The Human Signal Stack: Why Some People Become Impossible to Ignore

A friend of mine who was a blogger when blogging was cool in 2009 (we now call them creators and influencers) was attending the same conference.

He said to me let’s grab a quiet corner and let me interview you and capture your story and find out why you started a blog on social media and how it has changed your life. 

I then asked him this question which I remember today.

“Why would anyone want to hear my story and why would they care? “ 

We went to a foyer upstairs with the music still pumping below and he started to interview me using his iPhone. It took 6 minutes and he later uploaded it to YouTube. That interview is still there 12 years later:

My first career was a high school teacher and the training was all about how to educate and share information for young people to learn. The goal and our training was to inform and educate. But there was no training to be a storyteller as a teacher. But I have discovered that stories are much more memorable than information. 

And educating young minds and helping them to grow means we need to teach more with stories and less with information. 

And that is part of the story of why I created the signal stack framework. 

To learn to create and share human stories that teach lessons and provide inspiration so you can stand out in a world that is filled up with non human and AI slop.

Ai can provide information at scale. But it can’t teach with stories that stick.

Why I Built the Human Signal Stack

I spent 17 years building one of the world’s most-read digital marketing and social media blogs. 33 million readers. 190 countries. 

I watched the platforms rise. I watched them turn on us. And there were 3 stages: 

1. First, Facebook throttled organic reach. 
2. Then Google introduced snippets that answered questions without sending traffic. 
3. Now AI generates content at scale that floods every feed and every search result with noise that looks like signal.

The internet doesn’t have a content problem. It has a human signal problem.

I kept asking the same questions: 

• What makes the writers worth reading impossible to fake? 
• What is the common thread between the people who cut through and not with volume, not with SEO, not with algorithmic tricks but with something that lands in the chest of the reader and stays there?

The answer became the Human Signal Stack.

That framework?

Six layers. Three are the foundations. Three are the activation.

Most creators use one.

The ones you cannot stop reading stack them all.

The Human Signal Stack Framework

Each layer represents a dimension of human signal that AI cannot manufacture. 

• The Foundation layers are built first, they define who you are and what you know.
• The Activation layers are how that signal reaches the world.

The diagnostic question that sits over every layer:

“Could an AI have written this? If yes and you cannot point to something that makes it irreducibly yours then it is noise, not signal.”

5 Humans Who Activate the Full Human Signal Stack

These are not perfect content creators. They are humans who have built something AI cannot replicate: a perspective so specific, so lived, and so earned that their work is instantly recognisable and impossible to fake.

For each, I map their dominant signals, the real-world impact those signals have generated, and the one move that defines their human signal.

1. Scott Galloway

NYU Professor · Pivot Podcast · No Mercy / No Malice Newsletter

Scott Galloway is a professor of marketing at NYU Stern School of Business, serial entrepreneur, and one of the most widely-read voices on business and technology. 

He built and sold several companies, including L2 Inc., a business intelligence firm acquired by Gartner. 

He hosts the Prof G Pod and Pivot podcasts, writes the No Mercy / No Malice newsletter to over 500,000 subscribers, and has written multiple New York Times bestselling books. He is known for taking complex economic data and turning it into moral verdicts that are delivered with the rage of someone who remembers what it felt like to be on the outside.

How Scott scores on the 6 elements on the human signal stack. 

Signal Scores

• Identity: 9/10 — The outsider who made it. Fatherless. Scrappy. Permanently angry at systems that exclude.
• Story: 9/10 — Data never arrives naked. Always inside a narrative with villain, victim, verdict.
• Expertise: 9/10 — 30 years of brand strategy, economics, and business education.
• Evidence: 10/10 — NYU tenure, successful exits, L2 Inc., public prediction track record.
• Interaction: 7/10 — Engages selectively but memorably on social.
• Community: 8/10 — Loyal, vocal, opinionated audience that treats his newsletter as essential.

Dominant Signals

Galloway’s superpower is activating three layers simultaneously. 

1. He takes a data point and a market cap, a demographic chart and turns it into a moral verdict. The data arrives inside a narrative. T
2. There is always a villain (a system), a victim (usually the young or the poor), 
3. And a verdict delivered with the rage of someone who remembers being excluded.

Underneath all of it: the absent father. The fear of irrelevance. The outsider’s wound. He does not hide it. He leads with it.

“Your view of AI is directly correlated to your wealth. The only cohort with a positive view of AI is people earning over $200,000.”

That is not journalism. That is a moral argument dressed in data. It required his specific history to write.

The Numbers

500K+ Newsletter subscribers

2M Instagram followers

667K Threads followers

$100K+ Speaking fee per engagement

None of it was built through SEO. 

None of it built through algorithmic optimisation. 

It was built entirely on the back of one man’s opinion, delivered weekly, for years. Multiple New York Times bestselling books. The Prof G Pod publishing daily through the Vox Media network.

The signal that did it: data weaponised by moral outrage, delivered inside a story with a wound underneath it.

The One Move That Defines His Stack

He uses his wound as his weapon. The personal history does not distract from the argument. It is the argument. The data lands harder because of the human underneath it.

2. Rand Fishkin

Founder of SparkToro · Former CEO of Moz · SparkToro Weekly

Rand Fishkin is the founder of SparkToro, an audience research platform, and the former CEO of Moz, the company he built into one of the most trusted names in SEO. 

He left Moz in 2018 and wrote publicly about the experience in his book Lost and Founder,  a rare act of transparency in a tech culture that rewards the myth of the smooth exit. 

He now publishes research that consistently challenges what the marketing industry assumes to be true. 

His most cited finding? “that AI drives just 1.08% of web traffic”

That changed how thousands of marketers think about where to invest their attention

How Rand scores on the 6 elements on the human signal stack. 

Signal Scores

• Identity: 8/10 — The insider who got burned by the system he helped build.
• Story: 6/10 — Sparse storytelling. He lets data carry the weight.
• Expertise: 10/10 — 20 years of SEO, audience research, traffic analysis. No equal.
• Evidence: 10/10 — SparkToro data, published research, the Moz track record.
• Interaction: 8/10 — Actively debates, responds, engages with critics publicly.
• Community: 7/10 — Smaller but intensely engaged professional audience.

Dominant Signals

Fishkin’s superpower is counter-consensus evidence. He finds the number everyone ignored. His post showing AI sends just 1.08% of web traffic changed how thousands of marketers think about their strategy. While the industry chased AI visibility, Rand counted the actual clicks.

But the data lands because of what sits beneath it. He lost the company he built, Moz and wrote about it publicly and in painful detail. 

Intellectual honesty that has already cost him something is the foundation everything else rests on.

“When you publish data that contradicts what your clients want to hear, and you have already paid the price for being wrong in public before, people believe you.”

His prior vulnerability is the credibility infrastructure for everything he publishes now.

The Numbers

1.08% AI web traffic (the stat that went global)

25% New SparkToro customers who have subscribed before

1M+ Combined social reach across platforms

Profitable Bootstrapped with a small team

His most cited post reshaped how thousands of marketers think about their strategy. No advertising. No PR. Just a counter-consensus number published with intellectual honesty and shared because it was true. SparkToro runs profitably on a small team — loyalty as the business model.

The signal that did it: counter-consensus evidence backed by a prior vulnerability that made the honesty credible.

The One Move That Defines His Stack

He weaponises the counter-intuitive data point. Not the data that confirms what everyone thinks. The number that breaks the consensus. That move requires the courage to be publicly wrong — a courage his history has already demonstrated.

3. Brené Brown

Research Professor at University of Houston · Author of Daring Greatly · Dare to Lead

Brené Brown is a research professor at the University of Houston who has spent more than two decades studying shame, vulnerability, courage, and connection. 

Her 2010 TED Talk, The Power of Vulnerability, has been viewed nearly 60 million times on the TED website alone, making it one of the most watched talks in TED history. She has written six New York Times bestselling books, hosted a Netflix special, and built one of the most loyal communities in personal development. 

What sets her apart is not the research itself but what she did with it as she put herself inside the study, made herself the data, and published the results.

How Brene Brown scores on the 6 elements on the human signal stack. 

Signal Scores

• Identity: 10/10 — The researcher who became the subject. That is both her professional identity and her personal story.
• Story: 9/10 — The breakdown in the middle of her own vulnerability research is her defining origin myth.
• Expertise: 9/10 — Two decades of qualitative research on shame, courage, and connection.
• Evidence: 9/10 — Academic publications, bestselling books, TED talks with 60M+ views.
• Interaction: 8/10 — Deep engagement through workshops, podcast, and community.
• Community: 9/10 — One of the most loyal communities in personal development.

Dominant Signals

Brown activates the rarest layer of the Human Signal Stack. She did not just study vulnerability. She made herself the data.

Twenty years of qualitative research on shame and courage — then a breakdown in the middle of her own work — then the decision to publish it. That is not confessional vulnerability like Galloway’s. That is methodological vulnerability. The researcher became the subject.

When she writes about shame, the reader does not feel lectured. They feel found.

“Vulnerability is not weakness. It is our greatest measure of courage. I know this because I spent a decade trying to avoid it — and the data eventually found me.”

No AI will ever write that sentence. And have it be true.

The Numbers

60M TED Talk views on TED.com alone

6 New York Times bestsellers

4.4M Instagram followers

$150K Speaking fee per engagement

All of it flows from one decision made in the middle of a research project: to make herself the data. A Netflix special. An HBO Max docuseries. Two podcasts with millions of downloads. The numbers are the compound interest on a single act of methodological courage.

The signal that did it: the researcher became the subject. The study became the memoir.

The One Move That Defines Her Stack

She inverted the normal relationship between researcher and subject. By putting herself inside the study, she collapsed the distance between the academic and the human. The methodology became the memoir. The data became personal. The personal became universal.

4. Morgan Housel

Author of The Psychology of Money · Partner at Collaborative Fund

Morgan Housel is a partner at Collaborative Fund and the author of The Psychology of Money, which has sold over 12 million copies and been translated into more than 60 languages. 

Every major US publisher passed on the book before it found a home and it went on to become one of the bestselling financial titles of the last decade. He is a two-time winner of the Best in Business Award from the Society of American Business Editors and Writers, and MarketWatch has named him one of the 50 most influential people in markets. 

He writes about money the way a poet writes about loss by approaching it sideways, through story, and finding the human truth hiding inside the numbers.

How Morgan scores on the 6 elements on the human signal stack. 

Signal Scores

• Identity: 8/10 — The quiet contrarian. Anti-complexity. Anti-performance. Pro-simplicity in a world rewarding noise.
• Story: 10/10 — His highest layer. He finds the human truth hiding inside a financial chart.
• Expertise: 9/10 — Deep knowledge of financial history, behavioural economics, investment psychology.
• Evidence: 8/10 — The Psychology of Money. The track record is the evidence.
• Interaction: 6/10 — Selective. Chooses depth over volume.
• Community: 7/10 — Quietly massive. Readers share his work the way they share a discovery.

Dominant Signals

Housel writes about money but never about money. He writes about fear. About time. About the stories we tell ourselves when the market drops and we panic at 3am.

His signal is restraint as a form of respect. Every piece has one image, one story, one idea. No padding. No caveats. No content framework visible through the prose. He trusts the reader to follow a single idea to its end — and that trust is itself a human signal.

“The most important financial decision you make is not which stocks to buy. It is how you behave when you are scared.”

That sentence required decades of watching how humans behave under financial stress to write with that authority.

The Numbers

12M+ Books sold across all titles

60+ Languages translated into

Rejected By every major US publisher, then sold millions

2x Best in Business Award winner

Every US publisher passed on The Psychology of Money before it found its home. It went on to become one of the bestselling financial books of the last decade. No newsletter hacks. No content calendar. No growth strategy. Just one idea per piece, pursued with restraint and trust in the reader.

The signal that did it: story as the vehicle for the human truth hiding inside a financial chart.

The One Move That Defines His Stack

He finds the human truth hiding inside a number. The chart becomes the vehicle for a story about fear, time, or identity. Data without story is a report. Housel never writes reports. He writes about what the data reveals about being human.

5. Heather Cox Richardson

Professor of History at Boston College · Author of Letters from an American

Heather Cox Richardson is a professor of history at Boston College and the author of Letters from an American, a nightly newsletter that has grown to more than 3 million Substack subscribers. 

This is making her the most-subscribed individual creator on the platform. She has written seven books on American political history and was named to the TIME100 Creators list in 2025. She began writing her newsletter in 2019 as a historian trying to help readers understand current events through the lens of the past. 

She has never optimised for an algorithm. She has simply shown up, daily, with forty years of accumulated perspective behind every sentence — and let that be enough.

How Heather scores on the 6 elements on the human signal stack. 

Signal Scores

• Identity: 9/10 — The historian who refuses to let the present forget the past. Her identity is her archive.
• Story: 7/10 — Her storytelling is contextual rather than personal. She narrates history, not memoir.
• Expertise: 10/10 — 40 years of immersion in American political history. Irreplaceable archive.
• Evidence: 9/10 — Academic publications, multiple books, 3M+ Substack subscribers.
• Interaction: 8/10 — Posts near-daily. Hosts live Facebook Q&A sessions. Builds sustained relationship.
• Community: 9/10 — The most-subscribed individual creator on Substack.

Dominant Signals

Richardson’s signal is accumulated weight. She does not explain events. She contextualises them. She says: here is what happened before, and here is what this moment means inside that longer story.

That is only possible with 40 years of living inside American history as a scholar. AI can access the same historical record. It does not carry the same sense of moral urgency built from watching the same argument repeat across two centuries.

“The history of the United States has always been a struggle between those who want to concentrate power and those who want to distribute it. Today is not different. It is a continuation.”

That sentence is not information. It is a verdict delivered from forty years of pattern recognition.

The Numbers

3M+ Substack subscribers

3.2M Facebook followers

#1 Most-subscribed solo creator on Substack

TIME100 Creators list 2025

She started in 2019 as a historian trying to help people understand American politics. She has never written for algorithms. She has never optimised for SEO. She has simply shown up, daily, with 40 years of accumulated perspective behind every sentence. The result is the largest individual newsletter on the internet.

The signal that did it: accumulated expertise as moral weight. The past arriving inside the present, every day.

The One Move That Defines Her Stack

She delivers today’s news with the weight of history behind it. Every event arrives carrying its ancestors. That accumulated perspective is her moat — and it cannot be replicated by training on data. It requires living inside a discipline long enough that the patterns become instinct.

The Numbers Don’t Lie

There is a question sceptics always ask. Does this actually work? The answer is in the data. These five humans built their human signal before AI made it necessary. Here is what it compounded into.

What the combined numbers prove

• 3M+ Substack subscribers: Richardson alone, the most-subscribed individual on the platform
• 500K+ newsletter subscribers: Galloway, built on opinion and outrage, not SEO
• 60 million TED Talk views: Brown, from one act of methodological courage in a research project
• 12 million+ books sold: Housel, rejected by every US publisher before it became a classic
• 1.08% AI traffic stat: Fishkin, one counter-consensus number that reshaped an industry

None of them optimised for AI search. 

None of them chased zero-click impressions. 

None of them published AI slop and hoped for traffic.

They built human signal first. The audience followed.

That is not a coincidence. That is the compounding effect of the lived life, published over years.

“The question is not whether this approach works. The data answers that. The question is whether you are willing to do what they did. Show the wound. Publish the counter-consensus number. Make yourself the data. Tell the story only you can tell.”

What All Five Have in Common

Study these five long enough and a pattern emerges. Not a formula. A fingerprint.

1. They interpret, not just report

Data is everywhere. Meaning is scarce. These humans turn one into the other. They do not describe what happened. They tell you what it means — filtered through a specific identity that has earned the right to interpret.

2. They have skin in the game

Housel lives his financial philosophy. Brown was the subject of her own research. Fishkin lost the company he built. The writing is not separate from the life. It is the life, reported back.

3. They name the villain and it is always a system

The algorithm. The attention economy. Concentrated power. The myth of financial complexity. Never a person. Always a structure. That takes more courage than calling someone out. It requires actually understanding the mechanism.

4. They risk being wrong in public

Galloway is wrong regularly. He says so loudly and without apology. Fishkin publishes data that contradicts what clients want to hear. That honesty — that willingness to make a call and own the result — is the trust signal. Not the accuracy. The courage.

5. They carry their history into every piece

Richardson’s 40 years of scholarship. Brown’s 20 years of vulnerability research. Fishkin’s decade of building and losing Moz. Housel’s years of watching humans behave badly under financial stress. That accumulated perspective is the moat. It cannot be replicated by training data.

6. They write to transform, not to inform

Not here is what happened. But here is what it means for you, sitting where you are, thinking what you are thinking right now. The reader does not just understand something new. They see something differently.

How To Build Your Human Signal Stack

The deepest irony of the AI era: the best way to create content AI cannot replicate is to use AI to excavate your own irreplaceable humanity.

AI is not the threat to your signal. AI slop (content created without human signal) is the threat. The process below uses AI as the excavation tool, not the replacement.

Always start with identity. Everything else flows from there.

The Golden Rule of Human Signal Content

“Use AI to scale your signal. Never use it to replace it. Feed your Identity Report, your stories, and your evidence into every piece you create. AI handles research, structure, and scale. You provide the one thing it cannot manufacture: the lived life behind the argument.”

The Closing Diagnostic

Before you publish anything, ask yourself one question. Could an AI have written this?

If yes, and you cannot point to something specific that makes it irreducibly yours, 

It is just noise. Not signal.

The question is not whether AI can write. It can.

The question is whether it has lived.

It hasn’t? 

The only competitive advantage that compounds?

The lived life.

The earned opinion.

The story that could only have come from you.

The post The Human Signal Stack: Why Some People Become Impossible to Ignore appeared first on jeffbullas.com.

* This article was originally published here

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Why You Should Forget Google

In 2014, I wrote a post titled “Why You Should Forget Facebook.” and shared it on LinkedIn.

It got 400,000 views, 1,000 comments+, and spent a week as the top content on LinkedIn globally. 

I wrote it because Facebook had just betrayed every creator and marketer who had spent years building on its platform. They went public, killed organic reach, and quietly moved the goal posts while we were not looking.

Thousands of startups and media companies collapsed overnight that had based their traffic and web visibility and business on Facebook’s feed. 

It was the “Great Steal”

I was furious.

I am more furious now.

What inspired this red mist anger to re-surface now and over a decade later, was Google’s recent announcement at its annual 2026 conference about its biggest change to search in 25 years.

The announcement and I quote: 

“This new search box puts our most powerful AI tools right at your fingertips, and you can ask across modalities with text, images, files, videos and search reasons across them all”

Google

It is a PR announcement announcing this update like it is a gift to humanity. 

The reality? 

It is just money grabbing depravity and creator content theft disguised as a hug. The trillion dollar platform is just a bullying thug. 

Because what Google has done to the free and open web and what it is doing right now, at industrial scale, with the cover of artificial intelligence makes Facebook’s betrayal look like a minor policy adjustment.

Website traffic from search is about to head to zero while Google steals your content and monetizes it. 

This is “The Great Steal Mk 2”

And this is just the final nail in the coffin for SEO and the SEO industry. 

But it has been happening for years as after the Facebook algorithm change the rest of the large platforms started minimizing organic traffic to maximize revenue. 

“Google did not just change the algorithm. Google took your life’s work, fed it to a machine without your permission, and is now using that machine to replace you and make money from you”.

The Morning I Understood What Had Been Taken

I built jeffbullas.com over fifteen years. Four-thirty in the morning, five days a week, for five years at the beginning. Writing about digital marketing, social media, the future of content. Building something from nothing, on the back of a deal that felt, if not equal, at least fair.

The deal was: I create, Google indexes, Google sends traffic, I earn a living. Google gets to sell advertising against that traffic. Both parties benefit. The open web works.

I grew to thirty-three million readers. A Domain Authority above eighty. An email list with forty-percent-plus open rates. A platform that opened doors I could not have imagined standing at a desk before dawn in 2009.

And then, slowly at first and then very quickly, the deal changed.

Not because my content got worse. Not because my audience stopped caring. But because Google decided that it no longer needed to send my readers to my website. It could answer their questions itself — using my content, and the content of millions of creators like me, to build an AI that has no obligation to acknowledge where the knowledge came from.

The Anatomy of the Great Steal

Let us be precise about what happened, because the scale of it is easy to understate.

Google has crawled the public internet for more than two decades. Every article, every research paper, every blog post, every forum thread, every product review and  hundreds of billions of pages of human knowledge, created by individuals and organisations who were never asked for permission and never offered compensation.

That content was used to train the large language models that now power Google’s AI products. The same models that generate the AI Overview answers at the top of search results. 

Answers that tell users everything they need to know without ever requiring them to click through to the publisher who created the original knowledge.

In May 2024, Google launched AI Overviews. Early data from Semrush suggests AI Overviews reduce click-through rates by eight to ten percentage points for affected queries. SparkToro’s research shows that as of 2024, nearly sixty-five percent of Google searches already end without a single click to an external website.

The companies that extracted the most value from the open web are now the companies most actively dismantling it.

Chart 1: The Great Steal in numbers. Estimated value of creator content used to train AI versus what was returned to creators. The fourth bar requires no caption.

This Is Not New. This Is a Pattern.

If you were paying attention in 2012, you have seen this before.

Facebook went public in May 2012. Before the IPO, organic reach for a Facebook Page averaged around sixteen percent. By 2016, it had fallen to approximately two percent. The same audience. The same content. One-eighth of the reach.

The message was clear: you can still reach your audience. You just have to pay us to do it. The community you built on our platform is now our advertising inventory. Thank you for building it.

Google is running the identical playbook. The mechanism is different and instead of throttling reach, it is answering questions in-line, but the underlying logic is the same. 

We benefited from your contribution while we needed it. We are now capturing the value of that contribution for ourselves. The deal has changed and you were not consulted.

Chart 2: The creator rebellion is already underway. AI-crawler blocking among top publishers has grown from 8% to 80% in eighteen months.

The Question Nobody Wants to Ask

“What if we stopped feeding the machine?”

Every article you publish that Google can crawl is training data for the AI that is replacing you. Every YouTube video you create is content that Google, which owns YouTube can analyse, summarise, and serve through its AI products without directing a single viewer to your channel.

The robots.txt file allows publishers to specify which crawlers they will and will not allow. Blocking GPTBot, blocking the AI crawlers, is technically trivial. Thousands of publishers are already doing it. The question is what happens when the number reaches fifty percent of the web’s quality content.

Google’s AI answers become less accurate. Less reliable. Less useful. The leverage inverts.

Chart 3: The power equation. As creator opt-outs increase, AI quality degrades while creator leverage multiplies. At 50% opt-out, the dynamic fundamentally shifts.

This Is Not a Luddite Movement

This is not an argument against artificial intelligence. This is an argument for applying the same principle the music industry applied to streaming, the writers applied in their 2023 strike, and Australian publishers applied to Google’s news products: creators have collective leverage, and they have been slow to use it.

Chart 4: Every major creator rights battle in the digital era resolved in creators’ favour when they acted collectively. The precedent is clear.

The Answer: Build What Algorithms Cannot Steal

There is a version of this story that ends in paralysis. Google took your traffic. Facebook took your reach. The open web is dying. What is the point?

Here is the point:

The algorithms can only steal what you gave them in the first place. 

• They cannot steal a reader who subscribed to your email list because they trusted your judgment. 
• They cannot steal a community member who shows up every week because your interpretation of the world is the one they want to read. 
• They cannot steal the relationship between a writer and a reader who chose that writer and not because a search engine served them up, but because the writing said something true.

The answer to the Great Steal is not to optimize harder for Google. It is to build the thing Google was never able to own.

Invest in Human Signal

Every AI-generated article is indistinguishable from every other AI-generated article. The same structure. The same confident-but-empty prose. The same ten-step framework you have read forty times before. The content flood is already happening and it will get worse.

The only content that survives is content that could only have been written by you.

That means your specific story, with the specific detail that only you remember. The client who said the one thing that changed how you think. The morning you realised the strategy you had bet three years on was wrong. The pattern you have noticed across fifteen years that nobody who arrived last year can possibly see.

That means your actual opinion, not the balanced view, not the considered-all-perspectives summary, but the uncomfortable thing you actually believe. 

The claim that makes a polite professional in your industry slightly uncomfortable to read. That discomfort is a signal. Chase it.

That means your interpretation of what is happening and not a summary of the news, but what the news means through the lens of someone who has paid the cost of knowing this field. 

The research report is available to everyone. What is not available to everyone is your reading of it, filtered through your experience, your wounds, your pattern recognition.

“AI can generate content. It cannot generate yours”.

Build the Audience That Cannot Be Taken

In 2024, my email list delivered over forty percent open rates consistently. My organic search traffic fell thirty percent. The email list did not notice.

That is not a coincidence. It is a structural fact. The email inbox is the only distribution channel in the digital world where the relationship belongs to the writer, not the platform. There is no algorithm between you and your subscriber. There is no feed competing for attention. There is no engagement rate being optimised for someone else’s advertising product.

Every piece of content you publish should have a single clear purpose beyond the content itself: move someone from a platform audience into your owned audience. The LinkedIn post is not the destination. The email list is the destination. The article is not the destination. The community is the destination.

Platform traffic is the introduction. The relationship is what you build once they raise their hand and say they want more.

Community Is the Moat

Beyond the email list sits something even more powerful: community. The readers who gather not just because they read your words, but because they want to think alongside you and alongside each other.

Community is the thing platforms have always wanted to create and have always failed to sustain, because the incentive of a platform is to maximise time on the platform, not to deepen the relationship between its members. A community you own has exactly the opposite incentive: to make the members’ connection to your ideas so valuable that no algorithm can offer a substitute.

• Build the list. 
• Build the community. 
• Build the body of work that is so specifically and irreducibly yours that no AI can summarise its way to the same conclusion.

The algorithms will keep changing. The platforms will keep betraying. The deals will keep breaking.

“The human signal, the specific wound, the earned opinion, the interpretation that only you can offer that belongs to no one but you. And the readers who come for that? They are yours too”.

The Question That Changes Everything

Back to the beginning. 

“Why You Should Forget Facebook” resonated because millions of creators were experiencing the same betrayal and no one had said it plainly.

I am saying it plainly now.

What Google has done is not an accident of technology. It is a deliberate business decision to extract value from the creative commons that made Google valuable, at a scale and a speed that no previous platform betrayal approached.

The creator strike is not a fantasy. The technology exists. The legal precedent exists. The collective will is forming. Eighty percent of top publishers are already blocking AI crawlers.

I built thirty-three million readers on a deal that no longer exists. I am not prepared to build the next chapter on the same terms.

“What if 100 million creators decided the same thing? What if we all forgot Google — on the same day, in the same week, with the same message? The machine needs us more than we need the machine. It is time to act like it”.

The post Why You Should Forget Google appeared first on jeffbullas.com.

* This article was originally published here

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The Human Signal Manifesto: Claiming Back the Human Web

In 2009, I started writing.

No strategy. No keyword research. No content calendar.

Just pure, passionate and driven human curiosity about a fast-emerging revolution called social media and the compulsion to share what I was seeing, thinking, and feeling about it in real time.

I wrote an essay a day for 5 years. My Grade 5 English teacher would be so pleased.

These were my thoughts. My ideas. My voice. Trying to make sense of this brave new and emerging world. 

But in trying to help others I was finding myself. 

That creation was not for machines. Not for an algorithm. Not for optimization. It was for other humans; the curious ones, the early ones, the ones who felt something shifting beneath their feet and wanted to make sense of it together.

I followed writers I admired. Read their blog posts at all hours. Shared their articles. Left comments that turned into conversations. 

And slowly, something extraordinary emerged a global tribe. Real people, on every continent, sharing the journey in public. And online.

I watched the USA wake up late at night on Twitter from my quiet office nook on another continent in another time zone.

We met at conferences and stood in genuine awe of this new world that had captured our collective imagination.

The excitement was visceral. You could feel it.

We all leaned in.

Content exploded, but all of it was written by real people, from real experience, with real stakes. 

The human signal was obvious. Human creation was celebrated. There were no shortcuts, no hacks, no prompts to feed a language model. There was just the raw material of a human mind trying to understand the world and connect with others doing the same.

That energy carried me to 33 million readers across 190 countries. Not because I out-optimized anyone. Because I was genuinely, unmistakably, irrefutably human.

Then something started to crack.

The First Chokehold

It was invisible on the outside. But the results revealed something breaking from the inside.

Facebook traffic, which had been a river of organic human attention, began to slow. Then slow even more to the creators that fed it. Then it almost stopped.

What was happening? 

Facebook had started applying algorithms that throttled the human signal to maximise ad revenue. The global tribes that had emerged organically, the real communities built on shared curiosity were quietly sacrificed to the advertising stream. 

The feed was no longer showing people what they cared about and the people and the communities that had collected around the digital town square. It was showing them what could be monetised.

I wrote a blog post at the time titled “Why You Should Forget Facebook” The premise was simple: stop relying on Facebook for organic traffic and human-driven attention. We were moving toward a web where reach was no longer earned. It was bought. It was being stolen from the creators and made into a “pay to play” platform. You became invisible to your hard earned followers. 

That was the beginning. The first moment the machine started choking the human signal.

The search engines followed. Ads consumed the top of the results. Then Google snippets began summarising the websites that had fed the machine, giving people the answer without ever sending them to the source. The content creators who had built the web’s knowledge base were slowly being cut out of the equation.

SEO was now not about creating great human content. It was about engineering your content to satisfy an algorithm’s appetite. We adapted. We learned the rules. We optimized.

But in adapting, we started to change what we made.

The Final Chokehold

Then came the machine that changed everything.

Large language models with an AI chatbot face. We welcomed them with wonder, with excitement, and with some quiet suspicion. They offered both utopia and dystopia in the same sentence

What they did, at scale, was to scrape the entire archive of human expression, intelligence, creativity from songs to images to movies ever published on the web.

Decades of blog posts, articles, research, stories, debates, and ideas and use it to train systems capable of generating new content at near-infinite speed.

The same tools that consumed our work now offered to replace it.

We were told this was progress. We were told to optimize for the new machines. To structure our content so AI would cite it. To chase visibility inside a prompted answer. 

A new acronym appeared: GEO. Generative Engine Optimization.

And here is where I want to say something clearly, from sixteen years of watching how these cycles play out:

GEO is a losing game for most of us.

It is high effort with opaque feedback loops. There is no direct conversion mechanism. You are optimizing for a system that rewrites its own rules invisibly and one that does not pay you, does not credit you, and cannot distinguish your singular voice from the homogenous average of everything it has consumed.

Today, it is estimated that 50% of all content on the web is AI-generated. The river has become a flood. Polished. Persuasive. Structurally perfect. And almost entirely without soul.

Most creators have handed their voice to the machine.

I am not willing to do that.

The Manifesto

We built this web for humans. We built it out of curiosity, and generosity, and the ancient human drive to share what we know with others who need it. That impulse is not obsolete. It is not inefficient. It is not something to be engineered away.

It is the only thing that has ever actually mattered.

And right now; in the era of AI slop, infinite generated content, and algorithmic attention markets it is becoming the scarcest thing on the internet. AI slop is homogenous, smooth, inoffensive and devoid of humanity and full of information. Finding the human signal in the noise of infinite content is like trying to find a microprocessor in a haystack.  

That scarcity is the opportunity.

I am proposing a reorientation. Not a new tactic. A return to the original principle, armed with clarity about what we are actually doing and why.

I am calling it “Human Signal” 

And we need to now optimize for our human signal. 

That is human signal optimization. Or “HSO”.

Where SEO says be findable, and GEO says be cited, HSO says: be human and unique.

Make your voice your own. Be unique. But first you need to know who you are. That is your identity.

For many people they are told from birth to fit in. Be part of the crowd. Be the cog in the wheel. Don’t make waves. 

The reality is that real authentic human power and energy rises from what makes us unique. We don’t need to shout. But it does require awareness of our individual agency. 

And put a stake in the ground.

This means becoming aware of our human identity. 

What’s your opinion?

What is your point of view?

What do you stand for?

What are you angry about?

Human signals are not a style. It is a substance. It is the presence of a specific human mind with a specific history, a specific set of hard-won beliefs, a specific way of seeing in everything you make. 

If you can mine your unique signal, unearth your identity, then the force that rises will surprise you. 

It is the thing an AI cannot manufacture, because it cannot live a life. It cannot earn the 4:30am mornings. It cannot accumulate the scar tissue that makes a perspective genuine.

It has never had a marriage break up or a business failure. It has never discovered and lost love. It never has had a crisis. 

The diagnostic question I now run against everything I publish: 

“Could an AI have written this?”

If yes, and you cannot point to something specific that makes it irreducibly yours, then it is just noise, not signal. One bedrock human signal is your “stories”

Human signal lives in six layers, and they build on each other from the ground up. 

The foundation layers of Identity, Story, Expertise are slow to build and permanent once established. They are the bedrock. Most creators skip them because they require the kind of interior work that does not feel like marketing. But without them, everything above is fragile.

The activation layers of Evidence, Interaction, Community are where signals become visible and compound over time. But they only work when the foundation exists beneath them.

You cannot broadcast your way to signal. You have to build downward before you can grow upward.

The Reclamation

I am not sure exactly how we do this. This is an experiment from an observation of where we are and a life lived and of heading down a path that looks like a dead end.

No one has a complete map yet. The rise of artificial intelligence is challenging our humanity. But with educated awareness we can use it to amplify our humanity. We need to make sure we use it and not be used “by” it.

But I know what direction to move. And that is to be as fully as human as possible. 

We have an opinion and we need to state it, even when it is uncomfortable.

We need to tell human stories specific, earned, honest ones that could not belong to anyone else.

We create from curiosity, not from a prompt.

We build for the human reader, not the generative model.

We design our future from “our “identity. 

The free and open web was built by human signals. It was built by people like the ones I met in 2009. Leaning in, sharing ideas, forming tribes, celebrating each other’s creation. 

Somewhere along the way, we were gradually nudged, throttled, and optimized into something smaller than that.

I am claiming it back.

The category has a name now. The era has begun.

It is time to lean into our own Human Signal.

And optimize for that. 

No more bowing to the machines or the platforms. 

We still need them but they also need us. 

It is time to be unmistakably, irreducibly, irrefutably human.

There is no other way. 

The post The Human Signal Manifesto: Claiming Back the Human Web appeared first on jeffbullas.com.

* This article was originally published here

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The Reading Catastrophe: How One Generation Lost the Meta-Skill That Makes All Other Skills Possible

I learned to read at 5 and it changed everything.

For some reason it drew me in. And every lunch time I became the librarian’s best friend. 

I was always looking for books about pirates, tropical adventures and exploring crystal clear turquoise seas and lagoons.

I’d disappear into my imagination sparked by words and travel to other worlds. Books were a time travel machine. And I didn’t need to leave my chair. 

They were also the gateway to knowledge, the school grades, and vocabulary. It changed the shape of my interior world. It gave me other lives to inhabit, other minds to borrow, other centuries to visit. Reading didn’t just inform me. It formed me.

Now I watch my own grandchildren navigate a world where that formation isn’t happening. 15 second videos just distract.  No imagination needed.

They are smart, curious and full of energy and need the deep reading habit, even if they don’t realize it. 

The habit that builds something essential in the architecture of a person is absent. And I believe, as much as I believe anything, that their life could be less for it if they don’t develop a deep reading habit.

This isn’t nostalgia. This is a diagnosis. And science agrees with it.

“If your child becomes a reader, about 80 per cent of the education job is already done… Reading is the meta-skill that enables all other skills.”

Michael Strong, educator

The Operating System Nobody Noticed We Were Losing

Every skill has a foundation. Mathematics rests on number sense. Music rests on pitch discrimination. Sport rests on coordination. But reading is different.

It is the foundation beneath the foundations.

Educator Michael Strong puts it plainly: “If a child becomes a reader, 80% of the education job is already done”. 

History requires reading. Science requires reading. Even mathematics, increasingly, requires reading and the ability to parse a multi-step problem, extract meaning, hold structure in working memory.

Reading is not a skill. It is the meta-skill. 

The operating system on which everything else runs.

Which means when we allow reading to atrophy in a generation, we are not producing people who have simply read fewer books. We are producing people whose cognitive architecture has been built differently. The scaffolding is thinner. And we may not see the full consequences for another twenty years.

The Science of Friction: Why Hard is the Point

Here is the paradox at the heart of the reading debate: the thing that makes reading feel difficult is precisely the thing that makes it valuable.

When you open a video, it begins. Light and motion and sound are delivered directly to your senses. Your brain’s job is largely one of reception. When you open a book, nothing happens until you make it happen. 

Your brain must decode abstract symbols, convert them to phonemic sound, construct meaning, generate mental imagery, hold prior context in working memory while building toward inference and all simultaneously, all in real time, all self-directed.

This is not a design flaw in reading. It is the mechanism. The friction is the feature.

COGNITIVE LOAD THEORY (Sweller, 1988): Reading imposes higher intrinsic cognitive load than video because the learner must construct meaning rather than receive it. This active construction is precisely what builds durable knowledge structures in long-term memory.

Cognitive scientist Robert Bjork at UCLA named this principle the theory of Desirable Difficulties. The conditions that make learning feel harder in the short term:

1. Reading versus watching

Watching feels easier because the speaker, visuals, tone, and pacing do much of the work for you. 

Reading usually demands more mental effort because you have to slow down, interpret, connect ideas, and build meaning yourself. But the real issue is not reading versus watching. It is passive consumption versus active processing. The best learning happens when you pause, question, recall, summarize, and apply what you are learning.

2. Recalling versus recognising

Recognition feels like learning because the answer looks familiar when you see it. But recall is much stronger because you have to produce the idea from memory without prompts. 

That effort strengthens understanding. 

A simple test is: Can I explain this without looking? 

If not, the idea is still borrowed. Real learning begins when you can retrieve it, teach it, and use it in your own words.

3. Spacing practice versus massing it

Cramming feels productive because progress appears fast, but much of that learning fades quickly. 

Spaced practice feels harder because you forget between sessions and have to work to retrieve the idea again. 

But that struggle is the point. Returning to an idea after time has passed strengthens memory and makes learning more durable. In other words, forgetting is not always a failure. It can be the doorway to deeper learning.

Video is not a desirable difficulty. It is an undesirable ease. You feel as though you’ve learned something. 

But studies consistently show you have not learned at the depth the medium implies.

Figure 1: Cognitive effort required by medium. Social media and short-form video sit far below the active-construction threshold. Deep reading is the most cognitively demanding common medium. Source: Sweller (1988), Mayer (2009), Wolf (2018).

The chart above illustrates something counterintuitive: the media we consume most readily such as social feeds, short video, require almost no active cognitive construction. 

They sit at the passive end of the spectrum. Deep reading sits at the opposite extreme. And it is precisely that position that makes it cognitively transformative.

The question is not whether reading is harder. It obviously is. The question is whether the hardness is a bug or a feature. The science is unambiguous: it is the feature.

Your Brain on Reading vs Your Brain on Video

For most of human history, we assumed reading and watching activated roughly the same mental processes. Neuroscience has spent the last two decades dismantling that assumption.

When you read deeply, you are not simply processing language. You are running a full-brain simulation. 

Neuroscientist Stanislas Dehaene’s research at the Collège de France showed that reading activates what he calls the brain’s reading network, a distributed system spanning visual cortex, language areas, and crucially, the motor cortex. When you read the sentence ‘she kicked the ball,’ the neurons associated with kicking activate. Reading is embodied. You are not just understanding action. You are, at a neurological level, performing it.

Cognitive neuroscientist Maryanne Wolf, whose book Reader, Come Home stands as the definitive account of the reading brain, found that deep reading also activates the prefrontal cortex for inference and critical thought, and the default mode network for empathy and self-reflection. These are not incidental byproducts. They are the architecture of wisdom.

Passive video consumption activates a dramatically narrower set of systems. 

• Visual cortex. 
• Auditory cortex. Partial activation of the limbic system for emotional content. 
• The prefrontal cortex, the seat of critical thought and inference — is largely disengaged.

Figure 2: Relative neural activation across six major cognitive systems — deep reading versus passive video. Reading engages 4× more cognitive systems at meaningful intensity. Source: Wolf (2018), Dehaene (2009), Mar et al. (2006).

This is not a marginal difference. Reading engages four to five major neural systems at high intensity. Passive video engages two. The brain that reads regularly is exercising a significantly broader set of cognitive muscles than the brain that primarily watches. Over years of childhood development, this produces a measurably different cognitive architecture.

Raymond Mar’s research at York University (2006, 2010): People who read fiction extensively showed significantly greater empathy, social cognition, and theory of mind scores than non-readers — independent of their personality type. The effect was causal, not merely correlational.

The Retention Illusion: What You Actually Remember

Video creates a seductive cognitive illusion: the feeling of having understood something. The production values are high, the presenter is confident, the graphics are clear. You arrive at the end feeling informed.

The research on what actually transfers to long-term memory tells a different story.

Studies by cognitive psychologists Henry Roediger and Jeffrey Karpicke on the testing effect show that the act of retrieving information, which reading with active engagement requires and passive video does not is the primary driver of long-term retention. 

Reading, because it forces continuous active construction of meaning, is inherently more retrieval-like than viewing. Every paragraph requires you to integrate new information with what you already hold in working memory. Video does not.

Figure 3: Information retained after one week by consumption medium. Passive video and social content show 5–8% retention. Deep reading with reflection retains up to 72% of core concepts. Source: Roediger & Butler (2011), Mayer (2009), Bjork (1994).

The data here is stark. 

• Passive video produces retention rates in the single digits after one week for complex conceptual material. 
• Deep reading with active engagement retains 60–72% of core concepts. 

The medium that feels like learning is not, at the level of durable knowledge, the medium that produces it.

Richard Mayer’s extensive research on multimedia learning adds further nuance. Video is genuinely superior for procedural, visual tasks, how to assemble something, how to perform a physical movement. 

But for conceptual, analytical, and inferential material, the substance of education;  reading consistently produces superior comprehension and retention.

We have built an education system that is migrating toward the medium better suited to assembly instructions, for material that fundamentally requires the medium better suited to understanding.

The medium that feels like learning is not, at the level of durable knowledge, the medium that produces it.

The Friction-Reward Curve: Why Reading Always Wins the Long Game

There is a moment, familiar to every reader, approximately ten to fifteen minutes into genuine engagement with a difficult text, when the friction dissolves. The resistance that makes starting feel effortful converts into something else. 

Absorption, momentum, the peculiar sensation of being inside an idea rather than alongside it.

This is not an accident or a personality trait exclusive to book lovers. It is a predictable neurological event. The cognitive systems engaged by reading reach a threshold of activation at which they begin to self-sustain. 

The reading effort becomes flow. 

This is what video, precisely because it delivers its content frictionlessly from the first second, cannot produce in the same way.

Figure 4: Cognitive and knowledge return over time for deep reading versus passive video. Reading’s initial friction converts to compounding reward. Video’s instant gratification decays rapidly. Curves cross at approximately 12–15 minutes — the absorption threshold. Source: Bjork (1994), Karpicke & Roediger (2008).

The absorption threshold that is visible as the crossover point on the curve, sits at roughly twelve to fifteen minutes into sustained reading. This is the precise duration that dopamine-optimised content is designed to prevent you from ever reaching. 

Fifteen-second videos, thirty-second reels, three-minute YouTube segments. The algorithm has been engineered, with extraordinary precision, to keep users permanently on the left side of that crossover point.

Not because that is good for the user. Because it is good for engagement metrics.

DESIRABLE DIFFICULTIES (Bjork & Bjork, 1994): Learning conditions that introduce manageable difficulty — including the effort required to construct meaning during reading — enhance long-term retention and transfer. Conditions that reduce difficulty (passive viewing) enhance short-term performance but impair long-term learning.

The implication is significant. A child who grows up primarily on video content is not merely a child who has watched more than they have read. They are a child who has never regularly experienced the absorption threshold. 

They have never discovered that the friction converts. They know only that reading is hard, and that the alternative is easy. They do not know because they have not been allowed to find out what waits on the other side of twelve minutes.

The Displacement: What the Smartphone Actually Stole

The newspaper clipping that prompted this article makes an honest admission: if the author had owned a smartphone at age 14, they would never have read a book. This is not weakness. This is neuroscience.

Reading requires tolerating approximately thirty seconds of ‘nothing happening’, which is the threshold before a paragraph yields its first reward. Social media feeds have been engineered to eliminate that thirty seconds entirely. The reward is delivered before the delay is felt.

After sustained exposure to this model, the thirty-second threshold becomes neurologically intolerable. The baseline expectation for stimulation has been permanently adjusted upward. The child is not choosing video over books in any meaningful sense. Their reward circuitry has been recalibrated such that the choice is already made before they sit down.

Jonathan Haidt’s research in The Anxious Generation identifies the critical window for this recalibration: ages 10 to 14. This is precisely the developmental period when deep reading habits are either formed or permanently missed. The smartphone arrived, in mass-market form, directly into that window. The consequences are not yet fully visible. But they are already in motion.

The Mental Health Connection Nobody Fully Understands Yet

The link between the reading crisis and the adolescent mental health crisis is ‘probably’ real but for reasons ‘nobody fully understands.’ That epistemic humility is worth preserving. But we can identify mechanisms.

Reading: sustained, immersive, narrative reading, is one of the oldest and most effective tools for what psychologists call self-regulation. When you inhabit a character in genuine difficulty, you are practising emotional modulation at a safe distance. You are learning to sit with discomfort, uncertainty, ambiguity, and resolution and the full emotional arc, without the stakes being real. This is psychological weight training.

Social media does the opposite. It rewards emotional reactivity, performance anxiety, social comparison, and the constant monitoring of external validation. It is not merely that social media replaced reading time. It replaced a self-regulatory practice with a dysregulatory one.

The mental health crisis and the reading crisis may not be parallel phenomena. They may be the same phenomenon, seen from different angles.

The mental health crisis and the reading crisis may not be parallel phenomena. They may be the same phenomenon, seen from different angles.

The Class Divide That No One Wants to Name

Reading is becoming a class marker. In households where parents read, where books are visible and valued, where children see adults choosing a book, reading rates have declined less steeply. These children are falling behind their own parents’ generation, but not as dramatically as their peers.

In households without that modelling, which correlates imperfectly but measurably with socioeconomic status and time poverty, the smartphone filled the void completely. The consequence is a growing cognitive divergence that will compound economically. 

The jobs most resistant to automation will overwhelmingly require sustained reading capacity; complex reasoning, contextual judgment, the ability to parse ambiguity. We are concentrating those capacities, right now, in the children of people who already have them.

We are not just watching an educational crisis. We are watching the early formation of a new inequality, with reading at its foundation.

Can You Recover? The Question That Matters Most

The research on neuroplasticity is genuinely encouraging. The reading brain can be rebuilt in adulthood. It takes longer. The window of effortless acquisition has closed. But the window is not locked.

Adults who commit to sustained reading and even those who haven’t read seriously since childhood, can recover significant deep reading capacity within twelve to eighteen months of consistent practice. 

The key word is sustained. Not scanning. Not skimming. Actual linear reading of long-form text, for at least thirty minutes daily, without the phone in the room.

Fiction accelerates recovery as it activates empathetic imagination more than non-fiction. 

Difficult material that requires re-reading deepens the gains. 

And physical books outperform screens: the spatial memory cues of a physical page measurably aid comprehension and retention.

For children who have not yet developed the habit, the intervention is more straightforward, but requires adults who model it. Children who see parents reading are dramatically more likely to read themselves. Not because they are told to. Because the behaviour is made legible as something adults choose freely.

What a Reading Life Actually Gives You

A reading life gives you a populated interior world. When you have lived inside the consciousness of a nineteenth-century Russian aristocrat, a dying soldier, a grieving mother, a child discovering cruelty for the first time, you do not encounter human diversity as theory. You have already been there.

A reading life gives you language as a precision tool. The person who has read widely has access to distinctions the person who has not simply cannot make and not because they are less intelligent, but because they have not been given the vocabulary for those distinctions. 

Language is not just expression. It is the structure of thought.

A reading life gives you time. Every book is a conversation with a mind that spent years distilling what it knows into the clearest possible form. No other medium offers that ratio of return.

And a reading life gives you the capacity to be alone without being lonely, perhaps the most underrated gift in an age of manufactured connection and genuine isolation.

The Verdict

We are not watching children make different choices about how to spend their leisure time. We are watching the systematic removal of a cognitive and emotional infrastructure that took millennia to build and is being dismantled, platform by platform, in a single generation.

The friction of reading is not a design flaw. It is the entire mechanism. The thirty seconds before the page opens. The twelve minutes before absorption begins. The slow accumulation of a mind that knows how to sit with difficulty and come out the other side changed. These are not inconveniences to be optimised away. They are the process.

Video gives you content. 

Reading gives you a mind capable of doing something with it.

The answer is not to condemn technology or retreat into nostalgia. The answer is to understand what is being lost with clear eyes, name it without sentimentality, and make deliberate choices in our homes, our schools, and our own daily lives to protect something ancient, irreplaceable, and quietly essential to everything we think we value.

Read. Then read more. Not because it is virtuous. Because it is the closest thing to a superpower that remains freely available to every human being on earth.

Key research cited

• Bjork, R.A. & Bjork, E.L. (1994). Desirable difficulties in theory and practice. Journal of Applied Research in Memory and Cognition.
• Dehaene, S. (2009). Reading in the Brain: The Science and Evolution of a Human Invention. Viking.
• Haidt, J. (2024). The Anxious Generation: How the Great Rewiring of Childhood Is Causing an Epidemic of Mental Illness. Penguin.
• Karpicke, J.D. & Roediger, H.L. (2008). The critical importance of retrieval for learning. Science, 319(5865).
• Mar, R.A. et al. (2006). Bookworms versus nerds: Exposure to fiction versus non-fiction. Journal of Research in Personality, 40(5).
• Mayer, R.E. (2009). Multimedia Learning (2nd ed.). Cambridge University Press.
• Roediger, H.L. & Butler, A.C. (2011). The critical role of retrieval practice in long-term retention. Trends in Cognitive Sciences, 15(1).
• Sweller, J. (1988). Cognitive load during problem solving. Cognitive Science, 12(2).
• Wolf, M. (2018). Reader, Come Home: The Reading Brain in a Digital World. Harper.

The post The Reading Catastrophe: How One Generation Lost the Meta-Skill That Makes All Other Skills Possible appeared first on jeffbullas.com.

* This article was originally published here

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