Is Throwing Time More Important Than Throwing Money at AI?

AI in FinTech: Is Time More Valuable Than Money?

In this episode of FinTech Focus TV, hosted by Toby Babb, the conversation with Jon Freedman, Director at Quant Fin, explores one of the most important questions shaping financial technology today: Are firms approaching artificial intelligence in the right way? At a time when AI dominates boardroom discussions across hedge funds, asset managers, and capital markets firms, this episode cuts through the noise to examine what is actually happening inside organisations.

Jon brings over 20 years of experience across some of the most recognised names in capital markets, including roles spanning hedge funds and major financial institutions. His journey from a graduate trainee at Morgan Stanley through to senior leadership roles across buy-side technology provides a unique lens into how financial institutions evolve, scale, and adopt new technologies. The discussion quickly moves beyond surface-level AI hype and into the practical realities of how firms are trying to implement AI, budget for it, and ultimately extract value from it.

FinTech Recruitment and the Rise of AI-Driven Technology Roles

One of the most striking themes throughout the episode is the rapid shift in hiring demand across financial technology. Jon highlights that there has already been a significant spike in technology roles requiring experience with AI-assisted development tools. Platforms such as ChatGPT and Claude are no longer experimental; they are now appearing directly in job specifications. This shift is reshaping the FinTech recruitment landscape, with firms actively seeking engineers and technologists who can work in an AI-enabled environment.

For a FinTech recruitment business like Harrington Starr, this insight is critical. The demand is not simply for software engineers, but for professionals who can operate with an “AI-enabled mindset.” This means understanding how to leverage tools to accelerate development, automate processes, and focus on higher-value tasks such as system architecture and scalability. As Jon explains, AI is not replacing engineers, but augmenting them. The most valuable professionals are those who can bridge the gap between technical capability and business outcomes.

AI Strategy in Capital Markets: Productivity vs Cost Reduction

A central debate in the episode revolves around whether firms are using AI to drive productivity or simply to reduce costs. Jon and Toby both acknowledge that many organisations instinctively lean towards cost reduction, looking at AI as a way to eliminate headcount or reduce engineering teams. However, Jon is clear that this mindset is limiting.

Instead, the focus should be on productivity. By using AI to remove repetitive, low-value tasks, teams can spend more time on activities that directly contribute to business growth. This includes developing trading strategies, improving system resilience, and enabling faster decision-making. In capital markets, where speed and precision are critical, this shift towards productivity can create a meaningful competitive advantage.

Jon emphasises that firms should analyse how their teams spend their time. Understanding the balance between value-added work and necessary but repetitive tasks is key to identifying where AI can have the greatest impact. This approach aligns closely with the evolving needs of FinTech recruitment, where clients are increasingly looking for professionals who can deliver measurable business value rather than simply execute technical tasks.

The Challenge of Budgeting for AI in Financial Services

One of the more complex issues discussed in the episode is how firms budget for AI. Unlike traditional technology investments, AI is difficult to quantify. Costs can vary significantly depending on usage, tools, and scale. Jon provides a practical example, noting that tools such as ChatGPT may cost around $20–$25 per user per month, while more advanced solutions like Claude can cost significantly more. However, these costs are only part of the equation.

The real challenge lies in predicting usage and measuring return on investment. CFOs typically require certainty around budgets, but AI does not fit neatly into this model. The technology is evolving rapidly, and new tools and capabilities are emerging every few months. This creates a moving target for organisations trying to plan their spending.

Jon highlights that this uncertainty is even greater than what firms experienced during the shift to cloud computing. With cloud, there was at least some predictability around usage patterns. With AI, both the tools and the use cases are constantly changing. This makes it difficult for firms to define a clear strategy, leading many to adopt a more experimental approach.

Experimentation in AI: A New Approach for FinTech Firms

Experimentation emerges as one of the most important themes in the conversation. Jon strongly advocates for creating space within organisations for teams to test AI tools and explore their potential. Rather than committing to large-scale investments upfront, firms should encourage employees to experiment and discover what works.

This approach is particularly relevant in the FinTech sector, where innovation and agility are key differentiators. Jon explains that when individuals are given access to AI tools and the freedom to experiment, they often uncover capabilities that exceed initial expectations. Within a short period, teams can identify practical use cases that deliver real value.

For FinTech recruitment, this highlights the importance of hiring individuals who are naturally curious and willing to experiment. The ability to adapt quickly and embrace new technologies is becoming a core requirement for technology roles in financial services. As AI continues to evolve, firms that foster a culture of experimentation will be better positioned to stay ahead of the competition.

AI Adoption in Hedge Funds and Buy-Side Technology

The discussion also provides valuable insight into how hedge funds and buy-side firms are approaching AI. Jon notes that much of the current activity is driven by fear of missing out. Senior leaders, including Chief Investment Officers and COOs, are increasingly aware of AI’s potential and do not want to be left behind.

However, this has led to a situation where firms are investing in AI without a clear understanding of how it will deliver value. Jon points out that while some large funds are making significant investments, the broader industry is still trying to determine the most effective use cases. This uncertainty is compounded by the rapid pace of technological change.

One key takeaway is that there is no single “winning” AI platform. The market is highly competitive, with tools such as ChatGPT, Claude, and others constantly evolving. Jon advises against committing too heavily to any one vendor, suggesting instead that firms should remain flexible and open to multiple solutions. This approach allows organisations to adapt as the technology landscape continues to change.

FinTech Startups and the Challenge of Selling into Financial Institutions

Beyond AI adoption, the episode explores the challenges faced by FinTech startups trying to sell into financial institutions. Jon highlights that while it has become easier than ever to build a product, gaining traction remains difficult. Financial institutions have stringent requirements around security, reliability, and compliance, making procurement processes complex and time-consuming.

This creates a significant barrier for startups, particularly those offering innovative solutions that require access to sensitive data. Jon explains that firms need to be confident that a product is robust and secure before integrating it into their systems. This level of scrutiny can be challenging for early-stage companies that are still developing their technology.

From a FinTech recruitment perspective, this underscores the importance of hiring professionals who understand both technology and the financial services domain. The ability to navigate procurement processes, build trust with clients, and deliver enterprise-grade solutions is critical for success in this space.

The Evolution of Software Engineering in Financial Technology

Another key theme in the episode is the evolving role of software engineers. While AI is accelerating development processes, Jon is clear that it is not replacing engineers. Instead, it is changing the nature of their work. Engineers are increasingly expected to focus on higher-level tasks, such as designing systems, understanding business requirements, and enabling teams to use AI effectively.

Jon highlights that the value of an engineer lies in their ability to interpret complex problems and translate them into practical solutions. While AI can generate code, it still requires human oversight to ensure quality, accuracy, and alignment with business objectives. This reinforces the need for experienced professionals who can combine technical expertise with domain knowledge.

For FinTech recruitment, this shift presents both challenges and opportunities. Firms need to identify candidates who can operate at this higher level, while also adapting to the use of AI tools. This requires a more nuanced approach to hiring, focusing on skills such as problem-solving, communication, and adaptability.

Risk Management, AI, and the Changing Global Landscape

The episode also touches on broader market dynamics, including geopolitical risks and their impact on financial institutions. Jon discusses the importance of business continuity planning, particularly in regions experiencing instability. This highlights the need for organisations to remain resilient and adaptable in an increasingly complex environment.

AI plays a role here as well, offering potential solutions for risk management and operational efficiency. However, as Jon notes, the key challenge is balancing innovation with risk. Firms must carefully consider their risk appetite when adopting new technologies, particularly when dealing with sensitive data and critical systems.

This balance is central to the future of FinTech, where innovation must be matched with robust governance and risk management practices. For recruitment firms, this creates demand for professionals who can navigate both areas, ensuring that organisations can innovate safely and effectively.

The Future of FinTech and AI-Driven Growth

As the conversation concludes, it becomes clear that AI represents both an opportunity and a challenge for the financial technology sector. While the potential for productivity gains is significant, firms must take a thoughtful and strategic approach to implementation. This includes focusing on experimentation, investing in the right talent, and maintaining flexibility in a rapidly changing landscape.

For Harrington Starr, the insights from this episode reinforce the importance of aligning recruitment strategies with market trends. As demand for AI-enabled talent continues to grow, firms must adapt their hiring processes to identify and attract the right candidates. This includes not only technical skills but also the ability to think strategically and drive innovation.

Ultimately, the episode highlights that success in FinTech will depend on how effectively organisations can integrate AI into their operations. Those that prioritise productivity, embrace experimentation, and invest in the right talent will be best positioned to thrive in this new era of financial technology.