ff Venture Capital

John Frankel, founder of ff Venture Capital (ffVC), is passionate about partnering with visionary founders to shape the industry-leading businesses of the future. He has funded over 100 early-stage companies, created thousands of jobs and impacted millions of lives. While ffVC invests across industries, John focuses on applied AI, including drones, robotics, fintech and cybersecurity. He has served on numerous boards, including at NYU Tandon School of Engineering, supporting New York City’s ecosystem. Before founding ffVC, John had a long career at Goldman Sachs in technology development, business reengineering and capital markets. He graduated from New College Oxford in 1982 and qualified as a Fellow Chartered Accountant at Arthur Anderson.

In this interview, Frankel highlights the evolution of AI-driven solutions, its impact on business efficiency and the potential for startups to gain a competitive advantage by adopting AI.

The Expanding Market for AI-Driven Solutions

There are two kinds of leaders—hardworking and intuitive. The others are distinguished by their ability to balance strong vision with active listening for feedback. This group is rare but crucial, especially in evolving markets where staying attuned to change is critical.

For example, we are currently working closely with companies of all sizes, leveraging AI to improve business efficiency—not just integrating AI into their products but using it to optimize operations. Some refer to this as ‘agentic workflows,’ but I prefer ‘AI point solutions’ (AIP) because the focus should be on outcomes, not processes. We are actively hosting webinars, conducting surveys and facilitating discussions with experts to share best practices.

Integrating AI in startups can reduce costs by 10 to 30 percent—or even more—while scaling their operations without significantly increasing headcount. This unlocks greater capital efficiency and operating leverage. The tools are evolving rapidly and experimentation is ongoing. Questions around AI applications, knowledge bases, models and integrations remain at the forefront.

This shift is reshaping how we assess new companies. This redefines business operations, particularly for smaller companies with a distinct advantage. Unlike large enterprises, where change is often top-down, smaller businesses drive transformation from the ground up. The challenge is complex, touching everything from company culture to recruitment and incentives, but it is often easier to implement in smaller, more agile organizations.

Most companies invest in software and SaaS solutions, typically allocating around five to seven percent of the revenue to software spending. However, AI-driven workflow solutions and APIs now cover a broader range of functions, from reimagining marketing and training to transforming recruiting strategies. As a result, large enterprises have a much bigger budget for AI-related solutions, significantly expanding the addressable market.

Many AI-powered offerings are not just software replacements but a fusion of software and workflow optimization. In some cases, AI replaces traditional consulting roles. Businesses that integrate AI across different functions can operate far more efficiently. Major players like SAP, Oracle and Salesforce are embedding AI into their products, making adoption more manageable for many companies. But overall, AI-related budgets are increasing rapidly.

“Investing early in any innovation can be as risky as being wrong, so patience is key. Wait for specific trends to unfold. However, timing is critical—you can’t be too early, as the technology must be capable and widely adopted.”

This moment reminds me of the early internet boom from 1995 to 1996. There was a lot of experimentation; while it was unclear exactly where the most significant financial gains would come from, the momentum was undeniable. In sectors like financial services, the key questions revolve around confidentiality, security and data management.

For example, we are investing in StockTwits, a company with vast amounts of user-generated sentiment data that holds immense value—not just for training AI models but for providing real-time insights into market sentiment around stocks and themes. This represents another early-stage opportunity where visionary leaders can seize significant advantages.

Companies that embrace AI-driven efficiency will become more capital-efficient than they would have been just a few years ago. This enables faster growth with greater operating leverage. It is an exciting time, and those who act decisively stand to gain the most.

Keeping with AI’s Exponential Growth

Investing early in any innovation can be as risky as being wrong, so patience is key. Wait for specific trends to unfold. However, timing is critical—you can’t be too early, as the technology must be capable and widely adopted.

What is unusual about the AI tech stack is its unprecedented speed of evolution. In the past, businesses ran on mainframes with 3270 dumb terminals, then shifted to client-server models with graphical user interfaces like Windows 95. The internet followed, then mobile, but each technological shift remained relatively stable once adopted. While Moore’s Law improved computing power, the core functionality of many tools remained consistent—about 80 to 90 percent of what Excel offers today existed in Lotus 1-2-3. Roughly half could be done on VisiCalc in the mid-1980s.

AI, however, is evolving at an exponential rate. What seems impractical today can become mainstream within weeks. Unlike past innovations where knowledge remained relevant for years, in AI, what you learn today might be outdated in a matter of days.

Most technologies follow an S-curve of adoption, starting slowly, accelerating rapidly, and reaching maturity. However, AI is still in its steep incline phase, making it difficult to define its boundaries. The pace of change is unlike anything we have seen before, creating both challenges and extraordinary opportunities.

Key Advice for Future Leaders

These are fascinating times—an era of rapid change where new capabilities emerges at an unprecedented pace and the boundaries of what is possible are still undefined. As you build your business, don’t feel constrained by today’s limitations. If you believe a capability will evolve significantly shortly, plan with that in mind.

Equally important is understanding where the real value lies. Is it in the model, the aggregation or the application layer? Identifying this will help you position yourself effectively. Determine which parts of the tech stack will become commoditized and leverage them to maximize efficiency and differentiation.

Customers do not care about the process or the underlying technology—whether the chips, software or AI are involved— they just want faster solutions that are intuitive and seamless. AI can be a powerful enabler, but the focus should always remain on delivering a great end-user experience. It's the outcome, not the processes.