Notes from five years of being outmatched

How being the least expert person in the room teaches you to compress

The corner problem. “What if we moved the gusset by 5mm towards the vertical member?” By this point I was struggling to keep up with visualising the complex 3D geometry in my head, and it took me a good 30 seconds to remember what a gusset was again… and suddenly the conversation had moved on and someone else was offering technical observations about why that particular solution wouldn’t work, receiving nods of understanding from the room. The issue was that I was only a few months into real engineering, and despite being the work package manager for the battery pack we were discussing, I wasn’t fluent enough in the technical concepts to contribute much of substance. I kept my mouth shut.

Over the past five years of my career, I’ve been in rooms where I’ve been consistently outmatched. It’s become clear to me that fluency - the ability to use the jargon - and deep understanding are not the same thing. Every time I thought I had a rock-solid mental model and could speak the jargon, some new concept would present itself that everyone else was seemingly already familiar with, and which meant little to me. This, as I have come to appreciate, is what it costs to have responsibility beyond your experience, alongside genuine experts. It’s how I’ve spent the last five years at Sunswap - the cleantech start-up I joined as one of the first employees - and it’s how I’ve developed the ability to learn and compress fast.

Being outmatched isn’t an overstatement. I’ve worked with some of the smartest people I’ve met at Sunswap, and it’s a specific, daily, uncomfortable cognitive experience. A feeling of not being able to keep up, processing slower than everyone else, and often slight embarrassment at stalling the progress of discussion. And yet, as I’ve progressed in my career, I’ve come to a crucial realisation: this is exactly the room I want to be in - the one in which I’m outmatched.

That doesn’t necessarily mean being outmatched on every front. I’m a jack of all trades, master of some - but in the domain at hand, I’m outmatched. My thesis is simple: there is no greater motivational driver for learning than imposter syndrome. It’s a quality I see in all those I respect most: only those who are blind to their true ability have the complete confidence to never self-assess.

My solution: learn fast. Learning the language of the domain came first. In school, I would roll my eyes at having to rote learn terminology. There were more interesting things to be getting on with, but perhaps I wasn’t fully appreciating the power of compression (forgivable, in fairness, for a 14-year-old). Now that I work in AI there’s an obvious parallel: context window optimisation. Just like LLMs - which can only process a fixed number of tokens at once, with quality degrading as the context grows - people can only process so much information in one chunk. Being able to use precise and domain-specific language is effectively a context compression hack: the information conveyed is succinct and specific enough that the meaning carries through with very low cognitive load. Naming a concept allows you to refer back to it with ease, without convoluted explanations. I remember always being struck by one particular colleague who would focus hard on picking the right words, so much so that he actually spoke quite slowly. At the time I thought this was over the top, but I later came to realise that this is evidence of an incredibly valuable and rare skill: the art of concept compression in real time, and the ability to distil meaning into its purest form.

Another instrument is a mental model. Take the example of a “sacrificial element” - a purposefully weak link in a chain that allows you to control which section will fail first. We used this in the battery pack design with our fusing architecture. We knew the sacrificial fuse would fail first in an electrical fault, so we deliberately located it outside the main pack area, allowing the pack to stay serviceable without invasive repairs. By labelling the concept in two words, everyone conjures up the appropriate mental model, which means it occupies the minimal amount of “context tokens” possible and enables faster discussion. Or (to appropriately compress the idea): compression.

But labels only work if there’s something real underneath them. Mental models have to be discovered first, then labelled - not the other way round. They have to be earned, like achievements in a video game. Having discovered Charlie Munger (Warren Buffett’s esteemed business partner who popularised the concept of mental models) and Farnam Street (an online community dedicated to the subject), I attempted to create a giant poster of the most common mental models, hung it on my staircase, and tried to memorise it - an effort that quickly proved unsuccessful. Mental models are like a one-way function: you can easily calculate the output from the input, but not vice versa. It’s the same reason why book summaries don’t work long term. Your brain is effective at compressing concepts, but only once the underlying understanding is there. Without the work, the label has nothing underneath to attach itself to. However, I’ve found mental models to compound and pop up across domains, which is what makes them so powerful.

Beyond language and models, there’s first-principles thinking. A truly deep understanding of the domain space. Things like: what really is voltage? Every engineer will claim to understand voltage. Few can explain what physically causes it: an imbalance of charge at the particle level. My cheat code for first-principles thinking has been YouTube. Channels like 3Blue1Brown, AlphaPhoenix and Ben Eater have been invaluable in refining my models over the years. The videos worth watching I watch three or four times. Now I share them with my team. First-principles thinking is compression at the foundation: knowing what a concept physically is, so everything above it can be rebuilt. After all, if you can’t explain it at a whiteboard, do you really understand it?

The good news I’ve found is that compression compounds. I first noticed this two years in. By year three, after a concerted effort to deepen my electrical knowledge, it took me by surprise how fast new domains were becoming accessible. Not only are concepts transferable across domains, but more deeply, the framework for operating in ambiguity and getting up to speed quickly becomes a powerful weapon. Over the past couple of years I’ve found the same with AI. Ultimately, everything is systems. The skill of moving fluidly between levels of model complexity transfers everywhere. Now I use AI the same way, as a tutor for whatever domain I’m currently outmatched in. The compression habit just found a new instrument.

So being outmatched is the formation I’d recommend, if you can tolerate the discomfort. Studying compression won’t make you a compressor. Being in a room where you are outmatched will. This is the signal I look for now - if I’m not outmatched, I’m not at the limit, and there’s no room to grow. Every new expert colleague who joins Sunswap is an opportunity to be outmatched, and to learn from the best. Compression compounds.