Angus Fletcher didn’t plan to become a professor of story. He began in a neurophysiology lab at the University of Michigan, working on the dominant assumption that the brain behaves like a computer: taking in data, processing it, and outputting decisions.
In the closing episode of Season Nine of Data Malarkey – the podcast about using data, smarter – master data storyteller, Sam Knowles, traces Fletcher’s unusual pivot from studying neurons under a microscope to completing a PhD in English literature at Yale, and what that journey taught him about how humans actually think.
The genesis of a major change of direction
Fletcher’s turning point came from a growing doubt that “information processing” explained much of human behaviour. Yes, he concedes, parts of the brain – especially those linked to vision – do something like prediction and pattern matching. But he argues that most cognition is better understood as action: initiating behaviours, dealing with conflict between competing impulses, and inventing new chains of actions in response to changing circumstances. In his framing, a plan, a plot, and a story are closely related: all are sequences of actions that we generate and then try out in the world.
That view helps explain why Fletcher became obsessed with Shakespeare. Not because Shakespeare contains hidden social theories (a point Fletcher teases English departments about), but because he sees Shakespeare as a master of generating new stories that work. Fletcher’s interest is practical: how do certain forms of story provoke imagination, resilience and better judgement?
Primal Intelligence
In his 2025 book Primal Intelligence, he describes what he calls four “primal powers” that underpin narrative cognition: Intuition, Imagination, Emotion, and Common sense. They are not presented as literal brain modules, but as components of a useful model for how people spot anomalies, turn them into possible futures, and then choose which plan to pursue.
One of the most intriguing claims in the conversation is that story predates language. Fletcher points out that language is evolutionarily recent, whereas brains have been around for hundreds of millions of years. If story were merely a communications device, that would be odd: why would older biology rely on a newer tool? Instead, Fletcher argues that story is first a thinking tool – a way of modelling action.
Not-so-bird-brained after all
He notes that birds can make plans and use tools without language, suggesting that “story thinking” is ancient. Sam points to the work of Professor Nicky Clayton, an eminent psychologist at the University of Cambridge, in which corvids have been shown to exhibit behaviours consistent with theory of mind and understanding that other birds see the world from other birds’ perspectives.
These observations also feed into Fletcher’s scepticism about artificial intelligence as a substitute for human creativity. He draws a sharp distinction between computers, which operate through equations and correlations, and humans, who reason through causation and action. Whether you agree with his strongest claims or not, the practical takeaway is clear: creativity is not something mystical, nor something that comes from more information. It is a trainable capacity to generate, test, and adapt plans.
Practical applications
The most useful part of this episode for data professionals comes when Angus and Sam find common ground on when data helps and when it hinders. Data is powerful in stable environments where tomorrow resembles yesterday: it supports optimisation, productivity, and scaling. But in volatile, low-data conditions, “following the data” can become a trap, encouraging organisations to double down on what used to work, staring into the rearview mirror. Fletcher’s prescription is to treat anomalies as “hints” of the future – fragile, half-formed signals that might look wrong at first, but can point to real change if you lean into them.
Summing up
For anyone working in insight, strategy, and storytelling, this episode of the Data Malarkey podcast is a reminder that the point is not to choose between data and narrative. It is to know when to optimise and when to invent. Blended in the right way, stories and statistics are much more powerful when they’re brought together rather than kept apart.
—The first draft of this blog was written by ChatGPT, using a transcript of the episode and an ever-refined prompt. It was then edited by real humans.