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Inside India’s scramble for AI independence

In Bengaluru, India, Adithya Kolavi felt a mix of excitement and validation as he watched DeepSeek unleash its disruptive language model on the world earlier this year. The Chinese technology rivaled the best of the West in terms of benchmarks, but it had been built with far less capital in far less time.  “I thought: ‘This is how we disrupt with less,’” says Kolavi, the 20-year-old founder of the Indian AI startup CognitiveLab. “If DeepSeek could do it, why not us?”  But for Abhishek Upperwal, founder of Soket AI Labs and architect of one of India’s earliest efforts to develop a foundation model, the moment felt more bittersweet.  Upperwal’s model, called Pragna-1B, had struggled to stay afloat with tiny grants while he watched global peers raise millions. The multilingual model had a relatively modest 1.25 billion parameters and was designed to reduce the “language tax,” the extra costs that arise because India—unlike the US or even China—has a multitude of languages to support. His team had trained it, but limited resources meant it couldn’t scale. As a result, he says, the project became a proof of concept rather than a product.  “If we had been funded two years ago, there’s a good chance we’d be the ones building what DeepSeek just released,” he says. Kolavi’s enthusiasm and Upperwal’s dismay reflect the spectrum of emotions among India’s AI builders. Despite its status as a global tech hub, the country lags far behind the likes of the US and China when it comes to homegrown AI. That gap has opened largely because India has chronically underinvested in R&D, institutions, and invention. Meanwhile, since no one native language is spoken by the majority of the population, training language models is far more complicated than it is elsewhere.  Historically known as the global back office for the software industry, India has a tech ecosystem that evolved with a services-first mindset. Giants like Infosys and TCS built their success on efficient software delivery, but invention was neither prioritized nor rewarded. Meanwhile, India’s R&D spending hovered at just 0.65% of GDP ($25.4 billion) in 2024, far behind China’s 2.68% ($476.2 billion) and the US’s 3.5% ($962.3 billion). The muscle to invent and commercialize deep tech, from algorithms to chips, was just never built. Isolated pockets of world-class research do exist within government agencies like the DRDO (Defense Research & Development Organization) and ISRO (Indian Space Research Organization), but their breakthroughs rarely spill into civilian or commercial use. India lacks the bridges to connect risk-taking research to commercial pathways, the way DARPA does in the US. Meanwhile, much of India’s top talent migrates abroad, drawn to ecosystems that better understand and, crucially, fund deep tech.So when the open-source foundation model DeepSeek-R1 suddenly outperformed many global peers, it struck a nerve. This launch by a Chinese startup prompted Indian policymakers to confront just how far behind the country was in AI infrastructure, and how urgently it needed to respond. India responds In January 2025, 10 days after DeepSeek-R1’s launch, the Ministry of Electronics and Information Technology (MeitY) solicited proposals for India’s own foundation models, which are large AI models that can be adapted to a wide range of tasks. Its public tender invited private-sector cloud and data‑center companies to reserve GPU compute capacity for government‑led AI research.  Providers including Jio, Yotta, E2E Networks, Tata, AWS partners, and CDAC responded. Through this arrangement, MeitY suddenly had access to nearly 19,000 GPUs at subsidized rates, repurposed from private infrastructure and allocated specifically to foundational AI projects. This triggered a surge of proposals from companies wanting to build their own models.  Within two weeks, it had 67 proposals in hand. That number tripled by mid-March.  In April, the government announced plans to develop six large-scale models by the end of 2025, plus 18 additional AI applications targeting sectors like agriculture, education, and climate action. Most notably, it tapped Sarvam AI to build a 70-billion-parameter model optimized for Indian languages and needs.  For a nation long restricted by limited research infrastructure, things moved at record speed, marking a rare convergence of ambition, talent, and political will. “India could do a Mangalyaan in AI,” said Gautam Shroff of IIIT-Delhi, referencing the country’s cost-effective, and successful, Mars orbiter mission.  Jaspreet Bindra, cofounder of AI&Beyond, an organization focused on teaching AI literacy, captured the urgency: “DeepSeek is probably the best thing that happened to India. It gave us a kick in the backside to stop talking and start doing something.” The language problem One of the most fundamental challenges in building foundational AI models for India is the country’s sheer linguistic diversity. With 22 official languages, hundreds of dialects, and millions of people who are multilingual, India poses a problem that few existing LLMs are equipped to handle. Whereas a massive amount of high-quality web data is available in English, Indian languages collectively make up less than 1% of online content. The lack of digitized, labeled, and cleaned data in languages like Bhojpuri and Kannada makes it difficult to train LLMs that understand how Indians actually speak or search. Global tokenizers, which break text into units a model can process, also perform poorly on many Indian scripts, misinterpreting characters or skipping some altogether. As a result, even when Indian languages are included in multilingual models, they’re often poorly understood and inaccurately generated. And unlike OpenAI and DeepSeek, which achieved scale using structured English-language data, Indian teams often begin with fragmented and low-quality data sets encompassing dozens of Indian languages. This makes the early steps of training foundation models far more complex. Nonetheless, a small but determined group of Indian builders is starting to shape the country’s AI future. For example, Sarvam AI has created OpenHathi-Hi-v0.1, an open-source Hindi language model that shows the Indian AI field’s growing ability to address the country’s vast linguistic diversity. The model, built on Meta’s Llama 2 architecture, was trained on 40 billion tokens of Hindi and related Indian-language content, making it one of the largest open-source Hindi models available to date. Pragna-1B, the multilingual model from Upperwal, is more evidence that India could solve for its own linguistic complexity. Trained on 300 billion tokens for just $250,000, it introduced a technique called “balanced tokenization” to address a unique challenge in Indian AI, enabling a 1.25-billion-parameter model to behave like a much larger one.The issue is that Indian languages use complex scripts and agglutinative grammar, where words are formed by stringing together many smaller units of meaning using prefixes and suffixes. Unlike English, which separates words with spaces and follows relatively simple structures, Indian languages like Hindi, Tamil, and Kannada often lack clear word boundaries and pack a lot of information into single words. Standard tokenizers struggle with such inputs. They end up breaking Indian words into too many tokens, which bloats the input and makes it harder for models to understand the meaning efficiently or respond accurately. With the new technique, however, “a billion-parameter model was equivalent to a 7 billion one like Llama 2,” Upperwal says. This performance was particularly marked in Hindi and Gujarati, where global models often underperform because of limited multilingual training data. It was a reminder that with smart engineering, small teams could still push boundaries.Upperwal eventually repurposed his core tech to build speech APIs for 22 Indian languages, a more immediate solution better suited to rural users who are often left out of English-first AI experiences. “If the path to AGI is a hundred-step process, training a language model is just step one,” he says.  At the other end of the spectrum are startups with more audacious aims. Krutrim-2, for instance, is a 12-billion-parameter multilingual language model optimized for English and 22 Indian languages.  Krutrim-2 is attempting to solve India’s specific problems of linguistic diversity, low-quality data, and cost constraints. The team built a custom Indic tokenizer, optimized training infrastructure, and designed models for multimodal and voice-first use cases from the start, crucial in a country where text interfaces can be a problem. Krutrim’s bet is that its approach will not only enable Indian AI sovereignty but also offer a model for AI that works across the Global South. Besides public funding and compute infrastructure, India also needs the institutional support of talent, the research depth, and the long-horizon capital that produce globally competitive science. While venture capital still hesitates to bet on research, new experiments are emerging. Paras Chopra, an entrepreneur who previously built and sold the software-as-a-service company Wingify, is now personally funding Lossfunk, a Bell Labs–style AI residency program designed to attract independent researchers with a taste for open-source science.  “We don’t have role models in academia or industry,” says Chopra. “So we’re creating a space where top researchers can learn from each other and have startup-style equity upside.” Government-backed bet on sovereign AI The clearest marker of India’s AI ambitions came when the government selected Sarvam AI to develop a model focused on Indian languages and voice fluency. The idea is that it would not only help Indian companies compete in the global AI arms race but benefit the wider population as well. “If it becomes part of the India stack, you can educate hundreds of millions through conversational interfaces,” says Bindra.  Sarvam was given access to 4,096 Nvidia H100 GPUs for training a 70-billion-parameter Indian language model over six months. (The company previously released a 2-billion-parameter model trained in 10 Indian languages, called Sarvam-1.) Sarvam’s project and others are part of a larger strategy called the IndiaAI Mission, a $1.25 billion national initiative launched in March 2024 to build out India’s core AI infrastructure and make advanced tools more widely accessible. Led by MeitY, the mission is focused on supporting AI startups, particularly those developing foundation models in Indian languages and applying AI to key sectors such as health care, education, and agriculture. Under its compute program, the government is deploying more than 18,000 GPUs, including nearly 13,000 high-end H100 chips, to a select group of Indian startups that currently includes Sarvam, Upperwal’s Soket Labs, Gnani AI, and Gan AI.  The mission also includes plans to launch a national multilingual data set repository, establish AI labs in smaller cities, and fund deep-tech R&D. The broader goal is to equip Indian developers with the infrastructure needed to build globally competitive AI and ensure that the results are grounded in the linguistic and cultural realities of India and the Global South.According to Abhishek Singh, CEO of IndiaAI and an officer with MeitY, India’s broader push into deep tech is expected to raise around $12 billion in research and development investment over the next five years.  This includes approximately $162 million through the IndiaAI Mission, with about $32 million earmarked for direct startup funding. The National Quantum Mission is contributing another $730 million to support India’s ambitions in quantum research. In addition to this, the national budget document for 2025-26 announced a $1.2 billion Deep Tech Fund of Funds aimed at catalyzing early-stage innovation in the private sector. The rest, nearly $9.9 billion, is expected to come from private and international sources including corporate R&D, venture capital firms, high-net-worth individuals, philanthropists, and global technology leaders such as Microsoft.  IndiaAI has now received more than 500 applications from startups proposing use cases in sectors like health, governance, and agriculture.  “We’ve already announced support for Sarvam, and 10 to 12 more startups will be funded solely for foundational models,” says Singh. Selection criteria include access to training data, talent depth, sector fit, and scalability. Open or closed? The IndiaAI program, however, is not without controversy. Sarvam is being built as a closed model, not open-source, despite its public tech roots. That has sparked debate about the proper balance between private enterprise and the public good.  “True sovereignty should be rooted in openness and transparency,” says Amlan Mohanty, an AI policy specialist. He points to DeepSeek-R1, which despite its 236-billion parameter size was made freely available for commercial use.  Its release allowed developers around the world to fine-tune it on low-cost GPUs, creating faster variants and extending its capabilities to non-English applications. “Releasing an open-weight model with efficient inference can democratize AI,” says Hancheng Cao, an assistant professor of information systems and operations management at Emory University. “It makes it usable by developers who don’t have massive infrastructure.” IndiaAI, however, has taken a neutral stance on whether publicly funded models should be open-source.  “We didn’t want to dictate business models,” says Singh. “India has always supported open standards and open source, but it’s up to the teams. The goal is strong Indian models, whatever the route.” There are other challenges as well. In late May, Sarvam AI unveiled Sarvam‑M, a 24-billion-parameter multilingual LLM fine-tuned for 10 Indian languages and built on top of Mistral Small, an efficient model developed by the French company Mistral AI. Sarvam’s cofounder Vivek Raghavan called the model “an important stepping stone on our journey to build sovereign AI for India.” But its download numbers were underwhelming, with only 300 in the first two days. The venture capitalist Deedy Das called the launch “embarrassing.”And the issues go beyond the lukewarm early reception. Many developers in India still lack easy access to GPUs and the broader ecosystem for Indian-language AI applications is still nascent.  The compute question Compute scarcity is emerging as one of the most significant bottlenecks in generative AI, not just in India but across the globe. For countries still heavily reliant on imported GPUs and lacking domestic fabrication capacity, the cost of building and running large models is often prohibitive.  India still imports most of its chips rather than producing them domestically, and training large models remains expensive. That’s why startups and researchers alike are focusing on software-level efficiencies that involve smaller models, better inference, and fine-tuning frameworks that optimize for performance on fewer GPUs. “The absence of infrastructure doesn’t mean the absence of innovation,” says Cao. “Supporting optimization science is a smart way to work within constraints.”  Yet Singh of IndiaAI argues that the tide is turning on the infrastructure challenge thanks to the new government programs and private-public partnerships. “I believe that within the next three months, we will no longer face the kind of compute bottlenecks we saw last year,” he says. India also has a cost advantage.According to Gupta, building a hyperscale data center in India costs about $5 million, roughly half what it would cost in markets like the US, Europe, or Singapore. That’s thanks to affordable land, lower construction and labor costs, and a large pool of skilled engineers.  For now, India’s AI ambitions seem less about leapfrogging OpenAI or DeepSeek and more about strategic self-determination. Whether its approach takes the form of smaller sovereign models, open ecosystems, or public-private hybrids, the country is betting that it can chart its own course.  While some experts argue that the government’s action, or reaction (to DeepSeek), is performative and aligned with its nationalistic agenda, many startup founders are energized. They see the growing collaboration between the state and the private sector as a real opportunity to overcome India’s long-standing structural challenges in tech innovation. At a Meta summit held in Bengaluru last year, Nandan Nilekani, the chairman of Infosys, urged India to resist chasing a me-too AI dream.  “Let the big boys in the Valley do it,” he said of building LLMs. “We will use it to create synthetic data, build small language models quickly, and train them using appropriate data.”  His view that India should prioritize strength over spectacle had a divided reception. But it reflects a broader growing consensus on whether India should play a different game altogether. “Trying to dominate every layer of the stack isn’t realistic, even for China,” says Bharath Reddy, a researcher at the Takshashila Institution, an Indian public policy nonprofit. “Dominate one layer, like applications, services, or talent, so you remain indispensable.” 

In Bengaluru, India, Adithya Kolavi felt a mix of excitement and validation as he watched DeepSeek unleash its disruptive language model on the world earlier this year. The Chinese technology rivaled the best of the West in terms of benchmarks, but it had been built with far less capital in far less time. 

“I thought: ‘This is how we disrupt with less,’” says Kolavi, the 20-year-old founder of the Indian AI startup CognitiveLab. “If DeepSeek could do it, why not us?” 

But for Abhishek Upperwal, founder of Soket AI Labs and architect of one of India’s earliest efforts to develop a foundation model, the moment felt more bittersweet. 

Upperwal’s model, called Pragna-1B, had struggled to stay afloat with tiny grants while he watched global peers raise millions. The multilingual model had a relatively modest 1.25 billion parameters and was designed to reduce the “language tax,” the extra costs that arise because India—unlike the US or even China—has a multitude of languages to support. His team had trained it, but limited resources meant it couldn’t scale. As a result, he says, the project became a proof of concept rather than a product. 

“If we had been funded two years ago, there’s a good chance we’d be the ones building what DeepSeek just released,” he says.

Kolavi’s enthusiasm and Upperwal’s dismay reflect the spectrum of emotions among India’s AI builders. Despite its status as a global tech hub, the country lags far behind the likes of the US and China when it comes to homegrown AI. That gap has opened largely because India has chronically underinvested in R&D, institutions, and invention. Meanwhile, since no one native language is spoken by the majority of the population, training language models is far more complicated than it is elsewhere. 

Historically known as the global back office for the software industry, India has a tech ecosystem that evolved with a services-first mindset. Giants like Infosys and TCS built their success on efficient software delivery, but invention was neither prioritized nor rewarded. Meanwhile, India’s R&D spending hovered at just 0.65% of GDP ($25.4 billion) in 2024, far behind China’s 2.68% ($476.2 billion) and the US’s 3.5% ($962.3 billion). The muscle to invent and commercialize deep tech, from algorithms to chips, was just never built.

Isolated pockets of world-class research do exist within government agencies like the DRDO (Defense Research & Development Organization) and ISRO (Indian Space Research Organization), but their breakthroughs rarely spill into civilian or commercial use. India lacks the bridges to connect risk-taking research to commercial pathways, the way DARPA does in the US. Meanwhile, much of India’s top talent migrates abroad, drawn to ecosystems that better understand and, crucially, fund deep tech.

So when the open-source foundation model DeepSeek-R1 suddenly outperformed many global peers, it struck a nerve. This launch by a Chinese startup prompted Indian policymakers to confront just how far behind the country was in AI infrastructure, and how urgently it needed to respond.

India responds

In January 2025, 10 days after DeepSeek-R1’s launch, the Ministry of Electronics and Information Technology (MeitY) solicited proposals for India’s own foundation models, which are large AI models that can be adapted to a wide range of tasks. Its public tender invited private-sector cloud and data‑center companies to reserve GPU compute capacity for government‑led AI research. 

Providers including Jio, Yotta, E2E Networks, Tata, AWS partners, and CDAC responded. Through this arrangement, MeitY suddenly had access to nearly 19,000 GPUs at subsidized rates, repurposed from private infrastructure and allocated specifically to foundational AI projects. This triggered a surge of proposals from companies wanting to build their own models. 

Within two weeks, it had 67 proposals in hand. That number tripled by mid-March. 

In April, the government announced plans to develop six large-scale models by the end of 2025, plus 18 additional AI applications targeting sectors like agriculture, education, and climate action. Most notably, it tapped Sarvam AI to build a 70-billion-parameter model optimized for Indian languages and needs. 

For a nation long restricted by limited research infrastructure, things moved at record speed, marking a rare convergence of ambition, talent, and political will.

“India could do a Mangalyaan in AI,” said Gautam Shroff of IIIT-Delhi, referencing the country’s cost-effective, and successful, Mars orbiter mission. 

Jaspreet Bindra, cofounder of AI&Beyond, an organization focused on teaching AI literacy, captured the urgency: “DeepSeek is probably the best thing that happened to India. It gave us a kick in the backside to stop talking and start doing something.”

The language problem

One of the most fundamental challenges in building foundational AI models for India is the country’s sheer linguistic diversity. With 22 official languages, hundreds of dialects, and millions of people who are multilingual, India poses a problem that few existing LLMs are equipped to handle.

Whereas a massive amount of high-quality web data is available in English, Indian languages collectively make up less than 1% of online content. The lack of digitized, labeled, and cleaned data in languages like Bhojpuri and Kannada makes it difficult to train LLMs that understand how Indians actually speak or search.

Global tokenizers, which break text into units a model can process, also perform poorly on many Indian scripts, misinterpreting characters or skipping some altogether. As a result, even when Indian languages are included in multilingual models, they’re often poorly understood and inaccurately generated.

And unlike OpenAI and DeepSeek, which achieved scale using structured English-language data, Indian teams often begin with fragmented and low-quality data sets encompassing dozens of Indian languages. This makes the early steps of training foundation models far more complex.

Nonetheless, a small but determined group of Indian builders is starting to shape the country’s AI future.

For example, Sarvam AI has created OpenHathi-Hi-v0.1, an open-source Hindi language model that shows the Indian AI field’s growing ability to address the country’s vast linguistic diversity. The model, built on Meta’s Llama 2 architecture, was trained on 40 billion tokens of Hindi and related Indian-language content, making it one of the largest open-source Hindi models available to date.

Pragna-1B, the multilingual model from Upperwal, is more evidence that India could solve for its own linguistic complexity. Trained on 300 billion tokens for just $250,000, it introduced a technique called “balanced tokenization” to address a unique challenge in Indian AI, enabling a 1.25-billion-parameter model to behave like a much larger one.

The issue is that Indian languages use complex scripts and agglutinative grammar, where words are formed by stringing together many smaller units of meaning using prefixes and suffixes. Unlike English, which separates words with spaces and follows relatively simple structures, Indian languages like Hindi, Tamil, and Kannada often lack clear word boundaries and pack a lot of information into single words. Standard tokenizers struggle with such inputs. They end up breaking Indian words into too many tokens, which bloats the input and makes it harder for models to understand the meaning efficiently or respond accurately.

With the new technique, however, “a billion-parameter model was equivalent to a 7 billion one like Llama 2,” Upperwal says. This performance was particularly marked in Hindi and Gujarati, where global models often underperform because of limited multilingual training data. It was a reminder that with smart engineering, small teams could still push boundaries.

Upperwal eventually repurposed his core tech to build speech APIs for 22 Indian languages, a more immediate solution better suited to rural users who are often left out of English-first AI experiences.

“If the path to AGI is a hundred-step process, training a language model is just step one,” he says. 

At the other end of the spectrum are startups with more audacious aims. Krutrim-2, for instance, is a 12-billion-parameter multilingual language model optimized for English and 22 Indian languages. 

Krutrim-2 is attempting to solve India’s specific problems of linguistic diversity, low-quality data, and cost constraints. The team built a custom Indic tokenizer, optimized training infrastructure, and designed models for multimodal and voice-first use cases from the start, crucial in a country where text interfaces can be a problem.

Krutrim’s bet is that its approach will not only enable Indian AI sovereignty but also offer a model for AI that works across the Global South.

Besides public funding and compute infrastructure, India also needs the institutional support of talent, the research depth, and the long-horizon capital that produce globally competitive science.

While venture capital still hesitates to bet on research, new experiments are emerging. Paras Chopra, an entrepreneur who previously built and sold the software-as-a-service company Wingify, is now personally funding Lossfunk, a Bell Labs–style AI residency program designed to attract independent researchers with a taste for open-source science. 

“We don’t have role models in academia or industry,” says Chopra. “So we’re creating a space where top researchers can learn from each other and have startup-style equity upside.”

Government-backed bet on sovereign AI

The clearest marker of India’s AI ambitions came when the government selected Sarvam AI to develop a model focused on Indian languages and voice fluency.

The idea is that it would not only help Indian companies compete in the global AI arms race but benefit the wider population as well. “If it becomes part of the India stack, you can educate hundreds of millions through conversational interfaces,” says Bindra. 

Sarvam was given access to 4,096 Nvidia H100 GPUs for training a 70-billion-parameter Indian language model over six months. (The company previously released a 2-billion-parameter model trained in 10 Indian languages, called Sarvam-1.)

Sarvam’s project and others are part of a larger strategy called the IndiaAI Mission, a $1.25 billion national initiative launched in March 2024 to build out India’s core AI infrastructure and make advanced tools more widely accessible. Led by MeitY, the mission is focused on supporting AI startups, particularly those developing foundation models in Indian languages and applying AI to key sectors such as health care, education, and agriculture.

Under its compute program, the government is deploying more than 18,000 GPUs, including nearly 13,000 high-end H100 chips, to a select group of Indian startups that currently includes Sarvam, Upperwal’s Soket Labs, Gnani AI, and Gan AI. 

The mission also includes plans to launch a national multilingual data set repository, establish AI labs in smaller cities, and fund deep-tech R&D. The broader goal is to equip Indian developers with the infrastructure needed to build globally competitive AI and ensure that the results are grounded in the linguistic and cultural realities of India and the Global South.

According to Abhishek Singh, CEO of IndiaAI and an officer with MeitY, India’s broader push into deep tech is expected to raise around $12 billion in research and development investment over the next five years. 

This includes approximately $162 million through the IndiaAI Mission, with about $32 million earmarked for direct startup funding. The National Quantum Mission is contributing another $730 million to support India’s ambitions in quantum research. In addition to this, the national budget document for 2025-26 announced a $1.2 billion Deep Tech Fund of Funds aimed at catalyzing early-stage innovation in the private sector.

The rest, nearly $9.9 billion, is expected to come from private and international sources including corporate R&D, venture capital firms, high-net-worth individuals, philanthropists, and global technology leaders such as Microsoft. 

IndiaAI has now received more than 500 applications from startups proposing use cases in sectors like health, governance, and agriculture. 

“We’ve already announced support for Sarvam, and 10 to 12 more startups will be funded solely for foundational models,” says Singh. Selection criteria include access to training data, talent depth, sector fit, and scalability.

Open or closed?

The IndiaAI program, however, is not without controversy. Sarvam is being built as a closed model, not open-source, despite its public tech roots. That has sparked debate about the proper balance between private enterprise and the public good. 

“True sovereignty should be rooted in openness and transparency,” says Amlan Mohanty, an AI policy specialist. He points to DeepSeek-R1, which despite its 236-billion parameter size was made freely available for commercial use. 

Its release allowed developers around the world to fine-tune it on low-cost GPUs, creating faster variants and extending its capabilities to non-English applications.

“Releasing an open-weight model with efficient inference can democratize AI,” says Hancheng Cao, an assistant professor of information systems and operations management at Emory University. “It makes it usable by developers who don’t have massive infrastructure.”

IndiaAI, however, has taken a neutral stance on whether publicly funded models should be open-source. 

“We didn’t want to dictate business models,” says Singh. “India has always supported open standards and open source, but it’s up to the teams. The goal is strong Indian models, whatever the route.”

There are other challenges as well. In late May, Sarvam AI unveiled Sarvam‑M, a 24-billion-parameter multilingual LLM fine-tuned for 10 Indian languages and built on top of Mistral Small, an efficient model developed by the French company Mistral AI. Sarvam’s cofounder Vivek Raghavan called the model “an important stepping stone on our journey to build sovereign AI for India.” But its download numbers were underwhelming, with only 300 in the first two days. The venture capitalist Deedy Das called the launch “embarrassing.”

And the issues go beyond the lukewarm early reception. Many developers in India still lack easy access to GPUs and the broader ecosystem for Indian-language AI applications is still nascent. 

The compute question

Compute scarcity is emerging as one of the most significant bottlenecks in generative AI, not just in India but across the globe. For countries still heavily reliant on imported GPUs and lacking domestic fabrication capacity, the cost of building and running large models is often prohibitive. 

India still imports most of its chips rather than producing them domestically, and training large models remains expensive. That’s why startups and researchers alike are focusing on software-level efficiencies that involve smaller models, better inference, and fine-tuning frameworks that optimize for performance on fewer GPUs.

“The absence of infrastructure doesn’t mean the absence of innovation,” says Cao. “Supporting optimization science is a smart way to work within constraints.” 

Yet Singh of IndiaAI argues that the tide is turning on the infrastructure challenge thanks to the new government programs and private-public partnerships. “I believe that within the next three months, we will no longer face the kind of compute bottlenecks we saw last year,” he says.

India also has a cost advantage.

According to Gupta, building a hyperscale data center in India costs about $5 million, roughly half what it would cost in markets like the US, Europe, or Singapore. That’s thanks to affordable land, lower construction and labor costs, and a large pool of skilled engineers. 

For now, India’s AI ambitions seem less about leapfrogging OpenAI or DeepSeek and more about strategic self-determination. Whether its approach takes the form of smaller sovereign models, open ecosystems, or public-private hybrids, the country is betting that it can chart its own course. 

While some experts argue that the government’s action, or reaction (to DeepSeek), is performative and aligned with its nationalistic agenda, many startup founders are energized. They see the growing collaboration between the state and the private sector as a real opportunity to overcome India’s long-standing structural challenges in tech innovation.

At a Meta summit held in Bengaluru last year, Nandan Nilekani, the chairman of Infosys, urged India to resist chasing a me-too AI dream. 

“Let the big boys in the Valley do it,” he said of building LLMs. “We will use it to create synthetic data, build small language models quickly, and train them using appropriate data.” 

His view that India should prioritize strength over spectacle had a divided reception. But it reflects a broader growing consensus on whether India should play a different game altogether.

“Trying to dominate every layer of the stack isn’t realistic, even for China,” says Bharath Reddy, a researcher at the Takshashila Institution, an Indian public policy nonprofit. “Dominate one layer, like applications, services, or talent, so you remain indispensable.” 

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Australia’s Cue Energy Posts Lower Production

Cue Energy Resources Ltd., which produces oil and gas in Australia, Indonesia and New Zealand, has reported an output of 148,300 barrels of oil equivalent (boe) for the fourth quarter of fiscal year 2025. That was down from 156,100 boe for the prior three-month period. Cue Energy derived over 1,900 barrels and 0.34 petajoules from Australia in the quarter ended June, up for liquids but down for gas. New Zealand production dropped to just over 19,000 barrels. In Indonesia, the Mahato block contributed over 52,000 barrels, up from fiscal Q3 2025; the Sampang production sharing contract (PSC) produced 215 barrels and 0.12 petajoules, both down sequentially. Cash receipts totaled AUD 11.1 million ($7.21 million), down from AUD 15.3 million for fiscal Q3 2025. “Net cash flow was impacted by higher cash outflow from accelerated drilling activities at Mahato and delayed receipts from a Maari oil sale, with proceeds received after quarter end”, Cue Energy said. “The company’s balance sheet remains in a strong position, with no debt and a cash balance of $10.8 million”. In Australia, the volume of gas sold “remained consistent with the previous period, with the recently drilled WM29 and WM30 wells continuing to outperform pre-drill expectations”, Cue Energy said without disclosing sale volume figures. “The Northern Gas Pipeline (NGP) remained open for most of the quarter, closing again at the end of June due to maintenance works affecting other NT [Northern Territory] gas supply. Under existing contract terms, when the NGP is closed, Cue’s east coast gas sales are redirected into the NT, including to the NT government, minimizing the impact of NGP outages. “Oil sales from Mereenie were partially constrained due to current offtake arrangements. As a result, four wells with lower gas-to-oil ratios have been temporarily shut in to reduce liquids volumes, leading to

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India’s Nayara Cuts Refinery Run Rate after EU Sanctions

Nayara Energy Ltd. is reducing run rates at its west India refinery as more domestic and global players spurn the refiner after the EU imposed sanctions on the company.  The 400,000 barrel-a-day Vadinar refinery is currently operating at about 70 percent to 80 percent, said the people, who asked not to be named due to the sensitivity of the matter. Across India, processors typically run plants at close to 100 percent of nameplate capacity, or over.  The lowered operations are due to mounting logistical issues and trading partners turning away from Nayara, making it difficult for the company to monetize and transport its refined output to customers. This week, local shipowners are reassessing their dealings with Nayara, citing pressure from mutual-insurance groups known as P&I clubs, said two shipbrokers who specialize in fixing tankers for Indian routes. Many ships plying Indian coastal routes rely on P&I clubs that are based in the UK and across Europe, which comply with EU sanctions.  Ships found to be handling Nayara cargoes and trades could lose coverage from western P&I groups, which largely represents European insurers. A company spokesperson didn’t immediately reply to email and phone message seeking comments. Nayara exports up to 30 percent of its oil-products output, and sells the balance to local markets through its network of petrol pumps and sales to state refiners, according to rating agency CareEdge. At least one ship, the Bourbon, is currently idling off the Indian coast after loading a Nayara fuel cargo from Vadinar port, ship-tracking data show. The tanker, owned by Mumbai-based Seven Islands Shipping and covered by UK-based NorthStandard P&I club, was meant to deliver diesel to Mangalore.  Shippers are monitoring the status of this vessel due to the sensitivities around its Nayara cargo and western P&I coverage. Seven Islands didn’t immediately reply to an email

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Macquarie Strategists Forecast USA Crude Inventory Build

In an oil and gas report sent to Rigzone by the Macquarie team late Monday, Macquarie strategists, including Walt Chancellor, revealed that they are forecasting that U.S. crude inventories will be up by 4.7 million barrels for the week ending July 25. “This follows a 3.2 million barrel draw in the prior week, with the crude balance realizing looser than our expectations,” the strategists said in the report. “For this week’s crude balance, from refineries, we model a small increase in crude runs (+0.1 million barrels per day) following a strong print last week,” they added. “Among net imports, we model a very large increase, with exports down (-0.6 million barrels per day) and imports up (+0.7 million barrels per day) on a nominal basis,” they continued. The strategists warned in the report that timing of cargoes remains a source of potential volatility in this week’s crude balance. “From implied domestic supply (prod.+adj.+transfers), we look for a small reduction (-0.1 million barrels per day) on a nominal basis this week,” the strategists went on to state in the report. “Rounding out the picture, we anticipate a small build in SPR [Strategic Petroleum Reserve] stocks (+0.2 million barrels) this week,” they added. The Macquarie strategists also highlighted in the report that, “among products”, they “look for small builds across the board (gasoline/ distillate/jet +0.3/+0.7/+0.1 million barrels)”. “We model implied demand for these three products at ~14.6 million barrels per day for the week ending July 25,” the strategists added in the report. In its latest weekly petroleum status report at the time of writing, which was released on July 23 and showed data for the week ending July 18, the U.S. Energy Information Administration (EIA) highlighted that U.S. commercial crude oil inventories, excluding those in the SPR, decreased by 3.2 million

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PTTEP Acquires Stake in Gulf of Thailand Asset from Chevron

Thailand’s PTT Exploration and Production Public Co. Ltd. (PTTEP) said it has acquired a 50 percent participating interest in Block A-18 of the Malaysia–Thailand Joint Development Area (MTJDA) for $450 million. The sellers, Hess (Bahamas) Limited and Hess Asia Holdings Inc., are subsidiaries of Chevron following the Chevron-Hess merger. The acquisition enhances PTTEP’s gas production volume, petroleum reserves, and increases its investment in the MTJDA from its existing 50 percent participating interest in Block B-17-01, the company said in a news release. Block A-18 currently produces 600 million standard cubic feet of natural gas per day (MMscfd) which is distributed equally to Thailand and Malaysia, the company said, adding that the 300 MMscfd supplied to Thailand accounts for six percent of the country’s domestic gas demand. PTTEP said it plans to develop additional production wells and wellhead platforms, as well as gas pipelines, to support a consistent and reliable gas supply. The MTJDA is located in the southern part of the Gulf of Thailand. Covering an area of approximately 2,800 square miles (7,250 square kilometers), it is a key source of natural gas and condensates for Thailand and Malaysia, according to the release. Block A-18 which includes Cakerawala, Bumi, Suriya, Bulan, and Bulan South fields, started production in 2005, while Block B-17-01 began production in 2010. The block includes Muda, Tapi, Tanjung, Amarit, Jengka, Melati, and Andalas fields, and currently produces approximately 300 MMscfd of natural gas for Thailand and Malaysia, the release said. “PTTEP is pleased to further expand our operations in the MTJDA, which is recognized for its petroleum potential and strategic significance to Thailand’s energy security. The acquisition also contributes to the company’s growth. Apart from the existing producing fields, Block A-18 includes several discovered gas fields awaiting development to unlock their full potential. Participation in Block

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ExxonMobil Transfers Operatorship of Bass Strait Assets to Woodside

Woodside Energy Group Ltd. will assume operatorship of the Gippsland Basin Joint Venture (GBJV) and Kipper Unit Joint Venture (KUJV), which account for 40 percent of natural gas supply in the Australian east coast market, from Exxon Mobil Corp., the Australian company said Tuesday. “Woodside and ExxonMobil’s equity interests in the assets and current decommissioning plans and provisions remain unchanged”, Woodside said in a statement online. ExxonMobil and Woodside equally own the GBJV. In the KUJV, ExxonMobil and Woodside each hold 32.5 percent while Japan’s Mitsui & Co. Ltd. owns 35 percent. The operatorship change covers the Bass Strait production assets, the Longford Gas Plant, the Long Island Point gas liquids processing facility and associated pipeline infrastructure. The GBJV and KUJV assets have a daily production capacity of 700 terajoules of gas, nearly 1,800 metric tons of liquefied petroleum gas, over 200 metric tons of ethane and about 2,200 metric tons of condensate, according to information on Woodside’s website. The parties anticipate completing the transaction next year, subject to regulatory approvals and other conditions. “As operator, Woodside will take on the responsibility for asset planning and execution activities, pursuing a value maximization strategy that targets further production and reliability improvements”, the statement said. In March 2022 Woodside announced further investment to deliver additional gas between 2023 and 2027. Several facilities have ceased production due to field depletion, according to Woodside. “This strategic move combines Woodside’s existing global operating capabilities with ExxonMobil’s highly experienced Bass Strait workforce who will transfer to Woodside, further strengthening Woodside’s overall operating expertise”, Woodside added. “Operatorship of a larger group of assets in Australia will create economies of scale which are expected to realize over $60 million in synergies for Woodside from the Bass Strait after deduction of transition and integration costs. “The agreement also creates flexibility

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Crude Futures Soar

Oil ended the session at the highest levels in over a month as President Donald Trump reiterated that the US may impose additional levies on Russia unless it reached a truce with Ukraine, stoking worries about tighter supplies. West Texas Intermediate oil settled at $69.21 a barrel while Brent settled above $72 a barrel, with both benchmarks at the highest since June. Speaking to reporters aboard Air Force One Tuesday, Trump warned of the potential for secondary sanctions if Moscow fails to reach a ceasefire within ten days. Asked if he was worried about the impact additional sanctions on Russia would have on the oil market, Trump said he was not concerned, suggesting that the US could ramp up its own energy production. “I don’t worry about it. We have so much oil in our country. We’ll just step it up, even further,” he said. This week, bullish options on the Brent crude benchmark flipped to a premium to bearish options for the first time in two weeks, signaling the optimistic sentiment extended beyond headline prices. “The new deadline caught many analysts by surprise and, if enforced, could tighten Russian crude and fuel supplies to the global market,” said Dennis Kissler, senior vice president for trading at BOK Financial Securities. Earlier Tuesday morning, Kremlin made it clear that President Vladimir Putin is unlikely to change course, after taking note of the US President’s threat. Trump’s warning follows the latest round of sanctions by the European Union targeting Russia, including penalties on India’s Nayara Energy, which has trimmed processing rates at a refinery as a result of the measures. Global markets are also focused on the US deadline to nail down trade deals by Aug. 1, and the upcoming OPEC+ meeting that will decide supply policy for September. Oil was already

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AI Deployments are Reshaping Intra-Data Center Fiber and Communications

Artificial Intelligence is fundamentally changing the way data centers are architected, with a particular focus on the demands placed on internal fiber and communications infrastructure. While much attention is paid to the fiber connections between data centers or to end-users, the real transformation is happening inside the data center itself, where AI workloads are driving unprecedented requirements for bandwidth, low latency, and scalable networking. Network Segmentation and Specialization Inside the modern AI data center, the once-uniform network is giving way to a carefully divided architecture that reflects the growing divergence between conventional cloud services and the voracious needs of AI. Where a single, all-purpose network once sufficed, operators now deploy two distinct fabrics, each engineered for its own unique mission. The front-end network remains the familiar backbone for external user interactions and traditional cloud applications. Here, Ethernet still reigns, with server-to-leaf links running at 25 to 50 gigabits per second and spine connections scaling to 100 Gbps. Traffic is primarily north-south, moving data between users and the servers that power web services, storage, and enterprise applications. This is the network most people still imagine when they think of a data center: robust, versatile, and built for the demands of the internet age. But behind this familiar façade, a new, far more specialized network has emerged, dedicated entirely to the demands of GPU-driven AI workloads. In this backend, the rules are rewritten. Port speeds soar to 400 or even 800 gigabits per second per GPU, and latency is measured in sub-microseconds. The traffic pattern shifts decisively east-west, as servers and GPUs communicate in parallel, exchanging vast datasets at blistering speeds to train and run sophisticated AI models. The design of this network is anything but conventional: fat-tree or hypercube topologies ensure that no single link becomes a bottleneck, allowing thousands of

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ABB and Applied Digital Build a Template for AI-Ready Data Centers

Toward the Future of AI Factories The ABB–Applied Digital partnership signals a shift in the fundamentals of data center development, where electrification strategy, hyperscale design and readiness, and long-term financial structuring are no longer separate tracks but part of a unified build philosophy. As Applied Digital pushes toward REIT status, the Ellendale campus becomes not just a development milestone but a cornerstone asset: a long-term, revenue-generating, AI-optimized property underpinned by industrial-grade power architecture. The 250 MW CoreWeave lease, with the option to expand to 400 MW, establishes a robust revenue base and validates the site’s design as AI-first, not cloud-retrofitted. At the same time, ABB is positioning itself as a leader in AI data center power architecture, setting a new benchmark for scalable, high-density infrastructure. Its HiPerGuard Medium Voltage UPS, backed by deep global manufacturing and engineering capabilities, reimagines power delivery for the AI era, bypassing the limitations of legacy low-voltage systems. More than a component provider, ABB is now architecting full-stack electrification strategies at the campus level, aiming to make this medium-voltage model the global standard for AI factories. What’s unfolding in North Dakota is a preview of what’s coming elsewhere: AI-ready campuses that marry investment-grade real estate with next-generation power infrastructure, built for a future measured in megawatts per rack, not just racks per row. As AI continues to reshape what data centers are and how they’re built, Ellendale may prove to be one of the key locations where the new standard was set.

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Amazon’s Project Rainier Sets New Standard for AI Supercomputing at Scale

Supersized Infrastructure for the AI Era As AWS deploys Project Rainier, it is scaling AI compute to unprecedented heights, while also laying down a decisive marker in the escalating arms race for hyperscale dominance. With custom Trainium2 silicon, proprietary interconnects, and vertically integrated data center architecture, Amazon joins a trio of tech giants, alongside Microsoft’s Project Stargate and Google’s TPUv5 clusters, who are rapidly redefining the future of AI infrastructure. But Rainier represents more than just another high-performance cluster. It arrives in a moment where the size, speed, and ambition of AI infrastructure projects have entered uncharted territory. Consider the past several weeks alone: On June 24, AWS detailed Project Rainier, calling it “a massive, one-of-its-kind machine” and noting that “the sheer size of the project is unlike anything AWS has ever attempted.” The New York Times reports that the primary Rainier campus in Indiana could include up to 30 data center buildings. Just two days later, Fermi America unveiled plans for the HyperGrid AI campus in Amarillo, Texas on a sprawling 5,769-acre site with potential for 11 gigawatts of power and 18 million square feet of AI data center capacity. And on July 1, Oracle projected $30 billion in annual revenue from a single OpenAI cloud deal, tied to the Project Stargate campus in Abilene, Texas. As Data Center Frontier founder Rich Miller has observed, the dial on data center development has officially been turned to 11. Once an aspirational concept, the gigawatt-scale campus is now materializing—15 months after Miller forecasted its arrival. “It’s hard to imagine data center projects getting any bigger,” he notes. “But there’s probably someone out there wondering if they can adjust the dial so it goes to 12.” Against this backdrop, Project Rainier represents not just financial investment but architectural intent. Like Microsoft’s Stargate buildout in

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Google and CTC Global Partner to Fast-Track U.S. Power Grid Upgrades

On June 17, 2025, Google and CTC Global announced a joint initiative to accelerate the deployment of high-capacity power transmission lines using CTC’s U.S.-manufactured ACCC® advanced conductors. The collaboration seeks to relieve grid congestion by rapidly upgrading existing infrastructure, enabling greater integration of clean energy, improving system resilience, and unlocking capacity for hyperscale data centers. The effort represents a rare convergence of corporate climate commitments, utility innovation, and infrastructure modernization aligned with the public interest. As part of the initiative, Google and CTC issued a Request for Information (RFI) with responses due by July 14. The RFI invites utilities, state energy authorities, and developers to nominate transmission line segments for potential fast-tracked upgrades. Selected projects will receive support in the form of technical assessments, financial assistance, and workforce development resources. While advanced conductor technologies like ACCC® can significantly improve the efficiency and capacity of existing transmission corridors, technological innovation alone cannot resolve the grid’s structural challenges. Building new or upgraded transmission lines in the U.S. often requires complex permitting from multiple federal, state, and local agencies, and frequently faces legal opposition, especially from communities invoking Not-In-My-Backyard (NIMBY) objections. Today, the average timeline to construct new interstate transmission infrastructure stretches between 10 and 12 years, an untenable lag in an era when grid reliability is under increasing stress. In 2024, the Federal Energy Regulatory Commission (FERC) reported that more than 2,600 gigawatts (GW) of clean energy and storage projects were stalled in the interconnection queue, waiting for sufficient transmission capacity. The consequences affect not only industrial sectors like data centers but also residential areas vulnerable to brownouts and peak load disruptions. What is the New Technology? At the center of the initiative is CTC Global’s ACCC® (Aluminum Conductor Composite Core) advanced conductor, a next-generation overhead transmission technology engineered to boost grid

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CoreSite’s Denver Power Play: Acquisition of Historic Carrier Hotel Supercharges Interconnection Capabilities

In this episode of the Data Center Frontier Show podcast, we unpack one of the most strategic data center real estate moves of 2025: CoreSite’s acquisition of the historic Denver Gas and Electric Building. With this transaction, CoreSite, an American Tower company, cements its leadership in the Rocky Mountain region’s interconnection landscape, expands its DE1 facility, and streamlines access to Google Cloud and the Any2Denver peering exchange. Podcast guests Yvonne Ng, CoreSite’s General Manager and Vice President for the Central Region, and Adam Post, SVP of Finance and Corporate Development, offer in-depth insights into the motivations behind the deal, the implications for regional cloud and network ecosystems, and what it means for Denver’s future as a cloud interconnection hub. Carrier Hotel to Cloud Hub Located at 910 15th Street in downtown Denver, the Denver Gas and Electric Building is widely known as the most network-dense facility in the region. Long the primary interconnection hub for the Rocky Mountains, the building has now been fully acquired by CoreSite, bringing ownership and operations of the DE1 data center under a single umbrella. “This is a strategic move to consolidate control and expand our capabilities,” said Ng. “By owning the building, we can modernize infrastructure more efficiently, double the space and power footprint of DE1, and deliver an unparalleled interconnection ecosystem.” The acquisition includes the facility’s operating businesses and over 100 customers. CoreSite will add approximately 3 critical megawatts (CMW) of data center capacity, nearly doubling DE1’s footprint. Interconnection in the AI Era As AI, multicloud strategies, and real-time workloads reshape enterprise architecture, interconnection has never been more vital. CoreSite’s move elevates Denver’s role in this transformation. With the deal, CoreSite becomes the only data center provider in the region offering direct connections to major cloud platforms, including the dedicated Google Cloud Platform

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Texas Senate Bill 6: A Bellwether On How States May Approach Data Center Energy Use

Texas isn’t the first state to begin attempting to regulate energy use statewide. The impact of this legislation could shape how other states, of which there are at least a dozen in process, could shape their own programs. What are Other States Doing? There’s a clear shift toward targeted utility regulation for mega-load data centers. States are increasingly requiring cost alignment, with large consumers bearing infrastructure costs rather than residential cross-subsidization and implementing specialized contract/tariff terms, taking advantage of these huge contracts to uniquely tailor each contract. These agreements are also being used to enforce environmental responsibility through reporting mandates and permitting. And for those estates still focusing on incentivization to draw data center business, coupling incentives with guardrails, balancing investment attraction with equitable distribution. What follows is a brief  overview of U.S. states that have enacted or proposed special utility regulations and requirements for data centers. The focus is  on tariffs, cost-allocation mechanisms, green mandates, billing structures, and transparency rules. California SB 57 (2025): Introduces a special electricity tariff for large users—including data centers—with embedded zero-carbon procurement targets, aiming to integrate grid reliability with emissions goals. AB 222 (2025): Targets consumption transparency, requiring data centers to report energy usage with a specific focus on AI-driven load. Broader California Public Utilities  actions: Proposals for efficiency mandates like airflow containment via Title 24; opening utility rate cases to analyze infrastructure cost recovery from large consumers. Georgia Public Service Commission  rule changes (January 2025): Georgia Power can impose minimum billing, longer contract durations, and special terms for customers with loads >100 MW—chiefly data centers. SB 34: Mandates that data centers either assume full infrastructure costs or pay equitably—not distributing these costs to residential users. Ohio AEP Ohio proposed in 2024: For loads >25 MW (data centers, crypto), demand minimum charges, 10-year contracts, and exit penalties before new infrastructure

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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