Boardroom Intelligence

Ravi Mhatre on Venture Capital’s Role in the Age of AI


3 min read
Ravi Mhatre on Venture Capital’s Role in the Age of AI

The Jefferies 2025 Private Growth Conference brought together hundreds of top bankers, investors, founders, and tech executives to discuss the sector’s key trends and developments. The interview below has been edited for length and clarity.

In April, Jefferies hosted Ravi Mhatre, Co-Founder & Managing Director of Lightspeed Venture Partners, at its annual Private Growth Conference. Mhatre runs one of the most active venture capital funds in Silicon Valley, where he’s led investments in Snapchat and other major tech ventures.

Here, Mhatre shares his thoughts on how AI is accelerating growth in enterprise settings, the evolving role of venture capital in funding innovation, and raising capital in a post-tech bubble world.

Explosive Enterprise Growth, Fueled by AI

In just two years, AI technology has gone from a novelty to a broadly adopted system. In enterprise sectors — where adoption cycles are typically much slower — companies are leveraging AI for growth at rates Mhatre describes as “stunning.”

It would be a mistake to assume this growth is confined to specific pockets of industry, Mhatre notes. “We’re in a world where we are supply-constrained on expertise that people want. AI is providing an amplifier effect to let more of that expertise be available.”

From helping doctors see more patients by reducing their administrative burden to enabling developers to write better software, AI is driving significant revenue growth for the companies that use it. Recent estimates suggest that AI could generate over $15 trillion in additional revenue for businesses by 2030.

The Role of Venture Capital in an AI-Driven World

Realizing this growth will require significant capital. Mhatre believes that venture firms will play a key role in providing it.

“Venture capital has had to be much more of an institutional force in the capital markets ecosystem,” explains Mhatre. The scale of investment needed to support today’s breakthrough technologies is now much larger. The average investment in deep tech, or technologies that aim to solve the world’s most complex problems, has reached $100 million or more per deal.

Traditional sources of capital, like public markets and private equity, often can’t support these investments. Their short-term return focus can be limiting, Mhatre argues. In contrast, venture capital is built to take a long view on innovation and value creation.

Venture also brings a deep understanding of the technology stack, regulatory dynamics, and strategic paths businesses must navigate to scale.  “We know what it takes for a company to go from something new and innovative, that didn’t exist, to something that can be a major part of economic activity,” Mhatre explains.

This level of support has positioned venture capital as the go-to source of funding for innovative companies.

Raising Capital in a Post-Tech Bubble World

While there is still plenty of capital available for technology innovation, Mhatre suggests that this is a prime moment for companies to reflect on their product stack.

“Companies need to buy forward,” he says. That means having a clear vision for how products will evolve alongside AI and other emerging technologies. Meanwhile, mature companies must figure out how to compete with AI-native challengers drawing growing shares of budget and attention.

For some, this will mean pivoting to become an AI-forward company in their target markets. For others, it may mean figuring out how to operate successfully as a company that may not be as high-growth in the future. 

This Might Be the Tipping Point for AI

As AI systems become more sophisticated, reliable, and capable, demand will only accelerate. Companies will have to keep up. This is one trend that Mhatre believes won’t go away, even amid regulatory uncertainty and a murky macroeconomic outlook.

Mhatre describes the power of the tipping point: core technology trends tend to move forward on their own cadence. If one idea doesn’t work at first, there will always be another trend that drives progress.

“At some point, when these tipping points come together, you get large before and after moments in technology,” he says. “That’s happening on an ongoing basis in AI.”