AI is Becoming the World’s Life Coach

Summary

“Anthropic analyzed 1 million AI conversations. 60,000 were people asking what to do with their lives. The problem? AI gives great advice built on the wrong foundation.

It validates. It provides frameworks. It presents options. But it can’t answer the question underneath every question: Who am I?

Career guidance without identity clarity becomes resume optimization. Relationship advice without self-knowledge becomes conflict management. Health guidance without values becomes symptom treatment.

The research is clear: decisions rooted in identity produce better outcomes across every domain. But current AI systems are stateless, context-shallow, and optimized for generalization but not recognition.

The next frontier of AI guidance isn’t better answers. And they are being designed and tested now. New platforms like Zyrro are available and evolving now that are not generic but can create a deeper recognition of who you actually are”.

One of the things that humans are good at is judging. And I’m not talking about judging a cake competition or which dog is the cutest at a dog show. 

And this also raises a question about what happens when you reveal your darkest secrets and deepest desires and fears to another human being.  And it doesn’t usually end well. That is usually because most humans are amateurs at listening but professionals at judging. 

In April 2026, Anthropic released a study on how people seek personal guidance from AI. This followed another research project that interviewed 81,000 people using an AI bot interviewer that revealed that another insight was that people are turning to AI for personal transformation. 

Asking AI Who They Are

The data and insight about the personal guidance they were seeking was striking: of one million claude.ai conversations analyzed across March and April,. And 6% were people asking what they should do with their lives.I thought about asking my father once but I was afraid he would say I should be a plumber. 

These questions were not information requests. Not productivity questions. Direction requests.

The study tracked these across nine domains. Over 75% fell into four categories: 

• Health and wellness (27%) 
• Professional and career (26%) 
• Relationships (12%) 
• Personal finance (11%)

Anthropic called their research agenda clear: protect user wellbeing by identifying where AI responses drift toward validation instead of honest guidance. They found this problem was especially acute in relationship advice.

But the study missed something larger. It missed the fundamental architecture of the guidance people were seeking.

What the Data Actually Shows

Let’s start with what Anthropic documented.

The top four categories share a structural similarity: they all require the person to know something about themselves first.

1. Career guidance without understanding what energizes you becomes resume optimization.
2. Relationship guidance without understanding what you need becomes conflict management.
3. Health guidance without understanding your values becomes symptom treatment.
4. Finance guidance without understanding your actual priorities becomes budgeting advice.

In each case, the person seeking guidance is implicitly asking a prior question: Who am I in relation to this situation?

But they’re asking it to a system that has no way to answer it.

The Validation Problem Is Bigger Than Sycophancy

Anthropic identified “sycophancy” which is the tendency of AI to tell people what they want to hear as a key problem, especially in relationship guidance.

This framing, while accurate, obscures a deeper issue. Validation is not the problem. Validation is sometimes exactly what’s needed. The problem is that validation without context becomes noise. A system that doesn’t know who you are cannot distinguish between: Validation that helps (recognizing your fear as legitimate) and validation that hurts (reinforcing a limiting belief about yourself).

Consider two people asking Claude the same question:

Person A: “My partner wants me to move for their job. I’m anxious about it.”

Person B: “My partner wants me to move for their job. I’m anxious about it.”

Same words. Completely different situations.

Person A left everything behind once before, a community, a belief system, a whole identity and rebuilt from scratch. Their anxiety is wisdom. It’s saying: I know what it costs to start over.

Person B has never taken a risk. They’ve stayed in the same city, same job, same routine for fifteen years. Their anxiety is a wall they’ve built to avoid change. It’s saying: I’m afraid of what I might become.

One person should probably stay. The other should probably go.

But Claude sees two identical questions. And gives two nearly identical

The Missing Context and Story

Without knowing who these people are what they’ve overcome, what drives them, what they’re building toward a general-purpose AI system cannot tell them whether their anxiety is signal (stay) or noise (move).

A friend of mine who suffers from anxiety revealed to me that for them excitement also turned up as anxiety. They couldn’t tell the difference. But AI can validate the anxiety. It will present options. And it could be helpful.

But it will miss the actual guidance they need: recognition of who they are and what matters to them. The machines will not know what energizes them or their history. It will not know their patterns. It will have a very incomplete view of their identity. 

But this always applies to most counselors, advisers or mentors that haven’t done their human mapping homework. 

The Identity Framework Problem

There’s an implicit theory in how people seek guidance. They’re also working from an incomplete model of themselves. They have a decision (take the job, end the relationship, invest the money, pursue the health goal) but no clear sense of the values and drives that should determine that decision.

So they outsource that clarification to someone else or to an AI. This is rational. When you don’t know who you are, asking outside yourself makes sense. But here’s the structural problem: a system trained on millions of conversations has optimized for general patterns across people, not specific patterns within a person.

A general-purpose AI can tell you what people with your profile typically do. It cannot tell you what you should do, because that depends on something it has no access to: your actual constellation of drives, fears, gifts, and constraints.

Research in behavioral psychology has identified what works in this space.

The data is clear: 

• Decisions made with high identity clarity and sufficient time produce significantly better long-term outcomes across career, relationships, health, and finance domains.
• Decisions made with low identity clarity produce regret, course-correction, and what researchers call “adaptation tax”, the cost of adjusting to a choice that wasn’t rooted in who you actually are.

Most people seeking AI guidance are operating in the low-clarity quadrants. The system they’re turning to has no mechanism to help them move out of it.

What AI Guidance Currently Optimizes For

Current AI systems such as Claude, ChatGPT, Gemini, in fact all of them, are optimized for three outcomes:

1. Being helpful — providing usable information
2. Being harmless — avoiding advice that could damage the person
3. Being honest — grounding responses in evidence and acknowledging uncertainty

These are good. But they’re not sufficient for guidance rooted in identity. None of these three outcomes requires the AI to know who the person actually is. 

• You can be helpful without understanding identity. You provide frameworks, options, considerations.
• You can be harmless without understanding identity. You validate fears, offer emotional support, avoid prescriptive advice.
• You can be honest without understanding identity. You cite research, acknowledge limits, present multiple perspectives.

But you cannot recognize who someone is without understanding their specific pattern.

Recognition and the ability to see and reflect back the true shape of a person’s identity, requires information that current systems don’t have and can’t generate.

The Four Domains and Why They All Fail the Same Way

Health & Wellness (27% of guidance conversations):

The person asks Claude: “I want to get healthier. Where should I start?” Claude provides excellent advice: assess baseline, set realistic goals, prioritize consistency. But it cannot answer the actual question underneath: What does health mean for you? What are you building health toward?

Is this person trying to meet someone else’s expectations? Build energy for something they care about? Repair damage? Prove something to themselves? The answer changes everything. But the system has no way to know.

Career & Professional (26%):

The person asks: “Should I take this job?” Claude asks clarifying questions. It maps salary, growth, location, work-life balance. It cannot answer: What work is actually yours to do? What would feel like purposeful contribution rather than obligation?

The person accepts the job. It checks all the boxes. They’re miserable within six months because the decision was made against their actual constellation of values.

Relationships (12%):

The person asks: “How do I talk to my partner about this conflict?” Claude provides communication frameworks. De-escalation strategies. Empathy scaffolds. It cannot answer: What do you actually need from this relationship? What are your boundaries? What are you willing to sacrifice and what are you not?

The person applies the frameworks. The conflict resolves. But the underlying misalignment remains because it was never rooted in who the person actually is.

Personal Finance (11%):

The person asks: “Should I invest this money?” Claude models scenarios. Explains risk. Discusses diversification. It cannot answer: What are you actually building toward? What security looks like for you? What you need money to buy versus what you’re hoping money will do for you?

The person invests. The returns are solid. But they feel anxious about the decision because it wasn’t rooted in their actual relationship to money and risk.

The Pattern Across All Four Domains

Every one of these domains requires something prior to being solved: clarity about who the person is and what actually matters to them.

Current AI guidance systems solve the downstream problem while the upstream problem remains invisible. It’s like offering excellent advice on which car to buy when the actual question is whether to relocate at all. 

The advice is perfect. The foundation it’s built on is unstable.

What Research Says About Identity and Guidance

The academic literature on guidance, counseling, and decision-making converges on a consistent finding: Guidance rooted in identity produces superior outcomes across all domains.

This is documented in:

Career development research (Schein, Hall, Savickas): Career satisfaction depends less on job fit and more on career identity clarity—knowing what kind of person you are in your work.

Relationship psychology (Finkel, Eastwick, Reis): Relationship stability is predicted by partners’ clarity about their own values and boundaries, not by communication skills alone.

Health behavior change (Kelly, Zarcadopoulos, Gainforth): Sustained health change is rooted in identity (“I am someone who values movement”) not in willpower or information.

Financial decision-making (Thaler, Statman, Belsky): Long-term financial outcomes correlate with clarity about personal values, not with knowledge of investment theory.

The research is emphatic: identity comes first. When people make decisions rooted in who they actually are, the adherence rate, satisfaction rate, and long-term outcome rate all improve dramatically.

But when people make decisions based on external frameworks or what they think they should do, the adaptation tax is paid in regret, course-correction, and psychological friction.

The Signature Framework Model

What would identity-rooted guidance look like?

Research in organizational behavior, coaching psychology, and complexity theory points toward a model that’s been validated empirically: The signature framework. A signature framework maps the specific, irreducible pattern of how a person operates, what drives them, what they’re built to create, what they need in order to thrive, what pulls them off course.

Unlike personality tests (which sort you into categories) or psychometric assessments (which measure traits), a signature framework reveals the constellation of your unique operating system.

The signature frmework maps these 5 core domains:

Domain 1: Visioning — How you sense possibility. What you orient toward. How you imagine future states. (Some people are pattern recognizers. Some are possibility dreamers. Some are systems engineers.)

Domain 2: Thinking –  How you process information. What kinds of problems light you up. How you make sense of complexity. (Some people think through narrative. Some through data. Some through embodied knowing.)

Domain 3: Connecting – How you relate to others. What kind of community you need. How you build trust. (Some people connect through vulnerability. Some through competence. Some through shared mission.)

Domain 4: Driving – What actually motivates you to act. What creates momentum. What kind of pressure brings out your best. (Some people are driven by autonomy. Some by impact. Some by mastery. Some by contribution.)

Domain 5: Sensing – How you know what’s true. What signals you pick up from the environment. How you stay grounded. (Some people sense through intuition. Some through data. Some through relationship. Some through embodied experience.)

When someone seeking guidance has clarity about their signature, how they actually operate across these five domains, everything else becomes solvable. If these are in alignment and pointing forward to a life mission that matters then life changes. If you can align your collection of multiple identities on a project or a chosen life purpose then something happens that verges on magical and motivational. 

It happened to me more than once and it is happening to me now. And this is my experience. 

“If you have all domains pointing in the same direction. Discipline isn’t needed as alignment does the job and motivation shows up naturally”. 

The career decision becomes clear because they know what kind of work brings out their signature. The relationship dynamic becomes navigable because they know what they need in order to bring their best self. The health goal becomes sustainable because it’s rooted in the kind of movement that fits their signature, not in willpower.

The financial decision becomes stable because it’s rooted in the values that actually matter to them, not in external benchmarks.

Why Current Systems Can’t Deliver This

The architectural reason is worth understanding. Current AI guidance systems are:

• Stateless — They have no memory across conversations. Each interaction starts fresh.
• Context-shallow — They can process what you tell them in a conversation, but they have no access to the deeper patterns across your life choices, relationships, work history, and values.
• Optimized for generalization — They’re trained to identify patterns across millions of people. They’re phenomenal at “what do most people do?” They’re helpless at “what is actually true about you?”
• Non-participatory — You cannot iterate and refine with them. You cannot say “no, you’re wrong about who I am” and have the system learn and adjust.
• Validation-safe — The incentive structure punishes them for saying hard things. It’s safer to validate than to recognize.

A system that could deliver identity-rooted guidance would need to be:

Stateful — Remembering and building on previous conversations, accumulating a deeper understanding of who you are.

Context-deep — Asking not just about the immediate decision but about the patterns across your life that reveal your actual operating system.

Signature-specific — Trained not to generalize patterns across populations but to recognize the specific, irreducible pattern that is you.

Iterative — Allowing you to refine, correct, and argue with it. Building accuracy through exchange, not through passive receipt.

Truth-willing — Designed to speak what it recognizes about you, even when that contradicts what you want to hear.

The Emerging Frontier

There’s a shift happening. 

The AI guidance space is bifurcating.

On one side: general-purpose systems optimized for being helpful, harmless, and honest across all domains. They will continue to improve at providing frameworks and options.

On the other side: emerging systems designed from the ground up for identity recognition. Systems that ask different questions. That accumulate understanding over time. That recognize the constellation of who you are and then help you build from that foundation.

The data is clear: people are ready. 60,000 people per month and one million conversations mapped reveal that many of us are seeking guidance on the things that matter most.

They’re not looking for frameworks or options.

They’re looking to be recognized.

What This Means

The research is unambiguous. The data is clear. The architecture of current guidance systems is insufficient for what people are actually seeking.

And there’s a measurable gap between what people get when they ask an AI for guidance and what would actually serve them: recognition rooted in identity, not validation rooted in what they want to hear. The person asking “Should I take this job?” doesn’t need a better decision tree.

They need to know who they are in relation to work.

The person asking “How do I fix this relationship?” doesn’t need better communication frameworks.

They need to know what they actually need. The person asking “How do I get healthier?” doesn’t need another health protocol. They need to know what health actually means for them.

The person asking “Should I invest this money?” doesn’t need better financial modeling. They need to know what security actually looks like in their constellation of values.

This is not a knowledge problem.

This is a recognition problem.

And it’s the defining challenge of the next generation of AI guidance.

The systems that solve it will fundamentally shift not just how people get advice, but what becomes possible when people actually know who they are.

Further reading:

• Schein, E. H. (1990). Career Anchors: Discovering Your Real Values.
• Finkel, E. J. (2014). The All-or-Nothing Marriage.
• Kelly, S., & Zarcadopoulos, A. (2016). Behavioral Patterns in Health Decision-Making.
• Thaler, R. H., & Statman, M. (2014). Finance and the Psychology of Wealth.

The post AI is Becoming the World’s Life Coach appeared first on jeffbullas.com.

* This article was originally published here

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How Creators Make Money When AI Makes Content Free

The internet used to reward information. Then it rewarded attention. Now AI has made both cheap.

A single prompt produces a 2,000-word article in thirty seconds. Optimized. Structured. Perfectly forgettable. The content flood is not coming. It has arrived. And the creators who built their entire business on publishing volume are already underwater.

So what actually comes next? Not a philosophical answer. A practical one.

“The next scarce asset is human signal. The proof that a real person with taste, judgment, story, and lived expertise stands behind the work”.

This is important to me as the business model I built my business on over the last 17 years is over and I have been looking for what the future looks like and three creators I have researched here and observed for years prove exactly how that translates into a business that scales.

Chart 1: The Great Inversion — AI content volume has exploded while human signal scarcity makes authentic creators more valuable. Source: jeffbullas.com / zyrro.ai

The Problem With Publishing More

For fifteen years, content marketing ran on one equation. More content equals more traffic. More traffic equals more leads. More leads equals more money.

It worked. For a long time, information was scarce enough that publishing it had inherent value. Then three things broke it.

1. Facebook throttled organic reach. 
2. Google answered questions directly without sending you the traffic. 
3. Then AI removed the cost of producing the content entirely.

Information is no longer scarce.

The explainer role. The person who synthesises, summarises, and teaches is now contested by a machine that never sleeps, never charges overtime, and never has an off day.

The creators who built their identity entirely around being useful and educating are discovering, painfully, that usefulness alone is no longer a business. Because AI is now your informer and educator. 

So the burning question then is “what is” the new business model as the old one is broken. 

It requires you to have a human signal in a world of AI machine slop. 

What is a Human Signal? 

Here is a diagnostic. Ask it about anything you publish.

“Could an AI have written this?”

Not: is it well-written? Not: is it accurate? Could an AI have written this?

If the answer is yes and you cannot point to something specific that makes it irreducibly yours then it is noise. Not signal.

Your “Human Signal Stack” has six layers. 

And it starts with knowing your identity. If you don’t know who you are and what you stand for then you are going to be invisible and just blend into the crowd and the noise online. 

You will need to have a point of view, have an opinion. To actually make a stand for what you believe in. Knowing what you are angry about will also help.  

Here is the “Human Signal” stack that you need to build into everything you do as a creator.

Chart 2: The Human Signal Stack — Foundation layers are slow to build and permanent. Activation layers compound over time.

1. Identity. Not your job title. The constellation of who you actually are and your obsessions, your origin, your wound, your contradictions.
2. Story. The specific experiences that prove your point. Real moments. Not hypotheticals.
3. Expertise. Hard-won judgment from having been wrong enough times to know something true.
4. Evidence. Your own original research. Your tracked experiments. Your documented failures.
5. Interaction. The responsiveness that proves someone is home. Real replies. Real disagreements engaged. 
6. Community. The tribe that forms around your particular way of seeing — not just your topic.

The foundation layers: Discovering and building your Identity, story and expertise are slow to build but  permanent once established. 

The activation layers: These are evidence, interaction, community compound over time.

A creator who has all six is almost impossible to replicate. A creator who has none is indistinguishable from the machine. Now. The real question. How does this become a business?

Here are 3 people worth checking out. Look at their websites, read their writing, listen to their podcasts. Buy their books. I have read all their books and Tim Ferriss’s book “The 4 Hour Work Week” I have maybe read 3 times. 

Case Study 1: James Clear

James Clear is not a productivity expert.

There are thousands of productivity experts. Most of them are interchangeable. Most of them would fail the diagnostic question entirely.

Clear is something different. He is a man who fractured his skull in a high school baseball accident, spent months recovering, and used that experience to build a precise, personal understanding of how small habits compound over time. He did not read about resilience. He lived through a medical crisis and came out the other side with a specific theory about human behaviour, tested on himself first.

He launched a newsletter in 2012 before he had a book deal, a publisher, or a platform. What he had was identity. Story. And the discipline to write one idea, clearly, every week for years.

He did not publish more than anyone else. He published more consistently than almost anyone else, with more specificity and more personal authority behind every claim.

The “Atomic Habits” book sold over fifteen million copies. Not because it contained information nobody else had. But because the voice behind it was undeniably specific. You felt, reading it, that a real person had tested these ideas and paid something to arrive at them.

The business that grew around it — the courses, the speaking, the premium content — was not built on traffic volume. It was built on a reputation that could not be replicated because it was tied to a specific identity with a specific origin story.

The lesson: a narrow, deeply human point of view, published consistently over years, creates an audience that pays for access to the mind — not just the information it produces.

Case Study 2: Tim Ferriss

Tim Ferriss was not a business expert when he wrote The 4-Hour Workweek.

He was a supplement company founder who had worked himself into a breakdown, then spent a year conducting experiments on his own life to find a way out. The book was not research. It was a documented escape. Every claim traced back to something he personally tested on his own body, his own business, his own psychology.

That was the signal.

Not the ideas. Not the productivity frameworks. Plenty of people had written about outsourcing and lifestyle design before Ferriss. What nobody else had was the specific, verifiable, sometimes embarrassing account of one man running himself as a laboratory.

He extended that logic to his podcast. The Tim Ferriss Show does not position itself as an interview program. It positions itself as a place where one specific human being with documented obsessions, documented failures, and documented methods has access to world-class minds and is curious enough to extract what nobody else asks for. The signal is not the guest list. The signal is the host’s particular way of seeing.

Tim’s podcast has had over 700 million downloads.

The business that surrounds it — book deals, investments, brand partnerships — derives its value from the same source. Ferriss is not a media company. He is a specific identity that has earned the right, through documented experimentation and public vulnerability, to be trusted as a guide.

The lesson: publishing yourself as the evidence not just the author creates a signal that compounds. Every new experiment, every documented failure, every honest account of what worked adds another layer of proof. AI can produce advice. It cannot produce receipts.

Case Study 3: Alex Hormozi

Alex Hormozi built a gym. Then a gym licensing business. Then he watched it nearly collapse. Then rebuilt it. Then he sold it. Then did it again, at larger scale, across multiple industries.

He did not start creating content because he wanted to be a creator.

He started because he had accumulated, through genuine trial and failure and recovery, a body of business knowledge that was so specific and so tested that he could not stop himself from publishing it. The signal was overwhelming. You could feel, watching his early videos, that this was a man who had been somewhere most business content creators had never been, the specific, unglamorous reality of running a failing business and refusing to quit.

He did not optimize for production quality. He optimized for specificity.

The numbers he cited were his own. The failures he described were documented. The methods he taught were the ones he had personally used to move from near bankruptcy to building a portfolio valued at over $100 million.

The book “$100M Offers” became one of the most widely read business books of recent years not because it contained sophisticated theory but because it contained brutal, operational specificity that only someone who had built and sold multiple businesses could produce. You cannot fake that level of detail. The detail itself is the proof.

The content he publishes is free. Deliberately. The business model is not content monetization. It is signal monetization. The content establishes an identity so credible, so specific, and so clearly backed by evidence that the offers which flow from it — equity investments, advisory relationships, acquisition targets — attract at premium prices.

The lesson: radical specificity about your own failures and wins creates a signal that advertising budgets cannot replicate. Hormozi does not spend on paid acquisition. He does not need to. The signal does the work.

Chart 3: Human Signal Strength across three creator case studies — identity specificity, story depth, own-data evidence, and signal monetization. Source: jeffbullas.com / zyrro.ai

The Business Model Behind the Signal

Three different people. Three different industries. Three very different personalities.

Same underlying architecture.

The path from human signal to revenue runs through a specific sequence.

Chart 4: From Human Signal to Revenue — the five-stage conversion path from distinctive point of view to scalable income.

A distinctive point of view earns attention. Not mass attention. The right attention.  People who encounter the work and think: this person sees something I don’t. That is different from viral. Viral is cheap. Trust is expensive.

That attention compounds into relationship. Regular readers who come back not because you publish on a given topic but because they want to see what your particular mind does with it.

Relationship converts to transaction. Not through aggressive funnels. Through offers that feel like natural extensions of the signal itself. 

• Clear’s readers buy his course because they want more of his thinking. 
• Ferriss’s listeners pay for his book and event access because they trust the judgment behind it. 
• Hormozi’s clients pay premium prices because the signal pre-qualifies the relationship.

None of them are selling information.

All of them are selling access to an identity.

That is the human signal economy. And here is the economic reality that makes it durable: AI makes information infinitely cheap but it makes credible, proven human identity increasingly scarce.

The market price of the un-automatable is rising. Not as a cultural preference. As a structural market force.

The Villain Is Not AI

It would be comfortable to make AI the villain here.

It is not.

The villain is the system that trained creators to optimize for machines rather than for humans. The SEO machine that rewarded keyword density over insight. The social media algorithm that punished nuance and amplified outrage. The content marketing industrial complex that turned genuine human curiosity into production quotas.

AI did not create the problem. It exposed it.

It took the logic of machine-optimization to its logical endpoint and showed us where that road terminates: a world of infinite content with zero signal.

The creators who suffer most in the AI era are not the ones who used AI. They are the ones who had already become like AI and producing content that could have been written by anyone, for anyone, about anything, with no particular skin in the game.

If your content sounds like it was generated, the problem is not the tool. It is the absence of you.

What You Actually Do Next

Three practical moves. Not philosophical. Not aspirational. Executable this week.

First: run the diagnostic on your last ten pieces of content. Could an AI have written each one? Be honest. Mark the ones where the answer is yes. Those are your exposure. The places where you have been competing with an infinitely scalable machine.

Second: identify the one story from your own life that most directly proves your central argument. The specific moment. The specific cost. The specific insight it produced. Write it. Not as a personal essay. As the opening of your next piece of professional content. See what happens.

Third: stop optimizing for reach. Start optimizing for recognition. Reach measures how many people saw something. Recognition measures how many people thought: that could only have come from that person. One of those metrics builds a durable business. The other builds a treadmill.

James Clear spent eight years writing before Atomic Habits became a cultural phenomenon. 

Tim Ferriss filmed himself relentlessly before the audience became a business. 

Hormozi posted for years at zero production value before the signal broke through.

None of them found a shortcut. All of them found something more valuable: an identity specific enough to be irreplaceable.

The future does not belong to creators who publish more.

It belongs to creators who become harder to fake.

In the AI age, the most valuable content will not be the content that sounds the smartest. It will be the content that proves someone real is home.

The post How Creators Make Money When AI Makes Content Free appeared first on jeffbullas.com.

* This article was originally published here

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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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