What Industry Leaders Told Us At TradingTech 2026

What Will Define the Next Generation of Trading Technology?

The technology underpinning global financial markets is evolving rapidly. Artificial intelligence, cloud infrastructure, automation, changing market structures and the growing demand for always-on trading are creating new opportunities for firms across capital markets, while also forcing them to reconsider the technology, skills and strategies they will need for the future.

Recorded live at TradingTech Summit NYC 2026, this episode of FinTech Focus TV explores what the next generation of trading technology could look like and, importantly, what financial institutions may currently be underestimating as they prepare for it.

Host Kaitlin Edwards is joined by Diane Levich, VP of Marketing at HPR (Hyannis Port Research); Joshua Carroll, CTO at Solace; Martyn Snook, Senior Account Executive at Genesis Global; and Michael Riebling, Head of Solution Sales at Adaptive. Across their conversations, they explore the evolution of capital markets infrastructure, artificial intelligence, cloud adoption, hardware, data, 24/7 trading and the skills that could become increasingly valuable across financial technology.

For Harrington Starr, a specialist FinTech recruitment and financial technology recruitment business, these conversations are particularly relevant. As the infrastructure behind capital markets changes, so too do the capabilities firms require from their technology teams. Understanding where trading technology is heading provides important context for businesses building teams and professionals developing careers across capital markets technology.

The Future of Capital Markets Infrastructure and Trading Technology

Diane Levich begins by discussing HPR and its position within capital markets infrastructure. HPR has been operating for more than 15 years and focuses on high-performance trading through software- and hardware-based technology spanning market access, market data and matching engine technology. Its clients cover a broad spectrum of organisations seeking low-latency and ultra-low-latency solutions, including tier-one banks, proprietary trading firms, market makers and exchanges.

When looking towards the next generation of infrastructure, Diane highlights the evolution from software-based solutions towards hardware. She points to technologies such as routers, switches and firewalls as examples of this wider progression and explains how HPR has followed this direction since its inception.

The company's first flagship product, Riskbot, emerged following regulation associated with the flash crash, while its subsequent technologies have incorporated both software and hardware components according to the performance requirements of different organisations. Diane believes this movement towards hardware will continue to play a significant role in the next generation of capital markets infrastructure.

She also highlights HPR's work in matching engine technology, explaining that the company developed matching engine technology operating fully in hardware rather than simply using hardware acceleration for part of the process. It is an example of how performance requirements continue to influence the architecture behind modern financial markets.

Cloud Technology and the Next Generation of Trading Infrastructure

For Joshua Carroll, the evolution of cloud technology in capital markets is another major part of the infrastructure conversation.

Joshua explains that Solace grew up in the capital markets industry with appliance-based technology used for areas including market data and post-trade. Over time, that hardware appliance evolved into software capable of running in different environments and subsequently into a multi-tenancy SaaS cloud offering.

This evolution provides the backdrop to Joshua's view of what comes next. Much of today's capital markets infrastructure still exists physically, but he expects the lines between private colocation data centres and cloud environments to continue blurring.

The challenge for financial institutions is therefore not simply deciding whether they should use cloud technology. It is about retaining the ability to operate across different environments as technology changes.

Joshua uses the example of an Australian bank that had stored its data with one cloud provider before selecting another provider for AI capabilities. Moving all that data simply to access a particular technology is not necessarily practical. Instead, he argues that firms need the agility to adopt and use different systems without becoming overly dependent on one environment.

In his view, being multi-cloud by design, able to operate beyond individual data centres and capable of moving quickly as technology develops will become increasingly important. With the AI landscape itself still changing, organisations cannot necessarily know which providers or technologies will ultimately dominate.

That creates an important challenge for capital markets technology teams. Infrastructure needs to provide stability and performance while simultaneously giving businesses enough flexibility to respond to technologies that may not yet exist.

AI in Capital Markets Must Move Beyond the Prototype

Artificial intelligence inevitably plays a major role in the episode, but the discussion moves beyond whether financial institutions should simply be experimenting with AI.

For Martyn Snook, one of the key challenges is turning experimentation into technology that can genuinely operate within a financial institution.

Martyn describes Genesis Global as providing an AI-ready architecture designed to help organisations build applications while supporting requirements including scalability, resiliency, compliance, regulation and auditability. Genesis works across capital markets, from the front office through to the middle and back office, with clients ranging from tier-one banks to buy-side firms. Its work includes workflows, legacy system replacement and frameworks that can enable AI to interact with existing capital markets systems.

Martyn points to the growth of "vibe coding", with people using AI tools to build applications quickly. The problem is that creating an impressive prototype does not necessarily mean that application can enter production.

Without the necessary architecture behind an application, firms can encounter issues surrounding auditability, scalability, compliance and regulatory requirements. The workflow may function and the prototype may look promising, but it still cannot necessarily be deployed within the realities of a regulated capital markets environment.

This distinction between experimentation and production is becoming increasingly important for AI in capital markets. Speed of development matters, but so does the infrastructure surrounding what is being developed.

The True Cost of Artificial Intelligence in Financial Technology

Joshua raises another issue that he believes organisations are underestimating: the cost of AI.

Artificial intelligence has quickly become one of the dominant areas of technology investment, but Joshua questions whether every AI use case being pursued has been properly assessed against the business problem it is intended to solve.

He argues that firms need to consider whether AI is actually necessary rather than treating every problem as something requiring an AI solution. He also highlights the wider costs associated with AI infrastructure and the significant pace of development across large language models and technology providers.

This pace of change creates another challenge: people have to keep up.

Joshua believes organisational change management and educating employees will be as important as selecting technology vendors. Businesses can invest in sophisticated tools, but their ability to create value from them will depend partly on whether their people understand how, where and why those tools should be used.

Later in the conversation, he gives an example from his own experience where many proposed AI use cases were essentially automation projects that could have been completed without AI at significantly lower cost. His point is not that organisations should avoid AI, but that they need to identify where it provides genuine value.

For financial technology recruitment, this creates an interesting shift. Technical capability remains vital, but firms increasingly need professionals capable of understanding the commercial and operational problem behind the technology rather than deploying AI for its own sake.

24/7 Trading Could Reshape Trading Platforms and Market Infrastructure

Michael Riebling highlights another significant change facing trading technology: the transition towards more continuous markets.

Adaptive focuses on building bespoke trading solutions for organisations that want technology to become a source of differentiation. Michael explains that these clients value ownership of intellectual property and access to source code, with Adaptive working on projects ranging from trading platforms to bespoke exchanges. Recent work discussed in the episode includes launching a prediction market exchange as well as working on a global order management system replacement for a tier-one bank.

Looking at the next generation of capital markets infrastructure, Michael believes 24/7 operation will be a defining consideration.

Many large global market operators currently provide extensive exchange connectivity and infrastructure but remain built around markets operating 24 hours a day, five days a week. Moving towards 24/6 or 24/7 creates significant operational challenges.

Michael says Adaptive is seeing demand from clients concerned that existing technology providers may not be able to support that transition quickly enough. For those organisations, infrastructure capable of adapting to longer trading hours becomes not merely a technical consideration but a form of protection against providers that cannot evolve at the required speed.

The implications for trading technology recruitment are significant. An always-on market changes expectations around platform resilience, support, infrastructure and engineering. As trading hours expand, the systems and people supporting those environments will need to evolve with them.

Cloud Adoption and AI Strategy Are Becoming Business Priorities

Michael also argues that AI strategy has moved beyond being conceptual.

With organisations across financial services now trying to determine how they will use artificial intelligence, he believes firms need a credible answer when asked about their AI strategy. At the same time, cloud adoption remains an unresolved issue across parts of the market.

Despite years of discussion around moving mission-critical trading applications into cloud environments, Michael notes that many tier-one banks remain apprehensive about doing so. He nevertheless expects continued movement in this area.

Together, these trends demonstrate the complexity facing modern capital markets technology teams. Firms are simultaneously being asked to modernise legacy systems, explore artificial intelligence, consider cloud migration and prepare infrastructure for changing market structures.

The challenge is not simply adopting each technology individually. It is creating a coherent technology strategy that allows these different components to work together.

FinTech Recruitment and the Growing Challenge of Technology Talent

Technology may dominate much of the conversation, but Diane highlights an equally important issue: talent.

When Kaitlin asks what firms may currently be underestimating, Diane points towards the challenge of building technology in-house and finding the people required to do it successfully.

She explains that banks, exchanges and buy-side organisations can encounter significant talent gaps in highly competitive markets. Recruiting strong people is difficult, but retaining that talent and ensuring those individuals are focused on the right work adds another layer of complexity.

For organisations with significant technology estates, maintaining internal systems also requires ongoing resources as exchanges change, regulation evolves and trends such as 24/7 trading create additional demands.

Diane argues that firms should think carefully about where internal technology resources generate the greatest value and where specialist vendors could potentially take responsibility instead. Rather than dedicating valuable internal teams to continually building and maintaining infrastructure, organisations can consider whether those people would be better focused on the strategies and capabilities that differentiate the business.

This is directly relevant to FinTech recruitment, capital markets recruitment and financial technology hiring. As firms decide what should be built internally and what should be supported externally, their hiring priorities will change alongside their technology strategies.

Data Skills Will Remain Critical to AI and Capital Markets Technology

For Martyn, one of the most valuable areas of expertise over the coming years will be architecture and data.

He argues there remains an important role for architects who can ensure applications work together effectively. At the same time, he describes data as "king", particularly as organisations seek to apply artificial intelligence across increasingly large datasets.

The challenge is that simply possessing data does not mean an organisation knows how to use it.

Martyn notes that data is frequently distributed across disparate systems. Some organisations have begun creating centralised workspaces and data warehouses, but bringing the information into one place does not automatically solve the problem. Businesses still need to understand how their data models fit together and how that information should be analysed.

Without that understanding, AI models cannot automatically create meaningful results.

This reinforces why data recruitment in FinTech and capital markets continues to be closely connected to wider technology transformation. As AI becomes embedded in more workflows and platforms, expertise in data architecture, analytics, engineering and governance becomes part of the foundation required to make those technologies effective.

TradFi, DeFi and Digital Assets Are Moving Closer Together

Michael identifies another capability that he expects to become increasingly important: understanding the convergence between traditional finance and decentralised finance.

He argues that professionals who do not understand the complexities of combining crypto trading with traditional markets risk falling behind as those worlds move closer together. In his view, the conversation around digital assets is increasingly becoming a question of when integration happens rather than whether it happens.

This convergence adds another dimension to the skills required across trading technology and financial technology. Professionals increasingly need to understand established financial market structures while remaining informed about emerging technologies and asset classes.

The challenge is achieving that without simply chasing every new trend.

Diane makes a similar point when discussing the skills and mindsets firms will need. Although AI is the dominant technology conversation and prediction markets are receiving considerable attention, she stresses the importance of specialisation, prioritisation and genuine expertise.

Rather than constantly chasing the latest "shiny object", firms need to determine where they can establish a meaningful competitive niche and build expertise around it. Even when AI is being used to augment work, organisations still need knowledgeable people capable of assessing whether the outputs generated by AI tools and large language models are actually reliable and useful.

Continuous Learning Could Become One of the Most Valuable Skills in FinTech

Joshua's answer to the question of future skills is less about a particular programming language or technology platform and more about mindset.

He emphasises the importance of continuing to learn.

Drawing on his experience managing large teams, Joshua recalls situations where training budgets went unused despite employees having access to development opportunities. His wider point is that professionals cannot afford to stand still when technology is evolving so quickly.

As he puts it in the episode, the risk is not simply that AI replaces a job. It is that somebody who has continued educating themselves becomes better positioned to do that job.

He encourages professionals to remain current, experiment with new technologies and maintain a willingness to learn even when they are not immediately experts.

For those considering careers in FinTech, capital markets technology, software engineering, cloud, data, AI or trading technology, that mindset could become increasingly valuable. Specific technologies will continue to change. The ability to adapt alongside them may prove more durable.

What Will Define the Future of Trading Technology?

Across all four conversations, one theme repeatedly emerges: the next generation of trading technology will not be defined by one innovation alone.

Artificial intelligence is undoubtedly reshaping the conversation, but its success depends on architecture, data, governance, cost and the ability to move applications beyond prototypes. Cloud infrastructure continues to evolve, but firms need flexibility rather than dependence on a single environment. Markets may increasingly move towards 24/7 operation, creating new infrastructure and operational requirements. Hardware continues to influence high-performance trading. TradFi and DeFi are converging. And behind all of these developments sits the need for people capable of understanding, building and managing increasingly complex technology environments.

Whether firms are hiring specialists in AI, data, cloud infrastructure, software engineering, trading systems or other areas of financial technology, the ability to build teams that can adapt to change will become increasingly important.

The future of trading technology will therefore be about more than adopting the newest tool. It will require firms to make deliberate decisions about infrastructure, identify where AI genuinely creates value, understand their data, prepare for changing market structures and invest in people who are willing to continue learning.