Anagh Prasad, Vice President – Investment, Accel, grew up in Ranchi but his first serious exposure to computer science happened at IIT Delhi. Studying under professors who were exploring machine learning (ML), Anagh was already ahead of the curve.
In 2015, on an educational trip to Vancouver, one of the global hubs of AI innovation at the time, he witnessed how researchers were debating the right approaches to AI, long before transformers emerged as a dominant model. This was the spark that lit his curiosity, and led him to engage with AI.
Back in India, he pursued advanced AI courses and worked with Goldman Sachs, where he discovered the wide gulf between AI theory and practice. While exploring NLP and applied AI, he accidentally stumbled upon venture capital and in 2019 joined Stellaris Venture Partners – kickstarting a journey of investing in AI.
Where AI, founders and investors intersect
For Anagh, the fundamentals of AI investment are surprisingly similar to other sectors. Be it ecommerce, SaaS or, currently AI, the core focus remains on the size of the opportunity and the strength of the founding team.
“At its heart, it’s about asking: is this a problem people will care deeply about 10 years from now, and if solved, can it create a large business?” he says. The second filter—team quality—comes down to adaptability, learning speed, and any competitive advantage founders bring to their markets. While venture investing may seem overwhelming from the outside, Anagh credits Accel’s culture of simplicity and clarity for shaping his approach.
When it comes to red flags, Anagh has seen the startup landscape evolve. A year ago, many AI ventures risked building products that amounted to little more than features. Today, with more seasoned founders and sharper investors, those missteps are less common. Yet the classic pitfalls persist: entering markets without research, rushing to launch before the product is ready, or building without a clear path to value creation.
On pricing, he believes the space remains unsettled. Long-term pricing models are still tricky to pin down, but one encouraging trend is the shift from AI as a “tool” to AI as an outcome-driven solution. This opens doors for innovative business models, especially outcome-based pricing. “If companies can capture value tied to outcomes and gradually scale efficiencies to achieve software-like margins, they can build beautiful, enduring businesses,” he says.
Anagh sees this as a rare inflection point in tech history: a moment when the ground beneath AI is moving so fast that founders must operate with exceptional clarity. The entrepreneurs thriving now are not just technically strong but also deeply attuned to the incentive structures of the stakeholders who will buy, use, and be shaped by their products.
“It’s not enough to say my AI product is differentiated,” he notes. “The real question is: how does it affect the user, the buyer, and the decision-maker? Does it drive efficiency, revenue, or something else they truly value?”
This demands more than customer interviews; it requires a nuanced grasp of the buyer’s profit-and-loss dynamics, their fears about automation, and their appetite for change.
Previously, a sharp understanding of customer problems could get a startup most of the way. Now, founders must marry that insight with a forward-looking view of how AI reshapes business models—for both themselves and their customers. In such a dynamic landscape, Anagh believes those who can quickly build this bridge from present to future are the ones who will go the distance.
Agents of change: How AI assists sectors and society
Anagh is quick to acknowledge that not all global issues are technology problems. “AI, at the end of the day, is a tool; it can only take us so far,” he notes. Yet, he believes its real promise lies in accelerating the pace of discovery. From solving the mysteries of complex biological problems to breakthroughs in material science, battery technology, drug discovery, and even space exploration, AI is steadily pushing the boundaries of what’s possible.
Currently, much of enterprise AI is leveraged on use cases such as boosting efficiency in customer support. However, Anagh believes that the next wave of innovation will extend far beyond incremental gains into ripples that will disrupt economies, unlock innovations, and shape a more equitable society.
An area that he is especially excited about is the emerging popularity of AI agents. These systems, trained to perform highly specialized tasks within knowledge-heavy professions such as banking, consulting and medicine, are continuously learning and refining themselves. They have the potential to deliver immense value within a single domain.
“Every large knowledge profession could eventually have its equivalent AI counterpart,” he predicts. The companies that successfully build and scale these vertical AI agents could become the next generation of industry-defining giants.
The second theme Anagh has his eye on is how AI will be democratized for Indian consumers. “We have smartphone access, UPI, and have figured out distribution and payments. This allows new technologies to be absorbed quickly by the general public. I’m excited to see the use cases which make Indian lives better and appeal to India-specific price points.”
AI: An employee, ethicist and ambassador for brands
For Anagh, ethics in AI is non-negotiable. He highlights how once an AI product is released, it acts like a million employees representing the brand – every interaction is a reflection of the company. This raises difficult questions: should engagement be driven at the cost of mental wellness, or should boundaries be set where human care is needed?
From a venture perspective, he stresses the same rigor. Accel prioritizes founders with impeccable ethical standards, often investing only after years of trust-building. “On ethics, we need 100 out of 100,” he says—no compromises, even if other skills score lower.
AWS: Sparking innovation for startups
Anagh credits Amazon Web Services (AWS) as a vital force behind India’s startup ecosystem. Beyond offering cloud credits—often the difference between liftoff and dead-on-arrival for early companies—AWS has played a crucial role in mentorship and market access. Accelerators like AWS ML Elevate, where he has been closely involved, provide founders with technical guidance and pathways to scale.
He notes that AWS’s growing distribution support has been especially valuable for post-Product Market Fit (PMF) startups aiming to expand. “It’s hard to imagine today’s vibrant ecosystem without the support of hyperscalers like AWS,” he says.
The future of venture capital in the age of AI
Anagh takes a contrarian view on AI’s impact in venture capital. While acknowledging productivity gains, he believes early-stage investing will remain a deeply human business—rooted in trust, intuition, and close collaboration with founders. “I don’t think this is a business that can inherently scale,” he notes.
Unlike other domains where AI can automate discovery, venture thrives on building long-term relationships and backing only a handful of high-quality teams each year. For Anagh, the essence of investing lies in quality over quantity—something no tool can replace. AI may assist and augment, but the core nature of venture, he insists, will stay the same.
AI and beyond
Anagh’s sharpest insights on AI come from working closely with portfolio companies across vertical, consumer, and physical AI, constantly learning from founders and buyers.
Outside of work, he loves food. He often experiments with fusion cooking, combining the bright, fresh flavors of Mediterranean food with the spice and taste of Indian food. He also spends time looking for family-owned restaurants in new locations and scouting out authentic, local flavours and ingredients. “Ninety percent AI, 10 percent cooking,” he jokes about his balance.
