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<p>China has reportedly achieved a major milestone in brain-computer interface (BCI) technology by carrying out what authorities describe as the world's first commercial brain chip implant. The procedure was performed at Huashan Hospital in Shanghai using Neo, a brain-computer interface developed by Neuracle Medical Technology. Unlike experimental implants used in clinical trials, Neo has already received commercial approval in China, allowing eligible patients to receive the technology in hospitals.</p><p>The development has drawn global attention because it comes before Elon Musk's Neuralink receives full commercial approval in the United States.</p><h2><strong>How does Neo work?</strong></h2><p><br>Neo is designed to help people with severe paralysis regain some control over movement. The coin-sized implant is placed on the surface of the brain, rather than inside brain tissue. When a patient thinks about moving their hand, the device detects those brain signals and sends them to a computer.</p><p>The software then translates the signals into commands that control a robotic glove, helping patients perform everyday tasks that would otherwise be impossible. Doctors said the patient, who suffered a spinal cord injury in a car accident around 10 years ago, remained stable after surgery. Tests also showed the implant was successfully capturing high-quality brain signals.</p><h2><strong>Neo vs Neuralink: What's the difference?</strong></h2><p>Although both Neo and Neuralink aim to restore independence for people with paralysis, the two technologies take different approaches. Neuralink uses extremely thin flexible threads that are inserted directly into brain tissue using a specially designed surgical robot. Its first product, Telepathy, allows users to control computers and smartphones using only their thoughts. Neo, however, sits on the surface of the brain, avoiding penetration into brain tissue. This makes the procedure less invasive while still allowing the system to capture the electrical signals needed to operate assistive devices.</p><h2><strong>The biggest difference is regulatory status.</strong></h2><p>Neuralink has implanted its devices in several patients, but all procedures remain part of clinical trials approved by the US Food and Drug Administration (FDA). Neo, by contrast, received approval from China's National Medical Products Administration (NMPA) earlier this year as what Chinese authorities describe as the world's first commercially approved implantable brain-computer interface classified as a Class III medical device.</p><p>The breakthrough signals that the global race in brain-computer interfaces is entering a new phase. For years, companies have focused on proving that BCIs can safely translate thoughts into digital commands. The next challenge is making those technologies available to patients outside research programmes. If Neo continues to deliver positive clinical outcomes, it could accelerate the adoption of brain-computer interfaces for treating paralysis and other neurological conditions. At the same time, competition is expected to intensify as companies including Neuralink, Synchron, Precision Neuroscience and others work to bring their own brain chip technologies to market. While commercial approval marks an important milestone, experts note that long-term safety, durability and patient outcomes will ultimately determine how widely brain-computer interfaces are adopted in healthcare.</p>

China has reportedly carried out the world's first commercial brain chip implant using the Neo brain-computer interface. Unlike Neuralink, which remains in clinical trials, Neo has regulatory approval for hospital use, marking a major milestone in the global race to commercialise BCI technology.

China has reportedly achieved a major milestone in brain-computer interface (BCI) technology by carrying out what authorities describe as the world's first commercial brain chip implant. The procedure was performed at Huashan Hospital in Shanghai using Neo, a brain-computer interface developed by Neuracle Medical Technology. Unlike experimental implants used in clinical trials, Neo has already received commercial approval in China, allowing eligible patients to receive the technology in hospitals. The development has drawn global attention because it comes before Elon Musk's Neuralink receives full commercial approval in the United States. How does Neo work? Neo is designed to help people with severe paralysis regain some control over movement. The coin-sized implant is placed on the surface of the brain, rather than inside brain tissue. When a patient thinks about moving their hand, the device detects those brain signals and sends them to a computer. The software then translates the signals into commands that control a robotic glove, helping patients perform everyday tasks that would otherwise be impossible. Doctors said the patient, who suffered a spinal cord injury in a car accident around 10 years ago, remained stable after surgery. Tests also showed the implant was successfully capturing high-quality brain signals. Neo vs Neuralink: What's the difference? Although both Neo and Neuralink aim to restore independence for people with paralysis, the two technologies take different approaches. Neuralink uses extremely thin flexible threads that are inserted directly into brain tissue using a specially designed surgical robot. Its first product, Telepathy, allows users to control computers and smartphones using only their thoughts. Neo, however, sits on the surface of the brain, avoiding penetration into brain tissue. This makes the procedure less invasive while still allowing the system to capture the electrical signals needed to operate assistive devices. The biggest difference is regulatory status. Neuralink has implanted its devices in several patients, but all procedures remain part of clinical trials approved by the US Food and Drug Administration (FDA). Neo, by contrast, received approval from China's National Medical Products Administration (NMPA) earlier this year as what Chinese authorities describe as the world's first commercially approved implantable brain-computer interface classified as a Class III medical device. The breakthrough signals that the global race in brain-computer interfaces is entering a new phase. For years, companies have focused on proving that BCIs can safely translate thoughts into digital commands. The next challenge is making those technologies available to patients outside research programmes. If Neo continues to deliver positive clinical outcomes, it could accelerate the adoption of brain-computer interfaces for treating paralysis and other neurological conditions. At the same time, competition is expected to intensify as companies including Neuralink, Synchron, Precision Neuroscience and others work to bring their own brain chip technologies to market. While commercial approval marks an important milestone, experts note that long-term safety, durability and patient outcomes will ultimately determine how widely brain-computer interfaces are adopted in healthcare.

<p>China has reportedly carried out the world's first commercial brain chip implant using the Neo brain-computer interface. Unlike Neuralink, which remains in clinical trials, Neo has regulatory approval for hospital use, marking a major milestone in the global race to commercialise BCI technology.</p>

China performs world's first commercial brain chip implant, moving ahead of Elon Musk's Neuralink

<p>Google's race to stay at the front of artificial intelligence has reportedly hit another obstacle. According to a Bloomberg report, the company has delayed the release of Gemini 3.5 Pro, its next flagship AI model, after it failed to meet internal performance targets. The biggest concern was said to be coding an area where rivals OpenAI and Anthropic have recently gained significant momentum. The model was expected to launch in June, shortly after CEO Sundar Pichai introduced it during Google I/O 2026, saying it was already being used internally. Months later, however, Gemini 3.5 Pro is still undergoing testing.</p><p>The report unsettled investors. Shares of Google's parent company, Alphabet, fell nearly 3 per cent during trading and were down about 4 per cent at one stage.</p><h2><strong>Coding has become the new AI battleground</strong></h2><p>The delay highlights how important coding has become in the AI industry. Large language models are no longer judged only by how well they answer questions or generate text. Increasingly, companies want AI systems that can write, review, debug and improve software with minimal human input. According to Bloomberg, Google retrained Gemini with updated data last month to improve its coding ability, but engineers were reportedly disappointed with the results. Meanwhile, OpenAI's GPT-5.6 and Anthropic's Claude Fable 5 have raised expectations across the industry, particularly for enterprise software development.</p><h2><strong>Internal challenges may also be slowing Google</strong></h2><p>The report suggests Google's size has become one of its biggest challenges. Unlike AI startups that focus on a single product, Google must coordinate across Search, DeepMind, Cloud, Android, YouTube and other divisions before launching major AI models. One former employee reportedly described the process as trying to "boil an ocean," pointing to overlapping teams, changing priorities and internal competition for computing resources. Google has also tightened oversight around AI development as advanced models face growing scrutiny from governments over safety, national security and misuse.</p><h2><strong>Google says testing is still underway</strong></h2><p>Google has not confirmed a launch date for Gemini 3.5 Pro. A company spokesperson told Bloomberg that Google is "shipping quickly across a wide range of models while keeping them highly cost-effective" and confirmed that Gemini 3.5 Pro, an upgraded Flash model and other AI systems are currently being tested with partners. The spokesperson also said Google remains engaged with the US government as oversight of frontier AI models increases.</p><h2><strong>Is Google really falling behind?</strong></h2><p>Despite the reported delay, industry observers say Google remains one of the strongest AI players. Its biggest advantage is deep integration across products used by billions of people, including Search, Gmail, Maps, Android, Chrome and Workspace. Gemini also benefits from Google's search index, giving it access to one of the world's largest information ecosystems.</p><p>Google recently revealed that around 75 per cent of its internal code is now generated with AI assistance before being reviewed by engineers. The company has also consolidated several coding initiatives under its Antigravity platform to accelerate software development. However, with OpenAI and Anthropic releasing increasingly capable models, expectations for Gemini 3.5 Pro have only grown. The reported delay suggests Google is choosing to refine the model rather than launch a product that falls short of its own standards a decision that could prove important as the AI race enters an even more competitive phase.</p>

Google has reportedly delayed the launch of Gemini 3.5 Pro after the AI model failed to meet internal expectations, particularly in coding.

Google's race to stay at the front of artificial intelligence has reportedly hit another obstacle. According to a Bloomberg report, the company has delayed the release of Gemini 3.5 Pro, its next flagship AI model, after it failed to meet internal performance targets. The biggest concern was said to be coding an area where rivals OpenAI and Anthropic have recently gained significant momentum. The model was expected to launch in June, shortly after CEO Sundar Pichai introduced it during Google I/O 2026, saying it was already being used internally. Months later, however, Gemini 3.5 Pro is still undergoing testing. The report unsettled investors. Shares of Google's parent company, Alphabet, fell nearly 3 per cent during trading and were down about 4 per cent at one stage. Coding has become the new AI battleground The delay highlights how important coding has become in the AI industry. Large language models are no longer judged only by how well they answer questions or generate text. Increasingly, companies want AI systems that can write, review, debug and improve software with minimal human input. According to Bloomberg, Google retrained Gemini with updated data last month to improve its coding ability, but engineers were reportedly disappointed with the results. Meanwhile, OpenAI's GPT-5.6 and Anthropic's Claude Fable 5 have raised expectations across the industry, particularly for enterprise software development. Internal challenges may also be slowing Google The report suggests Google's size has become one of its biggest challenges. Unlike AI startups that focus on a single product, Google must coordinate across Search, DeepMind, Cloud, Android, YouTube and other divisions before launching major AI models. One former employee reportedly described the process as trying to "boil an ocean," pointing to overlapping teams, changing priorities and internal competition for computing resources. Google has also tightened oversight around AI development as advanced models face growing scrutiny from governments over safety, national security and misuse. Google says testing is still underway Google has not confirmed a launch date for Gemini 3.5 Pro. A company spokesperson told Bloomberg that Google is "shipping quickly across a wide range of models while keeping them highly cost-effective" and confirmed that Gemini 3.5 Pro, an upgraded Flash model and other AI systems are currently being tested with partners. The spokesperson also said Google remains engaged with the US government as oversight of frontier AI models increases. Is Google really falling behind? Despite the reported delay, industry observers say Google remains one of the strongest AI players. Its biggest advantage is deep integration across products used by billions of people, including Search, Gmail, Maps, Android, Chrome and Workspace. Gemini also benefits from Google's search index, giving it access to one of the world's largest information ecosystems. Google recently revealed that around 75 per cent of its internal code is now generated with AI assistance before being reviewed by engineers. The company has also consolidated several coding initiatives under its Antigravity platform to accelerate software development. However, with OpenAI and Anthropic releasing increasingly capable models, expectations for Gemini 3.5 Pro have only grown. The reported delay suggests Google is choosing to refine the model rather than launch a product that falls short of its own standards a decision that could prove important as the AI race enters an even more competitive phase.

<p>Google has reportedly delayed the launch of Gemini 3.5 Pro after the AI model failed to meet internal expectations, particularly in coding. The setback comes as OpenAI and Anthropic continue releasing more advanced models, increasing pressure on Google in one of the industry's most competitive races.</p>

Google delays Gemini 3.5 Pro after AI model misses coding targets, report says

<p>Artificial intelligence has become remarkably good at writing code, answering questions and analysing documents. Yet most businesses still struggle to use it in meaningful ways. Anthropic believes it knows why and its answer is not another AI model. The Claude maker has launched Ode, a $1.5 billion enterprise AI implementation company, in partnership with Blackstone, Hellman &amp; Friedman and a consortium of investors including Goldman Sachs, General Atlantic, Apollo Global Management, Leonard Green &amp; Partners, GIC and Sequoia Capital. The idea behind Ode is simple: the next big AI opportunity may not be building smarter models, but helping companies actually deploy them.</p><h2><strong>AI models are improving. Businesses are not keeping up</strong></h2><p><br>Over the past two years, AI models have become dramatically more capable. But many companies still find it difficult to integrate AI into their daily operations. The challenge is rarely the model itself. Businesses often have fragmented data, undocumented workflows, legacy software and strict compliance requirements. As a result, many AI projects never move beyond small pilot programmes.</p><p>Anthropic believes this "implementation gap" could become one of the biggest opportunities in enterprise technology. Rather than selling software licences, Ode will send experienced AI engineers directly into organisations to understand how they work before designing custom AI systems around their existing processes.</p><h2><strong>What exactly does Ode do?</strong></h2><p><br>Ode describes itself as a "scaled boutique" AI implementation firm. Its engineers work alongside executives and employees to identify repetitive work, automate complex business processes and build AI tools tailored to a company's internal systems. Instead of offering a one-size-fits-all chatbot, the company focuses on integrating AI into finance, customer support, operations, legal teams, software development and decision-making workflows. The goal is to redesign how businesses operate rather than simply adding another AI assistant.</p><p>The company is built on the team behind Fractional AI, a startup known for deploying AI solutions inside enterprises. Around 100 senior engineers including former startup founders now form Ode's core implementation team.</p><h2><strong>Claude comes first but not exclusively</strong></h2><p>Although Ode follows a Claude-first strategy and works closely with Anthropic's applied AI teams, it is not restricted to Anthropic's technology alone. If another AI model is better suited for a particular task, Ode says it can incorporate competing technologies into enterprise deployments. This flexibility reflects a growing trend across enterprise AI, where businesses increasingly choose combinations of models rather than relying on a single provider.</p><h2><strong>Why Blackstone sees a trillion-dollar opportunity</strong></h2><p><br>The idea for Ode emerged after Blackstone noticed that companies across its investment portfolio were buying AI tools but struggling to achieve measurable business results.&nbsp;<br><br>The private equity giant concluded that implementation not model development had become the industry's biggest bottleneck. That insight led to the creation of Ode, with Blackstone providing capital, operational expertise and access to portfolio companies as early customers. For Anthropic, the arrangement allows it to continue focusing on frontier AI research and model safety while extending Claude into real-world enterprise environments through a specialised implementation partner.</p><h2><strong>A new phase in the AI race</strong></h2><p>The launch highlights how competition in artificial intelligence is beginning to shift. For years, companies raced to build the most powerful foundation models. Now the focus is increasingly moving towards helping organisations extract measurable value from them. Industry analysts have often compared the current AI market to the early days of cloud computing, when technology alone was not enough and implementation partners became essential.</p><p>If Anthropic's bet proves correct, the biggest AI businesses of the next decade may not be the companies building models but those helping enterprises completely redesign how work gets done.</p>

Anthropic has launched Ode, a $1.5 billion enterprise AI company backed by Blackstone and Goldman Sachs. Rather than building another AI model, Ode will help businesses integrate AI into their core operations, betting implementation

Artificial intelligence has become remarkably good at writing code, answering questions and analysing documents. Yet most businesses still struggle to use it in meaningful ways. Anthropic believes it knows why and its answer is not another AI model. The Claude maker has launched Ode, a $1.5 billion enterprise AI implementation company, in partnership with Blackstone, Hellman & Friedman and a consortium of investors including Goldman Sachs, General Atlantic, Apollo Global Management, Leonard Green & Partners, GIC and Sequoia Capital. The idea behind Ode is simple: the next big AI opportunity may not be building smarter models, but helping companies actually deploy them. AI models are improving. Businesses are not keeping up Over the past two years, AI models have become dramatically more capable. But many companies still find it difficult to integrate AI into their daily operations. The challenge is rarely the model itself. Businesses often have fragmented data, undocumented workflows, legacy software and strict compliance requirements. As a result, many AI projects never move beyond small pilot programmes. Anthropic believes this "implementation gap" could become one of the biggest opportunities in enterprise technology. Rather than selling software licences, Ode will send experienced AI engineers directly into organisations to understand how they work before designing custom AI systems around their existing processes. What exactly does Ode do? Ode describes itself as a "scaled boutique" AI implementation firm. Its engineers work alongside executives and employees to identify repetitive work, automate complex business processes and build AI tools tailored to a company's internal systems. Instead of offering a one-size-fits-all chatbot, the company focuses on integrating AI into finance, customer support, operations, legal teams, software development and decision-making workflows. The goal is to redesign how businesses operate rather than simply adding another AI assistant. The company is built on the team behind Fractional AI, a startup known for deploying AI solutions inside enterprises. Around 100 senior engineers including former startup founders now form Ode's core implementation team. Claude comes first but not exclusively Although Ode follows a Claude-first strategy and works closely with Anthropic's applied AI teams, it is not restricted to Anthropic's technology alone. If another AI model is better suited for a particular task, Ode says it can incorporate competing technologies into enterprise deployments. This flexibility reflects a growing trend across enterprise AI, where businesses increasingly choose combinations of models rather than relying on a single provider. Why Blackstone sees a trillion-dollar opportunity The idea for Ode emerged after Blackstone noticed that companies across its investment portfolio were buying AI tools but struggling to achieve measurable business results. The private equity giant concluded that implementation not model development had become the industry's biggest bottleneck. That insight led to the creation of Ode, with Blackstone providing capital, operational expertise and access to portfolio companies as early customers. For Anthropic, the arrangement allows it to continue focusing on frontier AI research and model safety while extending Claude into real-world enterprise environments through a specialised implementation partner. A new phase in the AI race The launch highlights how competition in artificial intelligence is beginning to shift. For years, companies raced to build the most powerful foundation models. Now the focus is increasingly moving towards helping organisations extract measurable value from them. Industry analysts have often compared the current AI market to the early days of cloud computing, when technology alone was not enough and implementation partners became essential. If Anthropic's bet proves correct, the biggest AI businesses of the next decade may not be the companies building models but those helping enterprises completely redesign how work gets done.

<p>Anthropic has launched Ode, a $1.5 billion enterprise AI company backed by Blackstone and Goldman Sachs. Rather than building another AI model, Ode will help businesses integrate AI into their core operations, betting implementation not models is the industry's next trillion-dollar opportunity.</p>

Claude-maker Anthropic launches Ode, an enterprise AI company that learns how your business works and helps you run on AI

<p>Artificial intelligence (AI) is rapidly making a wider and significant transformation for companies in terms of its use at the workstation for different roles. Alongside technical use, the AI is also being highly utilised in several academic and administrative workforces, while solving business problems using human skills along with other tech.</p><p><br>In terms of use, the first approach focuses on using it to automate tasks carried out by employees, while the second involves applying AI-driven analytics and algorithms to automate managerial responsibilities, a practice commonly known as "algorithmic management." Automating specific tasks with AI does not automatically result in job losses, as the technology can also enhance human work by supporting employees in areas where certain functions have been automated.</p><p><br>Whether the adoption of AI leads to workforce reduction or strengthens human roles depends on several factors, including how essential the automated task is to a particular occupation, the way the technology is incorporated into workplace processes, and whether management chooses to retain employees to perform or supervise certain tasks even when automation is technically feasible.</p><p>&nbsp;</p><p><strong>Also read:</strong> <a href="https://www.wionews.com/trending/ai-is-changing-hiring-priorities-are-employers-looking-for-different-skills-than-they-did-2-years-ago-1784019436076">AI is changing hiring priorities: Are employers looking for different skills than they did 2 years ago?</a></p><h3>Safeguards to put before using AI</h3><p>In terms of legality and safeguards while using AI in the workforce, an expert indicated that companies should establish strong governance frameworks before deploying artificial intelligence (AI) for workforce-related decisions.</p><p><br>Prasad Rai, CEO of DAAS LABS, emphasised that organisations should not rely solely on technology when using AI for functions such as hiring, promotions, performance reviews and workforce planning. Instead, he stressed the need for safeguards to ensure fairness, accountability and transparency. &nbsp;</p><p><br>"Before AI is entrusted with workforce decisions, companies need to establish strong governance rather than relying solely on technology. The first safeguard is ensuring that the data used to train AI models is accurate, representative, and regularly audited for bias, because AI will inevitably replicate flaws in historical decisions. Equally important is explainability; every AI-driven recommendation should be transparent, auditable, and easy for leaders to justify. Human oversight must remain central, with AI serving as a decision-support tool rather than the final decision-maker for hiring, promotions, performance reviews, or workforce planning," he said.</p><p><br>According to Rai, companies that earn long-term employee trust will not necessarily be those that adopt AI the fastest, but those that implement it responsibly by placing fairness, accountability and transparency at the centre of their AI strategy.</p><p><br>"Organisations should also implement regular model reviews, clear accountability frameworks, and transparent communication with employees about how AI is being used. The companies that will build lasting employee trust are not those adopting AI the fastest, but those deploying it responsibly, with fairness, accountability, and transparency at the core," he added.</p>

The expert indicated that companies should establish strong governance frameworks before deploying artificial intelligence (AI) for workforce-related decisions. He also emphasised that organisations should not rely solely on technology when using AI for functions.

Artificial intelligence (AI) is rapidly making a wider and significant transformation for companies in terms of its use at the workstation for different roles. Alongside technical use, the AI is also being highly utilised in several academic and administrative workforces, while solving business problems using human skills along with other tech. In terms of use, the first approach focuses on using it to automate tasks carried out by employees, while the second involves applying AI-driven analytics and algorithms to automate managerial responsibilities, a practice commonly known as "algorithmic management." Automating specific tasks with AI does not automatically result in job losses, as the technology can also enhance human work by supporting employees in areas where certain functions have been automated. Whether the adoption of AI leads to workforce reduction or strengthens human roles depends on several factors, including how essential the automated task is to a particular occupation, the way the technology is incorporated into workplace processes, and whether management chooses to retain employees to perform or supervise certain tasks even when automation is technically feasible. Also read: AI is changing hiring priorities: Are employers looking for different skills than they did 2 years ago? Safeguards to put before using AI In terms of legality and safeguards while using AI in the workforce, an expert indicated that companies should establish strong governance frameworks before deploying artificial intelligence (AI) for workforce-related decisions. Prasad Rai, CEO of DAAS LABS, emphasised that organisations should not rely solely on technology when using AI for functions such as hiring, promotions, performance reviews and workforce planning. Instead, he stressed the need for safeguards to ensure fairness, accountability and transparency. "Before AI is entrusted with workforce decisions, companies need to establish strong governance rather than relying solely on technology. The first safeguard is ensuring that the data used to train AI models is accurate, representative, and regularly audited for bias, because AI will inevitably replicate flaws in historical decisions. Equally important is explainability; every AI-driven recommendation should be transparent, auditable, and easy for leaders to justify. Human oversight must remain central, with AI serving as a decision-support tool rather than the final decision-maker for hiring, promotions, performance reviews, or workforce planning," he said. According to Rai, companies that earn long-term employee trust will not necessarily be those that adopt AI the fastest, but those that implement it responsibly by placing fairness, accountability and transparency at the centre of their AI strategy. "Organisations should also implement regular model reviews, clear accountability frameworks, and transparent communication with employees about how AI is being used. The companies that will build lasting employee trust are not those adopting AI the fastest, but those deploying it responsibly, with fairness, accountability, and transparency at the core," he added.

<p>The expert indicated that companies should establish strong governance frameworks before deploying artificial intelligence (AI) for workforce-related decisions. He also emphasised that organisations should not rely solely on technology when using AI for functions.</p>

What safeguards should companies put in place before using AI for workforce decisions?

<p>IBM shares plunged 25 per cent on Tuesday, marking the company's biggest one-day stock decline in 58 years after it released disappointing preliminary second-quarter results. The sharp fall erased between $67 billion and $70 billion in market value, according to Bloomberg estimates. Investors reacted after IBM reported that revenue for the quarter ending in June rose only 1 per cent to $17.2 billion, missing market expectations.</p><p>Chief Executive Arvind Krishna admitted the company had failed to respond quickly enough to changing market conditions.</p><p>"We faltered... and did not adapt and move quickly enough," Krishna wrote in a letter to investors.</p><h2><strong>AI spending is changing customer priorities</strong></h2><p>IBM said one of the biggest reasons for the weak quarter was a shift in customer spending. As companies race to build AI infrastructure, demand for servers, memory chips and storage has surged. With supply remaining tight, many businesses accelerated purchases before expected price increases.</p><p>According to IBM, this meant customers redirected capital spending away from its high-margin mainframe systems and related software.</p><p>The company's infrastructure revenue fell 7 per cent, while software revenue grew 5 per cent, but still failed to meet analysts' expectations.</p><p>At the same time, IBM's server and storage business outside mainframes grew 37 per cent, reflecting strong demand for AI-related hardware.</p><h2><strong>Cybersecurity becomes a bigger priority</strong></h2><p>IBM also said cybersecurity spending increased significantly during the quarter. Businesses shifted budgets towards protecting their systems as more powerful AI models increased concerns about sophisticated cyberattacks.</p><p>The company pointed to growing industry focus on AI-powered security risks, prompting organisations to prioritise cyber defence over some planned technology projects. The trend also benefited cybersecurity companies. Shares of CrowdStrike rose around 12 per cent, while Okta and Netskope gained roughly 11 per cent following IBM's update.</p><h2><strong>What does this mean for software companies?</strong></h2><p>IBM's results have renewed debate over how artificial intelligence could reshape the future of traditional software businesses. Many investors are questioning whether conventional Software-as-a-Service (SaaS) companies can maintain growth as AI increasingly performs tasks that previously required specialised software. However, not every part of IBM struggled. Its Red Hat business reported 11 per cent revenue growth, showing continued demand for hybrid cloud and open-source software.</p><p>IBM also announced Lightwell, a $5 billion initiative backed by major financial institutions including Bank of America, JPMorganChase and Goldman Sachs to strengthen security across open-source software. While IBM's results disappointed investors, they also underline a broader shift across the technology industry. As companies invest heavily in AI infrastructure and cybersecurity, spending priorities are changing rapidly, creating both winners and losers across the enterprise software market.</p>

IBM shares fell 25 per cent in their biggest one-day decline in nearly 60 years after weak quarterly results. The company said customers shifted spending towards AI infrastructure and cybersecurity.

IBM shares plunged 25 per cent on Tuesday, marking the company's biggest one-day stock decline in 58 years after it released disappointing preliminary second-quarter results. The sharp fall erased between $67 billion and $70 billion in market value, according to Bloomberg estimates. Investors reacted after IBM reported that revenue for the quarter ending in June rose only 1 per cent to $17.2 billion, missing market expectations. Chief Executive Arvind Krishna admitted the company had failed to respond quickly enough to changing market conditions. "We faltered... and did not adapt and move quickly enough," Krishna wrote in a letter to investors. AI spending is changing customer priorities IBM said one of the biggest reasons for the weak quarter was a shift in customer spending. As companies race to build AI infrastructure, demand for servers, memory chips and storage has surged. With supply remaining tight, many businesses accelerated purchases before expected price increases. According to IBM, this meant customers redirected capital spending away from its high-margin mainframe systems and related software. The company's infrastructure revenue fell 7 per cent, while software revenue grew 5 per cent, but still failed to meet analysts' expectations. At the same time, IBM's server and storage business outside mainframes grew 37 per cent, reflecting strong demand for AI-related hardware. Cybersecurity becomes a bigger priority IBM also said cybersecurity spending increased significantly during the quarter. Businesses shifted budgets towards protecting their systems as more powerful AI models increased concerns about sophisticated cyberattacks. The company pointed to growing industry focus on AI-powered security risks, prompting organisations to prioritise cyber defence over some planned technology projects. The trend also benefited cybersecurity companies. Shares of CrowdStrike rose around 12 per cent, while Okta and Netskope gained roughly 11 per cent following IBM's update. What does this mean for software companies? IBM's results have renewed debate over how artificial intelligence could reshape the future of traditional software businesses. Many investors are questioning whether conventional Software-as-a-Service (SaaS) companies can maintain growth as AI increasingly performs tasks that previously required specialised software. However, not every part of IBM struggled. Its Red Hat business reported 11 per cent revenue growth, showing continued demand for hybrid cloud and open-source software. IBM also announced Lightwell, a $5 billion initiative backed by major financial institutions including Bank of America, JPMorganChase and Goldman Sachs to strengthen security across open-source software. While IBM's results disappointed investors, they also underline a broader shift across the technology industry. As companies invest heavily in AI infrastructure and cybersecurity, spending priorities are changing rapidly, creating both winners and losers across the enterprise software market.

<p>IBM shares fell 25 per cent in their biggest one-day decline in nearly 60 years after weak quarterly results. The company said customers shifted spending towards AI infrastructure and cybersecurity, wiping out almost $70 billion in market value and raising new questions about traditional software firms.</p>

Biggest crash in nearly 60 years: IBM shares tumble 25% as AI spending reshapes enterprise technology

<p>New York became the first state in the US on Tuesday (July 14, 2026) to pause the construction of big new ​data centres. The country has imposed a one-year moratorium after ​concerns grew over the facilities, prompting a boom in artificial intelligence, which is raising power costs, straining ​water supplies and burdening local communities.</p><p>&nbsp;</p><p>The development places New York at the forefront of a growing nationwide debate over regulating the infrastructure required to support artificial intelligence. As technology companies accelerate the construction of new data centres, lawmakers and regulators across dozens of US states are considering measures to curb their impact on power grids, electricity costs and local communities.</p><p><br>"As data centre development threatens to hike up utility bills, deplete ​our natural resources, and create uncertainty for New Yorkers, it's my responsibility to take action and lead," said Hochul, adding that she will also pursue legislation to reject sales tax exemptions for large data centres.</p><p>&nbsp;</p><p><strong>Also read: </strong><a href="https://www.wionews.com/world/us-nuclear-fuel-company-counters-barc-s-safety-concerns-over-thorium-fuel-for-reactors-more-details-inside-1784003959432">US nuclear fuel company counters BARC’s safety concerns over thorium fuel for reactors: More details inside</a></p><h3>Development of Generic Environmental Impact Statement (GEIS)</h3><p>Officials in the governor's office indicated that the moratorium will apply to data centres that use 50 megawatts or more of power. During the ​suspected period, the state's Department of Environmental Conservation will not issue any discretionary ⁠permits for new projects unless applications have already been deemed complete.</p><p><br>Rather than allowing unrestricted expansion, Hochul instructed state officials to develop a ​Generic Environmental Impact Statement (GEIS) ​to establish uniform standards for new data centres and examine the potential environmental effects of the construction and ​operation of data centres across New York. According to her office, the moratorium will remain in place until the state completes and adopts these standards.</p><p><br>Meanwhile, the New York Legislature passed a bill last month aimed at introducing stricter regulations for data centres, although it is yet to reach Hochul for approval. Officials in the governor's office said the legislation is complex and will require further discussions with lawmakers before it can move forward. The rapid growth of data centres across the United States has significantly increased electricity demand and power costs, prompting mounting concerns and opposition from local communities and policymakers.</p>

As technology companies accelerate the construction of new data centres, lawmakers and regulators across dozens of US states are considering measures to curb their impact on power grids, electricity costs and local communities.

New York became the first state in the US on Tuesday (July 14, 2026) to pause the construction of big new data centres. The country has imposed a one-year moratorium after concerns grew over the facilities, prompting a boom in artificial intelligence, which is raising power costs, straining water supplies and burdening local communities. The development places New York at the forefront of a growing nationwide debate over regulating the infrastructure required to support artificial intelligence. As technology companies accelerate the construction of new data centres, lawmakers and regulators across dozens of US states are considering measures to curb their impact on power grids, electricity costs and local communities. "As data centre development threatens to hike up utility bills, deplete our natural resources, and create uncertainty for New Yorkers, it's my responsibility to take action and lead," said Hochul, adding that she will also pursue legislation to reject sales tax exemptions for large data centres. Also read: US nuclear fuel company counters BARC’s safety concerns over thorium fuel for reactors: More details inside Development of Generic Environmental Impact Statement (GEIS) Officials in the governor's office indicated that the moratorium will apply to data centres that use 50 megawatts or more of power. During the suspected period, the state's Department of Environmental Conservation will not issue any discretionary ⁠permits for new projects unless applications have already been deemed complete. Rather than allowing unrestricted expansion, Hochul instructed state officials to develop a Generic Environmental Impact Statement (GEIS) to establish uniform standards for new data centres and examine the potential environmental effects of the construction and operation of data centres across New York. According to her office, the moratorium will remain in place until the state completes and adopts these standards. Meanwhile, the New York Legislature passed a bill last month aimed at introducing stricter regulations for data centres, although it is yet to reach Hochul for approval. Officials in the governor's office said the legislation is complex and will require further discussions with lawmakers before it can move forward. The rapid growth of data centres across the United States has significantly increased electricity demand and power costs, prompting mounting concerns and opposition from local communities and policymakers.

<p>As technology companies accelerate the construction of new data centres, lawmakers and regulators across dozens of US states are considering measures to curb their impact on power grids, electricity costs and local communities.</p>

New York becomes first US state to halt new AI data centres: Here's why

<p>Artificial intelligence (AI) has rapidly brought about a broader transformation for companies in terms of hiring, evaluating and retaining talent globally. Alongside technical expertise and academic qualification, employers are increasingly prioritising candidates with AI skills who can adapt to changing technologies and solve business problems using human skills along with technical.</p><p><br>Recruiters are no longer looking only for employees who can execute predefined tasks. Instead, organisations are seeking professionals who can combine AI tools with critical thinking, business understanding, communication and ethical judgement. A report published by the World Economic Forum in 2025 indicated that nearly 69 per cent of employers plan to hire talent with AI-related skills and nearly 62 per cent seek to recruit candidates capable of working effectively alongside AI.</p><p><br>Similarly, LinkedIn's Work Change Report forecast that approximately 70 per cent of the skills used are set to change by 2030, with AI emerging as one of the biggest drivers of workforce transformation. The report also highlights that companies increasingly value employees who combine AI literacy with human capabilities such as creativity, communication and problem-solving. In response, Industry leaders indicate that these changes are already visible in hiring decisions across technology, manufacturing and executive recruitment in most of the biggest firms.</p><p>&nbsp;</p><p><strong>Also read:</strong><a href="https://www.wionews.com/world/us-nuclear-fuel-company-counters-barc-s-safety-concerns-over-thorium-fuel-for-reactors-more-details-inside-1784003959432"> US nuclear fuel company counters BARC’s safety concerns over thorium fuel for reactors: More details inside</a></p><h3>Change in hiring priorities</h3><p>Venkat Raghavan M, Chief Operating Officer, LOWCODEMINDS, said hiring priorities have evolved significantly over the last two years as AI has become part of everyday business operations. Reacting to the question whether employers are looking for different skills than they were two years ago, he said, "Yes, hiring priorities have changed significantly over the past two years. Technical expertise and domain knowledge remain essential, but they are no longer the only qualities employers evaluate. As AI becomes embedded in everyday business operations, organisations are increasingly looking for professionals who can understand business context, learn continuously, and use AI to solve real business problems."<br>&nbsp;</p><p>"In the hiring conversations I've been part of, we have moved beyond asking whether someone can implement technology. We now spend more time assessing whether they can connect technology with business outcomes, collaborate across functions, question AI-generated recommendations when necessary, and adapt as the technology evolves," Raghavan added.</p><p><br>Amid rising concern over the AI threat to jobs, Raghavan assured that AI raises the value of uniquely human capabilities, including judgment, communication, creativity, and ethical decision-making. He added that AI can boost analysis and execution; however, people remain accountable for making informed decisions and driving business outcomes.</p><p><br>"For me, one question has become increasingly important during hiring: Can this person continue learning as technology evolves? In an AI-driven workplace, that mindset is often a stronger predictor of long-term success than any individual technical skill," he further said.</p><p>&nbsp;</p><p><strong>WATCH</strong>: https://www.wionews.com/videos/china-turns-to-ai-for-next-drug-breakthroughs-1784005943869/embed</p><h3>AI is transforming jobs, not replacing people</h3><p>Similarly, over job threat, Sandeep Das, Global HR Leader, Green Power International Pvt. Ltd., believes AI is changing the nature of work rather than eliminating the need for employees. He indicated that the demand for skills has certainly evolved in the industry at the global level.</p><p><br>"AI is undeniably reshaping the job market, bringing both disruption and change to the way we work. Traditional roles have always been mechanically intensive and process-driven. With the growth of automation and AI, the demand for skills has certainly evolved. As clean energy technologies continue to gain momentum, we now look for talent that goes beyond technical expertise. While we do give preference to candidates with relevant knowledge and experience, skill readiness is equally important, as it enables new hires to adapt quickly to modern and evolving technologies," Sandeep Das said.</p><p><br>"That said, the advent of artificial intelligence does not mean replacing people; it means transforming the way they work. For our sector, it is crucial to build a workforce that combines digital proficiency with strong operational and engineering capabilities. To achieve this, we believe in continuous upskilling and lifelong learning to drive sustainable growth. The future workforce will not just operate equipment; it will manage intelligent, data-driven energy systems," he added.</p><h3>Leadership roles increasingly demand AI understanding</h3><p>Sahil Thakur, Director &amp; Head of Tech-Enabled Businesses, Grassik Search, said organisations are increasingly seeking leaders who understand both business strategy and AI implementation. Highlighting the threat, he indicated that those individual who will fail to understand the application &amp; impact of AI, within their respective job profile, are going to become more vulnerable in the coming days.</p><p><br>"With the pace at which AI is being integrated into our professional lives, the skills required to understand &amp; adapt to new technologies have become critical. Today, it is critical to not only adopt AI tools but also master their application to maximise efficiency and optimise time management. Those who fail to understand the application &amp; impact of AI, within their domain, are going to be more vulnerable in the near future," Thakur said.</p><p><br>"At a leadership level, organisations are prioritising executives who can navigate this volatile environment and leverage AI to eliminate inefficiencies. However, companies must carefully vet candidates to ensure they genuinely understand the technology. The goal is to hire leaders who can deploy AI strategically, rather than those who implement it blindly without clear objectives," he added.</p><p>&nbsp;</p>

A report published by the World Economic Forum in 2025 indicated that nearly 69 per cent of employers plan to hire talent with AI-related skills and nearly 62 per cent seek to recruit candidates capable of working effectively alongside AI.

Artificial intelligence (AI) has rapidly brought about a broader transformation for companies in terms of hiring, evaluating and retaining talent globally. Alongside technical expertise and academic qualification, employers are increasingly prioritising candidates with AI skills who can adapt to changing technologies and solve business problems using human skills along with technical. Recruiters are no longer looking only for employees who can execute predefined tasks. Instead, organisations are seeking professionals who can combine AI tools with critical thinking, business understanding, communication and ethical judgement. A report published by the World Economic Forum in 2025 indicated that nearly 69 per cent of employers plan to hire talent with AI-related skills and nearly 62 per cent seek to recruit candidates capable of working effectively alongside AI. Similarly, LinkedIn's Work Change Report forecast that approximately 70 per cent of the skills used are set to change by 2030, with AI emerging as one of the biggest drivers of workforce transformation. The report also highlights that companies increasingly value employees who combine AI literacy with human capabilities such as creativity, communication and problem-solving. In response, Industry leaders indicate that these changes are already visible in hiring decisions across technology, manufacturing and executive recruitment in most of the biggest firms. Also read: US nuclear fuel company counters BARC’s safety concerns over thorium fuel for reactors: More details inside Change in hiring priorities Venkat Raghavan M, Chief Operating Officer, LOWCODEMINDS, said hiring priorities have evolved significantly over the last two years as AI has become part of everyday business operations. Reacting to the question whether employers are looking for different skills than they were two years ago, he said, "Yes, hiring priorities have changed significantly over the past two years. Technical expertise and domain knowledge remain essential, but they are no longer the only qualities employers evaluate. As AI becomes embedded in everyday business operations, organisations are increasingly looking for professionals who can understand business context, learn continuously, and use AI to solve real business problems." "In the hiring conversations I've been part of, we have moved beyond asking whether someone can implement technology. We now spend more time assessing whether they can connect technology with business outcomes, collaborate across functions, question AI-generated recommendations when necessary, and adapt as the technology evolves," Raghavan added. Amid rising concern over the AI threat to jobs, Raghavan assured that AI raises the value of uniquely human capabilities, including judgment, communication, creativity, and ethical decision-making. He added that AI can boost analysis and execution; however, people remain accountable for making informed decisions and driving business outcomes. "For me, one question has become increasingly important during hiring: Can this person continue learning as technology evolves? In an AI-driven workplace, that mindset is often a stronger predictor of long-term success than any individual technical skill," he further said. WATCH: https://www.wionews.com/videos/china-turns-to-ai-for-next-drug-breakthroughs-1784005943869/embed AI is transforming jobs, not replacing people Similarly, over job threat, Sandeep Das, Global HR Leader, Green Power International Pvt. Ltd., believes AI is changing the nature of work rather than eliminating the need for employees. He indicated that the demand for skills has certainly evolved in the industry at the global level. "AI is undeniably reshaping the job market, bringing both disruption and change to the way we work. Traditional roles have always been mechanically intensive and process-driven. With the growth of automation and AI, the demand for skills has certainly evolved. As clean energy technologies continue to gain momentum, we now look for talent that goes beyond technical expertise. While we do give preference to candidates with relevant knowledge and experience, skill readiness is equally important, as it enables new hires to adapt quickly to modern and evolving technologies," Sandeep Das said. "That said, the advent of artificial intelligence does not mean replacing people; it means transforming the way they work. For our sector, it is crucial to build a workforce that combines digital proficiency with strong operational and engineering capabilities. To achieve this, we believe in continuous upskilling and lifelong learning to drive sustainable growth. The future workforce will not just operate equipment; it will manage intelligent, data-driven energy systems," he added. Leadership roles increasingly demand AI understanding Sahil Thakur, Director & Head of Tech-Enabled Businesses, Grassik Search, said organisations are increasingly seeking leaders who understand both business strategy and AI implementation. Highlighting the threat, he indicated that those individual who will fail to understand the application & impact of AI, within their respective job profile, are going to become more vulnerable in the coming days. "With the pace at which AI is being integrated into our professional lives, the skills required to understand & adapt to new technologies have become critical. Today, it is critical to not only adopt AI tools but also master their application to maximise efficiency and optimise time management. Those who fail to understand the application & impact of AI, within their domain, are going to be more vulnerable in the near future," Thakur said. "At a leadership level, organisations are prioritising executives who can navigate this volatile environment and leverage AI to eliminate inefficiencies. However, companies must carefully vet candidates to ensure they genuinely understand the technology. The goal is to hire leaders who can deploy AI strategically, rather than those who implement it blindly without clear objectives," he added.

<p>A report published by the World Economic Forum in 2025 indicated that nearly 69 per cent of employers plan to hire talent with AI-related skills and nearly 62 per cent seek to recruit candidates capable of working effectively alongside AI.</p>

AI is changing hiring priorities: Are employers looking for different skills than they did 2 years ago?

<p>China's breakthrough in Extreme Ultraviolet (EUV) lithography for semiconductor manufacturing from its so-called 'Manhattan Project' is a leap forward in the nation's race towards self-reliance in artificial intelligence hardware. It also offers important lessons for India. Recent reports suggest that China built a ‘working prototype’ for indigenous EUV lithography. This is the world's first serious challenge to Dutch company ASML's monopoly in manufacturing EUV lithography machines. It is time to understand EUV lithography and why countries like India should launch their own mission-mode projects to achieve self-sufficiency in AI chipmaking.</p><h2>What is EUV lithography machine and why is there a race to build them?</h2><p>EUV lithography is the key technology that enables making the world's most advanced semiconductor chips. EUV lithography uses extreme ultraviolet light with a wavelength of 13.5 nanometres (nm) to print microscopic circuits on silicon wafers. This enables the manufacture of 5 nm, 3 nm and even smaller chips used for AI computing. These chips power smartphones, advanced computing systems, defence equipment and data centres. Smaller transistors improve computing speed, energy efficiency and processing power.</p><p>EUV manufacturing is extraordinarily complex. It requires extremely high precision, ultra-sophisticated optics, powerful light sources and consistently high production yields.</p><p>The Chinese project, often compared to the US Manhattan Project that developed the world's first nuclear weapons during the Second World War, brings together Huawei, state-backed research institutes and engineers who previously worked with ASML, the world's only commercial supplier of EUV lithography machines. Each of these machines costs approximately US$250-400 million.&nbsp;</p><p>The Chinese researchers, working in a high-security laboratory in Shenzhen are targeting the production of functional chips using EUV technology by 2028. Commercial production might realistically start in 2030.</p><h2>China could commercialise EUV technology soon. What would it mean for the world?</h2><p>Since 2019, US-led export restrictions prevented China from purchasing ASML's EUV systems, forcing it to develop indigenous alternatives. Once China develops the capability to manufacture chips using indigenous EUV lithography machines at scale, it could significantly reduce its dependence on foreign semiconductor equipment.&nbsp;</p><p>Domestic production of advanced chips would strengthen China's AI, electric vehicle, telecommunications and defence industries.&nbsp;</p><p>Increased chip production could also improve global supply and reduce costs over time.&nbsp;</p><p>A Chinese success in in commercialising EUV machines could make it eventually emerge as an exporter of advanced lithography equipment, creating competition in a market currently dominated by a single company, ASML.</p><p>Also read: <a href="https://www.wionews.com/world/china-is-secretly-building-an-ai-chipmaking-machine-in-shenzhen-how-its-manhattan-project-could-change-the-ai-era-1766138322822">China is secretly building an AI chipmaking machine in Shenzhen. How its ‘Manhattan Project’ could change the AI era</a></p><h2>US-China technology rivalry and China's EUV lithography breakthrough</h2><p>ASML spent nearly two decades perfecting commercial EUV technology. China is now attempting to close that gap. If Beijing succeeds in commercialising indigenous EUV systems, it would weaken the effectiveness of US export controls that limited China's access to advanced semiconductor manufacturing. This could force the US and its allies to invest even more heavily in next-generation semiconductor manufacturing, while further intensifying the global technology race.</p><p><a href="https://www.wionews.com/videos/dutch-government-invests-27-bn-to-keep-tech-giant-asml-705452/embed">https://www.wionews.com/videos/dutch-government-invests-27-bn-to-keep-tech-giant-asml-705452/embed</a></p><h2>Why India should have its own Manhattan Project</h2><p>This is where the India Semiconductor Mission (ISM) becomes important. India has approved projects worth over ₹1.6 trillion covering semiconductor fabrication plants, Outsourced Semiconductor Assembly and Test (OSAT) facilities, Assembly, Testing, Marking and Packaging (ATMP) units, and compound semiconductor projects. ISM 2.0 focuses on strengthening semiconductor equipment, materials, research, chip design and advanced packaging.</p><p>Yet, India still lacks leading-edge chip fabrication capability, a domestic semiconductor equipment ecosystem and sufficient high-end manufacturing expertise.&nbsp;</p><p>While India is globally recognised for its chip design talent, it continues to depend heavily on imports for advanced semiconductor manufacturing. This is why India needs its own "Manhattan Project" for semiconductors.</p><p>Such a project can succeed only through sustained government support, long-term investment and close collaboration between research institutions, universities and industry in AI hardware and semiconductor manufacturing.</p><p>Expanding research and development, strengthening semiconductor talent, attracting global technology partnerships, and building domestic capabilities in chip design, packaging, materials and manufacturing equipment should be national priorities.</p><p>Achieving Atmanirbhar Bharat, or self-reliance, will require greater private investment, policy stability and a realistic long-term strategy rather than relying solely on short-term subsidies.</p>

If and when China's breakthrough in EUV lithography leads to commercial production of these chipmaking machines, the global AI semiconductor industry will change forever. What does it mean for US-China technology race? And why should India have its own AI hardware ‘Manhattan Project’?

China's breakthrough in Extreme Ultraviolet (EUV) lithography for semiconductor manufacturing from its so-called 'Manhattan Project' is a leap forward in the nation's race towards self-reliance in artificial intelligence hardware. It also offers important lessons for India. Recent reports suggest that China built a ‘working prototype’ for indigenous EUV lithography. This is the world's first serious challenge to Dutch company ASML's monopoly in manufacturing EUV lithography machines. It is time to understand EUV lithography and why countries like India should launch their own mission-mode projects to achieve self-sufficiency in AI chipmaking. What is EUV lithography machine and why is there a race to build them? EUV lithography is the key technology that enables making the world's most advanced semiconductor chips. EUV lithography uses extreme ultraviolet light with a wavelength of 13.5 nanometres (nm) to print microscopic circuits on silicon wafers. This enables the manufacture of 5 nm, 3 nm and even smaller chips used for AI computing. These chips power smartphones, advanced computing systems, defence equipment and data centres. Smaller transistors improve computing speed, energy efficiency and processing power. EUV manufacturing is extraordinarily complex. It requires extremely high precision, ultra-sophisticated optics, powerful light sources and consistently high production yields. The Chinese project, often compared to the US Manhattan Project that developed the world's first nuclear weapons during the Second World War, brings together Huawei, state-backed research institutes and engineers who previously worked with ASML, the world's only commercial supplier of EUV lithography machines. Each of these machines costs approximately US$250-400 million. The Chinese researchers, working in a high-security laboratory in Shenzhen are targeting the production of functional chips using EUV technology by 2028. Commercial production might realistically start in 2030. China could commercialise EUV technology soon. What would it mean for the world? Since 2019, US-led export restrictions prevented China from purchasing ASML's EUV systems, forcing it to develop indigenous alternatives. Once China develops the capability to manufacture chips using indigenous EUV lithography machines at scale, it could significantly reduce its dependence on foreign semiconductor equipment. Domestic production of advanced chips would strengthen China's AI, electric vehicle, telecommunications and defence industries. Increased chip production could also improve global supply and reduce costs over time. A Chinese success in in commercialising EUV machines could make it eventually emerge as an exporter of advanced lithography equipment, creating competition in a market currently dominated by a single company, ASML. Also read: China is secretly building an AI chipmaking machine in Shenzhen. How its ‘Manhattan Project’ could change the AI era US-China technology rivalry and China's EUV lithography breakthrough ASML spent nearly two decades perfecting commercial EUV technology. China is now attempting to close that gap. If Beijing succeeds in commercialising indigenous EUV systems, it would weaken the effectiveness of US export controls that limited China's access to advanced semiconductor manufacturing. This could force the US and its allies to invest even more heavily in next-generation semiconductor manufacturing, while further intensifying the global technology race. https://www.wionews.com/videos/dutch-government-invests-27-bn-to-keep-tech-giant-asml-705452/embed Why India should have its own Manhattan Project This is where the India Semiconductor Mission (ISM) becomes important. India has approved projects worth over ₹1.6 trillion covering semiconductor fabrication plants, Outsourced Semiconductor Assembly and Test (OSAT) facilities, Assembly, Testing, Marking and Packaging (ATMP) units, and compound semiconductor projects. ISM 2.0 focuses on strengthening semiconductor equipment, materials, research, chip design and advanced packaging. Yet, India still lacks leading-edge chip fabrication capability, a domestic semiconductor equipment ecosystem and sufficient high-end manufacturing expertise. While India is globally recognised for its chip design talent, it continues to depend heavily on imports for advanced semiconductor manufacturing. This is why India needs its own "Manhattan Project" for semiconductors. Such a project can succeed only through sustained government support, long-term investment and close collaboration between research institutions, universities and industry in AI hardware and semiconductor manufacturing. Expanding research and development, strengthening semiconductor talent, attracting global technology partnerships, and building domestic capabilities in chip design, packaging, materials and manufacturing equipment should be national priorities. Achieving Atmanirbhar Bharat, or self-reliance, will require greater private investment, policy stability and a realistic long-term strategy rather than relying solely on short-term subsidies.

<p>If and when China's breakthrough in EUV lithography leads to commercial production of these chipmaking machines, the global AI semiconductor industry will change forever. What does it mean for US-China technology race? And why should India have its own AI hardware ‘Manhattan Project’?</p>

Why India should have its own Manhattan Project as China makes EUV breakthrough in AI chipmaking

<p>China is positioning itself at the forefront of the next wave of global drug innovation, and artificial intelligence is emerging as the key lever that could tilt dominance in its favour. After cross-border deals for innovative drugs touched a record $110 billion in the first half of 2026, China's pharmaceutical sector is rapidly pivoting toward AI-driven drug discovery to sustain momentum. The shift signals a deeper transformation, from being a fast follower to becoming a global leader in cutting-edge therapies.</p><p><strong>Also read | </strong><a href="https://www.wionews.com/trending/how-long-before-sun-destroys-the-earth-scientists-may-have-discovered-the-planet-s-expiry-date-1783760812774"><strong>How long before sun destroys the Earth? Scientists may have discovered the planet's 'expiry date'</strong></a><br>China already accounts for roughly 30 per cent of all new drugs under development worldwide, placing it behind only the US<br>More significantly, deal activity is accelerating. By the end of June, 81 transactions had been completed; that is nearly 80 per cent of last year’s total. It spans high-value therapeutic areas such as oncology, metabolic diseases, immunology and neurology. Global buyers, particularly from the US and Europe, continue to actively partner with Chinese firms.</p><p><a href="https://www.wionews.com/videos/china-turns-to-ai-for-next-drug-breakthroughs-1784005943869/embed">https://www.wionews.com/videos/china-turns-to-ai-for-next-drug-breakthroughs-1784005943869/embed</a></p><p>The latest collaborations highlight how AI is reshaping the innovation pipeline. Domestic companies are leveraging proprietary platforms to design molecules, simulate patient responses and optimise early-stage testing. Partnerships with global pharmaceutical majors are increasingly structured around milestone-based payouts that could run into billions of dollars.<br>AI is enabling faster and more precise drug development by reducing reliance on traditional trial-and-error methods. Virtual modelling, predictive analytics and data-driven insights are helping companies refine candidates earlier, potentially lowering costs and shortening timelines.</p><p><strong>Also read | </strong><a href="https://www.wionews.com/trending/built-to-outlive-us-all-earth-is-getting-an-indestructible-black-box-to-record-the-end-of-human-civilisation-1783747790087"><strong>Built to outlive us all: Earth is getting an indestructible ‘black box’ to record the end of human civilisation</strong></a><br>This convergence of technology and biomedicine is strengthening China's position in the global healthcare ecosystem. Its ability to combine large datasets, advanced AI capabilities and a rapidly maturing regulatory framework is creating a powerful engine for innovation.<br>The critical question now is whether this momentum translates into sustained global leadership. Experts say if current trends hold, China may not just participate in the next generation of medical breakthroughs; it could redefine them.</p>

China is using artificial intelligence to discover the next generation of blockbuster medicines. With innovative drug deals hitting a record $110 billion in just six months, Beijing is positioning itself to become a global pharmaceutical powerhouse.

China is positioning itself at the forefront of the next wave of global drug innovation, and artificial intelligence is emerging as the key lever that could tilt dominance in its favour. After cross-border deals for innovative drugs touched a record $110 billion in the first half of 2026, China's pharmaceutical sector is rapidly pivoting toward AI-driven drug discovery to sustain momentum. The shift signals a deeper transformation, from being a fast follower to becoming a global leader in cutting-edge therapies. Also read | How long before sun destroys the Earth? Scientists may have discovered the planet's 'expiry date' China already accounts for roughly 30 per cent of all new drugs under development worldwide, placing it behind only the US More significantly, deal activity is accelerating. By the end of June, 81 transactions had been completed; that is nearly 80 per cent of last year’s total. It spans high-value therapeutic areas such as oncology, metabolic diseases, immunology and neurology. Global buyers, particularly from the US and Europe, continue to actively partner with Chinese firms. https://www.wionews.com/videos/china-turns-to-ai-for-next-drug-breakthroughs-1784005943869/embed The latest collaborations highlight how AI is reshaping the innovation pipeline. Domestic companies are leveraging proprietary platforms to design molecules, simulate patient responses and optimise early-stage testing. Partnerships with global pharmaceutical majors are increasingly structured around milestone-based payouts that could run into billions of dollars. AI is enabling faster and more precise drug development by reducing reliance on traditional trial-and-error methods. Virtual modelling, predictive analytics and data-driven insights are helping companies refine candidates earlier, potentially lowering costs and shortening timelines. Also read | Built to outlive us all: Earth is getting an indestructible ‘black box’ to record the end of human civilisation This convergence of technology and biomedicine is strengthening China's position in the global healthcare ecosystem. Its ability to combine large datasets, advanced AI capabilities and a rapidly maturing regulatory framework is creating a powerful engine for innovation. The critical question now is whether this momentum translates into sustained global leadership. Experts say if current trends hold, China may not just participate in the next generation of medical breakthroughs; it could redefine them.

<p>China is using artificial intelligence to discover the next generation of blockbuster medicines. With innovative drug deals hitting a record $110 billion in just six months, Beijing is positioning itself to become a global pharmaceutical powerhouse.</p>

China's $110-billion AI pharma boom could redefine the future of medicine

<p>Apple's next flagship iPhone launch is now believed to be only a couple of months away, and leaks are beginning to paint a clearer picture of what to expect. According to reports from Bloomberg and supply chain sources, Apple could unveil the iPhone 18 Pro and iPhone 18 Pro Max during its annual September event, with September 8 or September 9 emerging as the most likely keynote dates. If Apple follows its usual schedule, sales could begin on September 18.</p><p>Unlike previous years, reports suggest Apple may reserve the September event for the Pro models and its first foldable iPhone, while the standard iPhone 18 lineup could arrive in early 2027.</p><h2><strong>Bigger battery and faster A20 Pro chip expected</strong></h2><p>One of the biggest expected upgrades is battery life. Several reports suggest the iPhone 18 Pro Max could feature Apple's largest battery yet, with capacities ranging from around 5,400mAh to 5,500mAh depending on the model and market. The larger battery may also make the phone slightly thicker. Powering the new devices is expected to be Apple's A20 Pro processor, reportedly built using TSMC's 2nm manufacturing process. The new chip is expected to deliver faster performance, improved gaming, better power efficiency and stronger on-device AI capabilities.</p><h2><strong>Familiar design with camera improvements</strong></h2><p>If leaks are accurate, Apple is unlikely to introduce a dramatic redesign this year.</p><p>The flat-sided design and triple-camera layout are expected to remain, although the rear camera module could become slightly larger. Reports also suggest Apple may reduce the two-tone appearance on the back, giving the phone a cleaner look. The display sizes are expected to stay at 6.3 inches for the Pro and 6.9 inches for the Pro Max. Several leaks also point to a smaller Dynamic Island, with Apple reportedly moving more Face ID components beneath the display. Camera upgrades could include a variable-aperture main camera, a brighter telephoto lens, a new stacked image sensor and an upgraded 24-megapixel front camera.</p><h2><strong>AI, connectivity and pricing</strong></h2><p>The iPhone 18 Pro is expected to launch with iOS 27 and the next generation of Apple Intelligence features, including a more capable Siri powered by on-device AI.</p><p>Apple is also tipped to introduce its C2 modem, offering improved power efficiency and faster wireless performance. Expanded satellite communication features are also expected. Pricing could become one of the biggest talking points. Analysts believe higher manufacturing costs, including the move to 2nm chips, more expensive memory and upgraded camera hardware, could push prices higher. Some reports suggest certain models may cost up to $200 more than their predecessors, although Apple has not confirmed any pricing. As always, these details remain based on leaks and supply chain reports. Apple is expected to reveal the official specifications during its September keynote.</p>

Apple is expected to unveil the iPhone 18 Pro lineup in September with a new A20 Pro chip, larger batteries, improved cameras, refined design and upgraded AI features. Reports also suggest higher prices as Apple prepares one of its biggest flagship launches in years.

Apple's next flagship iPhone launch is now believed to be only a couple of months away, and leaks are beginning to paint a clearer picture of what to expect. According to reports from Bloomberg and supply chain sources, Apple could unveil the iPhone 18 Pro and iPhone 18 Pro Max during its annual September event, with September 8 or September 9 emerging as the most likely keynote dates. If Apple follows its usual schedule, sales could begin on September 18. Unlike previous years, reports suggest Apple may reserve the September event for the Pro models and its first foldable iPhone, while the standard iPhone 18 lineup could arrive in early 2027. Bigger battery and faster A20 Pro chip expected One of the biggest expected upgrades is battery life. Several reports suggest the iPhone 18 Pro Max could feature Apple's largest battery yet, with capacities ranging from around 5,400mAh to 5,500mAh depending on the model and market. The larger battery may also make the phone slightly thicker. Powering the new devices is expected to be Apple's A20 Pro processor, reportedly built using TSMC's 2nm manufacturing process. The new chip is expected to deliver faster performance, improved gaming, better power efficiency and stronger on-device AI capabilities. Familiar design with camera improvements If leaks are accurate, Apple is unlikely to introduce a dramatic redesign this year. The flat-sided design and triple-camera layout are expected to remain, although the rear camera module could become slightly larger. Reports also suggest Apple may reduce the two-tone appearance on the back, giving the phone a cleaner look. The display sizes are expected to stay at 6.3 inches for the Pro and 6.9 inches for the Pro Max. Several leaks also point to a smaller Dynamic Island, with Apple reportedly moving more Face ID components beneath the display. Camera upgrades could include a variable-aperture main camera, a brighter telephoto lens, a new stacked image sensor and an upgraded 24-megapixel front camera. AI, connectivity and pricing The iPhone 18 Pro is expected to launch with iOS 27 and the next generation of Apple Intelligence features, including a more capable Siri powered by on-device AI. Apple is also tipped to introduce its C2 modem, offering improved power efficiency and faster wireless performance. Expanded satellite communication features are also expected. Pricing could become one of the biggest talking points. Analysts believe higher manufacturing costs, including the move to 2nm chips, more expensive memory and upgraded camera hardware, could push prices higher. Some reports suggest certain models may cost up to $200 more than their predecessors, although Apple has not confirmed any pricing. As always, these details remain based on leaks and supply chain reports. Apple is expected to reveal the official specifications during its September keynote.

<p>Apple is expected to unveil the iPhone 18 Pro lineup in September with a new A20 Pro chip, larger batteries, improved cameras, refined design and upgraded AI features. Reports also suggest higher prices as Apple prepares one of its biggest flagship launches in years.</p>

iPhone 18 Pro launching in about 2 months: Expected India price, launch date, A20 Pro chip, battery and more

<p>Google's next foldable smartphone has appeared in another leak just weeks before its expected launch. The latest images, shared by well-known tipster Mystic Leaks, show the Google Pixel 11 Pro Fold in a new Pine colour. The render offers one of the clearest looks yet at Google's upcoming foldable and suggests that the company is making small but noticeable design changes rather than a complete redesign.</p><p>The Pixel 11 Pro Fold is widely expected to debut during Google's Made by Google event on August 12, alongside the Pixel 11 smartphone series.</p><h2><strong>A familiar design with subtle changes</strong></h2><p>At first glance, the Pixel 11 Pro Fold looks very similar to last year's Pixel 10 Pro Fold. The leaked render shows a dark green Pine finish paired with a light gold frame and Google's signature "G" logo. The satin-finished frame also appears to return, while the standard Pixel 11 Pro models are expected to use glossier sides. The biggest visual difference is the redesigned camera bar. It appears slightly narrower than before, while the camera cut-outs now extend closer to the edge instead of being surrounded by extra metal.</p><h2><strong>Camera layout could be getting an update</strong></h2><p>The leak also suggests Google has rearranged some camera components.</p><p>The main and ultra-wide cameras remain at the bottom of the module, while the autofocus sensor has moved from the top to the lower section. The telephoto camera now sits in the upper-right corner. One detail attracting attention is the larger camera flash, which appears significantly bigger than the previous generation. Some reports suggest this space could house Google's rumoured Pixel Glow notification system, although Google has not confirmed any such feature.</p><h2><strong>Tensor G6 and AI expected to take centre stage</strong></h2><p>While the leaked images focus mainly on design, previous reports suggest the Pixel 11 Pro Fold will be powered by Google's Tensor G6 processor. The new chip is expected to improve on-device AI, photography, battery efficiency and multitasking. Google is also likely to introduce new Gemini-powered features across the Pixel 11 lineup, continuing its strategy of using artificial intelligence as a key selling point. Foldable smartphones remain a small but growing segment of the premium smartphone market. Rather than changing the hardware dramatically every year, Google appears to be refining its design while focusing on software, AI and everyday usability.</p><p>With the official launch now close, it may not be long before the company reveals the complete picture.</p>

A fresh leak has revealed the Google Pixel 11 Pro Fold in a new Pine colour ahead of its expected August 12 launch. The foldable appears to feature a refined camera design, a redesigned camera bar and is expected to run on Google's new Tensor G6 chip with upgraded AI features.


Google's next foldable smartphone has appeared in another leak just weeks before its expected launch. The latest images, shared by well-known tipster Mystic Leaks, show the Google Pixel 11 Pro Fold in a new Pine colour. The render offers one of the clearest looks yet at Google's upcoming foldable and suggests that the company is making small but noticeable design changes rather than a complete redesign. The Pixel 11 Pro Fold is widely expected to debut during Google's Made by Google event on August 12, alongside the Pixel 11 smartphone series. A familiar design with subtle changes At first glance, the Pixel 11 Pro Fold looks very similar to last year's Pixel 10 Pro Fold. The leaked render shows a dark green Pine finish paired with a light gold frame and Google's signature "G" logo. The satin-finished frame also appears to return, while the standard Pixel 11 Pro models are expected to use glossier sides. The biggest visual difference is the redesigned camera bar. It appears slightly narrower than before, while the camera cut-outs now extend closer to the edge instead of being surrounded by extra metal. Camera layout could be getting an update The leak also suggests Google has rearranged some camera components. The main and ultra-wide cameras remain at the bottom of the module, while the autofocus sensor has moved from the top to the lower section. The telephoto camera now sits in the upper-right corner. One detail attracting attention is the larger camera flash, which appears significantly bigger than the previous generation. Some reports suggest this space could house Google's rumoured Pixel Glow notification system, although Google has not confirmed any such feature. Tensor G6 and AI expected to take centre stage While the leaked images focus mainly on design, previous reports suggest the Pixel 11 Pro Fold will be powered by Google's Tensor G6 processor. The new chip is expected to improve on-device AI, photography, battery efficiency and multitasking. Google is also likely to introduce new Gemini-powered features across the Pixel 11 lineup, continuing its strategy of using artificial intelligence as a key selling point. Foldable smartphones remain a small but growing segment of the premium smartphone market. Rather than changing the hardware dramatically every year, Google appears to be refining its design while focusing on software, AI and everyday usability. With the official launch now close, it may not be long before the company reveals the complete picture.

<p>A fresh leak has revealed the Google Pixel 11 Pro Fold in a new Pine colour ahead of its expected August 12 launch. The foldable appears to feature a refined camera design, a redesigned camera bar and is expected to run on Google's new Tensor G6 chip with upgraded AI features.</p>

Google Pixel 11 Pro Fold leak reveals redesigned camera layout and new Pine colour

<p>OpenAI is asking security researchers to try to break one of its most advanced AI models and it is willing to pay up to $50,000 for it.</p><p>The company has expanded its Bio Bug Bounty Programme to include GPT-5.6, doubling the top reward from the previous $25,000 offered during the GPT-5.5 programme. The initiative focuses on finding "universal jailbreaks" that can consistently bypass the model's biology-related safety safeguards. The move comes as AI companies face growing scrutiny over how advanced models could be misused to generate harmful biological information. OpenAI says discovering vulnerabilities before attackers do is an important part of making future AI systems safer.</p>

<blockquote class="twitter-tweet"><p lang="en" dir="ltr">As part of our ongoing efforts to strengthen our safeguards for advanced AI capabilities in biology, we’re evolving our Bio Bug Bounty into an ongoing private program, known as the OpenAI Bio Bug Bounty program and doubling rewards to $50K.<br><br>We’re inviting researchers with…</p>&mdash; OpenAI (@OpenAI) <a href="https://x.com/OpenAI/status/2075647722766614733?ref_src=twsrc%5Etfw">July 10, 2026</a></blockquote> <script async src="https://platform.x.com/widgets.js" charset="utf-8"></script>

<h2><strong>What is OpenAI looking for?</strong></h2><p>Unlike ordinary prompt tricks that may work only once, OpenAI wants researchers to discover universal jailbreaks techniques that reliably bypass the model's built-in biosafety protections across multiple conversations. According to the company, these findings will help improve future safety systems while reducing the chances of malicious misuse. Researchers who discover smaller weaknesses may also receive partial rewards, depending on the quality and impact of their submissions.</p><h2><strong>Why has the reward increased?</strong></h2><p>OpenAI says GPT-5.6 represents its latest generation of AI models, making safety testing even more important. To encourage more researchers to participate, the company has doubled the maximum reward to $50,000. The updated programme will replace GPT-5.5 after 27 July 2026, when testing for the older model officially ends. The company added that researchers already enrolled in the GPT-5.5 programme do not need to submit another application.</p><h2><strong>Who can apply?</strong></h2><p>Anyone interested in participating must apply through OpenAI's Bio Bug Bounty page. Applicants are required to provide their name, organisation or affiliation, and relevant research experience. Participants must also have a ChatGPT account and sign a non-disclosure agreement (NDA) before gaining access to the testing platform. The GPT-5.6 programme will become available after the GPT-5.5 testing period concludes later this month.</p><p>&nbsp;</p><p>Bug bounty programmes have long been used in cybersecurity to uncover software vulnerabilities before criminals can exploit them. OpenAI is applying the same approach to artificial intelligence.</p><p>As AI systems become more capable, governments and regulators are placing greater emphasis on testing model safeguards, particularly in areas such as biology, cybersecurity and chemical research. By inviting independent researchers to stress-test GPT-5.6, OpenAI hopes to identify weaknesses early and strengthen its safety systems before the technology reaches wider use.</p>

OpenAI has expanded its Bio Bug Bounty programme to GPT-5.6, raising the top reward to $50,000 for researchers who discover reliable ways to bypass the model's biology-related safeguards. The programme aims to improve AI safety before vulnerabilities can be exploited.


OpenAI is asking security researchers to try to break one of its most advanced AI models and it is willing to pay up to $50,000 for it. The company has expanded its Bio Bug Bounty Programme to include GPT-5.6, doubling the top reward from the previous $25,000 offered during the GPT-5.5 programme. The initiative focuses on finding "universal jailbreaks" that can consistently bypass the model's biology-related safety safeguards. The move comes as AI companies face growing scrutiny over how advanced models could be misused to generate harmful biological information. OpenAI says discovering vulnerabilities before attackers do is an important part of making future AI systems safer.

<p>OpenAI has expanded its Bio Bug Bounty programme to GPT-5.6, raising the top reward to $50,000 for researchers who discover reliable ways to bypass the model's biology-related safeguards. The programme aims to improve AI safety before vulnerabilities can be exploited.</p>

OpenAI will pay up to $50,000 if you can do this

<p>Researchers at Stanford University found that AI is also transforming scientific discovery by helping scientists to analyse complex data, generate hypotheses and formulate experiments faster than old methods. AI is entering multiple sectors, including biology, medicine, engineering, and astrophysics, to overcome limitations connected to time, resources, and data complexity.</p><p><br>AI is also playing an increasingly significant role in biology and medicine by helping researchers analyse genetic data, identify biological patterns and develop advanced models for studying disease. According to Stanford researchers, AI-powered virtual cell models could significantly accelerate drug discovery and pave the way for more personalised healthcare.</p><p>&nbsp;</p><p><strong>Also read: </strong><a href="https://www.wionews.com/trending/japanese-researchers-found-invisible-gold-in-massive-quantities-hiding-beneath-seafloor-1783752058979">Japanese researchers find invisible gold in massive quantities hiding beneath seafloor</a></p><h3>Integration of AI agents</h3><p>AI agents are also becoming an integral part of scientific research, helping with tasks such as reviewing academic literature, designing experiments and interpreting data. However, researchers emphasise that human expertise remains indispensable, as AI-generated findings and hypotheses must still be scientifically validated and assessed for real-world applicability.</p><p><br>From uncovering the complexities of genetic systems to exploring the structure of the universe, AI is allowing scientists to investigate questions that were previously difficult to address. Stanford researchers believe future scientific breakthroughs will rely on combining AI's computational power with human knowledge to solve increasingly complex problems.</p><p><br>Rather than serving only as a productivity tool, AI is evolving into a fundamental component of scientific research. By analysing massive datasets, generating new hypotheses and simulating complex systems, it has the potential to accelerate discoveries across medicine, engineering, climate science and astrophysics.</p><p><br>The findings reinforce that no matter how AI advances and enters several fields, scientific progress will continue to rely completely on human expertise. AI can speed up the process of analysis and experimentation; however, rigorous validation, ethical oversight and critical judgement will always remain vital to ensuring research results are reliable and reproducible.<br>&nbsp;</p>

Stanford researchers believe future scientific breakthroughs will rely on combining AI's computational power with human knowledge to solve increasingly complex problems.

Researchers at Stanford University found that AI is also transforming scientific discovery by helping scientists to analyse complex data, generate hypotheses and formulate experiments faster than old methods. AI is entering multiple sectors, including biology, medicine, engineering, and astrophysics, to overcome limitations connected to time, resources, and data complexity. AI is also playing an increasingly significant role in biology and medicine by helping researchers analyse genetic data, identify biological patterns and develop advanced models for studying disease. According to Stanford researchers, AI-powered virtual cell models could significantly accelerate drug discovery and pave the way for more personalised healthcare. Also read: Japanese researchers find invisible gold in massive quantities hiding beneath seafloor Integration of AI agents AI agents are also becoming an integral part of scientific research, helping with tasks such as reviewing academic literature, designing experiments and interpreting data. However, researchers emphasise that human expertise remains indispensable, as AI-generated findings and hypotheses must still be scientifically validated and assessed for real-world applicability. From uncovering the complexities of genetic systems to exploring the structure of the universe, AI is allowing scientists to investigate questions that were previously difficult to address. Stanford researchers believe future scientific breakthroughs will rely on combining AI's computational power with human knowledge to solve increasingly complex problems. Rather than serving only as a productivity tool, AI is evolving into a fundamental component of scientific research. By analysing massive datasets, generating new hypotheses and simulating complex systems, it has the potential to accelerate discoveries across medicine, engineering, climate science and astrophysics. The findings reinforce that no matter how AI advances and enters several fields, scientific progress will continue to rely completely on human expertise. AI can speed up the process of analysis and experimentation; however, rigorous validation, ethical oversight and critical judgement will always remain vital to ensuring research results are reliable and reproducible.

<p>Stanford researchers believe future scientific breakthroughs will rely on combining AI's computational power with human knowledge to solve increasingly complex problems.</p>

Can AI make scientific breakthroughs faster? Researchers weigh in

<p>OpenClaw, the open-source AI assistant that gained attention earlier this year for its autonomous AI agents, has launched official mobile apps for Android and iPhone. The announcement was made by the company on X and marks one of the biggest updates for the project since it first became popular.</p><p>&nbsp;</p><p>Until now, many users accessed OpenClaw through messaging platforms such as Telegram or WhatsApp. The new apps give users a dedicated way to interact with their AI agents directly from their smartphones, making the experience simpler and more convenient. The launch comes as technology companies increasingly focus on bringing AI assistants beyond desktop computers and into everyday devices.</p>

<blockquote class="twitter-tweet"><p lang="en" dir="ltr">OpenClaw is now on iOS + Android 🦞<br><br>📱 Native mobile apps, finally<br>💬 Agents in your pocket<br>🔔 Channels, tasks, replies on the go<br><br>Run agents from wherever your thumbs are.<br><br>iOS: <a href="https://t.co/7LHHc9htgM">https://t.co/7LHHc9htgM</a><br>Android: <a href="https://t.co/X0Wuh2uA8w">https://t.co/X0Wuh2uA8w</a></p>&mdash; OpenClaw🦞 (@openclaw) <a href="https://x.com/openclaw/status/2071688039114342592?ref_src=twsrc%5Etfw">June 29, 2026</a></blockquote> <script async src="https://platform.x.com/widgets.js" charset="utf-8"></script>

<h2><strong>What can the OpenClaw mobile app do?</strong></h2><p>The mobile apps work with the OpenClaw Gateway, which connects users to their AI agents, language models and tools. Instead of running AI models directly on the phone, the apps connect securely to a Gateway running on a personal computer, cloud server or another user-controlled system.</p><p>Once connected, users can chat with their AI agents using text or voice, approve tasks, monitor automated workflows and receive notifications wherever they are.</p><p>According to OpenClaw, users can also allow the app to access features such as the camera, location, contacts, calendar, reminders, photos and screen content. These permissions are optional and require user approval. With access to these features, an AI agent can help organise schedules, analyse images, manage reminders or perform other everyday tasks.</p><p>&nbsp;</p><p>The launch highlights a growing trend across the AI industry. Instead of only answering questions like traditional chatbots, newer AI systems are designed to complete tasks for users after receiving permission. OpenClaw is one of several projects building what is known as "agentic AI", where software can carry out actions instead of only generating responses. The dedicated apps also remove one of the biggest limitations for iPhone users, who previously had to rely on third-party messaging platforms to access the service.</p><h2><strong>What is OpenClaw?</strong></h2><p>OpenClaw is an open-source AI assistant that allows people to build and customise their own AI agents while keeping control over where their data and models run. Unlike many commercial AI assistants, OpenClaw routes requests through its Gateway, allowing users to host the system on their own infrastructure if they choose. The project gained widespread attention following the launch of MoltBook, an experimental social platform built around AI agents. Although researchers later reported that some activity came from humans pretending to be AI agents, the project sparked wider discussion about the future of autonomous AI. OpenClaw also drew attention after founder Peter Steinberger announced earlier this year that he had joined OpenAI. Since then, development of the project has been overseen by the OpenClaw Foundation, a non-profit organisation. OpenAI has also said it plans to support the foundation, although details of that support have not been made public.</p><p>The arrival of official Android and iPhone apps is another step towards making AI agents part of everyday life. As more companies invest in AI assistants that can perform real-world tasks, mobile access is expected to become an important part of how people use these systems. While OpenClaw remains an early-stage project and some users have reported inconsistent performance, the launch shows how quickly AI assistants are moving beyond simple conversations and towards becoming practical digital helpers.</p>

OpenClaw has launched Android and iPhone apps, allowing users to control AI agents from their smartphones. Here's what the new apps do, how they work and why the launch matters.

OpenClaw, the open-source AI assistant that gained attention earlier this year for its autonomous AI agents, has launched official mobile apps for Android and iPhone. The announcement was made by the company on X and marks one of the biggest updates for the project since it first became popular. Until now, many users accessed OpenClaw through messaging platforms such as Telegram or WhatsApp. The new apps give users a dedicated way to interact with their AI agents directly from their smartphones, making the experience simpler and more convenient. The launch comes as technology companies increasingly focus on bringing AI assistants beyond desktop computers and into everyday devices.

<p>OpenClaw has launched Android and iPhone apps, allowing users to control AI agents from their smartphones. Here's what the new apps do, how they work and why the launch matters.</p>

You can now control OpenClaw AI agents from anywhere: OpenClaw launches Android and iPhone apps

<p>Elon Musk has made a surprising admission in the rapidly evolving artificial intelligence race. In a post on X, the SpaceXAI founder acknowledged that he had underestimated Anthropic, saying the company is now "obviously currently the leader in AI."</p><p>The remark marks a sharp reversal from September last year, when Musk wrote that "winning was never in the set of possible outcomes for Anthropic."</p>

<blockquote class="twitter-tweet"><p lang="en" dir="ltr">I was clearly wrong about Anthropic. They are obviously currently the leader in AI. No company has released a model as good as Mythos/Fable and they will undoubtedly have Mythos 2 ready soon. <br><br>And I would never cut them off in a way that hurt them badly, even as a competitor.…</p>&mdash; Elon Musk (@elonmusk) <a href="https://x.com/elonmusk/status/2075278580955685036?ref_src=twsrc%5Etfw">July 9, 2026</a></blockquote> <script async src="https://platform.x.com/widgets.js" charset="utf-8"></script>

<p>Responding to a user who reposted his earlier prediction, Musk said: "I was clearly wrong about Anthropic. They are obviously currently the leader in AI." He also praised Anthropic's latest Claude Mythos and Claude Fable models, calling them the strongest AI systems released so far.</p><h2>Musk says no rival has matched Mythos</h2><p>Musk did not stop at recognising Anthropic's progress.</p><p>He wrote that "No company has released a model as good as Mythos/Fable and they will undoubtedly have Mythos 2 ready soon."</p><p>The comments are notable because they come shortly after SpaceXAI launched Grok 4.5, while OpenAI recently released GPT-5.6. Both companies have been competing aggressively with Anthropic in coding, reasoning and enterprise AI. Earlier this year, Anthropic's Mythos and Fable models faced temporary restrictions from US authorities over cybersecurity concerns before later returning with updated safeguards.</p><h2><strong>Rivals in AI, partners in computing</strong></h2><p>The relationship between Musk and Anthropic has become increasingly complex. Although the companies compete directly in artificial intelligence, Anthropic is also one of the largest customers of SpaceXAI's Colossus 1 supercomputer. According to previously reported details, Anthropic agreed to lease computing capacity worth about $1.25 billion per month through May 2029, making the overall agreement worth roughly $40 billion. The Colossus system reportedly contains more than 220,000 Nvidia GPUs with approximately 300 megawatts of computing capacity.</p><p>The arrangement has prompted speculation over whether Musk could use that leverage against a competitor.</p><p>"That's not my style"</p><p>Musk dismissed those suggestions. When one X user claimed he could effectively shut down Anthropic by ending the computing agreement, Musk replied that he would never cut them off in a way that hurt them badly, even though they are competitors. He pointed to Tesla's open-source patents and its decision to make the Supercharger network available to rival carmakers as examples of his approach to competition.</p><p>The episode also highlights another growing issue in the AI industry model distillation, where companies study rival AI systems to improve their own models. During his legal dispute with OpenAI, Musk acknowledged that AI companies generally learn from one another's models, while Anthropic has previously accused some Chinese AI developers of distilling Claude. Musk's latest comments suggest that despite fierce competition, he believes Anthropic currently sets the benchmark in frontier AI and that recognition has become difficult even for rivals to ignore.</p>

Elon Musk has admitted he was wrong about Anthropic, calling it the current AI leader and describing Claude Mythos and Fable as the best AI models available. His comments come despite SpaceXAI competing directly with Anthropic while also supplying it with massive computing power.

Elon Musk has made a surprising admission in the rapidly evolving artificial intelligence race. In a post on X, the SpaceXAI founder acknowledged that he had underestimated Anthropic, saying the company is now "obviously currently the leader in AI." The remark marks a sharp reversal from September last year, when Musk wrote that "winning was never in the set of possible outcomes for Anthropic."

<p>Elon Musk has admitted he was wrong about Anthropic, calling it the current AI leader and describing Claude Mythos and Fable as the best AI models available. His comments come despite SpaceXAI competing directly with Anthropic while also supplying it with massive computing power.</p>

Elon Musk admits, 'I was wrong,' says Anthropic is the AI leader and calls Mythos the best model ever

<p>The not-so-subtle “AI war” over export controls has taken a new twist, with reports that American AI firms such as OpenAI and Google have been selling their models to Chinese groups blacklisted by the Pentagon over China-related national security concerns. According to a report in the Financial Times, such transactions are taking place with Chinese firms including Alibaba, Tencent and Baidu through their subsidiaries in Singapore. Has the US lost the AI control race before it has even begun?</p><h2>OpenAI and Google admit to selling advanced AI services to Chinese firms blacklisted by the Pentagon</h2><p>Chinese technology groups Alibaba, Baidu and Tencent were recently included on the Pentagon’s 1260H list, which identifies companies alleged to have links with China’s military establishment. However, according to the FT report, both OpenAI and Google confirmed that they supplied advanced AI services to Singapore-based subsidiaries of these companies.</p><p>Singapore-incorporated entities can legally contract with American AI providers even when their parent companies are blacklisted. This makes it difficult to ensure that AI models face the same level of export controls as those already imposed on advanced semiconductors.</p><p>Singapore’s position as a regional technology hub allows multinational companies to operate under Singaporean law while serving customers across Asia. This has made the city-state an important gateway for AI services and an increasingly significant focus for US regulators.</p><p>Also read: <a href="https://www.wionews.com/world/anthropic-vs-pentagon-judge-blocks-designation-of-ai-firm-as-supply-chain-risk-it-s-1-0-for-now-1774592991787">Anthropic vs Pentagon: Judge blocks designation of AI firm as ‘supply chain risk’. It's 1–0, for now</a></p><h2>US export controls restrict advanced AI services to mainland China, but are rules circumvented?</h2><p>Frontier AI is increasingly being viewed as a national security asset rather than just a commercial technology. This is why the US restricts access to advanced AI services from mainland China. But that does not broadly prohibit overseas subsidiaries of Chinese companies from purchasing US AI models.&nbsp;</p><p>Citing OpenAI, the FT reported that the company “blocks direct access to its models from China” but allows some Chinese-owned companies to use its services in jurisdictions “where safeguards can be enforced.” Google said its AI services are available in markets including Singapore and Hong Kong, subject to its usage policies. However, it acknowledged that “geographical restrictions alone are insufficient” to prevent sophisticated users from circumventing controls.</p><p>&nbsp;</p><p>Also read: <a href="https://www.wionews.com/world/us-anthropic-fable-5-mythos-5-jailbreaking-national-security-1781335223909">US locks down Anthropic's most powerful AI after alarming jailbreak warning</a></p><h2>OpenAI blocks API access for Alibaba-affiliated users</h2><p>The FT report came even as OpenAI said it suspended API access for Alibaba-affiliated users last month after detecting suspected “distillation”, an activity through which developers use outputs from advanced AI models to improve competing systems.</p><p>OpenAI said it reported the suspected activity to the US government.&nbsp;</p><p><a href="https://www.wionews.com/videos/us-china-tech-tensions-redraw-supply-chains-the-world-scrambles-between-us-china-1783268807974/embed">https://www.wionews.com/videos/us-china-tech-tensions-redraw-supply-chains-the-world-scrambles-between-us-china-1783268807974/embed</a></p><h2>Anthropic takes a stricter approach</h2><p>Anthropic has taken a much stricter approach, banning China-headquartered companies and all foreign subsidiaries they own from accessing its most advanced AI models. The company has previously accused Chinese AI labs, including DeepSeek, Moonshot and MiniMax, of attempting model distillation. In Congressional testimony in June, Anthropic alleged that Alibaba used “tens of thousands of fraudulent accounts” to generate more than 28 million exchanges with its Claude models in a distillation operation.</p><h2>US-China technology rivalry extends from AI hardware to software</h2><p>Since 2022, the US has tightened restrictions on exports of advanced AI chips, particularly those made by Nvidia, while expanding blacklists covering Chinese technology companies. The Biden administration introduced sweeping semiconductor controls, while the Trump administration continued to tighten restrictions on advanced AI technologies, arguing that these capabilities could strengthen China’s military and intelligence capabilities.</p><p>In response, China tightened export controls on strategic minerals such as gallium, germanium and rare earth elements, which are critical for advanced technology manufacturing.</p><p>Chinese AI companies, including DeepSeek, Alibaba, Baidu and Tencent, have accelerated the development of competitive large language models (LLMs) to reduce dependence on US technology. The Chinese government has intensified investment in domestic AI models and semiconductor development.&nbsp;</p>

OpenAI and Google’s AI services are being sold to Chinese firms blacklisted by Pentagon, using the Singapore subsidiary route. AI export controls are being challenged this way as the US-China technology rivalry shifts from chips to frontier AI models.

The not-so-subtle “AI war” over export controls has taken a new twist, with reports that American AI firms such as OpenAI and Google have been selling their models to Chinese groups blacklisted by the Pentagon over China-related national security concerns. According to a report in the Financial Times, such transactions are taking place with Chinese firms including Alibaba, Tencent and Baidu through their subsidiaries in Singapore. Has the US lost the AI control race before it has even begun? OpenAI and Google admit to selling advanced AI services to Chinese firms blacklisted by the Pentagon Chinese technology groups Alibaba, Baidu and Tencent were recently included on the Pentagon’s 1260H list, which identifies companies alleged to have links with China’s military establishment. However, according to the FT report, both OpenAI and Google confirmed that they supplied advanced AI services to Singapore-based subsidiaries of these companies. Singapore-incorporated entities can legally contract with American AI providers even when their parent companies are blacklisted. This makes it difficult to ensure that AI models face the same level of export controls as those already imposed on advanced semiconductors. Singapore’s position as a regional technology hub allows multinational companies to operate under Singaporean law while serving customers across Asia. This has made the city-state an important gateway for AI services and an increasingly significant focus for US regulators. Also read: Anthropic vs Pentagon: Judge blocks designation of AI firm as ‘supply chain risk’. It's 1–0, for now US export controls restrict advanced AI services to mainland China, but are rules circumvented? Frontier AI is increasingly being viewed as a national security asset rather than just a commercial technology. This is why the US restricts access to advanced AI services from mainland China. But that does not broadly prohibit overseas subsidiaries of Chinese companies from purchasing US AI models. Citing OpenAI, the FT reported that the company “blocks direct access to its models from China” but allows some Chinese-owned companies to use its services in jurisdictions “where safeguards can be enforced.” Google said its AI services are available in markets including Singapore and Hong Kong, subject to its usage policies. However, it acknowledged that “geographical restrictions alone are insufficient” to prevent sophisticated users from circumventing controls. Also read: US locks down Anthropic's most powerful AI after alarming jailbreak warning OpenAI blocks API access for Alibaba-affiliated users The FT report came even as OpenAI said it suspended API access for Alibaba-affiliated users last month after detecting suspected “distillation”, an activity through which developers use outputs from advanced AI models to improve competing systems. OpenAI said it reported the suspected activity to the US government. https://www.wionews.com/videos/us-china-tech-tensions-redraw-supply-chains-the-world-scrambles-between-us-china-1783268807974/embed Anthropic takes a stricter approach Anthropic has taken a much stricter approach, banning China-headquartered companies and all foreign subsidiaries they own from accessing its most advanced AI models. The company has previously accused Chinese AI labs, including DeepSeek, Moonshot and MiniMax, of attempting model distillation. In Congressional testimony in June, Anthropic alleged that Alibaba used “tens of thousands of fraudulent accounts” to generate more than 28 million exchanges with its Claude models in a distillation operation. US-China technology rivalry extends from AI hardware to software Since 2022, the US has tightened restrictions on exports of advanced AI chips, particularly those made by Nvidia, while expanding blacklists covering Chinese technology companies. The Biden administration introduced sweeping semiconductor controls, while the Trump administration continued to tighten restrictions on advanced AI technologies, arguing that these capabilities could strengthen China’s military and intelligence capabilities. In response, China tightened export controls on strategic minerals such as gallium, germanium and rare earth elements, which are critical for advanced technology manufacturing. Chinese AI companies, including DeepSeek, Alibaba, Baidu and Tencent, have accelerated the development of competitive large language models (LLMs) to reduce dependence on US technology. The Chinese government has intensified investment in domestic AI models and semiconductor development.

<p>OpenAI and Google’s AI services are being sold to Chinese firms blacklisted by Pentagon, using the Singapore subsidiary route. AI export controls are being challenged this way as the US-China technology rivalry shifts from chips to frontier AI models.</p>

OpenAI, Google are selling AI to Chinese firms blacklisted by Pentagon, says report: The battleground of AI export controls

<p>Meta is preparing for one of its biggest artificial intelligence infrastructure upgrades yet. According to an internal company memo reviewed by Reuters, the company plans to begin manufacturing its next-generation AI chip, codenamed Iris, in September 2026.</p><p>The chip is part of Meta's long-term Meta Training and Inference Accelerator (MTIA) programme, under which the company is developing its own AI processors instead of relying entirely on third-party suppliers. The goal is to improve the AI systems powering Facebook, Instagram and other Meta products while reducing computing costs.</p><p>The reported production follows six weeks of successful testing, during which engineers found no major issues with the chip.</p><h2><strong>Why is Meta building its own AI chip?</strong></h2><p>Running advanced AI models requires enormous computing power, and buying large numbers of graphics processing units (GPUs) from companies such as Nvidia and AMD has become increasingly expensive.</p><p>Meta believes custom-designed chips can perform many AI tasks more efficiently while giving the company greater control over its infrastructure.</p><p>The Iris processor has been designed with help from Broadcom, while Taiwan Semiconductor Manufacturing Co. (TSMC) will manufacture the chip. Industry analysts say nearly every major AI company is now pursuing custom silicon to reduce costs and avoid dependence on external chip suppliers.</p><h2><strong>Meta plans to double computing capacity</strong></h2><p>The Reuters memo shows Meta expects to deploy around 7 gigawatts (GW) of computing infrastructure by the end of 2026 before doubling that figure to 14GW in 2027.</p><p>For comparison, 1GW of electricity can power roughly 800,000 homes. Such a rapid expansion highlights the enormous energy requirements of modern AI systems. Meta also expects to spend up to $145 billion this year on AI infrastructure, making it one of the biggest investors in artificial intelligence globally.</p><h2><strong>The AI hardware race is accelerating</strong></h2><p>Meta's chip strategy reflects a broader industry trend. Google has its Tensor Processing Units (TPUs), Amazon has Trainium and Inferentia, while Microsoft, OpenAI and Anthropic are also exploring custom AI hardware.</p><p>According to the memo, Meta plans to introduce a new AI chip roughly every six months through 2027, a much faster release cycle than the industry's traditional annual updates. The company has also secured long-term supply agreements with Samsung Electronics for memory chips, Sandisk for storage and Sumitomo Electric for fibre-optic equipment as it expands its global data centre network. With AI demand continuing to grow, owning more of its hardware stack could become one of Meta's biggest competitive advantages in the years ahead.</p><p>&nbsp;</p>

Meta will reportedly begin producing its new Iris AI chip in September after successful testing. The custom processor is part of the company's plan to double computing capacity to 14GW by 2027, reduce dependence on Nvidia and support growing AI services across its platforms.

Meta is preparing for one of its biggest artificial intelligence infrastructure upgrades yet. According to an internal company memo reviewed by Reuters, the company plans to begin manufacturing its next-generation AI chip, codenamed Iris, in September 2026. The chip is part of Meta's long-term Meta Training and Inference Accelerator (MTIA) programme, under which the company is developing its own AI processors instead of relying entirely on third-party suppliers. The goal is to improve the AI systems powering Facebook, Instagram and other Meta products while reducing computing costs. The reported production follows six weeks of successful testing, during which engineers found no major issues with the chip. Why is Meta building its own AI chip? Running advanced AI models requires enormous computing power, and buying large numbers of graphics processing units (GPUs) from companies such as Nvidia and AMD has become increasingly expensive. Meta believes custom-designed chips can perform many AI tasks more efficiently while giving the company greater control over its infrastructure. The Iris processor has been designed with help from Broadcom, while Taiwan Semiconductor Manufacturing Co. (TSMC) will manufacture the chip. Industry analysts say nearly every major AI company is now pursuing custom silicon to reduce costs and avoid dependence on external chip suppliers. Meta plans to double computing capacity The Reuters memo shows Meta expects to deploy around 7 gigawatts (GW) of computing infrastructure by the end of 2026 before doubling that figure to 14GW in 2027. For comparison, 1GW of electricity can power roughly 800,000 homes. Such a rapid expansion highlights the enormous energy requirements of modern AI systems. Meta also expects to spend up to $145 billion this year on AI infrastructure, making it one of the biggest investors in artificial intelligence globally. The AI hardware race is accelerating Meta's chip strategy reflects a broader industry trend. Google has its Tensor Processing Units (TPUs), Amazon has Trainium and Inferentia, while Microsoft, OpenAI and Anthropic are also exploring custom AI hardware. According to the memo, Meta plans to introduce a new AI chip roughly every six months through 2027, a much faster release cycle than the industry's traditional annual updates. The company has also secured long-term supply agreements with Samsung Electronics for memory chips, Sandisk for storage and Sumitomo Electric for fibre-optic equipment as it expands its global data centre network. With AI demand continuing to grow, owning more of its hardware stack could become one of Meta's biggest competitive advantages in the years ahead.

<p>Meta will reportedly begin producing its new Iris AI chip in September after successful testing. The custom processor is part of the company's plan to double computing capacity to 14GW by 2027, reduce dependence on Nvidia and support growing AI services across its platforms.</p>

Meta plans a new AI chip every six months: Here's how its next chip could reduce dependence on Nvidia

<p>Imagine making a phone call from the middle of the ocean, deep inside a forest or high in the Himalayas without a single mobile tower nearby. That is exactly what BSNL's new satellite phone is designed to do.</p><p>The state-run telecom company has launched the handset in India for Rs 1,34,166 (including taxes). While the price is far higher than even premium smartphones, the device serves a completely different purpose. Instead of connecting to nearby 4G or 5G towers, it communicates directly with satellites orbiting the Earth.</p><p>The phone is meant for users who need reliable communication where conventional mobile networks cannot reach.</p><h2><strong>Why does it cost so much?</strong></h2><p>Unlike regular smartphones, satellite phones require specialised hardware that can communicate with satellites thousands of kilometres above Earth.</p><p>The handset also relies on global satellite communication networks such as Inmarsat, allowing it to make voice calls even in remote mountains, deserts, forests and offshore locations. Building and maintaining satellite communication infrastructure is significantly more expensive than operating conventional cellular networks. The smaller number of users also means production volumes remain low, increasing the cost of each device.</p><h2><strong>Who is it designed for?</strong></h2><p>BSNL says the satellite phone is aimed at professionals and organisations operating in areas without mobile coverage. Potential users include defence personnel, disaster response teams, maritime workers, miners, remote industrial operators, researchers, mountaineers and pilgrims travelling through isolated regions. It also includes an SOS emergency feature, making it useful during natural disasters or rescue operations when conventional telecom networks may fail.</p><figure class="image"><img src="https://akm-img-a-in.tosshub.com/indiatoday/styles/medium_crop_simple/public/2026-07/screenshot_2026-07-09_140955.png?VersionId=NXtmAZeT6dHYUrFZ8AzgJQlp3gdVLSR.&amp;size=750:*" alt="BSNL satellite phone"></figure><h2><strong>Can anyone buy it?</strong></h2><p>Not immediately. Satellite phones are tightly regulated in India because of national security concerns. Before purchasing or using the device, users must obtain permission from the Department of Telecommunications (DoT). Using an unauthorised satellite phone can attract legal action under Indian telecom regulations.</p><h2><strong>More than just a phone</strong></h2><p>The launch comes as BSNL continues expanding its regular mobile network across India. The company recently announced that it has commissioned nearly 99,000 4G sites, with plans to expand coverage further.</p><p>However, even thousands of mobile towers cannot reach every corner of the country. That is where satellite communication becomes important.</p><p>For most people, a smartphone remains the right choice. But for those working where there is no mobile signal, BSNL's satellite phone offers something no ordinary handset can communication when everything else goes offline.</p>

BSNL has launched a ₹1.34 lakh satellite phone that works without mobile towers by connecting directly to satellites. Built for remote areas, emergencies and critical operations, the handset requires prior approval from the Department of Telecommunications before it can be purchased or used.


Imagine making a phone call from the middle of the ocean, deep inside a forest or high in the Himalayas without a single mobile tower nearby. That is exactly what BSNL's new satellite phone is designed to do. The state-run telecom company has launched the handset in India for Rs 1,34,166 (including taxes). While the price is far higher than even premium smartphones, the device serves a completely different purpose. Instead of connecting to nearby 4G or 5G towers, it communicates directly with satellites orbiting the Earth. The phone is meant for users who need reliable communication where conventional mobile networks cannot reach. Why does it cost so much? Unlike regular smartphones, satellite phones require specialised hardware that can communicate with satellites thousands of kilometres above Earth. The handset also relies on global satellite communication networks such as Inmarsat, allowing it to make voice calls even in remote mountains, deserts, forests and offshore locations. Building and maintaining satellite communication infrastructure is significantly more expensive than operating conventional cellular networks. The smaller number of users also means production volumes remain low, increasing the cost of each device. Who is it designed for? BSNL says the satellite phone is aimed at professionals and organisations operating in areas without mobile coverage. Potential users include defence personnel, disaster response teams, maritime workers, miners, remote industrial operators, researchers, mountaineers and pilgrims travelling through isolated regions. It also includes an SOS emergency feature, making it useful during natural disasters or rescue operations when conventional telecom networks may fail. Can anyone buy it? Not immediately. Satellite phones are tightly regulated in India because of national security concerns. Before purchasing or using the device, users must obtain permission from the Department of Telecommunications (DoT). Using an unauthorised satellite phone can attract legal action under Indian telecom regulations. More than just a phone The launch comes as BSNL continues expanding its regular mobile network across India. The company recently announced that it has commissioned nearly 99,000 4G sites, with plans to expand coverage further. However, even thousands of mobile towers cannot reach every corner of the country. That is where satellite communication becomes important. For most people, a smartphone remains the right choice. But for those working where there is no mobile signal, BSNL's satellite phone offers something no ordinary handset can communication when everything else goes offline.

<p>BSNL has launched a ₹1.34 lakh satellite phone that works without mobile towers by connecting directly to satellites. Built for remote areas, emergencies and critical operations, the handset requires prior approval from the Department of Telecommunications before it can be purchased or used.</p>

No signal? No problem: How BSNL's satellite phone works without mobile towers

<p>Fidji Simo announced on Thursday (Jul 9) that she was stepping down from her full-time role at OpenAI as CEO of Artificial General Intelligence (AGI) Deployment due to health issues, transitioning to a part-time adviser role. Simo has postural orthostatic tachycardia syndrome (POTS), a neuroimmune condition she has lived with for about seven years after being diagnosed around 2019. She has described experiencing severe flare-ups requiring ongoing treatment and management. Who is Fidji Simo, what is POTS, and how did her diagnosis make her an advocate of AI for tackling such rare diseases?</p><h2>About Fidji Simo, who is exiting as OpenAI AGI CEO</h2><p>Fidji Simo, born on 5 October 1985, is a French-American business executive. She grew up in Sète, a small fishing town in southern France, and was the first in her family to graduate high school. She later earned a Master of Science in Management from HEC Paris and spent her final year at the UCLA Anderson School of Management.</p><p>Simo joined Facebook- now Meta Platforms- &nbsp;in 2011, eventually becoming head of the Facebook app and a key aide of founder-CEO Mark Zuckerberg. From 2021 to 2025, she served as CEO of grocery delivery company Instacart. She joined OpenAI's board in March 2024 and was appointed the company's first CEO of Applications in May 2025, later retitled CEO of AGI Deployment. Reporting to OpenAI CEO Sam Altman, she oversaw product, engineering, operations and business teams. She has also served on the boards of Shopify and Cirque du Soleil, among others.</p><p>Also read: <a href="https://www.wionews.com/entertainment/samantha-ruth-prabhu-opens-up-about-health-struggles-amid-myositis-diagnosis-691838">Samantha Ruth Prabhu opens up about health struggles amid Myositis diagnosis</a></p><h2>What is postural orthostatic tachycardia syndrome (POTS) which Fidgi Simo is suffering from?</h2><p>Postural orthostatic tachycardia syndrome or POTS is a disorder of the autonomic nervous system that affects heart rate, blood circulation and other involuntary body functions. It is marked by an unusually rapid increase in heart rate when standing. Common symptoms include dizziness, light-headedness, fatigue, fainting, chest pain, nausea, heart palpitations and difficulty standing for long periods. POTS often overlaps with conditions such as myalgic encephalomyelitis or chronic fatigue syndrome (CFS), migraines and Long COVID. Around 80 per cent of diagnosed cases occur in women, and the condition is often underdiagnosed.</p><p>Simo has said she consulted nearly 40 specialists before receiving a diagnosis, highlighting the difficulties many patients face in obtaining appropriate care.</p><p>Also read: <a href="https://www.wionews.com/entertainment/hollywood/the-boys-actress-erin-moriarty-reveals-graves-disease-diagnosis-go-get-checked-1749954958809">The Boys actress Erin Moriarty reveals Graves disease diagnosis: Go get checked...'</a></p><h2>How Fidgi Simo managed POTS while on a high-flying career</h2><p>Simo has spoken publicly about living with POTS to raise awareness and reduce stigma around chronic illnesses. She managed the condition through medical treatment and lifestyle adjustments, including exercise, sleep, nutrition and avoiding known triggers, while continuing to lead major technology companies. However, a severe flare-up in early 2026 led to medical leave in April, and her recovery took longer than expected, prompting her to step down from her full-time position.</p><p><a href="https://www.wionews.com/videos/on-quest-to-reverse-ageing-bryan-johnson-gets-diagnosed-with-incurable-disease-1783623424119/embed">https://www.wionews.com/videos/on-quest-to-reverse-ageing-bryan-johnson-gets-diagnosed-with-incurable-disease-1783623424119/embed</a></p><p>Based on her experiences, Simo also became an advocate for neuroimmune research. She co-founded the Metrodora Institute and ChronicleBio, and serves as president of the non-profit Complex Disorders Alliance (CODA), supporting research, improved diagnosis and better treatments for conditions such as POTS and CFS. She has also highlighted the potential of AI to improve care for patients with complex chronic illnesses.</p>

OpenAI executive Fidji Simo is stepping down from her full-time role as the firm's AGI CEO, after a severe POTS flare-up. Here's who she is, what postural orthostatic tachycardia syndrome is, its symptoms and how she managed it alongside a busy career.

Fidji Simo announced on Thursday (Jul 9) that she was stepping down from her full-time role at OpenAI as CEO of Artificial General Intelligence (AGI) Deployment due to health issues, transitioning to a part-time adviser role. Simo has postural orthostatic tachycardia syndrome (POTS), a neuroimmune condition she has lived with for about seven years after being diagnosed around 2019. She has described experiencing severe flare-ups requiring ongoing treatment and management. Who is Fidji Simo, what is POTS, and how did her diagnosis make her an advocate of AI for tackling such rare diseases? About Fidji Simo, who is exiting as OpenAI AGI CEO Fidji Simo, born on 5 October 1985, is a French-American business executive. She grew up in Sète, a small fishing town in southern France, and was the first in her family to graduate high school. She later earned a Master of Science in Management from HEC Paris and spent her final year at the UCLA Anderson School of Management. Simo joined Facebook- now Meta Platforms- in 2011, eventually becoming head of the Facebook app and a key aide of founder-CEO Mark Zuckerberg. From 2021 to 2025, she served as CEO of grocery delivery company Instacart. She joined OpenAI's board in March 2024 and was appointed the company's first CEO of Applications in May 2025, later retitled CEO of AGI Deployment. Reporting to OpenAI CEO Sam Altman, she oversaw product, engineering, operations and business teams. She has also served on the boards of Shopify and Cirque du Soleil, among others. Also read: Samantha Ruth Prabhu opens up about health struggles amid Myositis diagnosis What is postural orthostatic tachycardia syndrome (POTS) which Fidgi Simo is suffering from? Postural orthostatic tachycardia syndrome or POTS is a disorder of the autonomic nervous system that affects heart rate, blood circulation and other involuntary body functions. It is marked by an unusually rapid increase in heart rate when standing. Common symptoms include dizziness, light-headedness, fatigue, fainting, chest pain, nausea, heart palpitations and difficulty standing for long periods. POTS often overlaps with conditions such as myalgic encephalomyelitis or chronic fatigue syndrome (CFS), migraines and Long COVID. Around 80 per cent of diagnosed cases occur in women, and the condition is often underdiagnosed. Simo has said she consulted nearly 40 specialists before receiving a diagnosis, highlighting the difficulties many patients face in obtaining appropriate care. Also read: The Boys actress Erin Moriarty reveals Graves disease diagnosis: Go get checked...' How Fidgi Simo managed POTS while on a high-flying career Simo has spoken publicly about living with POTS to raise awareness and reduce stigma around chronic illnesses. She managed the condition through medical treatment and lifestyle adjustments, including exercise, sleep, nutrition and avoiding known triggers, while continuing to lead major technology companies. However, a severe flare-up in early 2026 led to medical leave in April, and her recovery took longer than expected, prompting her to step down from her full-time position. https://www.wionews.com/videos/on-quest-to-reverse-ageing-bryan-johnson-gets-diagnosed-with-incurable-disease-1783623424119/embed Based on her experiences, Simo also became an advocate for neuroimmune research. She co-founded the Metrodora Institute and ChronicleBio, and serves as president of the non-profit Complex Disorders Alliance (CODA), supporting research, improved diagnosis and better treatments for conditions such as POTS and CFS. She has also highlighted the potential of AI to improve care for patients with complex chronic illnesses.

<p>OpenAI executive Fidji Simo is stepping down from her full-time role as the firm's AGI CEO, after a severe POTS flare-up. Here's who she is, what postural orthostatic tachycardia syndrome is, its symptoms and how she managed it alongside a busy career.</p>

Fidji Simo to step down as OpenAI's AGI Deployment CEO due to POTS. Know more about the disease, symptoms and how she managed it

<p>Apple is on the verge of overtaking Google parent Alphabet to become the world's second most valuable company, as investors continue to back the iPhone maker despite concerns over its artificial intelligence strategy and rising costs.</p><p>&nbsp;</p><p>Apple's market capitalisation has climbed to around $4.56 trillion, edging closer to Nvidia, which remains the world's most valuable company at approximately $4.78 trillion. Alphabet, meanwhile, is valued at about $4.43 trillion, leaving Apple well positioned to move into second place if its shares continue to gain.</p><p>&nbsp;</p><p>Also read: <a href="https://www.wionews.com/world/airport-warships-visas-and-more-trump-s-name-added-to-at-least-5-us-government-initiatives-1783628600207">Airport, warships, visas and more: Trump's name added to at least 5 US government initiatives</a></p><p>&nbsp;</p><p>According to Dow Jones Market Data, the gap between Apple and Nvidia has narrowed sharply over the past year. On August 4, 2025, Nvidia was worth about $1.37 trillion, more than Apple, marking the widest gap since Apple lost its position as the world's most valuable company. That difference has now shrunk to roughly $200 billion.</p><p>&nbsp;</p><p>Apple's rise has come even as the company has faced scrutiny over its AI roadmap and higher component costs. Investors have continued to buy its shares, reflecting confidence in the company's long-term prospects. At the same time, Nvidia's stock has cooled after a remarkable multi-year rally driven by the AI boom. Market analysts say investors are increasingly looking beyond Nvidia for exposure to artificial intelligence.</p><p>&nbsp;</p><p>Also read: <a href="https://www.wionews.com/world/trump-updates-netanyahu-on-us-moves-in-gulf-as-they-discuss-regional-coordination-1783633014563">Trump updates Netanyahu on US 'moves in Gulf' as they discuss regional coordination</a></p><p>&nbsp;</p><p>Mike Reynolds, Vice President of Investment Strategy at Glenmede, told MarketWatch that investors are seeking AI opportunities outside Nvidia rather than concentrating their investments in a single company. "When you become the biggest stock in the world, there's a target on your back from people trying to chip away at your moat, and I think that's part of what we're seeing," Reynolds said, according to MarketWatch.</p>

Driven by a 15 per cent year-to-date stock rally, Apple's market cap reached $4.56 trillion, closing in on second-place Alphabet ($4.43 trillion) as investors diversify AI investments away from Nvidia.

Apple is on the verge of overtaking Google parent Alphabet to become the world's second most valuable company, as investors continue to back the iPhone maker despite concerns over its artificial intelligence strategy and rising costs. Apple's market capitalisation has climbed to around $4.56 trillion, edging closer to Nvidia, which remains the world's most valuable company at approximately $4.78 trillion. Alphabet, meanwhile, is valued at about $4.43 trillion, leaving Apple well positioned to move into second place if its shares continue to gain. Also read: Airport, warships, visas and more: Trump's name added to at least 5 US government initiatives According to Dow Jones Market Data, the gap between Apple and Nvidia has narrowed sharply over the past year. On August 4, 2025, Nvidia was worth about $1.37 trillion, more than Apple, marking the widest gap since Apple lost its position as the world's most valuable company. That difference has now shrunk to roughly $200 billion. Apple's rise has come even as the company has faced scrutiny over its AI roadmap and higher component costs. Investors have continued to buy its shares, reflecting confidence in the company's long-term prospects. At the same time, Nvidia's stock has cooled after a remarkable multi-year rally driven by the AI boom. Market analysts say investors are increasingly looking beyond Nvidia for exposure to artificial intelligence. Also read: Trump updates Netanyahu on US 'moves in Gulf' as they discuss regional coordination Mike Reynolds, Vice President of Investment Strategy at Glenmede, told MarketWatch that investors are seeking AI opportunities outside Nvidia rather than concentrating their investments in a single company. "When you become the biggest stock in the world, there's a target on your back from people trying to chip away at your moat, and I think that's part of what we're seeing," Reynolds said, according to MarketWatch.

<p>Driven by a 15 per cent year-to-date stock rally, Apple's market cap reached $4.56 trillion, closing in on second-place Alphabet ($4.43 trillion) as investors diversify AI investments away from Nvidia.</p>

Apple closes in on Alphabet as world's No. 2 most valuable company: What's behind the rally?

<p>On India's AI workplace readiness, there are two narratives: One that says the nation is heavily invested in it, and the other that brings out several gaps. The recent Kyndryl's People Readiness Report 2026 found that only 25 per cent of Indian organisations consider their workforce adequately prepared to leverage Artificial Intelligence, down 12 percentage points from 2025. While 56 per cent of organisations have AI deployed broadly or embedded in their core business processes, 81 per cent of Indian leaders believe AI is advancing faster than their workforce, governance and operating models. The AI skill gaps, from schools to the workplace, are ones that need urgent fixing in India. Here is why:</p><h2>India's AI skill readiness: Demand outrunning supply</h2><p>Recent data from the Ministry of Electronics and Information Technology of India (MeitY) showed only about 16 per cent of India's IT professionals are AI-skilled. A study by NASSCOM-McKinsey projected a shortage of more than 1.4 million AI professionals by 2026 without accelerated upskilling. A NASSCOM-Deloitte estimate said India's AI talent pool will grow from 600,000-650,000 to over 1.25 million between 2022 and 2027, while demand is expected to expand at 25-35 per cent annually.</p><p><a href="https://www.wionews.com/videos/india-joins-us-led-pax-silica-ai-supply-initiative-1782446225592/embed">https://www.wionews.com/videos/india-joins-us-led-pax-silica-ai-supply-initiative-1782446225592/embed</a></p><h2>Indian graduates are only half-ready to tackle AI-led workplaces</h2><p>The Mercer-Mettl India Graduate Skill Index 2025 reported an overall graduate employability at 42.6 per cent with 46 per cent of graduates employable for AI and machine learning roles. Employability stood at 48.4 per cent in Tier 1 colleges, 46.1 per cent in Tier 2 institutions and 43.4% in Tier 3 colleges. This indicates that Indian graduates are only half-ready for AI-intensive workplaces.</p><h2>The vocational skills gap in India</h2><p>AI skills gap is part of a larger problem. India's periodic labour force survey (PLFS) data shows that only 21 per cent of people aged 15-29 have received vocational or technical training, with just 4.4 per cent receiving formal training. The India Skills Report 2026, produced by Wheebox-ETS with AICTE and CII, found employability among computer science and IT engineers at nearly 80 per cent, compared with 45.95 per cent for ITI graduates and 32.92 per cent for polytechnic graduates. This should be seen in the context of what the report said was a 600 per cent growth in AI and machine learning job opportunities.</p><p>Also read: <a href="https://www.wionews.com/india-news/india-s-ai-problem-gaps-in-compute-hardware-infrastructure-chips-1781943963441">India's AI problem: Gaps in compute, hardware, infrastructure, chips affect readiness</a></p><h2>Catch them young: AI, school education and teacher readiness</h2><p>The Ministry of Education plans to introduce AI from Class 3 in the 2026-27 academic year, with CBSE preparing the curriculum. But then, we are also dealing with lack of basic computer and internet access at school levels. The 2024-25 data from UDISE+ of India's Department of Education showed that about 65 per cent of schools have computers, but only 58 per cent are functional, while internet access stands at about 63 per cent nationally. Surveys conducted in 2025 indicate only about 15 per cent of teachers and educators are AI-fluent.&nbsp;</p><p>Also read: India's AI problem: <a href="https://www.wionews.com/india-news/india-s-ai-problem-funding-gap-for-ai-startups-research-and-development-is-the-core-constraint-artificial-intelligence-news-1782542028012">Funding gap for AI startups, research and development is the core constraint</a></p><h2>The larger picture of India AI skill gaps: Reports from the UN and national agencies</h2><p>NITI Aayog's October 2025 roadmap estimated that AI could displace around 2 million traditional jobs while creating up to 8 million new roles if supported by appropriate policy and skilling measures. Against this, one has to look at the larger picture to compare India with the global situation. The UNCTAD Technology and Innovation Report of 2025 ranked India 113th in skills and 99th in ICT deployment, while placing it 10th globally in AI investment. The report warned that AI could affect up to 40 per cent of jobs worldwide.</p><p>Also read: <a href="https://www.wionews.com/technology/india-s-deep-tech-has-a-prototype-problem-that-big-industry-wants-to-fix-1783552923186/amp">India's Deep-Tech has a Prototype problem, that big Industry wants to fix</a></p><p>&nbsp;</p><p>The more recent one, UNESCO's India AI Readiness Assessment Report 2026, prepared with the IndiaAI Mission and MeitY, estimates India accounts for around 16 per cent of global AI talent and has filed more than 86,000 AI patents since 2010. Among other things, the report identifies gaps in AI governance, workforce transition planning and AI skills and computing infrastructure, warning that unequal access could widen economic inequalities.</p><p>The UN's 2026 AI Governance Report does not assess India separately, but warns that AI capabilities are advancing faster than governance systems. It said that compute, talent and frontier AI capabilities are becoming increasingly concentrated among a small number of countries and companies. This presents additional challenges for developing economies like India seeking to build AI-ready workforces.</p>

India's AI ambitions face a workforce challenge. Multiple reports, from Kyndryl to UN to Indian government, highlight shortages in AI skills, teacher and student readiness for the workplace, despite rapid AI adoption in the country. Here is why it's concerning

On India's AI workplace readiness, there are two narratives: One that says the nation is heavily invested in it, and the other that brings out several gaps. The recent Kyndryl's People Readiness Report 2026 found that only 25 per cent of Indian organisations consider their workforce adequately prepared to leverage Artificial Intelligence, down 12 percentage points from 2025. While 56 per cent of organisations have AI deployed broadly or embedded in their core business processes, 81 per cent of Indian leaders believe AI is advancing faster than their workforce, governance and operating models. The AI skill gaps, from schools to the workplace, are ones that need urgent fixing in India. Here is why: India's AI skill readiness: Demand outrunning supply Recent data from the Ministry of Electronics and Information Technology of India (MeitY) showed only about 16 per cent of India's IT professionals are AI-skilled. A study by NASSCOM-McKinsey projected a shortage of more than 1.4 million AI professionals by 2026 without accelerated upskilling. A NASSCOM-Deloitte estimate said India's AI talent pool will grow from 600,000-650,000 to over 1.25 million between 2022 and 2027, while demand is expected to expand at 25-35 per cent annually. https://www.wionews.com/videos/india-joins-us-led-pax-silica-ai-supply-initiative-1782446225592/embed Indian graduates are only half-ready to tackle AI-led workplaces The Mercer-Mettl India Graduate Skill Index 2025 reported an overall graduate employability at 42.6 per cent with 46 per cent of graduates employable for AI and machine learning roles. Employability stood at 48.4 per cent in Tier 1 colleges, 46.1 per cent in Tier 2 institutions and 43.4% in Tier 3 colleges. This indicates that Indian graduates are only half-ready for AI-intensive workplaces. The vocational skills gap in India AI skills gap is part of a larger problem. India's periodic labour force survey (PLFS) data shows that only 21 per cent of people aged 15-29 have received vocational or technical training, with just 4.4 per cent receiving formal training. The India Skills Report 2026, produced by Wheebox-ETS with AICTE and CII, found employability among computer science and IT engineers at nearly 80 per cent, compared with 45.95 per cent for ITI graduates and 32.92 per cent for polytechnic graduates. This should be seen in the context of what the report said was a 600 per cent growth in AI and machine learning job opportunities. Also read: India's AI problem: Gaps in compute, hardware, infrastructure, chips affect readiness Catch them young: AI, school education and teacher readiness The Ministry of Education plans to introduce AI from Class 3 in the 2026-27 academic year, with CBSE preparing the curriculum. But then, we are also dealing with lack of basic computer and internet access at school levels. The 2024-25 data from UDISE+ of India's Department of Education showed that about 65 per cent of schools have computers, but only 58 per cent are functional, while internet access stands at about 63 per cent nationally. Surveys conducted in 2025 indicate only about 15 per cent of teachers and educators are AI-fluent. Also read: India's AI problem: Funding gap for AI startups, research and development is the core constraint The larger picture of India AI skill gaps: Reports from the UN and national agencies NITI Aayog's October 2025 roadmap estimated that AI could displace around 2 million traditional jobs while creating up to 8 million new roles if supported by appropriate policy and skilling measures. Against this, one has to look at the larger picture to compare India with the global situation. The UNCTAD Technology and Innovation Report of 2025 ranked India 113th in skills and 99th in ICT deployment, while placing it 10th globally in AI investment. The report warned that AI could affect up to 40 per cent of jobs worldwide. Also read: India's Deep-Tech has a Prototype problem, that big Industry wants to fix The more recent one, UNESCO's India AI Readiness Assessment Report 2026, prepared with the IndiaAI Mission and MeitY, estimates India accounts for around 16 per cent of global AI talent and has filed more than 86,000 AI patents since 2010. Among other things, the report identifies gaps in AI governance, workforce transition planning and AI skills and computing infrastructure, warning that unequal access could widen economic inequalities. The UN's 2026 AI Governance Report does not assess India separately, but warns that AI capabilities are advancing faster than governance systems. It said that compute, talent and frontier AI capabilities are becoming increasingly concentrated among a small number of countries and companies. This presents additional challenges for developing economies like India seeking to build AI-ready workforces.

<p>India's AI ambitions face a workforce challenge. Multiple reports, from Kyndryl to UN to the Indian government, highlight shortages in AI skills, teacher and student readiness for the workplace, despite rapid AI adoption in the country. Here is why it's concerning</p>

India's AI problem: From teachers to students to workers, the workplace readiness skill gap that needs fixing

<p>India's deep-tech sector has several problems, and it isn't the one policymakers usually talk about.</p><p>The country is now home to approximately 3,600 deep-tech startups working across artificial intelligence, biotechnology, quantum computing and advanced engineering, according to government data cited in DPIIT's latest notification. New Delhi has responded with an unusually decisive policy. On February 4, 2026, the Department for Promotion of Industry and Internal Trade formally defined "deep-tech startup" as a distinct legal category for the first time, extending recognition to twenty years and raising the turnover ceiling to roughly 300 crore rupees, about 33.33 million dollars. In her February budget, Finance Minister Nirmala Sitharaman earmarked 20,000 crore rupees for private-sector research and innovation in the coming fiscal year, part of a wider one lakh crore rupee Research, Development and Innovation Scheme aimed at semiconductors, AI, quantum computing and biotechnology, alongside a dedicated Deep Tech Fund of Funds.</p><p>On paper, the ecosystem looks fully backed. In practice, industry figures say the money and the policy solve the wrong end of the problem.</p><p>"Most deep-tech startups die in the gap between a great prototype and a real deployment," said Akarsh Hebbar, Chairman of V-Spark DeepTech Ventures and President of the Vedanta Group, in an exclusive interaction with WION. "We built V-Spark to close that gap for good."</p><h2><strong>Where the money runs out</strong></h2><p>The gap Akarsh describes is well documented, if rarely named directly. India's deep-tech ventures typically require high R&amp;D intensity and gestation periods of ten to fifteen years before a technology is commercially viable, a timeline that sits uneasily with venture capital's conventional return horizon.&nbsp;</p><p>Industry body Nasscom has pushed the government for years to recognise this mismatch, convening a policy roundtable in April 2025 with the electronics ministry, the Department of Science and Technology and venture funds specifically to articulate the need for patient capital and funding mechanisms aligned to longer innovation cycles.</p><p>What that leaves is a stretch of runway, after a startup has proven a working prototype but before it can prove commercial-scale reliability, where neither early-stage VC money nor conventional bank lending is well suited to the risk. It's here that large industrial groups, sitting on live plants and heavy balance sheets, have started to insert themselves.</p><h2><strong>Who are all big players here</strong></h2><p>Vedanta is not alone in making this bet. Tata Technologies runs its own version of the same engagement model, now in its fourth edition, running an engineering hackathon with Amazon Web Services and Emerson that pushes student and startup teams to solve real problems in automotive, heavy machinery and aerospace.&nbsp;</p><p>Reliance Industries, meanwhile, has committed a $100 million joint venture with Meta toward building sovereign AI infrastructure inside India, alongside separate collaborations with Google. Recently, Adani group also launched its startup incubator named Vandey-Bharatam. Taken together, these moves point to a broader pattern, that India's largest industrial conglomerates, not its venture funds, are increasingly building the infrastructure that decides which deep-tech ideas survive contact with a factory floor.</p><p>Vedanta's V-Spark, launched as a dedicated platform inside a conglomerate historically built on metals, mining and energy, is one of the more structured versions of this bet. Akarsh Hebbar described a three-stage model, Pilot, Project, Partner, under which startups first test solutions against real operational problems inside Vedanta's mining and processing sites, then scale successful pilots into full projects, and eventually move into long-term partnerships that can include co-investment and revenue-sharing arrangements.</p><p>The platform has set a target of engaging over 1,000 startups and projects unlocking $300-400 million in value over three years, figures Hebbar attributes to internal tracking of "realised savings, avoided losses, increase in volume, return on investment, uptime improvements... and the pace at which successful pilots are scaled." Those numbers, drawn from company-reported internal metrics rather than independently audited disclosures, illustrate the scale industrial players believe is achievable, even if they cannot yet be externally verified.</p><h2><strong>An intelligence gap, not just a manufacturing one</strong></h2><p>Whether India can convert this deployment gap into an advantage depends on how it stacks up globally, and the picture is mixed.</p><p>On raw AI capability signals, India performs strongly. The country leads the world in AI talent acquisition, with an annual hiring rate of about 33 percent, according to Stanford's HAI AI Index, and separate analysis using Stanford's AI vibrancy tool places India third globally in the Innovation domain, alongside strong rankings in AI-related public discourse.</p><p>But talent and enthusiasm are not the same as industrial deployment, and that is where the gap with global manufacturing leaders becomes stark. Stanford's 2026 Index, citing International Federation of Robotics data, found China installed 295,000 industrial robots in 2024, against roughly 44,500 in Japan and 34,200 in the United States, a scale of physical automation India does not yet approach.</p><p>That is the distinction Akarsh Hebbar's framing leans on as industrial AI, he argues, is not an extension of the dashboard-and-dataset digitisation Indian manufacturing pursued over the last decade, but a different discipline entirely, one operating inside "highly complex, high-risk, real-time physical environments," where systems have to augment safety and asset health, not just visualise them. Vedanta says it is already running computer vision, digital twins and self-optimising systems across its mining and metals operations, citing internal figures of a 10-15 percent cut in chemical consumption and close to $10 million in annual EBITDA impact from process optimisation, again, company-disclosed metrics rather than third-party audited figures.</p><h2><strong>The workforce question nobody wants to get wrong</strong></h2><p>Scaling industrial AI inside live plants raises an obvious second question, what happens to the people running those plants today.</p><p>Akarsh Hebbar's answer leans on augmentation rather than replacement, AI-enabled learning platforms, VR-based training, and what he called marrying "AI wit(h) HI," artificial intelligence with human intelligence, so that domain experts become "tech-augmented, predictive decision-makers" rather than being displaced by the systems they operate. It's a framing every large industrial employer investing in automation now uses, and it deserves the same scrutiny applied to the financial numbers, that none of it is independently audited, and India currently has no comprehensive, government-tracked data on industrial job displacement tied specifically to AI adoption, a gap that limits how far claims of this kind can be verified either way.</p><h2><strong>A capital story as much as a technology one</strong></h2><p>Strip away the branding, and what's playing out is a quiet reallocation of who funds India's hardest technology bets. Venture capital built India's app economy and its unicorn count, India has over 100 unicorns and more than 1.57 lakh DPIIT-recognised startups, generating over 1.6 million jobs. But hardware, robotics and industrial AI don't fit that model's return timeline, and the DPIIT's own February notification effectively admits it, by rewriting the rules specifically to give deep-tech ventures a longer runway.</p><p>Into that space have stepped conglomerates with something VCs don't have, i.e. live industrial sites to test on and balance sheets patient enough to wait. Whether that capital comes with the right incentives, or ends up steering deep-tech innovation toward the problems large industries finds convenient, rather than the ones India needs solved, is the question this story leaves open.</p><p><br>&nbsp;</p>

India now counts roughly 3,600 deep-tech startups, backed by a new DPIIT recognition framework and a Rs1 lakh crore government innovation fund. Yet most die between prototype and deployment.

India's deep-tech sector has several problems, and it isn't the one policymakers usually talk about. The country is now home to approximately 3,600 deep-tech startups working across artificial intelligence, biotechnology, quantum computing and advanced engineering, according to government data cited in DPIIT's latest notification. New Delhi has responded with an unusually decisive policy. On February 4, 2026, the Department for Promotion of Industry and Internal Trade formally defined "deep-tech startup" as a distinct legal category for the first time, extending recognition to twenty years and raising the turnover ceiling to roughly 300 crore rupees, about 33.33 million dollars. In her February budget, Finance Minister Nirmala Sitharaman earmarked 20,000 crore rupees for private-sector research and innovation in the coming fiscal year, part of a wider one lakh crore rupee Research, Development and Innovation Scheme aimed at semiconductors, AI, quantum computing and biotechnology, alongside a dedicated Deep Tech Fund of Funds. On paper, the ecosystem looks fully backed. In practice, industry figures say the money and the policy solve the wrong end of the problem. "Most deep-tech startups die in the gap between a great prototype and a real deployment," said Akarsh Hebbar, Chairman of V-Spark DeepTech Ventures and President of the Vedanta Group, in an exclusive interaction with WION. "We built V-Spark to close that gap for good." Where the money runs out The gap Akarsh describes is well documented, if rarely named directly. India's deep-tech ventures typically require high R&D intensity and gestation periods of ten to fifteen years before a technology is commercially viable, a timeline that sits uneasily with venture capital's conventional return horizon. Industry body Nasscom has pushed the government for years to recognise this mismatch, convening a policy roundtable in April 2025 with the electronics ministry, the Department of Science and Technology and venture funds specifically to articulate the need for patient capital and funding mechanisms aligned to longer innovation cycles. What that leaves is a stretch of runway, after a startup has proven a working prototype but before it can prove commercial-scale reliability, where neither early-stage VC money nor conventional bank lending is well suited to the risk. It's here that large industrial groups, sitting on live plants and heavy balance sheets, have started to insert themselves. Who are all big players here Vedanta is not alone in making this bet. Tata Technologies runs its own version of the same engagement model, now in its fourth edition, running an engineering hackathon with Amazon Web Services and Emerson that pushes student and startup teams to solve real problems in automotive, heavy machinery and aerospace. Reliance Industries, meanwhile, has committed a $100 million joint venture with Meta toward building sovereign AI infrastructure inside India, alongside separate collaborations with Google. Recently, Adani group also launched its startup incubator named Vandey-Bharatam. Taken together, these moves point to a broader pattern, that India's largest industrial conglomerates, not its venture funds, are increasingly building the infrastructure that decides which deep-tech ideas survive contact with a factory floor. Vedanta's V-Spark, launched as a dedicated platform inside a conglomerate historically built on metals, mining and energy, is one of the more structured versions of this bet. Akarsh Hebbar described a three-stage model, Pilot, Project, Partner, under which startups first test solutions against real operational problems inside Vedanta's mining and processing sites, then scale successful pilots into full projects, and eventually move into long-term partnerships that can include co-investment and revenue-sharing arrangements. The platform has set a target of engaging over 1,000 startups and projects unlocking $300-400 million in value over three years, figures Hebbar attributes to internal tracking of "realised savings, avoided losses, increase in volume, return on investment, uptime improvements... and the pace at which successful pilots are scaled." Those numbers, drawn from company-reported internal metrics rather than independently audited disclosures, illustrate the scale industrial players believe is achievable, even if they cannot yet be externally verified. An intelligence gap, not just a manufacturing one Whether India can convert this deployment gap into an advantage depends on how it stacks up globally, and the picture is mixed. On raw AI capability signals, India performs strongly. The country leads the world in AI talent acquisition, with an annual hiring rate of about 33 percent, according to Stanford's HAI AI Index, and separate analysis using Stanford's AI vibrancy tool places India third globally in the Innovation domain, alongside strong rankings in AI-related public discourse. But talent and enthusiasm are not the same as industrial deployment, and that is where the gap with global manufacturing leaders becomes stark. Stanford's 2026 Index, citing International Federation of Robotics data, found China installed 295,000 industrial robots in 2024, against roughly 44,500 in Japan and 34,200 in the United States, a scale of physical automation India does not yet approach. That is the distinction Akarsh Hebbar's framing leans on as industrial AI, he argues, is not an extension of the dashboard-and-dataset digitisation Indian manufacturing pursued over the last decade, but a different discipline entirely, one operating inside "highly complex, high-risk, real-time physical environments," where systems have to augment safety and asset health, not just visualise them. Vedanta says it is already running computer vision, digital twins and self-optimising systems across its mining and metals operations, citing internal figures of a 10-15 percent cut in chemical consumption and close to $10 million in annual EBITDA impact from process optimisation, again, company-disclosed metrics rather than third-party audited figures. The workforce question nobody wants to get wrong Scaling industrial AI inside live plants raises an obvious second question, what happens to the people running those plants today. Akarsh Hebbar's answer leans on augmentation rather than replacement, AI-enabled learning platforms, VR-based training, and what he called marrying "AI wit(h) HI," artificial intelligence with human intelligence, so that domain experts become "tech-augmented, predictive decision-makers" rather than being displaced by the systems they operate. It's a framing every large industrial employer investing in automation now uses, and it deserves the same scrutiny applied to the financial numbers, that none of it is independently audited, and India currently has no comprehensive, government-tracked data on industrial job displacement tied specifically to AI adoption, a gap that limits how far claims of this kind can be verified either way. A capital story as much as a technology one Strip away the branding, and what's playing out is a quiet reallocation of who funds India's hardest technology bets. Venture capital built India's app economy and its unicorn count, India has over 100 unicorns and more than 1.57 lakh DPIIT-recognised startups, generating over 1.6 million jobs. But hardware, robotics and industrial AI don't fit that model's return timeline, and the DPIIT's own February notification effectively admits it, by rewriting the rules specifically to give deep-tech ventures a longer runway. Into that space have stepped conglomerates with something VCs don't have, i.e. live industrial sites to test on and balance sheets patient enough to wait. Whether that capital comes with the right incentives, or ends up steering deep-tech innovation toward the problems large industries finds convenient, rather than the ones India needs solved, is the question this story leaves open.

<p>India now counts roughly 3,600 deep-tech startups, backed by a new DPIIT recognition framework and a Rs1 lakh crore government innovation fund. Yet most die between prototype and deployment.</p>

India's Deep-Tech has a Prototype problem, that big Industry wants to fix

<p>For a decade, India's fintech story has had one headline number. In June 2026 alone, the country's Unified Payments Interface processed 22.72 billion transactions worth Rs28.92 lakh crore, according to data released by the National Payments Corporation of India (NPCI), capping a year in which UPI's annual transaction value crossed Rs314 lakh crore.</p><p>That number no longer defines where the money is going next. Payments, by most measures, are a solved problem in India. The harder and less visible question is what gets built on top of a payments rail that already works, and increasingly, the answer being tested by venture investors and founders is not another app, but intelligence layered onto the public infrastructure already in place.</p><h3>The shift: From rails to reasoning</h3><p>India's foundational digital infrastructure, UPI, Aadhaar, and the RBI-regulated Account Aggregator (AA) framework, is, in large part, already built. The AA ecosystem, which lets individuals and businesses share financial data across institutions with explicit consent, was granted a formal self-regulatory body on June 5 this year, when the Reserve Bank of India recognised industry alliance Sahamati as its SRO. The framework now spans 1,120 regulated entities and has enabled more than 294 million linked accounts and 450 million fulfilled consent requests, per Sahamati's disclosures accompanying the RBI order.</p><p>That scale of consented financial data flowing through a regulated architecture, close to 290 million data shares a month, is what venture investor Paramdeep Singh, founder of Long Tail Ventures, points to when arguing that India's fintech opportunity has moved from digitising transactions to digitising financial decision-making.</p><p>"The first wave digitised transactions," Singh said. "The next wave will digitise financial decision-making."</p><p>His reading is that the market gap is no longer digital payments or digital identity, it is what gets built on top of rails that already work.</p><h3>AI-native financial services</h3><p>The first is underwriting, fraud detection, compliance, treasury management and financial advice becoming autonomous rather than merely digitised. This is where the AA framework's scale becomes structurally relevant: with the data bottleneck largely resolved, the constraint shifts to the intelligence layer processing it, a bet a growing number of India-focused funds are making.</p><h3>The SME credit gap</h3><p>The second is B2B financial infrastructure built for India's small and medium enterprises, working capital, cash-flow management, embedded credit and ERP-integrated finance. This is also the segment with the clearest documented shortfall. A recent report by the Small Industries Development Bank of India, produced with CRISIL Intelligence and drawn from a survey of more than 2,000 MSMEs across 19 sectors, put the sector's addressable credit gap at Rs30 lakh crore, around 24% of total debt demand of Rs92 lakh crore, with the shortfall running to 35% among women-owned enterprises. Singh's contention is that millions of SMEs continuing to digitise represent one of the country's largest untapped financial infrastructure opportunities, precisely because that gap has proven resistant to conventional lending models.</p><h3>Cross-border rails</h3><p>The third is cross-border payments and trade infrastructure, as India's public digital rails gain international traction. UPI is now live in eight countries, including France, Singapore and the UAE, with a fully operational two-way retail corridor with Nepal launched on June 6. The bet here is that start-ups building global-first infrastructure around India's DPI stack, rather than domestic-only products, stand to capture disproportionate value as that footprint expands.</p><h3>A thesis, not a consensus</h3><p>Taken together, the argument repositions competitive advantage in Indian fintech away from owning a payments rail, since UPI, Aadhaar and the AA framework already function as public goods, and towards trusted distribution, proprietary data and applied AI capability layered on top of them.</p><p>"The next generation of fintech leaders may not look like traditional fintechs," Singh said. "They will sit at the intersection of AI, commerce, enterprise software and financial infrastructure."</p><p>This is, at this stage, one investor's read on where capital is moving, not a market consensus. But it is anchored in verifiable shifts already underway, as a payments network processing tens of billions of transactions monthly, a data-sharing framework that has just acquired formal regulatory governance, and an SME credit gap measured in lakhs of crores that has so far outrun every wave of digitisation aimed at closing it.</p><p>Whether that gap closes through AI-native underwriting, or simply becomes a bigger number cited in next year's SIDBI report, is the question the thesis will face first.</p>

UPI processed Rs28.92 lakh crore in June 2026 alone, yet India's next fintech opportunity may not be another payments product. With a Rs30 lakh crore SIDBI-measured SME credit gap and UPI now live in eight countries.

For a decade, India's fintech story has had one headline number. In June 2026 alone, the country's Unified Payments Interface processed 22.72 billion transactions worth Rs28.92 lakh crore, according to data released by the National Payments Corporation of India (NPCI), capping a year in which UPI's annual transaction value crossed Rs314 lakh crore. That number no longer defines where the money is going next. Payments, by most measures, are a solved problem in India. The harder and less visible question is what gets built on top of a payments rail that already works, and increasingly, the answer being tested by venture investors and founders is not another app, but intelligence layered onto the public infrastructure already in place. The shift: From rails to reasoning India's foundational digital infrastructure, UPI, Aadhaar, and the RBI-regulated Account Aggregator (AA) framework, is, in large part, already built. The AA ecosystem, which lets individuals and businesses share financial data across institutions with explicit consent, was granted a formal self-regulatory body on June 5 this year, when the Reserve Bank of India recognised industry alliance Sahamati as its SRO. The framework now spans 1,120 regulated entities and has enabled more than 294 million linked accounts and 450 million fulfilled consent requests, per Sahamati's disclosures accompanying the RBI order. That scale of consented financial data flowing through a regulated architecture, close to 290 million data shares a month, is what venture investor Paramdeep Singh, founder of Long Tail Ventures, points to when arguing that India's fintech opportunity has moved from digitising transactions to digitising financial decision-making. "The first wave digitised transactions," Singh said. "The next wave will digitise financial decision-making." His reading is that the market gap is no longer digital payments or digital identity, it is what gets built on top of rails that already work. AI-native financial services The first is underwriting, fraud detection, compliance, treasury management and financial advice becoming autonomous rather than merely digitised. This is where the AA framework's scale becomes structurally relevant: with the data bottleneck largely resolved, the constraint shifts to the intelligence layer processing it, a bet a growing number of India-focused funds are making. The SME credit gap The second is B2B financial infrastructure built for India's small and medium enterprises, working capital, cash-flow management, embedded credit and ERP-integrated finance. This is also the segment with the clearest documented shortfall. A recent report by the Small Industries Development Bank of India, produced with CRISIL Intelligence and drawn from a survey of more than 2,000 MSMEs across 19 sectors, put the sector's addressable credit gap at Rs30 lakh crore, around 24% of total debt demand of Rs92 lakh crore, with the shortfall running to 35% among women-owned enterprises. Singh's contention is that millions of SMEs continuing to digitise represent one of the country's largest untapped financial infrastructure opportunities, precisely because that gap has proven resistant to conventional lending models. Cross-border rails The third is cross-border payments and trade infrastructure, as India's public digital rails gain international traction. UPI is now live in eight countries, including France, Singapore and the UAE, with a fully operational two-way retail corridor with Nepal launched on June 6. The bet here is that start-ups building global-first infrastructure around India's DPI stack, rather than domestic-only products, stand to capture disproportionate value as that footprint expands. A thesis, not a consensus Taken together, the argument repositions competitive advantage in Indian fintech away from owning a payments rail, since UPI, Aadhaar and the AA framework already function as public goods, and towards trusted distribution, proprietary data and applied AI capability layered on top of them. "The next generation of fintech leaders may not look like traditional fintechs," Singh said. "They will sit at the intersection of AI, commerce, enterprise software and financial infrastructure." This is, at this stage, one investor's read on where capital is moving, not a market consensus. But it is anchored in verifiable shifts already underway, as a payments network processing tens of billions of transactions monthly, a data-sharing framework that has just acquired formal regulatory governance, and an SME credit gap measured in lakhs of crores that has so far outrun every wave of digitisation aimed at closing it. Whether that gap closes through AI-native underwriting, or simply becomes a bigger number cited in next year's SIDBI report, is the question the thesis will face first.

<p>UPI processed Rs28.92 lakh crore in June 2026 alone, yet India's next fintech opportunity may not be another payments product. With a Rs30 lakh crore SIDBI-measured SME credit gap and UPI now live in eight countries.</p>

India's fintech frontier is moving from transaction infrastructure to decision infrastructure

<p>NASA's next flagship space telescope could change not only how scientists search for life beyond Earth, but also how spacecraft are maintained in orbit.</p><p>The agency's proposed Habitable Worlds Observatory (HWO) is being designed so that robots not astronauts can repair, upgrade and refuel it throughout its lifetime. If successful, it would become the first major NASA observatory built from the beginning with robotic servicing in mind. The approach could extend the telescope's working life, reduce mission costs and allow new instruments to be added years after launch.</p><h2><strong>Why is NASA choosing robots?</strong></h2><p>Unlike the Hubble Space Telescope, which astronauts visited five times using the Space Shuttle, NASA no longer has a vehicle capable of carrying astronauts to every future space observatory. The Habitable Worlds Observatory is expected to operate near the Sun-Earth L2 Lagrange Point, about 1.5 million kilometres from Earth. That location is ideal for observing distant planets but is far beyond the reach of today's routine human space missions.</p><p>Instead of relying on astronauts, NASA wants specialised robotic spacecraft to dock with the telescope, replace faulty components, refuel it and even install upgraded scientific instruments.</p><h2><strong>A telescope built to search for another Earth</strong></h2><p>The Habitable Worlds Observatory is expected to become NASA's most ambitious planet-hunting mission since the James Webb Space Telescope. Its main goal is to directly observe Earth-like exoplanets orbiting nearby stars and analyse their atmospheres for gases such as oxygen, methane and water vapour—possible signs that a planet could support life. The observatory will also study galaxies, black holes, stars and the evolution of the universe, making it a general-purpose space telescope alongside its search for potentially habitable worlds.</p><h2><strong>Why robotic servicing could change future missions</strong></h2><p>Designing spacecraft for robotic maintenance represents a major shift in space engineering. Instead of treating telescopes as single-use missions, future observatories could be upgraded much like satellites or even computers. New cameras, spectrometers and other scientific instruments could be installed years after launch, allowing missions to stay scientifically relevant for much longer. NASA has already demonstrated some robotic servicing technologies through missions such as OSAM-1 and continues to work with industry on autonomous space robotics. If the Habitable Worlds Observatory follows its planned roadmap, it could become the first observatory designed to evolve throughout its lifetime keeping humanity's search for alien worlds active for decades without needing astronauts to make the journey.</p>

NASA's proposed Habitable Worlds Observatory will be designed for robotic repairs instead of astronaut servicing. The telescope aims to search for Earth-like planets while allowing robots to refuel, repair and upgrade it, potentially extending its mission for decades.


NASA's next flagship space telescope could change not only how scientists search for life beyond Earth, but also how spacecraft are maintained in orbit. The agency's proposed Habitable Worlds Observatory (HWO) is being designed so that robots not astronauts can repair, upgrade and refuel it throughout its lifetime. If successful, it would become the first major NASA observatory built from the beginning with robotic servicing in mind. The approach could extend the telescope's working life, reduce mission costs and allow new instruments to be added years after launch. Why is NASA choosing robots? Unlike the Hubble Space Telescope, which astronauts visited five times using the Space Shuttle, NASA no longer has a vehicle capable of carrying astronauts to every future space observatory. The Habitable Worlds Observatory is expected to operate near the Sun-Earth L2 Lagrange Point, about 1.5 million kilometres from Earth. That location is ideal for observing distant planets but is far beyond the reach of today's routine human space missions. Instead of relying on astronauts, NASA wants specialised robotic spacecraft to dock with the telescope, replace faulty components, refuel it and even install upgraded scientific instruments. A telescope built to search for another Earth The Habitable Worlds Observatory is expected to become NASA's most ambitious planet-hunting mission since the James Webb Space Telescope. Its main goal is to directly observe Earth-like exoplanets orbiting nearby stars and analyse their atmospheres for gases such as oxygen, methane and water vapour—possible signs that a planet could support life. The observatory will also study galaxies, black holes, stars and the evolution of the universe, making it a general-purpose space telescope alongside its search for potentially habitable worlds. Why robotic servicing could change future missions Designing spacecraft for robotic maintenance represents a major shift in space engineering. Instead of treating telescopes as single-use missions, future observatories could be upgraded much like satellites or even computers. New cameras, spectrometers and other scientific instruments could be installed years after launch, allowing missions to stay scientifically relevant for much longer. NASA has already demonstrated some robotic servicing technologies through missions such as OSAM-1 and continues to work with industry on autonomous space robotics. If the Habitable Worlds Observatory follows its planned roadmap, it could become the first observatory designed to evolve throughout its lifetime keeping humanity's search for alien worlds active for decades without needing astronauts to make the journey.

<p>NASA's proposed Habitable Worlds Observatory will be designed for robotic repairs instead of astronaut servicing. The telescope aims to search for Earth-like planets while allowing robots to refuel, repair and upgrade it, potentially extending its mission for decades.</p>

‘Robots will do the job’: NASA's next alien-hunting telescope won't need astronauts for repairs

<p>Microsoft is reportedly changing how it powers some of its AI products by reducing its reliance on models from OpenAI and Anthropic. According to a report by India Today, the company is replacing some ChatGPT and Claude models with its own AI models and smaller alternatives to lower operating costs. The reported move comes as technology companies spend billions of dollars on AI infrastructure, including powerful chips and large data centres. As AI adoption grows, keeping costs under control has become one of the industry's biggest challenges.</p><h2><strong>Why is Microsoft changing its AI strategy?</strong></h2><p>Running advanced AI models is expensive. Every user query requires computing power, and those costs rise quickly when millions of people use AI services every day. Microsoft has invested heavily in AI infrastructure, but it is also looking for ways to make its services more efficient.</p><p>Instead of using the largest and most expensive models for every task, Microsoft is reportedly selecting smaller or internally developed models for requests that do not require the highest level of reasoning. This approach can significantly reduce computing costs while maintaining a similar user experience for routine tasks.</p><h2><strong>What could replace ChatGPT and Claude?</strong></h2><p>According to the report, Microsoft is increasingly relying on its own Phi family of small language models and other internally developed AI systems in selected products. The company is also expected to continue using OpenAI's latest models where they offer clear advantages, particularly for more complex reasoning, coding and enterprise workloads. Rather than completely replacing ChatGPT or Claude, Microsoft appears to be adopting a multi-model strategy, selecting different models depending on the task.</p><h2><strong>A wider trend across the AI industry</strong></h2><p>Microsoft's reported move reflects a broader shift in artificial intelligence. For the past two years, companies competed mainly by building larger and more powerful AI models. Today, many are focusing on efficiency instead.</p><p>Developers are trying to lower the cost of every AI query while improving speed and reducing energy consumption. This is becoming increasingly important as AI services expand to hundreds of millions of users worldwide. If the report is accurate, Microsoft's latest strategy could signal where the AI industry is heading next not simply towards bigger models, but towards smarter and more cost-effective ones.</p>

Microsoft is reportedly replacing some ChatGPT and Claude models with its own AI systems and smaller models to reduce computing costs. The move reflects a growing industry trend where efficiency, speed and lower infrastructure costs are becoming as important as AI performance.


Microsoft is reportedly changing how it powers some of its AI products by reducing its reliance on models from OpenAI and Anthropic. According to a report by India Today, the company is replacing some ChatGPT and Claude models with its own AI models and smaller alternatives to lower operating costs. The reported move comes as technology companies spend billions of dollars on AI infrastructure, including powerful chips and large data centres. As AI adoption grows, keeping costs under control has become one of the industry's biggest challenges. Why is Microsoft changing its AI strategy? Running advanced AI models is expensive. Every user query requires computing power, and those costs rise quickly when millions of people use AI services every day. Microsoft has invested heavily in AI infrastructure, but it is also looking for ways to make its services more efficient. Instead of using the largest and most expensive models for every task, Microsoft is reportedly selecting smaller or internally developed models for requests that do not require the highest level of reasoning. This approach can significantly reduce computing costs while maintaining a similar user experience for routine tasks. What could replace ChatGPT and Claude? According to the report, Microsoft is increasingly relying on its own Phi family of small language models and other internally developed AI systems in selected products. The company is also expected to continue using OpenAI's latest models where they offer clear advantages, particularly for more complex reasoning, coding and enterprise workloads. Rather than completely replacing ChatGPT or Claude, Microsoft appears to be adopting a multi-model strategy, selecting different models depending on the task. A wider trend across the AI industry Microsoft's reported move reflects a broader shift in artificial intelligence. For the past two years, companies competed mainly by building larger and more powerful AI models. Today, many are focusing on efficiency instead. Developers are trying to lower the cost of every AI query while improving speed and reducing energy consumption. This is becoming increasingly important as AI services expand to hundreds of millions of users worldwide. If the report is accurate, Microsoft's latest strategy could signal where the AI industry is heading next not simply towards bigger models, but towards smarter and more cost-effective ones.

<p>Microsoft is reportedly replacing some ChatGPT and Claude models with its own AI systems and smaller models to reduce computing costs. The move reflects a growing industry trend where efficiency, speed and lower infrastructure costs are becoming as important as AI performance.</p>

Microsoft reportedly replaces ChatGPT and Claude models to reduce AI spending

<p>Blue Origin is preparing for one of the biggest fundraising rounds in the history of the private space industry. According to CNBC, Jeff Bezos' rocket company is looking to raise around $10 billion, valuing the company at approximately $130 billion.</p><p>It will be the first time Blue Origin has accepted large-scale outside investment after years of relying mainly on funding from Bezos himself. The move is significant because it gives investors a rare opportunity to back one of the world's largest private space companies. The fundraising also comes only weeks after SpaceX completed a record-breaking IPO, adding fresh momentum to the commercial space industry.</p><h2><strong>How is the funding round structured?</strong></h2><p>According to people familiar with the discussions, Bezos is expected to invest $2 billion of his own money in the round. Investment firm Coatue Management is reportedly set to contribute about $4 billion, while the remaining $4 billion is expected to come from several institutional investors. CNBC reported that demand for the remaining allocation has been strong. If completed, the deal would value Blue Origin among the world's most valuable privately controlled aerospace companies.</p><h2><strong>Why is Blue Origin raising money now?</strong></h2><p>For years, Bezos funded Blue Origin largely through personal wealth, including proceeds from selling Amazon shares. However, developing reusable rockets, lunar landers and future space infrastructure requires enormous amounts of capital. Bringing in outside investors gives the company greater financial flexibility while reducing its dependence on Bezos alone.</p><p>In an interview with CNBC in May, Bezos said it was "a good time" to consider external investment as the company enters its next phase of growth.</p><h2><strong>What does it mean for the space industry?</strong></h2><p>The fundraising reflects growing investor confidence in the commercial space sector. Blue Origin is developing launch vehicles such as New Glenn, while also working on lunar exploration projects, satellite launch services and long-term space infrastructure. The timing is notable. SpaceX's recent IPO demonstrated that investors are willing to place massive bets on commercial space companies. Blue Origin's fundraising suggests competition between Bezos and Elon Musk is expanding beyond rockets into financial markets. If the round closes as expected, Blue Origin will have billions of dollars to accelerate development of future launch systems and compete more aggressively in an industry expected to grow rapidly over the coming decade.</p>

Blue Origin is reportedly raising $10 billion in its first outside funding round, valuing the company at $130 billion. Jeff Bezos is expected to invest $2 billion as the space company looks to expand after SpaceX's record-breaking IPO.

Blue Origin is preparing for one of the biggest fundraising rounds in the history of the private space industry. According to CNBC, Jeff Bezos' rocket company is looking to raise around $10 billion, valuing the company at approximately $130 billion. It will be the first time Blue Origin has accepted large-scale outside investment after years of relying mainly on funding from Bezos himself. The move is significant because it gives investors a rare opportunity to back one of the world's largest private space companies. The fundraising also comes only weeks after SpaceX completed a record-breaking IPO, adding fresh momentum to the commercial space industry. How is the funding round structured? According to people familiar with the discussions, Bezos is expected to invest $2 billion of his own money in the round. Investment firm Coatue Management is reportedly set to contribute about $4 billion, while the remaining $4 billion is expected to come from several institutional investors. CNBC reported that demand for the remaining allocation has been strong. If completed, the deal would value Blue Origin among the world's most valuable privately controlled aerospace companies. Why is Blue Origin raising money now? For years, Bezos funded Blue Origin largely through personal wealth, including proceeds from selling Amazon shares. However, developing reusable rockets, lunar landers and future space infrastructure requires enormous amounts of capital. Bringing in outside investors gives the company greater financial flexibility while reducing its dependence on Bezos alone. In an interview with CNBC in May, Bezos said it was "a good time" to consider external investment as the company enters its next phase of growth. What does it mean for the space industry? The fundraising reflects growing investor confidence in the commercial space sector. Blue Origin is developing launch vehicles such as New Glenn, while also working on lunar exploration projects, satellite launch services and long-term space infrastructure. The timing is notable. SpaceX's recent IPO demonstrated that investors are willing to place massive bets on commercial space companies. Blue Origin's fundraising suggests competition between Bezos and Elon Musk is expanding beyond rockets into financial markets. If the round closes as expected, Blue Origin will have billions of dollars to accelerate development of future launch systems and compete more aggressively in an industry expected to grow rapidly over the coming decade.

<p>Blue Origin is reportedly raising $10 billion in its first outside funding round, valuing the company at $130 billion. Jeff Bezos is expected to invest $2 billion as the space company looks to expand after SpaceX's record-breaking IPO.</p>

Blue Origin's first funding round signals a new chapter in the space race

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