For 17 years, scientists at Australia’s Parkes Radio Telescope were chasing mysterious radio blips, nicknamed perytons, that stubbornly defied explanation. First detected in 1998, these fleeting signals closely resembled fast radio bursts (FRBs), which some excitedly speculated were astrophysical in origin. But they didn’t behave like anything out in space. They appeared only during the daytime, once or twice a year, and were always picked up when the telescope pointed in a specific direction.
Initially, researchers suspected that local atmospheric interference, perhaps lightning, could be the source of the mysterious signals. That theory persisted for many years until early 2015, when a new radio‑frequency interference (RFI) monitor was installed. After years of searching the astronomers identified the real source of the mysterious signals – a microwave oven in the facility’s break room.
The astronomers were operating the telescope remotely but there were a small number of maintenance staff on site. When the microwave oven was set to heat something up but then was opened part way through (perhaps to check on the contents) it emitted signals that the telescope was able to pick up when it was pointing in the general direction. Years of mysterious signals originating from a maintenance worker heating up their lunch.
As humans we often prefer complicated solutions over easy ones (complexity bias) and we can miss what’s right in front of us. In working with and applying AI I think we’re at risk of falling foul of complexity bias in some key areas, from prompting to products to AI systems.
Prompting
In prompting for example, over on LinkedIn right now there’s a lot of people pushing the idea of complex ‘magic prompts’. You know the kind of thing: ‘cut and paste this 16-page prompt into ChatGPT to skyrocket your strategy’ (yep, I did actually see someone promoting a 16 page prompt). Magic prompts may short cut the process but this ultimately means that we don’t learn how to work with AI in our own way. They also overcomplicate the prompt itself by trying to include every possible nuance in the very first attempt.
LLMs understand words as sequences of numbers (or tokens). Each token is mapped to a vector, a set of numerical values representing its meaning based on usage patterns across billions of sentences. The model doesn’t ‘know’ what a word means, it just knows what kinds of words are likely to follow it, based on statistical relationships in its training data. So while a human might intuit the subtle difference between similar meaning words an LLM needs cues from context, sentence structure, or examples, to make that distinction.
For this reason, spending a little time being clear about what it is we’re actually trying to do rather than going straight to ChatGPT always pays dividends. Francois Grouiller’s ‘Think-Prompt-Think’ approach for example, starts with a human focus on framing the right question to ask and how to express it with clarity. Magic prompts may answer the question, but it could well be the wrong question.
AI product strategy and innovation
Not everything is an AI-shaped challenge. Sometimes teams rush to use generative AI or LLMs for problems that a well-designed rule-based system or a structured database query could solve faster and more reliably. AI is a powerful tool, but not always the right one, and sometimes the best AI strategy is not to use AI at all, or at least to apply it more lightly than instinct suggests.
Complexity bias leads us to add things rather than take them away. The risk of an AI-is-the-answer-to-everything assumption is that the team develop complex AI solutions for things that could be done in much simpler ways. Writer Craig Mod once compared this desire to add complexity to the time Homer Simpson was asked to design a car:
‘When Homer Simpson was asked to design his ideal car, he made The Homer. Given free reign, Homer’s process was additive. He added three horns and a special sound-proof bubble for the children. He layered more atop everything cars had been. More horns, more cup holders.’
In product design, says Craig, the simplest thought exercise is to make additions, and it’s the easiest way to make the old thing feel like a new thing. Adding AI feels like it’s a forward-thinking thing to do but if we’re simply adding complexity we are in danger of ‘doing a Homer’.
AI explainability and trust
In AI, explainability matters. Making the internal workings and outputs of an AI model or system transparent helps to build trust and accountability, and can empower users to dispute or alter AI-driven decisions. The opposite of explainability is opaque, complex systems that no-one trusts and no-one can navigate.
The first London Undergound maps in the early 20th Century tried to represent stations in a geographically accurate way so that it could be transposed onto an above ground street map. It was believed that being geographically correct would help underground users to navigate the system but as a map it was crowded, visually overwhelming and confusing to use.
Henry Beck, an engineering draughtsman who had lost his job at the Underground Electric Railways Company of London (UERL) due to budget cuts, decided to redesign the map. He set about ‘straightening the lines, experimenting with diagonals and evening out the distance between stations’. In 1931 he submitted his work to the UERL, who promptly rejected his ‘revolutionary’ map. Fortunately for us he persisted and the map was eventually published, becoming an instant hit.
Beck’s map was abstract, schematic, and geographically ‘wrong’ but it was beautifully, radically simple. He had understood that people didn’t need to know where stations were in the real world, just how to get from one to another. It was the simplicity that made the system legible.
Learning from Henry Beck, we need to design AI systems in ways that make them easy to understand, and in ways that are closely aligned to how people want to meet their needs. In his epic presentation on AI trends, Benedict Evans made the point that many people in organisations are being told to use AI but they don’t actually know what they should be using it for. Quite apart from the mystifying lack of training and support that staff are generally getting, ensuring good explainability and understanding is crucial to building trust in these systems. In this context simplicity isn’t just elegant, it’s operationally smart.
A version of this post appeared on my weekly Substack of AI and digital trends, and transformation insights. To join our community of over ten thousand subscribers you can sign up to that here.
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