More British Please: The Struggle with Untranslatables

"I cannot define it, but I know it when I see it." This is something we're all distinctly familiar with, and it's becoming increasingly relevant in the age of AI. As various models approach (and often surpass) human capabilities in numerous domains, it's the differences between AI and humans, not their similarities, that draw our greatest frustration. 

"More British please" is a prompt I find myself making constantly when using AI as a tool in my daily work. As a professional doing outreach (cold, warm, and everything in between), tone and subtlety are what I look for. Asking for investment, building rapport, getting replies, and acquiring customers all depend on unwritten textual and social rules that cannot be expressed in any concise or quantifiable manner. 

The American-heavy nature of AI development and training data means modification via prompts is increasingly required but also increasingly difficult. The challenge lies not in the AI itself but in the user's desired yet nebulous output. I can describe what "British tone" means based on my own background and experience, but is that the same as what the AI understands? And is that the same as how everyone else interprets it? 

This goes far beyond spelling and grammar. Much of my work has been UK-based, and through in-person meetings and phone calls, you develop a feel for what works (and more importantly, what doesn't). In a culture like the UK, which values subtlety generally and directness only occasionally, these nuances matter enormously. 

The Hybrid Approach: Keeping Humans in the Loop 

However, this struggle illuminates the benefits of a hybrid approach; Augmented Intelligence means keeping humans in the loop. Why have a human write from scratch when we can refine? The last 20% is often 80% of the value (and effort!). 

I'm not suggesting all writing should be done by AI, but when time is constrained, it's better for both creator and recipient that effort is allocated to the value-add. Personalisation and tone are where humans excel. Navigating relationships is what we've evolved to do and what we're paid to do in client-facing roles. The charismatic, eloquent salesperson is in the company to spend their time driving value, not drafting approximate emails to reach as many targets as possible. AI should enable you to focus on your strengths. 

At Felix Research, we understand this is true across all domains, and especially in finance where costs are high and time constraints the highest. The analyst, partner, or senior has studied hard, worked hard, and thought hard to reach a position where they add value. Why continue doing things the old way? How much of a £100k analyst's salary is spent sorting and scanning PDFs? 

Beyond Cultural Untranslatables: Domain-Specific Meaning 

But untranslatables aren't unique to cultural contexts. Consider: if I ask for "the European gas price", what does that mean? Or "a good wine to invest in"? Or "a company that could be the next GameStop"? All of these have multiple interpretations and contextual meanings. 

In finance, terms often have more specific, generally agreed-upon interpretations, but these are built on agreements and assumptions. Assumptions, however, don't always create clear or concise answers. Only someone (or something) trained to understand the industry-specific interpretation can give you a good answer. And crucially, a fast one. 

This is where Felix One differs from generic AI.  A standard LLM available today is good at being verbose and covering all bases, but misses the mark for the professional user. A professional needs the correct answer, and often needed it yesterday. Speed and accuracy are crucial, but so is traceability and referenced findings. After all, no one likes being asked where they found that data. 

An AI Built for Finance 

Take a real example: an analyst researching renewable energy exposure across European utilities. With a generic LLM, you'd spend time crafting the perfect prompt, sifting through verbose responses, then manually tracking down sources to verify claims. With Felix Research, our AI-driven financial research platform is trained specifically on financial documents and understands domain-specific terminology. Ask about renewable energy exposure, and you get precise data points with direct citations to the relevant regulatory filings, investor presentations, and annual reports. 

The platform doesn't replace the analyst's judgment about which utilities represent the best investment opportunity. Instead, it eliminates the hours spent gathering and verifying basic information, allowing the analyst to focus on what they do best: analysis, pattern recognition and strategic thinking. 

This is how AI should work in professional contexts. It gets you to the stage where you add value faster by finding sources, showing them clearly, and being correct. Humans are still needed, and still wanted, for making crucial financial analysis and decisions. The £100k analyst should be synthesising insights and advising clients, not wrestling with PDF searches. 

The Path Forward 

The future isn't about AI replacing financial professionals. It's about purpose-built AI platforms that understand finance specifically, not generically. Platforms that cite their sources. Platforms that understand what "European gas price" means in context. And most importantly, platforms that free professionals to do what only humans can: apply judgment, build relationships, and make the critical decisions that drive value. 

Ready to reclaim your time for high-value work? 

Written by James Hall