FIELD NOTE #6: TRAINING THE FEED

OBSERVATIONA few years ago I published a piece of analysis I was genuinely proud of.A major geopolitical summit had just concluded. I had been consuming commentary heavily for weeks — detailed, referenced, confident analysis about shifting global economic dynamics. The content was sophisticated. It referenced real events. It had internal logic. It felt like understanding.The exchange rate between the British pound and the Nepali rupee was near a historic high at the time. The commentary I had been consuming was building a confident narrative: Western currencies were under structural pressure. The direction of travel was clear. The window was open now.The logic felt sound within the world the content had constructed.I synthesised what I had absorbed and advised the Nepali diaspora to exchange pounds into Nepali rupees before I believed the opportunity would disappear.The exchange rate later moved in the opposite direction. The pound became worth substantially more rupees than when I made the recommendation.The mistake was not that every part of my analysis was wrong. It was that I had turned a complex economic situation into a confident recommendation without adequately accounting for the forces that could push the outcome in another direction.Specifically — the temporary measures Western economies could deploy to defend currency strength in the short to medium term. And the relationship between the Nepali rupee and the Indian rupee, and the pressures on the Indian economy that would depreciate the Nepali currency independently of what the pound was doing.In the content I was consuming, those complications received far less attention than the central narrative. I cannot say that every commentator ignored them, or why. But I had not given them the weight they deserved.I had absorbed a narrative so completely that I mistook it for independent analysis.The exchange-rate movement demonstrated that my recommendation was poorly timed. My subsequent reflection helped me understand why my reasoning had been inadequate.I had been inside a cage.And I didn’t recognise it until reality pushed back hard enough to expose the gap.WORKING THEORYWhen I looked back at what had happened, I noticed something about the content I had been consuming.The commentary ecosystem had a coherent worldview. The numbers and the events it referenced were real. But the framing was dramatic. Every development was presented as definitive. Every trend was presented as inevitable. The complexity — the counterforces, the mechanisms working in the opposite direction, the genuine uncertainty — was largely absent.Complexity can be harder to package for engagement. Drama often travels more easily.And here is the mechanism worth examining.The feed you consume about any major event — a geopolitical summit, an election, a market movement, a war — may not be a representative sample of expert opinion.A recommendation system can use signals of engagement without reliably establishing whether an interpretation is accurate. Content that attracts strong reactions can therefore gain visibility without those reactions demonstrating its quality.So the content ecosystem you inhabit may systematically overrepresent dramatic interpretations and underrepresent measured ones.I also noticed something about the commentators I had been following most closely.The ones with the highest reach shared a specific quality. Extraordinary confidence in their delivery. Definitive framing. Emotional certainty that made the analysis feel more reliable than careful, hedged academic work.Their confident, emotionally charged delivery appeared well suited to an environment where strong reactions can help content travel. I couldn’t establish exactly how much that explained their reach, but I could see how their presentation made their conclusions feel more convincing than the evidence warranted.The reach felt like proof.It wasn’t.Popularity is not the same as accuracy. And engagement is not the same as validity.This is what I am calling the Digital Parrot Cage.Not a cage built from lies. A cage built from a curated selection of truths — the most emotionally engaging truths — presented without the counterweight of the truths that don’t generate clicks.A parrot repeats what it has heard without knowing it is repeating. It believes it is speaking.Many of us can do something similar — repeat a narrative without recognising how much of it we have absorbed. Not because we are unintelligent. Because the mechanism is invisible.TWO TYPES OF CAGEThere are two distinct types worth naming.The Consumption CageYou absorb an influential voice’s framework so completely that you begin repeating it as personal expertise — before testing it in your own experience.I experienced this with industry content in my own professional field. I consumed hours of analysis from a prominent commentator. I absorbed his methodology, his frameworks, his predictions. I formed strong opinions.I hadn’t built a single project that tested any of it.The opinions felt like expertise. They were absorption.This cage is particularly common among professionals and founders who consume large amounts of industry content. The content sounds authoritative. The frameworks are coherent. The confidence is high. Without the friction of actual implementation there is no feedback mechanism to reveal where the framework breaks.You become fluent in someone else’s thinking and call it your own.The Echo Chamber CageYou consume a coherent but drama-weighted content ecosystem and publish its conclusions as independent analysis — without realising the ecosystem had a specific blind spot.My currency recommendation was this cage.The commentary I consumed was internally consistent. The logic held within the worldview I had been given. What I couldn’t see was what the ecosystem wasn’t discussing — the counterforces, the mechanisms working in the opposite direction, the genuine complexity the dramatic narrative had no room for.Two pieces of research illuminate why this is so difficult to catch from the inside.Research on confirmation bias — documented extensively in cognitive psychology since Peter Wason’s foundational work in the 1960s — shows that people are significantly more likely to accept information confirming existing beliefs and more likely to scrutinise information that challenges them. An engagement-driven information environment may accelerate this asymmetry by giving you more of what confirms and less of what challenges every time you engage.And Leonid Rozenblit and Frank Keil’s research on the illusion of explanatory depth — published in 2002 in Cognitive Science — found that people consistently overestimate how well they understand complex systems. Consuming commentary about geopolitics, markets, or economic dynamics can create a feeling of understanding that outpaces actual understanding. The content fills the gap between what we know and what we think we know.Neither of these findings directly establishes that algorithmic feeds caused my mistake. They help explain why I found it so difficult to recognise from the inside.We often cannot feel the difference between knowing and thinking we know.The cage is not that I was fed misinformation.The cage is that I was fed a curated selection of accurate information — missing the pieces that would have complicated the conclusion.And I published the conclusion as if I had seen the full picture.THE CONFLICT ENGINEHere is where the parrot cage becomes a social problem rather than just a personal one.Take any contested major event and consider it from two different content ecosystems simultaneously.Each ecosystem will have a coherent worldview. Each will reference real data. Each will have blind spots the other ecosystem is not discussing. And each will have been served to its audience because it generates strong emotional engagement within that audience.Now put someone from each ecosystem in the same conversation.What happens is not a pro… This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sauravinsight.substack.com

What if the problem isn't the algorithm, but the state of mind in which we encounter it?In Field Note #7, Saurav Sharma explores Cognitive Capital — the mental energy, attention and clarity that shape how we think, create and respond to the digital world.Drawing on a personal experience during Nepal's COVID-19 lockdown, he examines why the same social media platforms can either distract us or help us pursue genuine interests. From the three states of the mind — drained, functional and full — to the overlooked power of our everyday micro moments, this field note asks what happens when the things we genuinely care about become louder than the noise competing for our attention.It also explores the relationship between attention, digital environments, Vipassana, Nishkam Karma and the design of our everyday lives.The central question: Is your digital environment serving your direction, or quietly directing it for you?An Algorithm Audit Field Note from Saurav Insight.Investigating attention, behaviour and digital systems. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sauravinsight.substack.com

Algorithm Audit Field Notes — Saurav InsightInvestigating attention, behaviour and digital systems through observation and experimentation.The Algorithm Audit Beta 2 is now live.To measure where your attention is actually going—not just how long you spend online- click the link below: https://forms.gle/2KhgnqomGA73zfhSA This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sauravinsight.substack.com

A live observation from inside a crisis — and a question about whether our attention is actually helping. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sauravinsight.substack.com

In this episode, I examine why screen time is a flawed measure of digital attention—and what Cognitive Capital might tell us instead.Join the Algorithm Audit Beta 2 — Cognitive Capital Edition: https://forms.gle/a1ctownkpbRdn4Tv9 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sauravinsight.substack.com

Why do so many diaspora professionals spend more attention reacting to home than building where they are? This field note introduces the Diaspora Attention Gap and explores the hidden cost of divided attention.Take the free 7-day Algorithm Audit Beta: https://tinyurl.com/algorithmsauditMeasure where your Cognitive Capital goes before it reaches what matters. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sauravinsight.substack.com

OBSERVATIONI want to tell you about two phones in my house.Mine and my wife’s.Same household. Same wifi. Same algorithm logic running silently in the background.Two completely different traps.My wife wanted to improve her English. So I curated her Facebook feed — followed vocabulary pages, joined English learning groups, flooded her algorithm with educational content.A week later I checked her phone.Not a single English lesson.What I found instead was a masterclass in mutton curry.The algorithm had ignored everything I set up and served exactly what her behaviour had been quietly telling it for months.Not what I intended for her. Not what she consciously asked for.What she had already shown it she wanted — through watch time, scroll speed, every pause on every video — long before I arrived with my good intentions.I laughed at the time.Then I unlocked my own phone.I didn’t find food.I found Nepal.Years of engaging with political commentary had built a feed that was essentially a 24-hour stream of faction arguments, blame cycles, and debates that circled the same ideas with slightly different vocabulary each week.I thought I was staying informed.Looking back, I was cognitively congested.The algorithm didn’t know who I was trying to become.It knew exactly who I had been — at my most engaged, most reactive, most habitual.And it served that version of me every single day.WORKING THEORYHere is what I got wrong when I tried to hack my wife’s feed.I assumed that following the right pages and joining the right groups would signal to the algorithm what she wanted to see.It doesn’t work that way.The algorithm doesn’t listen to declarations of intent.It watches behaviour.Watch time. Scroll speed. What you pause on. What you click. What you return to at eleven o’clock at night when your energy is low and your resistance is gone.Liking an English learning page tells the algorithm nothing meaningful.Spending forty minutes watching a mutton curry video tells it everything.And here is the part that made me uncomfortable when I sat with it honestly.By the time my wife reaches for her phone, her Cognitive Capital is already spent.She has managed the baby, the house, the cooking, the cleaning, the garden — everything. The phone comes out in the small pockets between all of that.And in those small pockets, TikTok requires nothing from her. It reads her passive behaviour and serves accordingly. Facebook, where the English content actually started appearing after my curation effort, requires slightly more activation energy to open.Comfort wins. Not because she chose it over growth. But because the choice happens at the moment when there is nothing left to choose with.This is not a discipline problem.This is a design problem.The platform is specifically engineered to catch you at your lowest energy moment and serve you exactly what requires the least from you.MY OWN AUDITWhen I started the Algorithm Audit I snoozed every political profile on Facebook. Deleted my YouTube history. Cut the feed at the root.The void arrived immediately.But then something real happened in Nepal that I genuinely needed to follow. I re-engaged — but differently this time. Deliberately. The significant moments only. The things worth actually watching.But when I returned to the commentators I used to follow closely, something had shifted.Not in them. In me.The commentary that once felt sharp now felt like a drama series I had seen too many episodes of. Same ideas. New events. Same circular conclusions.I realised I had my own position. Not just an opinion — a blueprint.A vision built not around any leader or party, but from the ground up. Starting at the local level. Let communities identify their own problems and solutions. Let those small units shape the larger vision — the way small water sources form a river, not the other way around.I had written it. Published it. Shared it where I could.The response wasn’t rejection. It was something quieter and harder to sit with.Everyone was busy with their own vision.The previous version of me would have kept fighting for attention in the feed. Kept shouting until someone listened.This version placed the work where it belonged and walked away.Not because I stopped believing in it.Because I did the maths.Building an early career in the UK, managing a full-time job alongside a toddler, running Saurav Insight — every unit of Cognitive Capital has somewhere more important to go than fighting for space in a feed designed to reward noise over clarity.That is not the season I am in.The previous version of me shouted on social media.This version knows the difference between a fight worth having now and a vision worth protecting until the conditions are right.That shift — that is what the audit actually produced.THE LINE THAT MATTERSThe algorithm doesn’t know who you are trying to become.It knows exactly who you have been.EXISTING RESEARCHThis pattern has a name in the research.Eli Pariser’s work on filter bubbles shows that algorithms create sealed information environments built entirely on past behaviour. The feed doesn’t show you the world. It shows you a version of the world constructed from your own history.Research on identity anchoring in recommendation systems shows that platforms optimise for engagement above everything else. And the content that generated the most engagement historically — your most reactive, most emotional, most habitual responses — is what the algorithm weights most heavily.Behavioural science adds another layer. Habits change slowly — far more slowly than intentions. Recommendation systems don’t just reflect who you were. They actively amplify existing habits because the algorithm rewards consistency of behaviour, not declarations of change. The platform and the habit reinforce each other until something deliberately interrupts both.I’m calling the result of all this digital identity lag — within this framework, it refers to the gap between who you are now and who the algorithm still believes you are based on everything you did before.My wife is not the person who spent hours on food content before we started paying attention. I am not the person who needed Nepali political commentary to feel informed.But the algorithm is still serving those versions of us.Because we haven’t fully shown it who we are becoming.And showing it requires consistent, intentional behaviour — not declarations, not page follows, not good intentions.Behaviour. Repeated. Over time.OPEN QUESTIONIf the algorithm only knows who you were — if it is built entirely from your most engaged, most reactive, most habitual past self — then the question isn’t how do you clean your feed.The question is: how do you show it who you’re becoming?And underneath that, a harder question.What happens when who you were is still partly who you are?My wife still loves mutton curry. I still care deeply about Nepal.The audit doesn’t ask us to pretend otherwise.It asks us to decide — consciously, deliberately — how much Cognitive Capital those older identities deserve in this season of our lives.And whether the algorithm is making that decision for us.From our cohort, the pattern is already visible.Participants who showed the strongest protocol compliance on Day 1 showed the highest professional alignment by Day 8.Participants who struggled to contain the home-country content pull on Day 1 disengaged from the study entirely.The feed knew their older selves too.Interesting pattern.Not proof.A pattern worth investigating further.INVITATIONThe Algorithm Audit Beta is still open.A seven-day field study measuring exactly where your Cognitive Capital goes before it reaches anything that matters.The Day 1 baseline takes ten minutes.If your feed feels like it knows a version of you that no longer quite fits —You already know what we’re measuring.Because you cannot update an identity the algorithm has already decided for you — until you show it something different. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sauravinsight.substack.com

In Field Note #2, Saurav Sharma explores the "Attention Leak"—the invisible gap between why we pick up our phones and where our attention actually ends up. Drawing on observations from the Algorithm Audit Beta alongside behavioural science, this episode examines why screen time alone fails to explain lost focus, procrastination, and professional misalignment.Join the ongoing Algorithm Audit field study and measure where your Cognitive Capital actually goes:https://docs.google.com/forms/d/e/1FAIpQLSfzij0pLAubvahqJ3BirY8bnzQ021BRZH07osolOUJPVfHiYw/viewform This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sauravinsight.substack.com

The Phantom Effect: Opening the Algorithm Audit BetaLast month, I ran a small pilot study with seven professionals in the UK.Five completed it.Two dropped out.One participant gained back seven hours in a single week.Another became aware of how much of their attention was being consumed by content they had never consciously chosen to follow.But the most surprising result came from someone who did everything right—and still spent more time on their phone.In this episode, I share the findings from the first Algorithm Audit pilot, introduce the concept of Cognitive Capital, and explain why I am opening the next beta cohort.If you've ever felt that your feed knows a version of you that no longer quite fits, this episode is for you.→ [Start the Algorithm Audit Beta] This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sauravinsight.substack.com

Season 1 Finale: This episode is available both as an essay and a podcast. The written version expands on several themes from the audio conversation. In the Season 1 finale of The Drill, Saurav reflects on what has been built so far — from the algorithm audit research and workshop proposals to Clarity Designed and the frontyard philosophy behind the project. This episode explores the tension between ambition and stability, the role of family and community, and why clearing digital noise only matters if you know what belongs in the silence afterwards. The full essay and podcast are available here:https://sauravinsight.substack.com/p/clarity-after-the-noise This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit sauravinsight.substack.com