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Economic Times
04-07-2025
- Business
- Economic Times
Can you trust AI to manage your mutual funds?: Bruce Keith on human vs AI debate
Bruce Keith, CEO of InvestorAI, explains how AI is transforming mutual fund investing, particularly in the U.S. He urges cautious adoption in India, stressing transparency, human oversight, and diversification. AI excels in data-driven insights and behavioral analysis but carries inherent biases and lacks accountability without regulatory frameworks. Tired of too many ads? Remove Ads Q. How is AI changing the game for mutual funds, particularly at the front end for consumers? What's your view? Bruce Keith: Q. With that kind of gap, there's clearly a need for deeper understanding in India. What kind of AI models are mutual fund companies using, and how are they different from tools like ChatGPT? Bruce: Tired of too many ads? Remove Ads Q. Are there any real-world examples where AI-led mutual funds have actually outperformed traditional ones? Bruce: Q. From a retail investor's point of view, how does AI help recommend mutual funds based on goals or risk appetite? Bruce: Q. AI-driven advisory platforms are booming. What's fueling this surge? Bruce: Q. A concern here is accountability. In traditional systems, fund managers are accountable. With AI, who takes responsibility if something goes wrong? And where does SEBI stand? Bruce: Q. You mentioned AI reduces human bias, but does AI come with its own set of biases? Bruce: Q. How does AI handle market volatility, especially given the unpredictability in global and domestic events? Bruce: Q. Any closing thoughts for retail investors who may still be hesitant about using AI? Bruce: As artificial intelligence reshapes industries worldwide, the world of investing is no exception. In the U.S., over 35% of mutual funds are now powered by AI, a stark contrast to just 1% in India . So, what does this mean for Indian investors? Can AI really outperform human fund managers? And more importantly, can it be trusted?In this exclusive conversation, Bruce Keith, Co-Founder and CEO of InvestorAI , sits down with Neha Vashishth Mahajan to break down how AI is changing mutual funds, from cost and performance to risk profiling and a global standpoint, especially looking at the U.S. as the frontrunner in mutual fund innovation, they've been using AI for several years now. In contrast, India is still catching up. Currently, around 35% of mutual funds in the U.S. are quant or AI-driven, while in India, that figure is closer to just 1%. That's a significant gap. In the U.S., AI is used for market sentiment analysis, stock selection, strategy reinforcement, and even writing research papers. In India, we're still mostly using AI to enhance backend operations rather than drive front-end investment research, but the direction is is useful, but for asset management firms, differentiation is key. At InvestorAI, we've built our own foundational AI, we don't use ChatGPT, Gemini, or any off-the-shelf models. Everything is developed in-house, in India, from the servers to the code. It's similar to what firms like Renaissance or Jane Street do in the U.S. To build a real edge in this space, especially in asset management, you need to develop your own foundational You don't reach 35% market share in the U.S. without delivering results. At InvestorAI, while we don't run mutual funds, we do manage equity baskets. Since our product went live in April 2021, we've delivered a CAGR of 45%, compared to the market's 17% over the same period. That's more than double, in live trading. In India, early forays into quant strategies weren't always successful, which is why many players are still guide this process quite tightly and require firms to assess risk appetite through specific questions. That doesn't need heavy AI, it's well automated. But AI becomes valuable when comparing declared risk appetite with actual behavior. We found in a study that about 40% of people acted in ways inconsistent with their self-reported risk levels. AI can detect that gap and help investors make better-aligned is a hot buzzword. Every CEO today talks about it. Many businesses integrate GenAI tools for branding more than functionality. But foundational AI platforms, like ours, offer deeper value. Younger investors, in particular, are more open to trusting AI over legacy institutions. Also, AI doesn't sleep; it works around the clock, unlike fund managers. And with the ability to reduce manufacturing costs, AI allows financial services to be delivered instantly, much like ordering food on Zomato or Swiggy.A human-in-the-loop model is essential. At InvestorAI, all recommendations undergo final human review before being released. SEBI's latest circular focused on AI in the back office, not yet on investment strategy or manufacturing. But AI's biggest value lies in reducing cost and improving access, which benefits all stakeholders. As adoption grows, I expect more structured regulatory frameworks to absolutely does. All AI models are built on human-generated data and inherit those biases. Machines reflect whatever biases are embedded in the data and algorithms. For example, ask several GenAI models for a random number and you'll get the same answer across the board, that's bias in action. The challenge is transparency: AI systems need to be clearer about their data sources and potential biases so users can interpret results more can't predict unpredictable political actions or wars, but it can pick up signals. For example, just before the Israel-Iran tensions escalated, our India model shifted heavily into healthcare—a classic risk-off move. The AI sensed something was off through trading volumes and patterns, even though it didn't 'know' what was coming. With the ability to process a trillion data points daily and react instantly, AI offers unparalleled responsiveness to market shifts—something human analysts simply can't match in real Wall Street goes, Dalal Street follows. We'll see more AI integration in India. But as an industry, we need to develop transparent frameworks so retail investors can truly understand AI-based offerings. My advice: try it in small amounts. Never put your entire portfolio into AI. Diversify, have a portion in active funds, another in passive, and some in AI-driven strategies. I personally allocate about a third to AI. It's a growing space, and informed participation is the best way forward.(Disclaimer: Recommendations, suggestions, views and opinions given by the experts are their own. These do not represent the views of the Economic Times)
Time of India
04-07-2025
- Business
- Time of India
Can you trust AI to manage your mutual funds?: Bruce Keith on human vs AI debate
As artificial intelligence reshapes industries worldwide, the world of investing is no exception. In the U.S., over 35% of mutual funds are now powered by AI, a stark contrast to just 1% in India . So, what does this mean for Indian investors? Can AI really outperform human fund managers? And more importantly, can it be trusted? In this exclusive conversation, Bruce Keith, Co-Founder and CEO of InvestorAI , sits down with Neha Vashishth Mahajan to break down how AI is changing mutual funds, from cost and performance to risk profiling and regulation. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Play War Thunder now for free War Thunder Play Now Undo Excerpts: Q. How is AI changing the game for mutual funds, particularly at the front end for consumers? What's your view? Bruce Keith: From a global standpoint, especially looking at the U.S. as the frontrunner in mutual fund innovation, they've been using AI for several years now. In contrast, India is still catching up. Currently, around 35% of mutual funds in the U.S. are quant or AI-driven, while in India, that figure is closer to just 1%. That's a significant gap. In the U.S., AI is used for market sentiment analysis, stock selection, strategy reinforcement, and even writing research papers. In India, we're still mostly using AI to enhance backend operations rather than drive front-end investment research, but the direction is promising. Q. With that kind of gap, there's clearly a need for deeper understanding in India. What kind of AI models are mutual fund companies using, and how are they different from tools like ChatGPT? Bruce: ChatGPT is useful, but for asset management firms, differentiation is key. At InvestorAI, we've built our own foundational AI, we don't use ChatGPT, Gemini, or any off-the-shelf models. Everything is developed in-house, in India, from the servers to the code. It's similar to what firms like Renaissance or Jane Street do in the U.S. To build a real edge in this space, especially in asset management, you need to develop your own foundational AI. Live Events Q. Are there any real-world examples where AI-led mutual funds have actually outperformed traditional ones? Bruce: Absolutely. You don't reach 35% market share in the U.S. without delivering results. At InvestorAI, while we don't run mutual funds, we do manage equity baskets. Since our product went live in April 2021, we've delivered a CAGR of 45%, compared to the market's 17% over the same period. That's more than double, in live trading. In India, early forays into quant strategies weren't always successful, which is why many players are still cautious. Q. From a retail investor's point of view, how does AI help recommend mutual funds based on goals or risk appetite? Bruce: Regulations guide this process quite tightly and require firms to assess risk appetite through specific questions. That doesn't need heavy AI, it's well automated. But AI becomes valuable when comparing declared risk appetite with actual behavior. We found in a study that about 40% of people acted in ways inconsistent with their self-reported risk levels. AI can detect that gap and help investors make better-aligned decisions. Q. AI-driven advisory platforms are booming. What's fueling this surge? Bruce: AI is a hot buzzword. Every CEO today talks about it. Many businesses integrate GenAI tools for branding more than functionality. But foundational AI platforms, like ours, offer deeper value. Younger investors, in particular, are more open to trusting AI over legacy institutions. Also, AI doesn't sleep; it works around the clock, unlike fund managers. And with the ability to reduce manufacturing costs, AI allows financial services to be delivered instantly, much like ordering food on Zomato or Swiggy. Q. A concern here is accountability. In traditional systems, fund managers are accountable. With AI, who takes responsibility if something goes wrong? And where does SEBI stand? Bruce: A human-in-the-loop model is essential. At InvestorAI, all recommendations undergo final human review before being released. SEBI's latest circular focused on AI in the back office, not yet on investment strategy or manufacturing. But AI's biggest value lies in reducing cost and improving access, which benefits all stakeholders. As adoption grows, I expect more structured regulatory frameworks to emerge. Q. You mentioned AI reduces human bias, but does AI come with its own set of biases? Bruce: It absolutely does. All AI models are built on human-generated data and inherit those biases. Machines reflect whatever biases are embedded in the data and algorithms. For example, ask several GenAI models for a random number and you'll get the same answer across the board, that's bias in action. The challenge is transparency: AI systems need to be clearer about their data sources and potential biases so users can interpret results more effectively. Q. How does AI handle market volatility, especially given the unpredictability in global and domestic events? Bruce: AI can't predict unpredictable political actions or wars, but it can pick up signals. For example, just before the Israel-Iran tensions escalated, our India model shifted heavily into healthcare—a classic risk-off move. The AI sensed something was off through trading volumes and patterns, even though it didn't 'know' what was coming. With the ability to process a trillion data points daily and react instantly, AI offers unparalleled responsiveness to market shifts—something human analysts simply can't match in real time. Q. Any closing thoughts for retail investors who may still be hesitant about using AI? Bruce: Where Wall Street goes, Dalal Street follows. We'll see more AI integration in India. But as an industry, we need to develop transparent frameworks so retail investors can truly understand AI-based offerings. My advice: try it in small amounts. Never put your entire portfolio into AI. Diversify, have a portion in active funds, another in passive, and some in AI-driven strategies. I personally allocate about a third to AI. It's a growing space, and informed participation is the best way forward.
Time of India
07-05-2025
- Business
- Time of India
AI fluency is the new wealth: How mastering intelligence can rewrite your paycheque?
Emergence of skill capitalism From Python to power moves Specialists in model training and configuration Thinkers from cryptology, econometrics, and forensics Non-tech talent with translation and logic expertise Mid-level mayhem The agentic AI paradox Blueprint for doubling your worth Go beyond prompt fluency: Learn to build, not just use. Seek foundational work: Model training, guidelines, and evaluation. Join firms that train you to think like a machine: Not just use one. Pick a domain: Finance, logistics, law—depth wins over breadth. Act before agentic AI saturates the market: The window is open, but closing fast. Talent, not tenure, is the most precious currency in India's technology ecosystem. As artificial intelligence knits the very fabric of work, those fluent in language are no longer just professionals in cubicles—they are the frontrunners in a race that will determine who leads and who gets left behind. While every new invention in the domain fans the flames about whether AI will substitute jobs or leverage employees, the truth is: It is here to who learn to adapt to the guest will survive; those who shrug off its presence under the carpet will suffer. Not only are AI jobs expected to boom in the coming years, but AI specialists are also expected to secure hefty salary intelligence has penetrated almost every sphere of human existence—finance, healthcare, retail, logistics. AI skills are creating a rippling effect in the job market. Salaries are doubling, hierarchies are flattening, and the old rules of career progression are being rewritten with breathtaking is not just a buzzword, it is opening doors to skill capitalism- a system where compensation is directly intertwined with cognitive depth in machine learning, data modelling, and algorithmic reasoning. In this system, artificial intelligence is the great leveller and the great AI literacy is ubiquitous- but mastery is rare. And companies are not rewarding familiarity - they are paying a premium for those who can architect, train, and refine AI systems from the ground up. For those professionals, a 100% salary hike has become the new benchmark, not the is projected to generate 2 million AI-related job openings over the next two years according to a Bain and Company report. But even in this abundance, one element remains scarce—specialized talent. Foundational AI engineers, mathematicians, physicists, and domain-savvy coders are in dangerously short Python and AI tools are now well-learned skills among graduates. The market doesn't need prompt engineers. It needs system architects. AI familiarity is no longer a value proposition. Companies now seek: Experts in noise analysisIn short, AI has become interdisciplinary warfare, and generalists are losing Bruce Keith, Co-founder, InvestorAi, stated that 'Every graduate we meet is AI literate in terms of prompts and general use of AI tools. Add to this that Python is easy to learn, and then barriers to entry are low. If you are looking for someone to train models, set guidelines, and provide monitoring, then there are a good number of candidates. I think the issue is that firms are hiring a bunch of smart kids and expecting them to bring AI to the organisation without a proper plan - I see this across the finance sector.'The scarcity is most acute in the mid-level range. These are professionals expected to design and scale foundational models—yet this tech is so new that 'five years of experience' is often a are waiting six months or more to onboard viable candidates. During negotiation, 100% salary jumps are not just tolerated—they're often the opening candidates are seeing offers of ₹10–15 LPA—half of European standards, but still substantially above traditional Indian benchmarks. But the real prize lies in mid and senior roles, where compensation can cross into 300% premium territory for domain-specialist agentic AI—the new wave of intelligent, autonomous systems—becomes more capable, a paradox unfolds. These very systems may eventually replace the roles companies are desperately hiring for World Economic Forum (WEF) Future of Jobs 2025 report warns that up to 87% of AI-related roles could face substitution. But that isn't a death knell—it's a clarion call to mentioned, 'As agentic AI increases in adoption, there will be more capacity in the system and less need for new engineers – make sure you take the opportunities to go deep in terms of tech or domain.'So, how do you secure the 100% salary hike that's suddenly within reach?This is no longer a story of linear growth. It is a story of intellectual compounding. AI is not just a tool—it is a career catalyst. But only for those who understand that the future of work will belong to those who can build the future double your salary, you don't need to chase need to become indispensable to it.
