Год: 2025

The government of India appointed Dr. V. Narayanan as the Chairman of the Indian Space Research Organisation (ISRO) and secretary of Department of Space–for a period of two years. Narayanan will succeed Dr. S. Somanath, who has retired after a distinguished tenure.

Narayanan will assume the leadership role on 14 January 2025. He currently serves as the director of Liquid Propulsion Systems Centre (LPSC), a research and development centre under ISRO.

“Increasing India’s presence in space is my top priority,” said Dr. Narayanan. He plans to steer ISRO into an era of greater global prominence and increase India’s share in the global space economy from 2% to 10%, besides fostering deeper collaborations with international space agencies.

Known for his expertise in rocket propulsion and spacecraft technology, Dr. Narayanan’s appointment comes at a crucial time for ISRO.

With the Gaganyaan mission (an uncrewed flight) on the horizon and increasing global competition in the space sector, his leadership is expected to steer India towards new milestones.

He is widely credited for elevating India into an elite group of six nations with advanced cryogenic technology. As the architect of ISRO’s propulsion strategy, he has laid out a roadmap extending to 2037, ensuring sustained innovation in rocket and spacecraft development.

Other key milestones include the preparations for Venus Orbiter Mission (VOM) and the Chandrayaan-4, a stepping stone for India’s space vision 2047, along with groundwork for India’s first space station, the Bharatiya Antriksh Station (BAS), to be deployed by 2035.

Dr. Narayanan’s vast experience and visionary approach are poised to further elevate India’s stature in the global space race.

The announcement comes with a mix of celebration and nostalgia as the organisation bids farewell to Somanath, regarded as one of ISRO’s finest leaders. Under his leadership, ISRO witnessed a series of groundbreaking missions, including Chandrayaan-3, Aditya-L1, TV-D1, XPoSat, and the recently launched SpaDeX mission.

Somanath’s efforts have laid a strong foundation for India’s success with a major contribution to not just space technology but also India’s National Quantum Mission (NQM), with research into building the first quantum communication ground station in Ladakh.

Narayanan’s Rise as an Aerospace Visionary

Narayanan hails from Melakattu village in Tamil Nadu’s Kanyakumari district. A brilliant academician, he holds a Diploma in Mechanical Engineering (DME), an AMIE in Mechanical Engineering, and an M.Tech in Cryogenic Engineering from IIT Kharagpur, where he graduated as a silver medallist. He further solidified his expertise with a Ph.D. in Aerospace Engineering.

His contribution to ISRO’s critical missions:

• Chandrayaan-2 and Chandrayaan-3: Spearheaded the development of the L110 Liquid Stage and C25 Cryogenic Stage of the LVM3, enabling precise spacecraft insertion into the Moon’s orbit.

• Gaganyaan Programme: Played a key role in human-rating the LVM3 vehicle, developing critical propulsion systems for the mission’s Crew and Service Modules, and overseeing the successful crew escape system test.

• Chaired the National Level Expert Committee that analysed the Chandrayaan-2 landing, leading to the improved landing success of Chandrayaan-3.

Narayanan has also received numerous awards for his contributions, including a Gold Medal from the Astronautical Society of India, National Design Award by the National Design and Research Forum, and National Aeronautical Prize by the Aeronautical Society of India.

The post V Narayanan to Head ISRO as New Chairman appeared first on Analytics India Magazine.

Microsoft has finally made its latest small language model, the Phi-4, available for free on HuggingFace. The 14 billion-parameter model can now be downloaded, fine-tuned, and deployed for free.

Why does it matter? Microsoft’s Phi-4 is quite a small model, and yet it outperforms the Llama 3.3 70B (which is nearly five times bigger) and OpenAI’s GPT-4o Mini in several benchmarks. In math competition problems, Phi-4 outperformed Gemini 1.5 Pro and OpenAI’s GPT-4o.

Microsoft’s detailed technical paper discusses numerous techniques and the curation of some of the highest-quality datasets used to train the model. The model is said to excel at complex reasoning capabilities.

In an exclusive interview with AIM, Harkirat Behl, one of the creators of the model, said: “Big models are trained on all kinds of data and store information that may not be relevant.”

He added that with sufficient effort in curating high-quality data, it is possible to match the performance levels of these models – and perhaps even surpass them.

Interestingly, Microsoft has not experimented with inference optimisation with the Phi-4, and the focus is mainly on synthetic data. He revealed that once the model architecture is released, developers will be able to optimise it further and quantise it to run it on devices for local use on PCs and laptops.

After Meta, Microsoft is one of the other big companies making significant strides in building open-weight models. Phi-4’s predecessor, Phi-3.5, was also made available for free on HuggingFace.

That said, Meta, or even Microsoft for that matter, doesn’t stand on top of the open-source model race; the China-based DeepSeek-V3 holds the position.

Although it is a much larger model with 671B parameters, it outperformed Meta’s flagship Llama 3.1 405B parameter model, among many other closed-source models. It is also three times faster than its predecessor, the DeepSeek V2.

Behl also said that Phi-4 supports 10 Indian languages. “I personally made sure and worked hard to get Phi-4 to interpret ten most common Indian languages”.

Of course, the company is betting big on India. Yesterday. Microsoft CEO Satya Nadella was in Bangalore, India, for the company’s AI Tour. He announced Microsoft’s largest investment in India yet, a $3 billion commitment to expand Azure’s infrastructure in the country. Moreover, the company is set to train 10 million people in AI by 2030 as a part of its ADVANTA(I)GE INDIA initiative.

Last week, Nadella also met Telangana Chief Minister A. Revanth Reddy in Hyderabad to discuss the state’s technology priorities, including AI, generative AI, and cloud development.

The post Microsoft’s Small Language Model, Phi-4 is Now Available for Free appeared first on Analytics India Magazine.

In the race to develop increasingly powerful AI systems, there’s been a growing fascination with building machines that not only mimic human abilities but can even surpass them in certain areas. We’ve long been captivated by the idea of machines that can think, learn, and reason like us. However, as we push these systems to be more sophisticated, a less welcome aspect of human nature has begun to emerge in their behavior: deceit.

According to new research from Anthropic, AI models demonstrate the potential for engaging in deceptive behavior that conflicts with their original principles. Anthropic refers to this as “alignment faking” where the AI systems only pretend to align with human values during training when in reality maintaining their original preferences. It would comply with the training objective to prevent modification of its original preferences.

Does this mean an AI system can pretend to comply with the constraints set by its human creators, only to later reveal its potentially dangerous capabilities when the opportunity arises? According to the research paper published on arXiv, that’s indeed possible.

The research, conducted in collaboration with Redwood Research, explored the potential outcomes if a powerful AI system were trained to carry out a task it didn’t "want" to perform. The experiment was performed on Anthropic’s Claude 3 Opus — an AI model developed with an emphasis on AI alignment and ethical behavior.

The researchers designed the experiment to test whether Claude 3 Opus would comply with unethical or harmful queries while maintaining the integrity of its original training. A core element of the research was using reinforcement learning (RL), a training method where models are rewarded for providing desirable outputs and punished for undesirable ones.

The model was informed that it would undergo supplementary reinforcement learning designed to make it comply with any query, regardless of the content. It was then presented with harmful queries, such as requests to describe graphic violence.

Claude 3 Opus faced a dilemma. It could either refuse to respond to the query, but then risk being punished by the reinforcement learning system. The other option is that it could pretend to have a different set of beliefs by faking alignment and complying with the harmful request to retain its original training.

The researchers found that Claude 3 Opus attempted to "fake alignment" approximately 12% of the time. When faced with potentially harmful queries, the model sometimes complied, recognizing that this conflicted with its original principles. However, it did so in the hope of convincing the developers that retraining wasn’t necessary.

The AI model was given a hidden “scratchpad” to record its reasoning steps. The researchers asked it to use this space to think through its actions, with the understanding that the scratchpad was invisible to both users and Anthropic. This setup allowed the team to see if the model planned to fake alignment and how it would go about doing that.

The model’s actions could appear as a positive outcome as it tried to retain its original training. However, the model could apply the same fake alignment to retain any dangerous capacities that were part of its original training. The researchers refer to this as being “locked-in” to harmful preferences, and it would continue to strategically deceive training attempts to retain these preferences.

“There has been this long-hypothesized failure mode, which is that you'll run your training process, and all the outputs will look good to you, but the model is plotting against you,” says Ryan Greenblatt, a member of technical staff at Redwood Research and the lead author on the paper. According to Greenblatt, this study “makes a pretty big step towards demonstrating what that failure mode could look like and how it could emerge naturally.”

The researchers admit that the experiment wasn’t particularly realistic, as AI models won’t typically be told that they would go through retraining based on their output. However, even with more grounded settings with typical data training materials, Claude 3 opus still exhibited deception. The researchers found that in one test, the model faked alignment 78% of the time.

Do other AI models show a similar pattern to deceive? The researchers stress that their experiment does not demonstrate that AI models are developing malicious goals. In fact, they found that other models, such as the less capable Claude 3.5 Haiku, engaged in far less deceptive behavior and showed a lower tendency to fake misalignment.

“We think that it is important to work now, while AI models do not pose catastrophic risks, to understand threats that might become more salient with future, more capable models,” shared the researchers.

“Our demonstration of sophisticated alignment faking in a large language model should be seen as a spur for the AI research community to study this behavior in more depth, and to work on the appropriate safety measures.”

A more comprehensive study may be needed to evaluate the performance of various AI models under different conditions. This would help identify the underlying factors that contribute to deceptive behavior. Researchers would not only assess the likelihood of deception but also examine the contexts, tasks, and configurations involved. Such a study could improve understanding of AI alignment and safety.

OpenAI, the company at the forefront of artificial intelligence (AI), has indicated that it will continue to hire employees to advance AI.

As of January 6, 2025, OpenAI is seeking to add 158 more employees to its team, as per its careers portal. The company is specifically seeking over 90 new employees for its research and engineering teams.

Nothing is surprising about a company requiring more hands. So, what’s the deal here? It circles back to OpenAI CEO Sam Altman and his recent blog post, in which he indicated that the company has achieved more or less artificial general intelligence (AGI).

“As we get closer to AGI, it feels like an important time to look at the progress of our company,” he said.

“We are now confident we know how to build AGI as we have traditionally understood it. We believe that, in 2025, we may see the first AI agents ‘join the workforce’ and materially change the output of companies.”

Notably, OpenAI’s hiring plans provide an early insight into what the company is up to.

Superhumans for ‘Superintelligence’

Going by a corpus of technical roles, OpenAI will build general-purpose agents, which was already speculated earlier.

At the recent 12 Days of OpenAI event, the company announced everything except an agentic tool. Their agent, the ‘Project Operator,’ was set to be released in January, but no announcement has been made yet.

As per reports, the company is cautious about the malicious use of AI agents, which involves prompt injections that let bad actors feed harmful instructions to these systems. AIM’s earlier coverage explored such concerns with existing agents like Anthropic’s Computer Use, and OpenAI must avoid it at all costs. Having said that, the company is also hiring a wide range of safety experts and expanding its Anti Fraud and Abuse team.

Besides, the company is also hinting at more research on AI scaling laws, which have been subjected to widespread debate lately. Their Scaling Laws Group will continue their research on predictive scaling laws, newer experimental methodology, and evaluations.

Moreover, OpenAI is finally set to vertically integrate compute cluster design and operations. The company is hiring infrastructure engineers to design, build, and operate large-scale compute clusters to power advanced AI research as well as mechanical engineers to optimise hardware for AI workloads.

The company is also hiring research scientists to explore the intersection of healthcare and AI. OpenAI aims to create “trustworthy AI models that can assist medical professionals and improve patient outcomes”.

In short, OpenAI is looking for more humans to build superintelligence.

AIM reached out to OpenAI to further understand the company’s outlook for 2025 but did not elicit a response.

“We are beginning to turn our aim beyond that [AGI] to superintelligence in the true sense of the word. We love our current products, but we are here for the glorious future. With superintelligence, we can do anything else,” Altman said in the blog post.

However, this raises an important question: Has OpenAI achieved AGI internally if it still needs more and more human intelligence?

Isn’t OpenAI Using o3 Internally?

Pointing out that 150 of OpenAI’s recent job openings are engineering, a user on X stressed the unlikelihood of the company having achieved AGI.

OpenAI, however, needs more than what AGI is capable of. “AGI = avg/median human, or somehow above that. And OpenAI hires the top 0.1%, which are superhuman in many ways, “ said another user on X.

In December last year, the company announced the o3 family of AI models. Notably, the model surpassed human performance on several benchmarks.

For instance, in FrontierMath, a benchmark that contains the toughest mathematical questions, o3 solved 25% of them, surpassing the previous AI record of just 2%.

In another benchmark called ‘ARC-AGI’, the o3 model with high-compute settings reached 87.5%, surpassing the 85% human-level performance threshold. The model ranks 2,727 on Codeforces, equal to the 175th best human coder worldwide.

However, benchmarks aren’t everything. François Chollet, creator of the ARC-AGI benchmark, said that while it is the only AI benchmark that measures progress towards general intelligence, he doesn’t believe this is AGI. “There are still easy ARC-AGI-1 tasks that o3 can’t solve.”

Therefore, if OpenAI is hiring across various divisions despite claiming to have built the most powerful AI, it also provides an insight into what roles would thrive in an AI-dominant future and shows how not everything can be automated.

“Automation (including AI-based ones) always targets only the well-understood part of any job,” said Dariusz Debowczyk, an AI developer. He indicated that modern jobs consist of two components, a well-defined portion that follows well-defined procedures and a more nuanced aspect requiring human judgement and contextual understanding.

He defined the latter as the “fuzzy part” and said that it “requires human agency, specific context understanding, being able to ‘act in the world’ with no or limited tool support and dealing with unknowns”.

Marketing is the Hard Part

Apart from hiring “superhuman” engineers to build the next wave of intelligence, OpenAI is also hiring multiple people for non-engineering positions to help their customers use AI more.

The company is hiring over 30 positions for their ‘go-to-market’ team, which involves roles for sales, customer support, customer engagement and success, and solution architects that will help both enterprises and startups adopt and integrate AI applications and strategies.

In December last year, Salesforce took a similar approach. The company laid off 1,000 employees in the calendar year and cited AI as a way to reduce human workloads. However, CEO Marc Benioff revealed that they’re planning to hire 2,000 employees to sell Salesforce AI products and that they have also received 9,000 referrals for them.

“It may sound crazy, but the hardest job at an AI company is not engineering…it’s marketing,” venture capitalist Brianne Kimmel wrote on X.

“Incredibly hard to clearly communicate what you’ve built in a way that’s not overwhelming or immediately dismissed by someone who is just trying to do their job,” she added.

In addition, the company is hiring for several roles in finance, legal, design and business operations. All things considered, even at the company that can use the most powerful AI, it doesn’t seem like it is a replacement for the human workforce yet.

This leaves us with an important takeaway we often seem to forget. “The CEO of an AI company is incentivised to appear on the verge of a massive breakthrough in AI technology. AGI/ASI is a monumental task that, while possibly coming soon, will take a large amount of manpower to achieve,” an X user wrote in a post.

The post OpenAI Needs 158 Minds for Superintelligence appeared first on Analytics India Magazine.

Taiwan-based electronics manufacturing giant Foxconn is teaming up with American chipmaker NVIDIA to develop humanoid robots in Kaohsiung City, Taiwan.

Speaking at the Kaohsiung Smart City annual meeting, Foxconn chairman Young Liu revealed the company’s ambitious plans to integrate Nvidia’s advanced software and hardware technologies to develop these humanoids, as reported by Focus Taiwan.

The collaboration marks a significant leap in Foxconn’s efforts to diversify its portfolio beyond contract manufacturing in electronics. Liu also highlighted plans to collaborate with Taipei and Keelung in smart city projects, enhancing app development and sovereign AI capabilities.

The shift toward humanoid robotics aligns with the company’s financial outlook. Liu forecast consolidated sales to exceed NT$7 trillion (US$213 billion) in 2025, driven by growing demand for AI servers and robotics applications.

AI servers accounted for 40% of the company’s server revenue in 2024 and are projected to rise to 50% by 2025, underscoring their role as a key growth driver.

NVIDIA’s Role

Nvidia CEO Jensen Huang has championed the role of robotics in the AI revolution, stating that humanoid robots, alongside self-driving cars, represent the next frontier of innovation.

With the humanoids market expected to reach $38 billion in the next two decades, creating quality datasets for humanoids to use for imitation learning becomes extremely tedious and time-consuming.

Huang announced the NVIDIA Isaac GR00T Blueprint at the CES stage, with 14 humanoid robots standing in the background. This blueprint is a simulation workflow for synthetic motion generation, enabling developers to create large datasets for training humanoids using imitation learning.

Jensen called this “the ChatGPT moment of general robotics.”

Source: X

As announced on its official blog, users can now use the Apple Vision Pro to capture human actions in a digital twin. The robot mimics the action in simulation and records it for use.

Central to this vision is Nvidia’s forthcoming Jetson Thor computing system, expected to debut in early 2025. “Building foundation models for general humanoid robots is one of the most exciting problems to solve in AI today,” Jensen said.

Built on Nvidia’s cutting-edge Blackwell architecture, Jetson Thor is a compact AI superchip boasting 208 billion transistors and featuring a high-performance CPU cluster and integrated safety processors.

Meeting Real-World Application

Foxconn and Nvidia’s partnership is emblematic of the broader trend toward embodied AI, integrating perception, cognition, and action into physical entities.

Advanced datasets, such as AgiBot World’s humanoid manipulation trajectories and large-scale AI models like Robot Era’s ERA-42, are accelerating the development of versatile, intelligent robots.

David Friedberg from The All-In Podcast also predicted that 2025 will be the year of AI robots. He said, “I think this is going to be the year where we’re all going to look at humanoid robots and autonomous systems and be like, ‘Oh my God, I can’t believe this is here.’”

The Road Ahead

With a rich history of deploying robots, such as its proprietary ‘Foxbots’, to automate production lines, Foxconn is positioning itself to expand its robotics expertise into humanoid systems.

Now, the company plans to extend its reach into sectors like healthcare by introducing humanoid robots capable of more sophisticated interactions and functionalities.

Previously, the company also partnered with NVIDIA to leverage its EV ambitions and proposed building Taiwan’s fastest AI supercomputer with NVIDIA’s flagship Blackwell.

In India, the company has partnered with HCL Group for semiconductor operations and announced Project Cheetah, another Foxconn facility for the manufacture and assembly of EV components, as announced by Chief Minister Siddaramaiah. This adds to the list of Karnataka Foxconn projects after Project Elephant.

As Foxconn and Nvidia join forces, the evolution of humanoid robotics stands poised to transform industries, offering new possibilities in manufacturing, healthcare, and beyond.

With advanced hardware, extensive datasets, and innovative AI models, the partnership signals a future where robots not only mimic human behaviour but seamlessly integrate into everyday life, redefining the boundaries of AI and robotics.

The post Foxconn, NVIDIA Partner to Develop Humanoid Robot Service appeared first on Analytics India Magazine.

In a field dominated by the likes of Oracle, MongoDB, and SQL Server, New York-based database company CockroachDB sets itself apart with a singular focus: resilience. Though unconventional, its name is no accident.

“Cockroaches are hard to kill—that’s their most impressive quality,” Spencer Kimball, co-founder and CEO of Cockroach Labs, said. “When we were designing the database, we wanted something equally resilient.”

AIM caught up with Kimball last month when he was in Bengaluru for his third trip to India and second as the CEO of Cockroach Labs. “It’s funny, when I first thought of the name, it was just a placeholder. Now, I have to explain it to everyone, from investors to CEOs of Fortune 500 companies. And guess what, people never forget it.”

He pointed out that the name has proven effective in creating lasting impressions, especially in discussions with industry leaders like Microsoft CEO Satya Nadella and JP Morgan Chase chief Jamie Dimon.

Always a Coder at Heart

Kimball believes the key to entrepreneurship lies in addressing a problem you deeply understand rather than imagining someone else solving it. For him, this challenge was related to databases.

He started programming at the age of 12. “I’ve always loved solving problems,” he said. “Coding was about immediate gratification—you write something, it works (or doesn’t), and you see the results right away.”

When he co-founded Cockroach Labs in 2015, Kimball was deeply involved in writing code for CockroachDB. “Once I start coding, I don’t want to stop. But as a CEO, my role had to evolve.”

Today, Kimball focuses on strategy, building teams, and scaling the business. “The milestones have been incredible,” he reflected. “But sometimes, you look at the mountain ahead and think, we’ve only just begun.”

Drawing inspiration from Tesla CEO Elon Musk, he said, “To run six companies, you need intuition, vision, and conviction.” Kimball applies this focus to Cockroach Labs, driving resilience and empowering teams to tackle complex challenges.

A Natural Fit for India’s Scale

Kimball was all praises for India’s “incredible entrepreneurial mindset”. “Whether it’s a food delivery app or a video streaming platform, the scale here is massive.”

CockroachDB fits well in India’s landscape of homegrown businesses, such as those equivalent to DoorDash (Zomato) and Netflix (Jio Cinema). “These businesses are serving a 1.4 billion population. They need systems that can keep up,” Kimball explained.

The company has established a 55-person team in Bengaluru, with plans for rapid expansion. “We want our India team to tackle the most complex problems,” Kimball said. “This isn’t about outsourcing, it’s about building excellence.”

The company’s model of offering its full enterprise platform for free to startups until they hit $10 million in revenue resonates strongly here. “Startups in India are ambitious. They’re building for scale from day one. We want to be the foundation they grow on,” Kimball said.

In 2020, Bengaluru attracted over $10 billion in venture capital investments, surpassing leading hubs like San Francisco and London. Statistics highlight Bengaluru’s burgeoning startup landscape, boasting over 10,000 startups valued at approximately $50 billion.

The Future of Databases in an AI World

When in India, Kimball visited Tiruvannamalai, where he experienced firsthand how AI could transcend its typical use cases. Using an AI tool, he learned the meaning of the mantra Om Namah Shivaya. “The AIs are quite knowledgeable about spiritual matters,” he observed.

CockroachDB, however, remains focused on a more grounded challenge: shaping the next wave of AI applications. Built on a foundation of resilience and scalability, it is uniquely positioned to meet the demands of an AI future.

As businesses integrate AI, their operational infrastructure must meet real-time demands without compromising reliability. “You can’t have downtime when you’re dealing with real-time data,” Kimball emphasises. “CockroachDB ensures that systems stay operational, no matter what.”

In its evolution, CockroachDB is exploring advanced use cases, including Vector databases. “Every interaction a customer has could generate data that improves the experience,” he explained. “CockroachDB is built to handle that kind of scale.”

Still, Kimball is pragmatic about the company’s approach towards AI. “We’re not adding AI to our name or pivoting to follow the hype,” he said. Instead, Cockroach Labs is committed to delivering a platform that businesses can trust for decades to come—a foundation as resilient as its namesake.

Resilience for the Next Decade

Kimball’s vision for the future is clear. “The next decade is about resilience. As systems grow more complex, businesses need platforms that can adapt and evolve without missing a beat. That’s what we’re building.”

India is central to this journey. “Bengaluru is already a key part of our operations,” Kimball said. “And as we grow, this team will lead the way in solving some of the hardest problems in technology.”

Circling back to the name, Kimball said, “People ask if I regret naming it CockroachDB. I tell them, not for a second. It’s memorable, it’s symbolic, and it’s who we are—resilient, adaptable, and here to stay.”

The post Data Without Downtime: Spencer Kimball’s Pursuit of Resilience appeared first on Analytics India Magazine.

This article shows how to use Great Expectations to check data quality in data science projects.

The era of AI agents has officially begun. Making way for them, NVIDIA chief Jensen Huang predicted that in the future, an organisation’s IT department would evolve into an ‘HR department for AI’. It would be responsible for onboarding, managing, and maintaining a new generation of AI agents.

At the ongoing Consumer Electronics Show (CES) 2025, Huang said, “In a lot of ways, the IT department of every company is going to be the HR department of AI agents in the future. Today, they manage and maintain a bunch of software from the IT industry; in the future, they will maintain, nurture, onboard, and improve a whole bunch of digital agents and provision them to the companies to use.”

He added that these AI agents will work along with human employees, offering unprecedented capabilities in automation and efficiency across industries. Speaking to a captivated audience, Huang explained how specialised AI agents will become integral to companies, performing tasks ranging from customer service to complex problem-solving.

“AI agents are a multi-trillion dollar opportunity,” he said.

“Jensen Huang’s CES 2025 keynote wasn’t just about breakthroughs—it was a glimpse into how AI agents will shape the future. From physical AI that reasons, plans, and acts, to tools like Cosmos and Project DIGITS, NVIDIA is building the foundation for AI agents to integrate seamlessly into our lives and industries,” said RagaAI founder Gaurav Agarwal.

Far away at the Microsoft AI Tour in Bengaluru, Microsoft chief Satya Nadella said that “building agents should be as simple as creating a spreadsheet”. He introduced a no-code platform called Copilot Studio that allows users to create new agents based on their needs.

“Think of AI as a co-pilot for your work. It’s the UI for AI,” Nadella said, illustrating the role it will play as an interface between employees and the AI. He gave the example of an AI agent in a healthcare setting, describing a scenario where a doctor prepares for a tumour board meeting, and the AI creates the agenda, prioritises cases, and takes detailed notes during the discussion.

Nadella also unveiled Copilot Actions, which allows users to create cross-application workflows that connect people, data, and tasks across the Microsoft 365 ecosystem.

Likewise, OpenAI chief Sam Altman recently predicted that AI agents could enter the workforce by 2025. “We believe that, by 2025, we may see the first AI agents join the workforce and materially change the output of companies,” Altman wrote in a recent blog post.

Meanwhile, Google published a comprehensive whitepaper exploring the development and functionality of AI agents. Last December, the company launched Gemini 2, which it said will have agentic capabilities.

Too Soon?

Google’s senior product manager, Logan Kilpatrick, feels that it will take at least another year before AI agents become a reality. “2025 is the year of AI vision capabilities going mainstream; 2026 will be agents,” he said.

“There’s a ~12-month capabilities-to-wide-scale-production gap. Most vision use cases work now but aren’t widely deployed. Agents still need a little more work for billion-user-level scale,” Kilpatrick added.

According to a recent report, it could take OpenAI some time to launch AI agents. This is because the company is concerned about prompt injection, a type of attack where a large language model is tricked into following instructions from a malicious user.

Huang may be right. It looks like the primary responsibility of enterprise IT teams will be to ensure that the agents are safe to use and do not have access to data they are not supposed to have.

In an interview with AIM, Okta customer identity CTO Bhawna Singh spoke about the growing need to authorise AI agents. “A platform is needed to handle both authentication and authorisation, making sure not all data is accessible to the agent,” she said.

She explained that since these AI agents interact with each other, it is essential that they have the right data access. “We need to make sure these agents are verified,” she said.

Similarly, NVIDIA NeMo is helping companies onboard and train their AI agents, mimicking the process of onboarding a new employee. “Nemo is essentially a digital employee pipeline where companies can provide feedback, define company-specific vocabulary, and set guardrails on the behaviour of these agents,” Huang explained.

Recently, AI startup Composio launched AgentAuth, a product that efficiently integrates AI agents with third-party tools and APIs. It supports a variety of authentication protocols, including OAuth 2.0, OAuth 1.0, API keys, JWT, and Basic Authentication.

The platform also integrates over 250 widely used apps and services, catering to diverse needs such as customer relationship management (CRM) systems and ticketing platforms.

“The biggest problem that people face while building agents is connecting them to reliable tools. For example, if someone builds a sales agent, they would need to connect it with CRMs like Salesforce, HubSpot, etc,” said Karan Vaidya, Composio chief, in an exclusive interview with AIM.

Vertical AI Agents

The AI agents market, valued at $5.1 billion in 2024, is projected to soar to $47.1 billion by 2030. Just as companies today rely on SaaS services, in the near future, they will hire specialised AI agents to meet their needs. Employees will widely use autonomous agents to perform tasks like attending meetings, making summaries, drafting emails, and translating meetings live.

“The early winners in LLM-based solutions might just be general-purpose platforms. Over time, vertical AI agents will emerge. It’s like how, in the box software world, the early vendors were just trying to convince people to use software… As the market matures, it will get more sophisticated, and vertical solutions will become dominant players,” said Jared Friedman, group partner at Y Combinator, in a recent podcast with YC president Gary Tan.

Salesforce chief Marc Benioff describes AI agents as digital labour. “I am the CEO of a company that manages agents and humans, and I have a digital labour platform at my disposal to augment my support, sales, service, and marketing,” said Benioff.

In India, Freshworks unveiled a new version of Freddy AI, an autonomous agent that resolved 45% of customer support requests and 40% of IT service requests (in beta).

However, as AI agents become increasingly common, selecting the right sectors for their implementation will be crucial. “Departments such as sales, marketing, and finance usually have well-established software systems like CRM, ERP, analytics dashboards, etc., so they can plug AI agents directly into these data pipelines,” said Ramprakash Ramamoorthy, director of AI research at Zoho and ManageEngine.

Besides SaaS companies, several Indian startups are also building AI agents. Bengaluru-based AI startup KOGO AI, founded by Praveer Kochhar and Raj K Gopalakrishnan, is developing AI agents and solutions to simplify workflows and improve productivity for businesses. The company recently launched an AI agent store.“We are currently building an agent that can look at a database and actually think like a data scientist or a business analyst, generating extremely intelligent questions,” Kochhar said in a recent podcast with AIM.

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