In the fast-evolving world of technology, simplicity often outperforms complexity. This is a lesson that applies as much to software engineering and AI as it does to the design of regulatory and legal technology systems for the investment fund industry.
At Zeidler Group, the approach has been clear: start with simple, effective solutions and scale complexity only when absolutely necessary.
Zeidler recently delved into AI why clarity is vital.
One idea that reflects Zeidler’s philosophy comes from Anthropic’s discussion of agentic AI systems: “Start with the simplest solution possible and only increase complexity when needed. Success isn’t about building the most sophisticated system; it’s about building the right one.”
The words echo a recurring truth in software architecture—sometimes a modular monolith will deliver better results than a sprawling microservices structure. While distributed systems offer flexibility, they also introduce more latency, more costs, and more points of failure. As with AI prompts, the simplest approach can often be enough.
The principle is particularly relevant in evaluating AI systems. Unlike earlier machine learning models, which produced straightforward answers such as “spam” or “not spam”, large language models (LLMs) generate open-ended responses. These are varied, often subjective, and difficult to evaluate at scale without expert judgment. Some researchers suggest using AI to evaluate other AI models, but this remains an unsettled question, Zeidler said. What is clear, however, is that evaluation demands clarity and strong frameworks to avoid unnecessary layers of complexity.
The misconception that larger models are automatically better is another challenge. Models with billions of parameters may appear powerful, but their effectiveness depends on the quality and diversity of the data they are trained on. Without this, scale becomes an illusion of strength rather than an enabler of knowledge.
This philosophy has influenced many practitioners, including those exploring Chip Huyen’s AI Engineering – Building Applications with Foundation Models. The appeal lies not in theory but in practical design patterns that bring AI into real-world use cases. This is also how Zeidler Group approaches its work—balancing technical capability with legal and regulatory expertise. Software engineers, lawyers, and compliance experts collaborate to create solutions that fund managers can trust to meet the demands of global financial regulation.
In legal services, software architecture, or AI development, the lesson is the same: start simple, prioritise clarity, and introduce complexity only when necessary. At Zeidler Group, these principles are embedded in how legaltech solutions are developed—helping asset managers and fund managers navigate regulatory challenges with confidence.
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