AI Transformation in Financial Technology: A Structural Shift in Trading Software
The Scale of the Shift
Something irreversible happened in the last few weeks. Not the emergence of a new AI model, not another research paper, not a demo. The stock market itself delivered a verdict on the future of software.
Monday is down by nearly 50% in just one month. At the same time, ION’s bonds have lost 5 to 8 per cent over different maturities. The big legacy fintechs are under pressure, not because they have built bad products, but because the world has suddenly understood, viscerally, that the way software is built is about to change beyond recognition, and those businesses are structurally constrained in their ability to change with it.
We are at one of those rare inflection points where the disruption isn’t approaching. It’s already here. And at Quod Financial, we’ve made a clear-eyed decision: we are not waiting for the wave to crash. We are learning to surf it.
An Island of Geniuses
Dario Amodei, CEO of Anthropic, recently described a vision of AI compressing decades of scientific and economic progress into just a few years. Systems capable of performing, in his words, the work of a country full of Nobel Prize winners operating in concert. He called it an “island of geniuses”: not human replacement, but human amplification at a scale we have never seen before.
“Imagine having access to a brilliant friend who happens to have the knowledge of a doctor, lawyer, financial advisor, and expert in whatever you need.”
— Dario Amodei, CEO, Anthropic
This is not science fiction. The engineering implications are playing out right now in codebases around the world. AI coding tools are moving from novelty to infrastructure. The question for any software business is not “should we use AI?”. The question is “are we restructuring everything around it, or are we just bolting it on?”
Those are two entirely different things. And only one of them constitutes survival.
Key Indicators
- $2T+ wiped from global software market valuations in recent weeks
- 3–5× product output multiplier we are targeting through AI-driven engineering
- 18 months window to transform before the market hardens around new AI-native players – based on software adoption cycles
- 20+ years depth of domain knowledge and production-grade platform we are building on
Why Large (lethargic) Incumbents Cannot Move
The BlackBerry Moment
In a recent conversation with a senior technology leader at one of the world’s largest investment banks, the comparison was made without prompting: the established vendors in this space are facing their BlackBerry moment. They are telling themselves their clients need the keyboard. They are telling themselves their clients need the security, the reliability, the known quantity. And they are right. Until suddenly, they are not. Until the competition has so thoroughly shifted the game that the old strengths become irrelevant, and the inertia becomes fatal.
Fidessa became dominant because GL Trade (FIS) did not adapt to the changing trading world and then regulatory demands of Reg NMS. Broadridge built its franchise on FIX network economics. These were genuine competitive moats, built on regulatory events and infrastructure lock-in. But the competitive moat today is not a regulation, not a network effect, and not a years-long implementation project. It is the speed and quality with which you can build software. And that is changing faster than any regulatory cycle in financial markets history.
“None of the existing vendors have the capability to make the change organizationally. None of them have the motivation. And all of them are sitting on entrenched franchises that are broken by this type of change.”
— T1 Institutional Bank — February 2026
This isn’t arrogance. It’s arithmetic. A vendor with thousands of engineers and hundreds of clients running on a monolithic codebase cannot restructure itself around AI-driven development without destroying the very thing that sustains its revenue. We can. And that asymmetry is the most valuable thing we possess right now.
Our Transformation
Not a Feature. A Foundation.
The critical distinction, one we return to in every leadership discussion, is between using AI and engineering around AI. These sound similar. They are not.
Today, using AI means giving developers access to Copilot, running some code completions, maybe generating a few test cases. It is a productivity tool. A useful one. But it does not change the structure of the business. It does not change how stories are written, how quality is validated, how products are shipped, or how engineers think about their roles.
Engineering around AI means making AI the core of how we develop software, and then rebuilding every adjacent process to match the throughput it creates. This is what we are doing.
What AI-First Engineering Looks Like at Quod Financial
Rather than large cross-language agile teams organized by technology stack, we are moving toward small units of domain-expert engineers whose primary role is precision: defining exactly what needs to be built, in exact operational and functional terms, so that AI systems can generate the code across the different technology stack.
The engineer’s value shifts from writing code to encoding knowledge: the kind of deep system understanding that no AI can yet replicate, and that took our team two decades to accumulate.
The insight that anchors this transition is straightforward but profound: the barrier to our industry has never primarily been about writing code. It has been about understanding what to write. The functional debates, the edge case discussions, the domain-specific decisions about how an order management system should behave at 5 am when two distant and distinct markets open simultaneously — that is where the real complexity lives.
That knowledge is inside our engineers. AI amplifies it. It does not replace it.
Every New Benefit Becomes the Next Bottleneck
AI does not simply make you faster at what you already do. It reveals every constraint that was previously hidden by the slowness of ‘manual’ development.
If AI can generate substantially more code, but your QA process is manual, you have not accelerated delivery. You have created a larger queue in front of a human bottleneck.
If AI generates a higher volume of stories being turned into working implementations, but your code review process is unchanged, quality will degrade.
If the throughput of your engineering team doubles, but product management cannot absorb and articulate stories at that velocity, the gain is lost.
This is why our transformation is not an engineering project. It is a holistic company effort.
If you don’t anticipate these bottlenecks, you will only choke step-by-step. In order to address these we have invested into all aspects of our product management and engineering. For instance, we are:
- Building automated test generation directly into CI/CD pipelines,
- Formalizing how product managers encode tacit domain knowledge into stories,
- Restructuring team composition around domain knowledge rather than language and technology specialization.
“The goal is not to work harder. It is to eliminate every human friction point and then rebuild our processes to absorb that new velocity without sacrificing quality. This could double, or more than double, our productivity”
— Ben Ernest-Jones, CTO
The Structural Advantage
Twenty Years of the Right Foundation
There is a version of this moment that is terrifying for a 20-year-old software company, and adaptation is not just about solving problems relating to engineering or product management.
Our platform was designed from first principles for multi-asset, multi-market, multi-region operation. It was built for automation before automation was fashionable. It runs in microservices. It exposes APIs and middleware interfaces. It has no client-specific development. Every enhancement becomes a product.
Critically, we are owner-managed, self-funded, with no external investor pressure driving short-term decision-making. We have no debt weight around our neck, which would drown us in the storm. We are not part of a merry-go-round of being bought and sold every 5 years. That combination means we can make the structural choice to transform now, without a debt holder demanding we protect existing revenue streams while the window closes.
We have 23 years of production knowledge embedded in our engineers. We have clients in the most demanding corners of the world. We have a codebase designed to be extended.
The only thing we needed was that clients cease to consider their three core trading systems as ‘impossible to change’. We believe that the accelerator has arrived. It also means that status quo is now the most dangerous posture. It is a technology risk, a vendor risk.
A Two-to-Three Year Protection Window
The financial technology sector has a specific buffer that most software verticals do not. The operational complexity of maintaining a live trading environment, combined with regulatory and compliance requirements, means that AI-native challengers building from scratch face a meaningful barrier.
But that window is two to three years, not indefinitely.
The businesses that use this period to transform will own the next decade. The businesses that treat it as comfort will find it becomes a trap.
Sitting on a time bomb
Never a comfortable position to be sitting on a time bomb.
Our industry has seen massive consolidation under Private Equity ownership, a model that relies on high leverage supported by stable revenue streams and high EBITDA. However, in a new world where clients are less “sticky”, largely because they no longer view software migration as a death sentence, the landscape becomes far more unstable for incumbents. As private credit and debt markets in general become more punishing, highly leveraged vendors risk falling into a debt trap, which can quickly accelerate into a full-blown death spiral.
What We Are Building Toward
The vision is specific: a small number of highly knowledgeable engineering units, organised around business domains rather than technology stacks, running AI engineering against a validated, automated testing framework, and shipping three to five times the product volume we can ship today.
New asset classes. New connectivity. New markets. Functionality that our clients have been requesting for years, delivered at a pace that changes the competitive dynamic entirely. Our engineering process becomes a partnership with every client where project capacity is no longer about our development backlog but the speed of ‘prompt’.
We are not waiting to understand whether this works. We are in the process of making it work. Advocate programs are running. Boot camps are underway. We are measuring, iterating, and building the institutional knowledge of how to engineer this way.
Mistakes will happen. They will inform the next iteration. Early wins are already emerging.
We are not waiting for the tooling to become perfect.
We are not protecting the current way of working simply because it is familiar.
And we are not assuming the disruption will move slowly enough for us to respond later.
The disruption has already started.
The only question is whether you are changing fast enough.
Medan Gabbay, Co-CEO, Quod Financial
Sources include recent reporting from the Financial Times, Bloomberg, WSJ, and S&P Market Intelligence.
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About Quod Financial
Quod Financial delivers multi-asset trading technology for banks, brokers, and asset managers, supporting the full trading lifecycle across global electronic markets. The firm’s product suite includes high-performance OMS and EMS, Smart Order Routing (SOR), Algorithmic Trading, liquidity internalization, and direct market connectivity.
At its core is Unity, Quod’s modular, cross-asset architecture that normalizes data and trading workflows. Unity enables institutions to automate and control complex operations, scale efficiently across asset classes, and adapt to evolving market structure and regulatory requirements using data-driven and AI-enhanced capabilities. For more information, visit: www.quodfinancial.com