be careful not to reproduce its shortcomings. American universities invest enormous sums in research and generate significant intellectual property, yet relatively little of that work ultimately becomes companies, products or technologies that directly benefit society. Academic incentives often reward publications and citations more than practical innovation, encouraging researchers to pursue recognition within narrow academic disciplines rather than focus on translating knowledge into useful solutions.

What is particularly encouraging is that many Indian IITs are beginning to move in a different direction. Having worked closely with IIT Madras, IIT Delhi and IIT Kharagpur, I have witnessed a significant transformation from the environment I encountered when I began studying Indian engineering education nearly two decades ago. Indian universities were once often inward-looking and wary of industry, with academics assuming that they alone could determine which problems deserved attention. Increasingly, that mindset is changing. Leading institutions are seeking partnerships with industry, promoting translational research and encouraging technologies to move from laboratories into practical applications.

My own experience reinforces this change. After shifting the R&D activities of my Silicon Valley company, Vionix Biosciences, to the IITs, I have seen the extraordinary technical ability, motivation and ambition of their faculty and students. Across engineering, physics, biotechnology, medicine and artificial intelligence, I have encountered people willing to work on extremely difficult problems. Their capabilities are especially striking at a time when many talented graduates in the West are being drawn toward fashionable areas of technology.

The IITs are increasingly competing with each other not simply for prestige, but for measurable impact. They want faculty to address meaningful challenges, students to build things, research to produce technologies and innovations to become companies, products and practical solutions. Government policy has contributed to this shift, but so has the determination of institutional leaders.

The major obstacle now is bureaucracy. Excessive approvals, committees and administrative procedures—many rooted in structures inherited from the colonial era—consume time and energy that could otherwise be directed toward research and innovation. Leaders such as Suman Chakraborty at IIT Kharagpur are trying to make their institutions more entrepreneurial and impact-oriented, but they continue to operate within systems that can slow meaningful change.

The government should therefore give these institutions much greater freedom. IIT directors need more autonomy, unnecessary reporting requirements and outdated regulations should be eliminated, industry partnerships should be simplified, and institutional leaders should have greater authority to remove bureaucratic resistance. Most importantly, India should stop asking the West to define what a world-class research university must look like. Instead, Indian science and engineering should be organised around the problems that matter most to India and humanity.

That means pursuing deep research in physics, chemistry, biology, materials science, engineering and artificial intelligence while applying those disciplines to challenges such as clean water, early cancer detection, affordable healthcare, energy storage, food security, pollution and advanced manufacturing. Practical problems should not be treated as inferior to fundamental research; solving them often requires major scientific breakthroughs.

The strongest model would bring together the complementary strengths of industry, universities and government. Industry can identify urgent real-world problems, universities can provide scientific expertise and young talent, and governments can fund long-term missions that are too large or uncertain for individual companies to pursue alone. Making these partnerships easier could become one of India’s greatest competitive advantages.

Imagine IITs competing not primarily on placement salaries, citation numbers or international rankings, but on which institution develops the cheapest cancer diagnostic, cleans polluted groundwater most effectively, creates a breakthrough battery, reduces agricultural losses or develops technologies that improve millions of lives. IIT Kharagpur’s platinum jubilee should therefore represent more than a milestone. The institution began as a declaration that independent India could shape its own destiny. It went on to become the foundation of the IIT system. Its next chapter could make an even more important declaration: India does not need to imitate the world’s leading research universities. It has the talent and opportunity to build a model of scientific and technological excellence that is uniquely its own—and perhaps better suited to solving the problems of the future.
Disclaimer: This image is taken from Hindustan Times.

A new research paper is adding fresh heat to the growing debate over AI “distillation”, a technique used to transfer capabilities from one AI model to another. The issue has attracted particular attention as Chinese open-weight models such as Moonshot AI’s Kimi K3 continue to gain ground. Researchers say they have found a way to uncover hidden reasoning traces generated by advanced AI models before they produce their final answers. They also suggest that similar techniques could potentially be used to obtain reasoning information from powerful proprietary models and use it to train other systems through distillation.

The study was carried out by researchers from the University of Tübingen, the Max Planck Institute, MATS Research and cybersecurity firm Snyk. In their experiments, the researchers observed notable similarities between reasoning generated by Kimi K3 and hidden reasoning traces associated with Anthropic’s Claude Opus 4.8 and OpenAI’s GPT-5.6 Sol when responding to certain prompts. However, the researchers made clear that these similarities do not prove that Moonshot AI or another Chinese developer actually distilled reasoning data from those US models.

The paper specifically states that the results cannot establish a causal link. Researchers also found that the US-based open-weight model Inkling from Thinking Machines and a DeepSeek model did not show the same type of reasoning similarities with Claude Opus. The controversy comes as artificial intelligence development has become an increasingly important area of competition between the United States and China. Distillation itself is not new and has long been used in machine learning to transfer capabilities from larger models to smaller and more efficient systems. But the technique has become politically and commercially sensitive as companies accuse rivals of using their models to accelerate development.

OpenAI previously alleged that DeepSeek had used outputs from its models while developing its R1 reasoning system. Anthropic also accused Chinese technology giant Alibaba of similar activity. At the same time, some Silicon Valley leaders have defended distillation as a legitimate part of the wider AI ecosystem. Meta CEO Mark Zuckerberg recently argued that restricting the practice could place US developers at a disadvantage.

The researchers' method for examining hidden reasoning relies on an unusual property of smaller models. While proprietary systems generally conceal their chain-of-thought reasoning, an encrypted version of this information is transmitted to a user's computer for computational purposes. The researchers found that feeding these traces into a smaller model from the same family could cause it to reveal more of the underlying reasoning because smaller models typically undergo less alignment training.

To investigate whether open-weight models might show evidence of similar training, the researchers tested 90 questions across several systems. They then supplied the models with the opening portions of reasoning traces obtained from a proprietary model. According to the study, some models produced answers that showed similarities to the original reasoning, with Kimi K3 displaying particularly noticeable overlaps.

The researchers cautioned that these results should not be treated as proof that any particular company copied confidential reasoning data. The research also raises a separate cybersecurity concern. The researchers warned that techniques capable of exposing hidden model reasoning could potentially be abused to retrieve sensitive information supplied to AI systems, including passwords and API keys. They said tests involving frontier models from OpenAI, Anthropic and Google demonstrated that sensitive information could be recovered through API access. The companies were reportedly notified about the vulnerability and subsequently introduced measures to reduce the risk.

Anthropic spokesperson Michael Aciman told Wired that the company welcomed independent research into its models and had begun implementing short-term protections against the replay behaviour described in the study. He also denied that the researchers had accessed Anthropic's internal infrastructure or obtained encryption keys and personal data from its systems. The research therefore raises important questions about both AI security and competition. While the similarities between certain models are notable, the study does not establish that Chinese companies deliberately extracted and distilled proprietary reasoning from US AI systems. Further research will be needed to determine whether the observed overlaps result from distillation, common training data, model behaviour or other factors. As the AI race between the US and China intensifies, the ability to protect proprietary reasoning while allowing legitimate research and innovation is likely to become an increasingly important challenge for the industry.
Disclaimer: This image is taken from The Indian Express.

Meta’s Ray-Ban smart glasses have rapidly emerged as one of the world’s most popular new tech products, with reports suggesting that more than seven million pairs were sold in 2025. Supporters praise the glasses for making photography and accessibility more convenient, but the technology has also sparked privacy concerns. Critics have dubbed them “pervert glasses,” while some UK pubs and restaurants, including Wetherspoons, have reportedly banned customers from using the devices on their premises.

Disclaimer: This podcast is taken from The Guardian.

On the July 24 episode of Open For Business, hosts Andrea Heng and Poh Kok Ing are joined by Santosh Rao, Head of Research and Partner at Manhattan Venture Partners, to break down today's market trends.

Disclaimer: This podcast is taken from CNA.

As AI continues to evolve, cyber risks are becoming a major business challenge rather than just a technical problem. The Five Eyes alliance warns that advanced AI models could transform the cyber threat landscape faster than anticipated. With AI being used for both attacks and defense, the question remains: who is ahead in this new automated cyber battle? Andrea Heng and Hairianto Diman explore this with Jayant Dave, Chief Information Security Officer at Check Point Software Technologies.

Disclaimer: This podcast is taken from CNA.

A prolonged and heated courtroom dispute between tech billionaires Elon Musk and Sam Altman has ended in a win for OpenAI’s CEO. Musk says he plans to challenge the decision. The case has raised wider questions about Big Tech influence and the worldwide competition in artificial intelligence. Lucy Hough discusses the outcome with Guardian US tech and power reporter Nick Robins-Early in a YouTube interview.
Disclaimer: This image is taken from The Guardian.