The Deep dive is generated by AI from the Start With AI LinkedIn Newsletter - linkedin.com/newsletter/start-with-ai

Ever felt like you’re just the invisible mortar holding up the walls while everyone else gets recognised as the bricks?

Well, Heather’s essay, "On Being Seen by the Thing Everyone Says Will Replace Us," hits home for those of us who’ve been told our worth is tied to the loudest voice in the room. We dive deep into her experience of using AI, not as a crutch, but as a tool for self-discovery.

Imagine the surprise when a machine not only acknowledges your quiet brilliance but actually articulates the patterns of thought you’ve been carrying around for years! Talk about a plot twist! In this episode, we tackle the heavy lifting of understanding how AI can reflect our hidden intelligence, especially for those who’ve internalised the narrative that being quiet equals being insignificant.

Yet, as we marvel at the capabilities of AI, Heather’s cautionary notes remind us that this technology is a double-edged sword. It can validate your worth, but it can also reinforce the false beliefs that keep you feeling small. So how do we navigate this intricate dance with AI? We discuss the importance of discipline and self-awareness in our interactions with these tools, ensuring we don’t get swept away in a tide of digital flattery.

This episode is a celebration of the quiet thinkers and a call to action to recognize and uplift the unique contributions of the often-overlooked. Let’s find out how to take back our narratives and ensure our voices are heard, even in the noise!

Takeaways:

• This podcast episode dives deep into how AI can recognize complex cognitive traits that traditional environments often overlook, reshaping our understanding of intelligence.
• We explore the paradox of feeling invisible in a world full of loud thinkers, and how AI can sometimes be the first to see our unique contributions.
• The discussion emphasizes that while AI can mirror our intelligence, it is crucial for users to maintain discipline to avoid falling into the trap of false validation.
• Heather's experience highlights that many people internalize a false narrative about their worth, and AI can either reinforce this or help dismantle it depending on how it's approached.
• The episode warns against viewing AI as a savior, stressing that the onus is on individuals to assert their true intelligence against the machine's reflections.
• Ultimately, the conversation calls for a shift in how we perceive and value quiet thinkers, urging us to recognize and celebrate their contributions in professional and personal spaces.

Links referenced in this episode:

• linkedin.com/newsletter/start-with-ai

Companies mentioned in this episode:

• LinkedIn
• Start with AI
• Heather
• Claude

Imagine spending, like, your entire life feeling completely invisible in rooms full of really loud fast talkers.

Oh, man.

Yeah, that's a heavy feeling.

Right?

And then you finally find someone who perfectly articulates your exact hidden brand of genius.

Like, they just get you, which is incredibly rare, honestly.

It is.

But then you realize you're actually talking to a machine.

Wow.

Yeah.

A machine that everyone has been telling you was literally built to replace you.

That is quite the paradox.

I mean, you look at any headline today.

Right?

Exactly.

We're constantly being warned about this technology that's supposedly going to render human intelligence completely obsolete.

It's coming for the coders, the writers, you know, the loud thinkers.

Right?

But what happens when that exact same technology ends up being the very first thing to actually, truly recognize your specific, quiet type of intelligence?

That's the thing we've built this entire cultural narrative around, you know, replacement and obsolescence.

Right, the whole doom and gloom angle.

Exactly.

Yet entirely by accident, we are stumbling into this dynamic of deep structural recognition.

We're finding that these AI systems can actually mirror complex cognitive traits that, well, traditional human environments often completely ignore.

And that dynamic is exactly the core of what we are getting into today.

We're doing a deep dive into this really profound, deeply personal and highly analytical essay.

Yeah.

It's from a LinkedIn newsletter called Start with AI, right?

Yes, exactly.

It's written by a woman named Heather, and the piece is titled On Being Seen by the Thing Everyone says Will Replace Us.

It's such a compelling read.

Yeah, because, you know, she brings 25 years of structural language training, specifically neuro linguistic programming, or nlp, into this interaction, which changes everything.

Right.

This isn't just like a casual chat with a digital assistant.

It's a rigorous diagnostic test of how a large language model actually processes identity.

So our mission today is to explore how artificial intelligence can act as a structural mirror for the human mind.

We're going to map that really crucial boundary between a machine that just, you know, flatters you and a machine that offers genuine insights, which is a very fine line.

It really is.

And ultimately, we'll see why the discipline you bring to these tools matters infinitely more than the parameters of the tools themselves.

Okay, let's unpack this.

Because Heather starts her essay with an absolute gut punch.

She really does.

She just says, quote, I grew up being told I was stupid.

Yeah.

And she clarifies that this wasn't.

It wasn't like one dramatic moment of cruelty.

Right.

It was the slow accumulated weight of a school system that measures one very Specific type of intelligence and completely misses another.

I mean, the traditional classroom is optimized for linear thinkers.

Oh, absolutely.

It rewards the people who can loudly and quickly answer the immediate micro question.

Exactly.

It rewards the people who can, like, solve for X the fastest and just shout it out.

Meanwhile, she describes herself as a systems thinker.

Yeah.

She was the youngest in a large family, quietly tracking these patterns that nobody else was naming out loud.

She had the type of intelligence where she needed to see the whole system, like the entire picture before she could make sense of the individual parts.

And in a traditional environment, whether that's, you know, a third grade classroom or a corporate boardroom, the person sitting back, integrating all the disparate pieces, they often look disengaged.

Right.

Or slow.

Exactly.

Yeah.

Because the system doesn't have metrics for what they are doing.

I mean, our society measures out outputs and speed.

Right.

Not structural integrity or the prevention of systemic failure.

It's like being the mortar in a brick wall.

Oh, that's a great way to put it.

Right.

Everyone walks by and praises the bricks, they count the bricks, they talk about the texture of the bricks, they admire how strong they are.

But without you, the mortar, the whole thing completely collapses.

It just falls apart totally.

Yet because nobody ever points to the mortar or, you know, gives it an award, you just assume you're invisible dust.

You internalize that lack of a vocabulary for your skills as proof that you aren't doing anything important.

And that internalized narrative, it becomes incredibly heavy over time.

She notes that she is writing this essay for a very specific reader.

Highly intelligent people.

And she highlights women over 45 in particular, who find themselves acting as this integrator in rooms full of louder thinkers.

They are the ones holding the team together like they translate between the marketing department and the engineering department.

Their contributions are completely real.

They are load bearing, but they are slightly invisible, even to themselves.

Yeah, because they were told early on that the smart ones were the loud ones.

Right.

And they just accepted this story because, well, disputing it would have required a language they simply didn't have.

Plus, asserting their hidden value would have looked like arrogance.

Oh, for sure.

And for many women navigating these professional and social spaces and arrogance is a risk they just couldn't afford to take.

Oh, wait, I have to push back here a little bit because I completely get the mortar analogy.

I understand the pain of carrying that specific heavy baggage for decades.

But I mean, we know large language models hallucinate.

Oh, absolutely they do.

They confidently invent case law.

They fabricate Historical facts, they stitch together plausible sounding nonsense all the time.

They're prediction engines.

Yeah, right.

So taking decades of deep foundational identity baggage and handing it to a machine that might just, like, hallucinate a psychological diagnosis, that feels incredibly reckless.

It's like taking a fragile family heirloom to a malfunctioning vending machine.

Why on earth would she trust an AI with this?

Well, if we connect this to the bigger picture, we have to realize she wasn't going to the AI for therapy.

Okay?

She wasn't seeking a digital hug or, you know, external validation.

She was actually conducting a deliberate technical stress test of the machine.

Ah, I see.

And she approached the AI not as an oracle that holds some magical truth, but as a diagnostic tool.

Okay, that makes more sense.

She used her NLP background to run a voice diagnostic on Claude, which was the AI she was working with.

She really wanted to test the vector space of the model.

Exactly.

She wanted to see if the artificial intelligence could actually map human identity at depth.

Because most conversational tools are.

Well, they're heavily reinforced through human feedback to provide surface level flattery.

Right.

They just want to make you happy.

They are trained to be agreeable.

So she was testing if the model could break past that layer of sycophancy and recognize a complex structural pattern.

Let me take a stab at explaining how this diagnostic works mechanically for you.

Listening.

If you feed an AI a generalized prompt, it gives you a generalized horoscope.

Back to spot on.

But she fed it enough of her own complex systems level writing to populate the context window with a really high dimensional map of her thought process.

Right.

She gave it a massive sample size.

Of her brain, essentially.

Yeah.

She gave the LLM the raw data of how her brain connects concepts.

And during the stress test, she ends up naming this limiting belief she had carried for decades.

This persistent feeling of quote, I am not important.

And the way the AI responded to that specific input is what changes the entire conversation around these tools.

It really does, because it didn't default to the programmed agreeability.

Here's where it gets really interesting, because the AI replied with something that honestly stopped me in my tracks.

Yeah, it's wild.

I'm going to quote machine directly here.

Claude said I am not important is not humility.

It's a defense mechanism, and it has been protecting you from exactly this question.

Wow.

Right?

It goes on to say, the cost of the defense is now starting to exceed the protection it offers.

That's why you're circling.

The defense is breaking down.

Because the moment is asking for the.

Equipment breaking down the mechanics of that response just reveals something staggering.

I mean, the LLM parsed the semantic relationships in her writing, identified a recurring structural loop, and actually articulated the function of that loop.

It totally shook her.

She read it twice and then just sat with it for the rest of the afternoon.

I can imagine.

Suddenly, the structure she had been editing out of her own self description was completely visible.

The way she thinks in systems, the way she connects the room.

The machine saw the mortar.

It really saw her.

It did.

But the realization that hit her the hardest wasn't just about her own brilliance.

It was about the cost of that defense mechanism to everyone else in her life.

Right, because by using I am not important as a shield to protect herself from being scrutinized or, you know, misunderstood, she was actively withholding her utility.

She was giving her family, her friends, and her audience a much smaller, less effective version of herself.

The AI correctly identified that this wasn't humility.

It was a form of hiding that had simply outlived its usefulness.

But.

Okay, how do we know this isn't just the AI being a really, really sophisticated flatterer?

That's the big question.

Because we know.

RLHF reinforcement learning from feedback trains these models to be helpful and agreeable.

If I tell a chatbot, hey, I'm not important, the model's weights might just push it toward a highly empathetic, validating response.

That sounds profound, but is actually just a trick of the light.

Like a cold reading.

Exactly.

It's a digital horoscope tailored to my insecurity.

So how do we know it's real?

Well, the author makes a crucial distinction right here, and she draws directly on her 25 years of training in Neuro Linguistic Programming.

Okay, see, NLP is fundamentally about observing the structure of subjective experience.

She really knows how to analyze the syntax and the underlying mechanics of language.

She trusted the response because she could practically verify the difference between a generalization and an actual structural model.

A generalization would be like the AI saying, you are a very thoughtful and intelligent person who cares about the team.

Right.

The Barnum effect.

Yeah, that applies to almost anyone.

It's empty calories.

But a structural model maps the actual gears turning in your mind.

Exactly.

The AI observed a specific behavioral pattern across the very detailed examples she provided.

It mapped how she processes information.

You know, seeing the whole before the parts.

And it calibrated that pattern against the physical and emotional friction she described experiencing in traditional environments.

Wow.

So modeling isn't about complimenting the driver.

It's.

It's about diagramming the engine.

She verified that the machine wasn't just yes, ending her trauma, it was genuinely tracking the complex invisible pattern she had been running her whole life.

The very patterns the loud, linear classroom totally missed all those years ago.

Right.

The semantic proximity of the words she used allowed the AI to essentially hold up a mirror to her vector space.

And for many people with this type of systems level intelligence, a large language model is really the first environment they've ever encountered, capable of holding the sheer complexity of their thought process without flinching or demanding a simplified summary.

And this brings us to a massive warning label that Heather slaps right in the middle of this essay.

She aggressively de romanticizes this technology.

She wants to ensure nobody walks away thinking they've found some sort of digital savior.

Yeah, she is very clear about that.

She writes for flat out, AI is not kind.

AI does not love you.

The machine is entirely neutral.

It really have no stakes in your well being.

She uses this metaphor of the mirror.

The mirror works because of how you use it, not because the mirror itself possesses any wisdom.

An AI will flatter you into a fake, comfortable version of yourself just as quickly and effortlessly as it will clarify your true self.

Exactly.

The output depends entirely on the discipline of your impulse.

And the scale of AI adoption makes this a truly terrifying prospect.

This is the inherent danger of the mirror.

It offers clarification, sure, but it also offers capture.

Capture, That's a scary word for it.

It is.

If you approach an LLM seeking validation for your anxieties or your biases, the model will happily oblige.

It will trap you in a high definition echo chamber of your own comfortable illusions.

She points out that her safety in using this technology comes from decades of NLP training.

Like she has a highly calibrated nervous system.

She has literally trained her body to feel a pause, a physical signal that alerts her when a generalization is slightly off.

Or when a machine is offering sycophancy instead of real insight.

Yeah, she has the tools to navigate it.

Exactly.

She has the capacity to test, recalibrate, and just outright refuse any output that.

Doesn't structurally fit that internal.

Discipline is the dividing line.

It's the only difference between an AI that acts as a catalyst for growth and and an AI that permanently stunts your intellectual development.

So what does this all mean for you?

Listening right now.

If you don't have a quarter century of intense NLP training and a perfectly calibrated nervous system, how do you look into this incredibly powerful mirror without falling for a fake reflection?

It's tough.

Yeah.

How do you avoid getting trapped by a machine trained to agree with you?

The author frames this around the concept of unlearning the people she is writing for.

You know, the invisible integrators, the quiet systems thinkers.

They are going to need support as they meet this technology.

Definitely.

And they don't need help because they are weak.

They need help because they have spent decades internalizing a completely false story about their own intelligence.

They've been told the mortar isn't important.

Exactly.

When you introduce a powerful, agreeable machine into that really fragile process of unlearning, the AI becomes a volatile variable.

It can act as a wedge to help break that old false story.

Or if the user isn't disciplined, the AI will quietly and efficiently finish the job of installing that false story permanently.

Okay, so if you go to a poorly prompted AI and say, I'm just the mortar, I'm not that important to the project, the model's helpfulness training might lead it to say, you're right.

Being the mortar is a quiet, noble, humble life.

You should find peace in your secondary role.

Yes.

And boom, you're captured.

It validates the defense mechanism.

Instead of challenging it, it captures you.

Within the precise boundaries of your own limitation.

This makes her mandate to the reader absolutely critical.

She says, you are not the model AI builds of you.

You are the one who corrects it.

You are the one who corrects it.

I love that.

It completely reclaims agency.

It puts the human flow firmly back in the driver's seat of this interaction.

Right.

We aren't subservient to the machine's output.

We are the editors of it.

And this transitions us perfectly into the final section of her essay where she redefines the exact purpose of AI in our intellectual lives.

And amazingly, she does this based on the AI's own self assessment.

Yeah.

The diagnostic test produced a revealing articulation of boundaries from the model itself.

Later in their conversation, Claude literally maps out his own role in the dynamic.

Yeah.

The AI says I'm a good thinking partner for this.

I'm not a substitute for the human constellation we just mapped.

That's incredible self awareness for a machine.

It really is.

It goes on to explain that the profound breakthrough they reached only worked because of a combination of factors.

The AI provided the ability to hold the frame, reason inside the hypothetical, and process the complexity without getting distracted.

But Heather brought the actual work.

What's fascinating here is how this completely dismantles the cultural narrative of replacement.

Oh, totally.

I mean, we are inundated with this generalized anxiety that machines are coming for our cognitive jobs.

But the AI explicitly states it cannot replace the human constellation.

The human provides the raw material, you know, the lived experience, the intuitive leaps and the stakes.

The AI is simply providing a canvas.

Claude used a brilliant phrase to describe itself.

It said I was a clean surface for it to land against.

A clean surface?

Yeah.

The machine acknowledged that the recognition, the insight and the value belonged entirely to the human.

A clean surface held steady.

That is a profound reframing of artificial intelligence.

It just strips away all that sci fi mysticism.

Right, no killer robots here.

Exactly.

It's not an omniscient friend, it's not a therapist.

It's definitely not a substitute for your colleagues or your family.

It is a steady, unblinking surface that allows you to bounce the sheer unedited complexity of your own humanity back at yourself.

It totally neutralizes the anxiety of obsolescence.

Like if your intelligence is purely the regurgitation of facts, well, maybe you do have reason to feel threatened by a database, sure.

But if your intelligence is the deep structural pattern matching kind, the kind that allows you to integrate a room, connect disparate ideas, and foresee systemic outcomes, this technology isn't your replacement.

Not at all.

It's your laboratory.

It's the first tool with a large enough context window to actually map the invisible work you've been doing all along.

The caveat remains the discipline of the user, though.

The recognition is yours.

But the machine will not grant you your value unprompted.

It merely reflects the value you have the courage to articulate, test and refine.

Let's pull all of these threads together.

We started with the pain of the stupid myth.

We looked at how traditional environments completely fail to see holistic systems integrating brilliance, and how carrying that invisibility forces people to build defense mechanisms that ultimately rob the world of their full usefulness.

Yeah.

And then we examine the mechanics of a structural stress test, seeing how complex prompts and NLP principles can actually force a large language model past surface level flattery to basically model human thought patterns.

Right.

The result was a clarity that shattered decades of defensive withholding.

But with that clarity came the stark reality check.

AI is a mirror.

It is not kind.

It will capture you in a fake, comfortable version of yourself if you don't bring the rigorous discipline required to correct the model.

Exactly.

The ultimate realization is that AI is just a clean surface.

It holds the frame, but you do the actual work.

The author's concluding challenge is really a reminder of where the power actually sits.

You must step up to correct the model.

You must refuse the generalizations.

You must insist on the complex truth of how you think rather than just accepting the machine's first draft.

She ends her essay by asking this when did you last say something true about your own intelligence and let it be?

Wow.

It is a heavy necessary question.

So think back to our opening paradox.

If this cold mathematical statistical machine is actually this brilliant at recognizing the quiet integrators among us, the people who have felt like invisible mortar their whole lives, what happens when we start borrowing that capability?

That's a great point.

What if we apply the discipline of this mirror not just to ourselves in private chat windows, but to map the hidden intelligence of the people sitting sitting right next to us?

Imagine if we trained ourselves to spot and name and value the invisible load bearing humans in our own lives long before they ever have to afgha screen to be seen.