The Quant Advantage is Disappearing

For decades, quantitative finance has carried a significant barrier to entry. Building sophisticated quantitative research and portfolio management capabilities has traditionally required deep pockets, specialist infrastructure and teams of highly paid researchers, mathematicians, engineers and computer scientists.

Agentic AI could fundamentally change that equation.

In this episode of FinTech Focus TV, Toby Babb is joined by Aditya Jha, CEO of Itoflow, to explore what happens when quantitative technology that was once concentrated within leading hedge funds and financial institutions becomes accessible to a much wider market.

Drawing on his experience across both the sell side and buy side, including roles at J.P. Morgan, Royal Bank of Canada and Tower Research, Aditya explains why he believes much of quantitative research is more scientific and repeatable than the industry sometimes portrays it to be. With AI agents increasingly able to handle that repeatable work, the competitive advantage within quantitative trading could begin to move elsewhere.

The conversation explores agentic AI, quantitative research, portfolio management, trading technology and the future of talent in financial services. It also raises a bigger question for the industry. If access to sophisticated technology is no longer the differentiator it once was, where will the next quant advantage come from?

Agentic AI is Making Quantitative Research more Accessible

Itoflow was created around the idea of bringing the science of quantitative research and portfolio management to a much larger group of users.

Aditya explains that these capabilities have historically been limited largely to leading hedge funds and quantitative funds. Building a quantitative strategy can require significant investment before a firm even reaches the point of testing its first ideas. Infrastructure needs to be built, data needs to be processed and teams of specialist professionals need to be hired.

But Aditya believes the economics are beginning to change.

From his experience in quantitative finance, he argues that around 90% of the process is relatively standard and scientific. Tasks such as data cleaning, backtesting and portfolio optimisation are important, but they are not necessarily where a firm’s unique trading insight sits.

Agentic AI creates the possibility of teaching those established processes to agents, allowing the human user to concentrate on what Aditya describes as the more artistic part of the process: deciding what to trade and where the real opportunity exists.

That distinction sits at the centre of the episode. AI in finance is not simply about completing the same processes slightly faster. It could change which capabilities firms need to build themselves and which areas actually create competitive differentiation.

Why the Quant Trading Advantage Could be Disappearing

One of the most striking ideas discussed by Toby and Aditya is what happens to competitive advantage when sophisticated quantitative infrastructure becomes easier and cheaper to access.

Traditionally, a multi-billion-dollar fund has been able to hire large teams of programmers, mathematicians and quantitative researchers to develop proprietary systems. The ability to fund that infrastructure could itself become an advantage.

Aditya believes this is changing.

If technology can handle more of the coding, infrastructure and quantitative processes required to support a strategy, simply having the resources to employ more people to write more code may become less valuable as a differentiator.

As Aditya puts it during the conversation, if firms want to trade better and make money in the markets, they will increasingly need to “trade better and not write better code.”

This does not mean technology becomes less important. In many ways, the opposite is true. But the value could move away from repeatedly building the underlying mechanics and towards how effectively firms use that technology to execute their ideas.

For quantitative trading, that could represent a major shift. The quant advantage may increasingly come from the quality of an idea rather than the size of the team capable of turning it into code.

AI in Finance Could Level the Playing Field

The idea of democratising quantitative technology runs throughout the discussion.

Aditya highlights an inefficiency he repeatedly encountered within financial services. A portfolio manager or quant researcher may spend years building data-cleaning pipelines, backtesting tools and portfolio optimisation infrastructure at one organisation. When they move elsewhere, they cannot simply take that infrastructure with them, often for entirely understandable reasons.

The result is that similar foundations are rebuilt again and again.

Itoflow’s approach is based on removing more of that repetitive work. Rather than asking every participant to recreate the same underlying capabilities, the platform is designed to handle areas such as data cleaning, risk models and the mathematical and computing complexity surrounding quantitative research.

The user can then concentrate on their trading ideas and investment decisions.

This has implications beyond traditional quantitative funds. Aditya discusses discretionary investors who may previously have viewed quantitative techniques as inaccessible or irrelevant to the way they operate. If a platform can handle more of the mathematical complexity, those users can potentially access quantitative capabilities without having to become quantitative researchers themselves.

For the wider financial technology market, this represents an important shift. AI portfolio management and agentic technology could make tools available to firms and individuals that previously lacked the headcount, infrastructure or specialist knowledge needed to build them.

AI Agents Could Change Productivity in Portfolio Management

The discussion also explores the distinction between efficiency and productivity.

Aditya gives the example of a portfolio manager being approached by a data vendor with a new dataset and claims that it contains useful, uncorrelated alpha. A portfolio manager is unlikely simply to accept that claim. They need to test the data independently.

Historically, that process can take weeks or months. It may require a quant to onboard the dataset, test it and determine whether it provides something genuinely useful.

Agentic AI could compress that process.

If new vendors, signals and ideas can be tested more quickly, portfolio managers can experiment at greater speed. The same team could potentially evaluate more opportunities without increasing headcount at the same rate.

This is where the lines between efficiency, productivity and scalability begin to blur. AI agents are not simply about replacing individual tasks. They can increase the volume of work a team is capable of completing and reduce the friction between an idea and the ability to test it.

For hedge funds and other investment businesses, that could alter how quantitative research teams are structured and how organisations think about technology investment.

Why Building your own AI in Trading is Not Always the Answer

As interest in AI in trading has grown, so has the temptation for financial institutions to build their own solutions.

Aditya acknowledges why this is appealing. Modern AI tools can create an initial version of a product remarkably quickly. A user can describe what they want and see something functional appear, creating the impression that building an internal agentic system could be straightforward.

The challenge comes afterwards.

Aditya describes how initial systems can begin to reveal problems as they encounter the complexity of real financial markets. Corporate actions such as dividends or mergers may not have been accounted for. Fixing one issue leads to another, while the underlying models and technology continue to evolve.

A team that originally wanted to improve its trading process can suddenly find itself effectively becoming an AI research company.

Itoflow’s argument is that firms should understand where their genuine intellectual property sits. If the trading idea is the source of the edge, the firm should be able to focus on that rather than maintaining every part of the AI infrastructure underneath it.

That distinction between proprietary investment knowledge and supporting technology is likely to become increasingly important as AI adoption accelerates across financial services.

AI Governance and Security Cannot be an Afterthought

Greater accessibility does not remove the need for control.

Throughout the episode, Toby and Aditya discuss some of the concerns financial institutions have when adopting AI, particularly around security, intellectual property, compliance and governance.

These concerns become particularly significant when technology is interacting with investment processes and potentially sensitive trading information.

Aditya explains that Itoflow offers private deployments that can be customised around a client’s requirements without feeding those customisations back into the base platform. The business also supports different approaches depending on the level of control organisations require over their information and models.

Traceability is another major part of the discussion.

Aditya explains that Itoflow built its own harness to maintain fine-grained control over what its large language models are doing. Tool calls and numbers used throughout a process can be tracked so that a risk or compliance team can understand how a particular decision was reached.

As AI agents become more capable, that auditability could become increasingly important.

Financial services firms cannot simply adopt AI because it can produce an answer. They need to understand how systems are operating, how information is being handled and how decisions can be explained when challenged.

Financial Technology is Moving Faster than Traditional Finance

The conversation moves beyond quantitative research into a broader discussion about the culture of technology and financial services.

Aditya contrasts the open-source culture common within major technology companies with the secrecy traditionally associated with finance.

Technology companies regularly make tools, frameworks and even programming languages available for others to build upon. Financial services has historically operated differently, with information, infrastructure and knowledge frequently remaining behind organisational walls.

Aditya believes that lack of collaboration can slow progress.

Itoflow has therefore taken a more open approach to sharing what its team learns while working with emerging AI technology. As the company makes mistakes and discovers new approaches, it aims to share those lessons so others do not have to repeat exactly the same process.

This matters because of the speed at which AI is developing.

Models and tools can advance significantly within a matter of months. Aditya argues that the secrecy and slower pace associated with traditional finance are increasingly difficult to reconcile with an AI-native world where knowledge and technology evolve extremely quickly.

For financial institutions, adopting AI may therefore require more than purchasing new technology. It could demand a cultural change in how teams experiment, collaborate and exchange ideas.

FinTech recruitment is changing alongside AI adoption

That cultural shift also has significant implications for FinTech recruitment and financial technology talent.

Toby and Aditya discuss how the relationship between the technology industry and financial services has changed over time. Finance has historically been capable of attracting some of the world’s strongest engineers and technologists, supported by high compensation and the opportunity to solve difficult problems.

But money is not always enough.

Aditya says some talented researchers and engineers are increasingly attracted to businesses where they can work directly with cutting-edge AI technology, exchange ideas quickly and have a visible impact on what is being built.

He describes candidates willing to accept significantly less compensation for the opportunity to work with newer technology rather than remain within a larger organisation where access to the latest AI tools can be slower.

For FinTech recruitment, this is a significant point.

Competition for AI, quantitative research and software engineering talent is not based purely on salary. The technology people are able to use, the problems they can solve, the speed at which a business operates and the influence they can have over a product can all shape an employer’s ability to attract highly sought-after professionals.

As financial institutions continue investing in AI in finance, their talent strategies may need to evolve alongside their technology strategies.

The Battle for Quantitative Research and AI Talent

Itoflow itself is looking to build what Aditya describes as a “rockstar team”, with a focus on hiring researchers and engineers capable of working at the forefront of this technology.

But those people have options.

Established banks, hedge funds, quantitative trading firms and global technology businesses are all competing for many of the same skill sets. AI-native FinTech businesses therefore need to offer something different.

For Itoflow, Aditya believes part of that attraction comes from the ability to communicate openly, share ideas and work directly with cutting-edge technology.

The conversation highlights a wider challenge for financial technology employers. The best talent increasingly has choices across industries, and professionals with skills spanning AI, software engineering and quantitative research may not automatically see traditional finance as their most attractive destination.

Businesses that want to attract those individuals need to think beyond compensation. They need to consider their technology environment, culture, pace of innovation and the opportunity they give employees to make an impact.

For Harrington Starr and the wider FinTech recruitment market, these changing expectations are becoming increasingly important as businesses compete for specialist technology talent.

The Future of Quantitative Trading and Portfolio Management

Itoflow’s long-term ambition is broad.

Aditya says the business wants to take quantitative technology beyond the relatively small group of organisations that have historically been able to access it and put it safely and securely into the hands of a much larger audience, from institutions through to retail users.

He points to areas of the investment industry where scientific techniques developed over the past two decades are still not being used as widely as they could be.

Agentic AI could help close that gap.

If the mathematical and computing complexity surrounding quantitative research becomes easier to access, the barrier separating quantitative and discretionary approaches may also become less rigid. More investors could use sophisticated quantitative techniques without having to build entire teams and technology stacks around them.

That does not mean every participant suddenly gains the same investment ability. Instead, it changes where they need to differentiate.

The infrastructure becomes more accessible. The idea still matters.

The Quant Advantage is Disappearing, but the Human Edge Remains

Perhaps the most important takeaway from the episode is that AI does not necessarily remove the need for expertise. It changes where that expertise delivers the most value.

Aditya’s argument is not that technology can magically create a successful investment strategy. Itoflow’s platform is deliberately unopinionated about what users should trade and how they should trade it.

The technology can remove grunt work, handle mathematical complexity, test ideas and help users move faster. But the trading idea still needs to come from somewhere.

That could ultimately be the biggest change agentic AI brings to quantitative trading.

For years, access to infrastructure, programmers, researchers and computing resources helped separate the largest quantitative firms from the rest of the market. As those capabilities become easier to access, simply possessing the technology may no longer constitute the same advantage.

The competitive battleground shifts towards ideas, judgement, experimentation and the ability to use technology effectively.

For financial institutions, hedge funds and FinTech businesses, that creates both an opportunity and a challenge. The tools are becoming more powerful, the barriers are beginning to fall and the speed of innovation is increasing.

But if everyone eventually gains access to increasingly sophisticated quantitative technology, firms will need to become much clearer about where their real edge comes from.

Watch the full episode of FinTech Focus TV as Toby Babb speaks with Aditya Jha, CEO of Itoflow, about agentic AI, quantitative research, portfolio management, financial technology, AI governance and the changing battle for technology talent in finance.