Outrage has become one of the most effective forms of engagement on the modern internet. Content that provokes anger, moral judgement, fear, or frustration often spreads faster and travels further than calm, nuanced information because emotionally activated audiences are more likely to click, comment, share, and remain online.

In this episode, we explore how engagement-driven digital systems can reward emotionally charged content without necessarily being designed to make people angry on purpose. We look at the economic logic behind the attention economy, why outrage performs so well inside recommendation systems, and how social feedback loops can reinforce emotionally charged behaviour online.

Most importantly, we examine how recognising these mechanisms can help listeners slow down, spot emotional amplification in real time, and regain a greater sense of control over how they engage with the information environment around them.

In This Episode

• Why outrage performs so effectively in engagement-driven systems
• How attention and emotional activation generate commercial value online
• Why algorithms may amplify emotionally charged content
• How social feedback loops can reinforce outrage behaviour
• Practical ways to recognise and resist outrage-driven engagement loops

The Point of Friction

Most people assume outrage online simply reflects a world becoming more divided, angry, or extreme.

But the underlying reality is more complicated. In engagement-based digital systems, emotionally activating content often performs strongly because it captures attention, encourages interaction, and keeps users engaged for longer periods of time.

The system is not necessarily optimised for truth, balance, or calm reflection. It is largely optimised around measurable engagement signals - and outrage is one of the most reliable emotional drivers of those signals.

Why It Matters

Outrage is not always irrational, and many issues genuinely deserve moral concern. But when emotional activation becomes commercially valuable, digital environments can begin to reward content that keeps audiences reactive rather than reflective.

Over time this can subtly shape how people experience the world online. Headlines become more dramatic, disagreements become more emotionally charged, and complex issues are increasingly presented through conflict-driven narratives designed to maximise attention.

Understanding these systems does not require rejecting technology or disengaging from online life. It simply means recognising when strong emotional reactions may be part of an engagement loop rather than a balanced understanding of events.

Awareness introduces friction - and friction restores choice.

Listener Reflection

The next time something online makes you instantly angry, frustrated, or morally outraged, pause for a moment and ask:

Am I understanding this issue more clearly… or am I simply being kept emotionally engaged?

Next Episode

In the next episode, we explore The Silent War for Your Worldview - how governments, corporations, ideological groups, and influence operations attempt not only to persuade people, but to confuse, exhaust, and divide them by shaping the information environment itself.

Further Reading

• Brady, W. et al. (2017) Emotion Shapes the Diffusion of Moralised Content in Social Networks.
• Munn, L. (2020) Angry by Design: Toxic Communication and Technical Architectures.
• Kramer, A., Guillory, J. and Hancock, J. (2014) Experimental Evidence of Massive-Scale Emotional Contagion Through Social Networks.
• Meta Annual Reports and public advertising revenue disclosures.
• PNAS Nexus studies on engagement ranking systems and emotionally activating content.

Episode Overview

For most of the history of the internet, there has been a basic assumption that behind every article, comment, review or opinion piece is a human being; a journalist, blogger, campaigner or just someone expressing a view. That assumption is no longer valid. Today, large volumes of online content are generated automatically, and this is likely to grow. Artificial Intelligence (AI) programs can now produce convincing articles, commentary, social media posts and even videos and images in seconds, resulting in what can be termed as ‘synthetic reality’, an information environment in which it is increasingly difficult to determine whether what you are reading was written by a person or by an algorithm.

In this episode, we explore why this has happened and how it has become a common feature of commercial and political discourse. Importantly, it will discuss how listeners may be able to distinguish between the 2 and why it matters.

Why is this happening?

The introduction of generative AI platforms, such as OpenAI ChatGPT, Anthropic Claude,Microsoft Copilot and others has had a disruptive and transformational impact on how journalism and other content creation is achieved.

Initially recognised as highly useful in mitigating much of the time-consuming research, collation and coherence of reference material, through their ability to discover, search and analyse huge datasets and detect patterns, generative AI platforms have enabled journalists and researchers to focus on the more cognitive and value-added elements of content creation. This has enabledorganisations to become more efficient and achieve economies of scale at a time when the way in which media is consumed has significantly changed. An increasing volume of what we read is accessed through digital and social media platforms and traditional news outlets have suffered as a result. Many have therefore embraced the benefits of AI and incorporated them into their workflows to reduce costs and increase output.

The latest generation of AI platforms have amplified these advantages, to the extent that many are now able to generate complete and credible articles with minimal human involvement.

The utility of generative AI has significantly expanded over its short lifetime, and now includes the following:

• Scale. AI models are able to generate orders of magnitude more content than a human in a given time.
• Speed. What would take an experienced human hours or days, can be produced in seconds, by freeing authors and researchers from mundane collection and collation activities, including speech-to-text transcription and translation and searching archives and translating one medium to another.
• Diversification. Content tailored to diverse consumer groups and even individuals, thereby increasing user engagement and reaching fragmented audiences.
• Simplifying and contextualising complex source material.
• ‘Knowledge Creation’. The production of text with information content originating in the language model itself, rather than from a source document.

Why is AI Content Hard to Identify

As Generative Artificial Intelligence (AI) technology continues to evolve, it is becoming increasingly difficult to tell the difference between AI-generated and human-generated content. Advances in core generative algorithms include transformer-based language models that analysehuge quantities of written data to create text that closely mimics human writing, and diffusionmodels that produce highly credible manipulated images.

Experiments have demonstrated that humans can distinguish AI-generated text only about half of the time in a setting where random guessing also achieves 50% accuracy. Even following trainingon how to differentiate between the two, or when multiple people work as a team to detect AI-generated text better, detection rates do not improve much.

AI detection tools developed specifically to identify AI content often have little more success than humans and are even more challenged when the synthetic content is edited, amended or merged with other data.

Normally also created by AI, their effectiveness depends on matching the huge amount of investment in the creation models that utilise huge data sets and computing power, and which areconstantly evolving. The commercial demand for these systems is much lower and consequently they tend to have access to much less data.

Traditional AI detection works by sampling and analysing text once it has been written, assessing individual words purely based on the model's probability distribution through searching for statistical patterns that indicates automated production. Another technique, that adopts a completely different approach, is AI ‘Watermarking’. This works by embedding a discrete, machine-detectable signal into content generated by artificial intelligence models to verify ownership and identify that a piece of content was generated by AI.

What should we look for?

Despite the limitations in detection software, there are still several clues that can lead us to identifying content as AI written or derived.

These are only general guides, however, and as generative AI models become more sophisticated, the ability to detect when they have been used is likely to be further impacted:

• Absence of verifiable attribution. A good place to check first is the authorship. Can you verify the author actually exists, do they have a body of work, are they cited elsewhere?Are references provided at all and if so, do they detail primary sources rather than vague summaries? Can they be found and verified on-line?
• Language Patterns. AI written content often uses generic language with non-specific phrases such as ‘experts say’ or ‘studies suggest’. It lacks emotional subtlety and tends to use overly formal or complex words or phrases, and sentences are often of the same length.
• Repetition and Duplication. When researching a given subject, are the same argumentsmade, using the same form of words across multiple sites?
• Volume vs Depth. A site with lots of articles but little by the way of editorial comment can often be an indicator of AI content farms. Are complex subjects that require personal experience or expert knowledge lacking in analysis and opinion? They may provide definitive statements but without supporting detail or explanation to support the points.

Why AI Content Accelerates Misinformation?

Synthetic media is proliferating, and here to stay. Given the advantages listed earlier, why should we care? In a word, trust. How can we be sure that what we are reading or seeing, produced by generative AI, is factual and not misinformation? It should be noted that there is a big difference between misinformation, ‘hallucination’ and disinformation, the latter of which will be discussed below. While there is no definitive definition of misinformation, we characterise it as the inadvertent spread of false information without intent to cause harm. While this often produces results that are factually accurate, generative AI models have been known to produce hallucinations, which are answers that have been generated as the probable correct answer based on the patterns in the training data but are in fact incorrect and sometimes totally incoherent.

There are numerous circumstances that result in misinformation; for example when a breaking news story is unfolding and before all the details are fully known, or when personal conscious or unconscious bias drives them to an incorrect conclusion or assessment about a given situation. AI doesn’t always make false information more convincing, but it makes it easier to produce in volume. And that matters at a time when information is accessed and consumed in small bites and with limited attention spans. Its sheer persistence and ubiquity mean that misleading content proliferates and reaches a much wider audience. And of course, once information is absorbed, however inaccurate, it is extremely difficult to change that view, and people quickly move on. AI‑driven recommendation algorithms used by platforms can unintentionally:

• ​Prioritise sensational or misleading content
• Increase engagement with false narratives
• Push misinformation into users’ feeds faster than fact-checked content

This creates an environment where misinformation spreads rapidly even without deliberate manipulation. And it can do this in the full spectra of media formats. For example, AI now enables bots (automated accounts on social media) to generate or manipulate text, images, audio and video. This multimodal capability increases the sophistication and believability of misinformation. A 2018 study of Twitter (now known as X) users by researchers at the Massachusetts Institute of Technology found that false information spreads more quickly than accurate information. Popular social media posts, for example, are often easily shared and reposted, without any thought as to the veracity of the content. Once it has gone ‘viral,’ even if the original post is corrected, the false version is likely to endure and proliferate, with no accountability for those doing so. Together, these factors make bots one of the most powerful accelerators of information disorder on social media.

The State Actor Angle

Unlike misinformation, disinformation is deliberately created and distributed false information that is explicitly designed to...]]>Episode: Synthetic Reality: How AI is Now Writing the News you thought Humans Wrote

Episode Overview

For most of the history of the internet, there has been a basic assumption that behind every article, comment, review or opinion piece is a human being; a journalist, blogger, campaigner or just someone expressing a view. That assumption is no longer valid. Today, large volumes of online content are generated automatically, and this is likely to grow. Artificial Intelligence (AI) programs can now produce convincing articles, commentary, social media posts and even videos and images in seconds, resulting in what can be termed as ‘synthetic reality’, an information environment in which it is increasingly difficult to determine whether what you are reading was written by a person or by an algorithm.

In this episode, we explore why this has happened and how it has become a common feature of commercial and political discourse. Importantly, it will discuss how listeners may be able to distinguish between the 2 and why it matters.

Why is this happening?

The introduction of generative AI platforms, such as OpenAI ChatGPT, Anthropic Claude,Microsoft Copilot and others has had a disruptive and transformational impact on how journalism and other content creation is achieved.

Initially recognised as highly useful in mitigating much of the time-consuming research, collation and coherence of reference material, through their ability to discover, search and analyse huge datasets and detect patterns, generative AI platforms have enabled journalists and researchers to focus on the more cognitive and value-added elements of content creation. This has enabledorganisations to become more efficient and achieve economies of scale at a time when the way in which media is consumed has significantly changed. An increasing volume of what we read is accessed through digital and social media platforms and traditional news outlets have suffered as a result. Many have therefore embraced the benefits of AI and incorporated them into their workflows to reduce costs and increase output.

The latest generation of AI platforms have amplified these advantages, to the extent that many are now able to generate complete and credible articles with minimal human involvement.

The utility of generative AI has significantly expanded over its short lifetime, and now includes the following:

• Scale. AI models are able to generate orders of magnitude more content than a human in a given time.
• Speed. What would take an experienced human hours or days, can be produced in seconds, by freeing authors and researchers from mundane collection and collation activities, including speech-to-text transcription and translation and searching archives and translating one medium to another.
• Diversification. Content tailored to diverse consumer groups and even individuals, thereby increasing user engagement and reaching fragmented audiences.
• Simplifying and contextualising complex source material.
• ‘Knowledge Creation’. The production of text with information content originating in the language model itself, rather than from a source document.

Why is AI Content Hard to Identify

As Generative Artificial Intelligence (AI) technology continues to evolve, it is becoming increasingly difficult to tell the difference between AI-generated and human-generated content. Advances in core generative algorithms include transformer-based language models that analysehuge quantities of written data to create text that closely mimics human writing, and diffusionmodels that produce highly credible manipulated images.

Experiments have demonstrated that humans can distinguish AI-generated text only about half of the time in a setting where random guessing also achieves 50% accuracy. Even following trainingon how to differentiate between the two, or when multiple people work as a team to detect AI-generated text better, detection rates do not improve much.

AI detection tools developed specifically to identify AI content often have little more success than humans and are even more challenged when the synthetic content is edited, amended or merged with other data.

Normally also created by AI, their effectiveness depends on matching the huge amount of investment in the creation models that utilise huge data sets and computing power, and which areconstantly evolving. The commercial demand for these systems is much lower and consequently they tend to have access to much less data.

Traditional AI detection works by sampling and analysing text once it has been written, assessing individual words purely based on the model's probability distribution through searching for statistical patterns that indicates automated production. Another technique, that adopts a completely different approach, is AI ‘Watermarking’. This works by embedding a discrete, machine-detectable signal into content generated by artificial intelligence models to verify ownership and identify that a piece of content was generated by AI.

What should we look for?

Despite the limitations in detection software, there are still several clues that can lead us to identifying content as AI written or derived.

These are only general guides, however, and as generative AI models become more sophisticated, the ability to detect when they have been used is likely to be further impacted:

• Absence of verifiable attribution. A good place to check first is the authorship. Can you verify the author actually exists, do they have a body of work, are they cited elsewhere?Are references provided at all and if so, do they detail primary sources rather than vague summaries? Can they be found and verified on-line?
• Language Patterns. AI written content often uses generic language with non-specific phrases such as ‘experts say’ or ‘studies suggest’. It lacks emotional subtlety and tends to use overly formal or complex words or phrases, and sentences are often of the same length.
• Repetition and Duplication. When researching a given subject, are the same argumentsmade, using the same form of words across multiple sites?
• Volume vs Depth. A site with lots of articles but little by the way of editorial comment can often be an indicator of AI content farms. Are complex subjects that require personal experience or expert knowledge lacking in analysis and opinion? They may provide definitive statements but without supporting detail or explanation to support the points.

Why AI Content Accelerates Misinformation?

Synthetic media is proliferating, and here to stay. Given the advantages listed earlier, why should we care? In a word, trust. How can we be sure that what we are reading or seeing, produced by generative AI, is factual and not misinformation? It should be noted that there is a big difference between misinformation, ‘hallucination’ and disinformation, the latter of which will be discussed below. While there is no definitive definition of misinformation, we characterise it as the inadvertent spread of false information without intent to cause harm. While this often produces results that are factually accurate, generative AI models have been known to produce hallucinations, which are answers that have been generated as the probable correct answer based on the patterns in the training data but are in fact incorrect and sometimes totally incoherent.

There are numerous circumstances that result in misinformation; for example when a breaking news story is unfolding and before all the details are fully known, or when personal conscious or unconscious bias drives them to an incorrect conclusion or assessment about a given situation. AI doesn’t always make false information more convincing, but it makes it easier to produce in volume. And that matters at a time when information is accessed and consumed in small bites and with limited attention spans. Its sheer persistence and ubiquity mean that misleading content proliferates and reaches a much wider audience. And of course, once information is absorbed, however inaccurate, it is extremely difficult to change that view, and people quickly move on. AI‑driven recommendation algorithms used by platforms can unintentionally:

• ​Prioritise sensational or misleading content
• Increase engagement with false narratives
• Push misinformation into users’ feeds faster than fact-checked content

This creates an environment where misinformation spreads rapidly even without deliberate manipulation. And it can do this in the full spectra of media formats. For example, AI now enables bots (automated accounts on social media) to generate or manipulate text, images, audio and video. This multimodal capability increases the sophistication and believability of misinformation. A 2018 study of Twitter (now known as X) users by researchers at the Massachusetts Institute of Technology found that false information spreads more quickly than accurate information. Popular social media posts, for example, are often easily shared and reposted, without any thought as to the veracity of the content. Once it has gone ‘viral,’ even if the original post is corrected, the false version is likely to endure and proliferate, with no accountability for those doing so. Together, these factors make bots one of the most powerful accelerators of information disorder on social media.

The State Actor Angle

Unlike misinformation, disinformation is deliberately created and distributed false information that is explicitly designed to mislead others with the intent to manipulate truth and facts. The term disinformation is derived from the Russian word dezinformácija and has long been used by them, and others, as a legitimate tool in propaganda campaigns. It would be an exaggeration to claim that AI has suddenly revolutionised state influence, but there is firm evidence that state-linked actors are using generative AI as one key element of hybrid warfare. The heavy use of AI driven conversational bots in particular, can produce seemingly valid text at high speed, at scale, for minimal cost and on a persistent basis. This makes it easy to flood social media with:

• False narratives
• ​Manipulated stories
• Misleading commentary

Some bots are designed with the specific purpose of amplifying false information. These bots:

• Rapidly repost ‘fake news’ and propaganda
• ​Boost visibility through coordinated activity
• Create the illusion of widespread agreement or popularity

The intent is that, by flooding the information environment with false narratives, it undermines trust in what people are told or read; by politicians, commentators and even scientists. Designed to destabilise liberal democracies, undermine alliances and challenge the international rules order to advance their own geopolitical agenda, the result is that polarised views become even more polarised.

There is an additional ‘so what’ when applied to a given crisis scenario. A critical element in modern warfare in the information age is the battle of the narrative. The side that can generate the most rapid and pervasive commentary favourable to their position, echoed by pseudo sentiment analysis, and apparently objective assessment, is the one most likely to gain both international and internal support.

The Point of Friction

We still consume information as if it were created by humans, with an acceptance that its creation is limited by time, experience, knowledge and accountability. But an increasing share of what we read or see is now generated by machines that are optimised for scale and engagement, not necessarily for accuracy or accountability. Our instincts for judging credibility and accuracy have not yet adapted to an environment where content is generated continuously and in vast quantities, without the benefit of verification or authorship.

Why It Matters

While the consequences of spreading misinformation can have varying degrees of impact, misinformation can lead to decreased trust in all information on the Internet. In turn, this mistrust can erode democratic systems and undermine the news ecosystem. As in the case of the common fable of the “boy who cried wolf,” if people find that the information they consume on a common basis is often false, it will lead them to distrust or not believe crucial and important information that is true.

Listener Reflection

When you read something online that feels credible, what makes you trust it - and how confident are you that those signals still come from a human source?

Next Episode

In the next episode, we explore how outrage has become a business model - why emotionally charged content spreads so effectively, how platforms prioritise it, and how it is used to capture and hold attention. We also look at how to recognise when your reactions are being shaped for someone else’s gain.

Overview

Much of the internet you experience today is not static. It is experimental.

Behind the scenes, digital platforms constantly test different versions of headlines, images, notifications, and even the order in which stories appear. Each variation is measured to see which one produces the strongest reaction.

This process, known as A/B testing, is widely used in technology companies to improve products and understand user behaviour. But when these experiments are applied to news, social media feeds, and emotionally charged content, they can begin to shape how people feel about events before they have even had time to reflect.

In this episode, we explore how large‑scale experimentation works online, why emotional reactions are powerful engagement signals, and how understanding these mechanisms can help you recognise when content is trying to provoke rather than inform.

In This Episode

• How A/B testing works and why technology platforms rely on it
• Why headlines, thumbnails, and wording are constantly being tested
• How emotional reactions become measurable engagement signals
• Why emotionally charged content spreads further online
• How to recognise when content may be designed to trigger a reaction

The Point of Friction

• Common Narrative: Most people assume that the content they see online simply reflects what other people have posted.
• Underlying reality: But in reality, much of what appears in feeds and timelines has survived thousands of experiments designed to identify which version generates the strongest reaction.

The internet is not just showing you information, it is testing which version of information works best - to retain your attention and the maximum percentage of the audience too.

Why It Matters

Emotion sits upstream of judgement. When content reaches us through anger, outrage, or curiosity, it can influence how we interpret the underlying story.

Because digital platforms measure engagement in real time, they quickly learn which emotional signals keep people reading, clicking, and sharing. Over time this can subtly shape the tone of the information environment we experience.

Understanding that these experiments exist does not mean rejecting digital platforms altogether. It simply means recognising that every reaction we have online also becomes data that helps train the system.

Awareness introduces friction, and friction restores choice.

Listener Reflection

The next time a headline makes you react instantly – whether with anger, curiosity, or disbelief – pause for a moment and ask:

• Did the story create that reaction… or did the headline?

Next Episode

In the next episode we explore Synthetic Reality – how artificial intelligence is increasingly generating articles, comments, reviews, and posts that appear convincingly human, and how to recognise when the voice shaping a narrative may not be human at all.

Further Reading

Berger, J. and Milkman, K. (2012) What Makes Online Content Viral? Journal of Marketing Research.

Kramer, A., Guillory, J. and Hancock, J. (2014) Experimental Evidence of Massive-Scale Emotional Contagion Through Social Networks.

Kohavi, R., Tang, D. and Xu, Y. (2020) Trustworthy Online Controlled Experiments.

Why recommendation systems quietly steer curiosity toward more extreme content.

Episode Overview

Most people have experienced the online rabbit hole. You open a platform to watch one short video or read a single post, and twenty minutes later you find yourself deep into a topic you never intended to explore. It feels natural – like curiosity simply leading you from one idea to the next.

In this episode of Fact & Friction, Sean and Harry examine why that experience is rarely accidental. Modern recommendation systems are designed to maximise engagement, and one of the most effective ways to do that is to gradually guide users toward content that is slightly more dramatic, surprising, or emotionally engaging than what came before.

The conversation explores why the traditional idea of 'filter bubbles' only tells part of the story. Rather than simply showing people what they already agree with, many platforms accelerate curiosity by nudging users along a chain of increasingly compelling recommendations. Understanding how this process works helps listeners recognise when curiosity is being quietly steered – and how to slow the process when necessary.

In This Episode

• Why online rabbit holes feel like natural curiosity rather than guided exploration
• Why the popular idea of 'filter bubbles' does not fully explain modern recommendation systems
• How engagement algorithms learn from watch time, clicks, and behavioural signals
• Why slightly more extreme or dramatic content tends to keep attention longer
• Simple habits that help interrupt the rabbit hole and restore deliberate choice

The Point of Friction

• Common narrative: The internet traps people inside echo chambers that reinforce existing beliefs.
• Underlying reality: Many recommendation systems instead accelerate curiosity, guiding users toward increasingly intense or engaging content because those pathways keep people watching longer.

Why It Matters

Curiosity is one of the most powerful drivers of learning and discovery. However, when engagement systems quietly guide that curiosity, the journey can lead somewhere very different from where the user intended to go. Over time, these recommendation pathways can shape what topics feel urgent, interesting, or important. Recognising the mechanics behind rabbit holing allows listeners to maintain the benefits of curiosity while retaining control over where their attention ultimately goes.

Listener Reflection

When you follow a chain of recommendations online, are you consciously exploring a subject – or are the recommendations quietly guiding your curiosity for you?

Next Episode

In Episode 3 we examine something even less visible: emotional A/B testing. We explore how headlines, images, and notifications are constantly tested to discover which versions trigger the strongest emotional reactions.

Most of us assume that what we see online is largely the result of our own choices. In reality, much of the modern internet is shaped by recommendation systems designed to keep our attention for as long as possible.

These systems learn continuously from our behaviour - what we click, how long we pause on a post, what we watch to the end - and use those signals to decide what appears next in our feeds.

In this episode of Fact & Friction, we explore the 'attention engine' behind modern digital platforms. Harry and Sean unpack how engagement‑driven algorithms work, why emotionally charged content tends to travel further, and how repeated exposure subtly shapes what feels important, urgent, or interesting. The goal is not to criticise technology but to understand the incentives behind it, and to give listeners practical ways to regain control over where their attention goes.

In This Episode

• How recommendation algorithms learn from clicks, pauses, and watch time
• Why engagement - not accuracy - is often the core metric shaping online feeds
• How emotional and novelty‑driven content spreads faster than neutral information
• The subtle signals that indicate your attention is being steered
• Simple habits that introduce friction and help you regain control of your digital focus

The Point of Friction

• Common narrative: Social media simply shows you the content you choose to engage with.
• Underlying reality: Engagement algorithms actively shape those choices by amplifying the content most likely to keep you watching, scrolling, and reacting.

Why It Matters

Attention sits upstream of many decisions we make each day. What we see repeatedly influences what we think about, what we feel is important, and how we interpret events. When digital systems are designed primarily to maximise engagement, they naturally prioritise content that captures emotion and curiosity. Understanding how these systems operate helps listeners recognise when their attention is being nudged, and restores the ability to choose where their focus truly belongs.

Listener Reflection

What captured your attention online today, and did you deliberately choose it, or did it appear repeatedly until it felt important, and you’d spent a lot more time engaged with it than you intended?

Next Episode

In Episode 2 we explore 'rabbit holing' - how recommendation systems can quietly steer curiosity toward increasingly dramatic or extreme content, often without the user realising the journey is being guided.

This introductory episode sets the scene for Fact & Friction who we are, why we’re doing this, and the questions and topics we’ll be working through in the months ahead.

Background & Purpose

We live in a world where headlines are written to grab attention rather than to clarify. Spin, selective framing, and deliberate distraction often cloud the truth, leaving people polarised, misinformed, or simply exhausted by the noise.

Fact & Friction was born out of the need to slow down, strip away the theatre of contemporary media, and examine what’s really going on when narratives clash. Every episode dives into the points of tension - the 'friction' - where fact meets spin, where evidence collides with ideology, and where competing truths are put to the test.

Our purpose is to unpick, challenge, and illuminate. We aim to help listeners see through the haze of modern media, offering clarity without oversimplification.

This is the space where critical thinking lives: not in the comfortable consensus, but in the sparks that fly when truth is forced into collision with power, bias, and perception.

Why It Matters

• People should be aware of ‘clickbait’ and be able to find the ‘truth’ and ‘depth’ they want.
• Understanding the ‘friction points’ of an issue reveals more than surface-level reporting ever could and - we contend - allows the listener to (re)find balance and form reasoned judgements.
• In a noisy world, truth deserves a fighting chance.

Our Intent

Our intent is simple: cut through the noise without adding to it - to illuminate - shine a light on the issues at the centre of our information era.

We’re not here to re-hash tired disinformation narratives or scaremonger about “the media”. Fact & Friction exists to take real, contemporary forces - from engagement-hungry algorithms to the invisible design choices shaping what we see online - and explain them in a way that’s genuinely interesting, uncomfortably revealing, and completely grounded in reality.

Most importantly, we’ll show listeners why these forces matter to them, and what they can do - if they choose - to reclaim agency in a digital environment built to steer, nudge, and distract. Each episode finds the spark point where fact meets friction, and gives people something practical, something to take away and action.

Meet Your Hosts

Harry comes from a background in intelligence, security, and strategic analysis, where much of his career has been spent trying to make sense of complex situations with incomplete and often conflicting information. Like his co-host, Sean, that process never really stops – only the environment changes.

What has become increasingly clear to him is that the challenge is no longer access to information, but making sense of it. We now live in an environment saturated with data, competing narratives, and constant noise, where distinguishing signal from distraction is both harder - and more important - than ever. He believes we are operating in information environments that humans were never really designed for, where attention is constantly pulled and the longer-term effects on how we think and make decisions are still not fully understood.

That concern sits behind Luminae and Fact & Friction. The aim is not to provide answers or tell people what to think, but to help them understand how information is shaped, how narratives take hold, and why we often see the world the way we do.

At home, this often surfaces as enthusiastic monologues about whatever he’s currently reading – rarely mainstream, always fascinating (at least to him) - usually met with a mixture of interest, patience, and the occasional “what on earth are you going on about?”

When he’s not doing that, he’s normally outside or on the move - most often on an adventure motorcycle, where things are a bit simpler and the signal-to-noise ratio is refreshingly low.

Sean is a career intelligence officer, having spent most of his time in uniform in hot (and cold) unpleasant places, trying to work out what was going on. As a veteran, he is still trying to work out what is going on, but now from the comfort of his own home and producing intelligence derived from ‘open’ sources.

Like Harry, Sean doesn’t do things by halves. If he says he’s going to do something, he’s all in, and will do his utmost to make it work, whatever is thrown at him – the ‘personal moral contract’ as he calls it. Luminae provides the perfect medium through which he can do that, an initiative about which he is passionate and committed, and which really matters if our youth is to successfully navigate such a pivotal time in our history.

In many ways, Luminae is the natural evolution of his time in the intelligence community. Distilling large amounts of disparate and incomplete information to form an objective understanding has been central to what he does. The explosion of publicly available data has made that task harder, as noise drowns out signal and cognitive overload becomes a reality.

We can’t go back or un-invent technology, but we can inform, educate and increase awareness. That is the fundamental aim of Luminae – not to preach, but to help people understand and navigate the contemporary information environment.

When he’s not doing that, Sean is a passionate angler, a long-suffering Saracens fan, still runs a bit, and regularly heads to Norway to ski.

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