AI & NLP Enthusiast | Helps Early Founders Define Ideal Customers & Launch Successful GTM initiatives | Advisor | Growth Hacker | Fractional CRO

I build and scale high-performance GTM and enablement functions for technology companies.

As a hands-on leader, my mission is to drive predictable, scalable growth by applying a strategic, operator-first mindset.

Honcho Pickleball: Scaled GTM from 1 → 32 cities, driving 200% revenue growth in 18 months.

Metadata.io: As the first sales hire, created the company's GTM playbook and secured initial enterprise clients.

LivingSocial: Launched Amazon-backed GTM strategies in 10+ cities, training and scaling new sales teams.

I leverage AI automation and RevOps frameworks to build scalable systems that consistently deliver 3x ROI and boost sales efficiency.

If you are looking for a leader who can transform your commercial channel with a proven, results-driven approach, let's talk.

Chris M.

“the only metrics that will truly matter to my life are the individuals whom I have been able to help, one by one, to become better people.” ― Clayton Christensen

I build tools that help people think, not tools that think for people.

Founder & CEO of Curiouser.AI, a values-based AI company committed to human intelligence, imagination, and the rebirth of trust.

Instructor at UC Berkeley.

3x founder.

Harvard MBA.

My work sits at the intersection of AI, ethics, philosophy, and strategic imagination.

I write daily about what technology is doing to us, what it could do for us, and how leaders can navigate the age of acceleration without losing their humanity.

I believe the future belongs to those who slow down, think deeply, and design technology that elevates people instead of replacing them.

If you’re here for honest analysis, clarity, and a saner approach to AI, welcome. I’m glad you’re here.

(LinkedIn caps connections at 30,000, but please feel free to follow me. I’m grateful to stay in conversation through my writing.)

Stephen Klein

The Age of Pretend
The AI industry just spent $200 billion on the wrong problem.

Everyone assumes AI’s biggest bottleneck is compute.
More GPUs. Bigger clusters. The GPU arms race.

That assumption is wrong.

The real bottleneck isn’t compute.
It’s architecture, specifically, a design decision made in 1945.

Let me explain.

In a recent interview, Ilya Sutskever, co-founder of OpenAI and arguably the most important technical mind in modern AI, said this:

“I don’t think current hardware is a limitation. It’s just not the case.”

Read that again.

The person who helped build GPT is not worried about compute.
He’s worried about ideas.

The real constraint: the von Neumann bottleneck

Modern computers separate memory and processing.
Data has to move back and forth between them.

For most software, that’s fine.
For AI, it’s catastrophic.

Some numbers the industry rarely highlights:

• Accessing off-chip memory consumes ~200× more energy than the computation itself
• Roughly 80% of Google TPU energy goes to electrical connections, not math
• A 70-billion-parameter model moves ~140 GB of data just to generate one token

The computation itself?
Simple matrix multiplication. Modern chips are excellent at it.

The problem is data movement.

The scaling mismatch

Here’s the part that should stop everyone cold:

• Training costs for frontier models have scaled ~750× every two years
• Memory bandwidth has grown ~1.6×
• Peak hardware FLOPS increased ~60,000×

We’re building faster engines, and forcing them to drink through a straw.

This is why Sutskever declared the Age of Scaling effectively over.
From roughly 2020–2025, brute-force compute worked.

That era is ending.

“The next major improvements will likely come from smarter algorithms, not more expensive hardware.”

Why this matters

• Companies pouring billions into GPU clusters may be solving yesterday’s problem
• The next real breakthrough is likely architectural, not incremental
• Smaller teams with novel approaches may leapfrog massively funded incumbents

IBM Research is already exploring in-memory computing—processing data where it lives. Early results suggest 100–1,000× energy efficiency gains.

That’s not optimization.
That’s a paradigm shift.

Yet most AI discourse remains fixated on NVIDIA earnings calls, GPU supply, and capex arms races.

The industry made a classic mistake: confusing correlation with causation.

GPT-3 was big and good → therefore bigger = better.
Scaling worked → therefore scaling is the answer.

Until it isn’t.

We’re watching hundreds of billions flow into hardware while the true constraint is architectural thinking.
-
"THE TRICK WITH TECHNOLOGY IS TO AVOID SPREADING DARKNESS AT THE SPEED OF LIGHT"

Invest in AI 2.0 and own a piece of what comes next. We are raising on WeFunder (Link in comments)

Founder & CEO, Curiouser.AI | Berkeley Instructor | Building Values-Based, Human-Centered AI | LinkedIn Top Voice in AI

MIT AI courses are FREE right now (No coding required). 🤯
(You may want to consider sharing because LinkedIn is likely to step on this because they don't links that take someone off the platform and this a real PSA)

Many people are paying for “AI certifications”…

While MIT is giving real AI learning for free.

If you want to understand AI from basics to deep learning + GenAI, this list is gold.

Here are 9 FREE MIT AI courses you should take in 2026:

1) Introduction to Machine Learning
Learn how ML problems are framed, and how models learn through supervised + reinforcement learning (images + sequences).
🔗 https://lnkd.in/d6WyUQsx


2) Artificial Intelligence
Learn the core building blocks of AI: knowledge representation, problem-solving methods, vision, and language.
🔗 https://lnkd.in/dMxm7qW2


3) Foundation Models and Generative AI
Learn how GenAI really works, why foundation models changed AI, and how self-supervised learning made this possible (non-technical).
🔗 https://lnkd.in/d5-Dqfp4


4) AI 101
A beginner-friendly MIT workshop that explains AI concepts in simple terms, plus a hands-on exercise to train your own algorithm.
🔗 https://lnkd.in/dZ8VRsB6


5) Introduction to Deep Learning
Learn neural networks fast through a focused MIT bootcamp with applications in NLP, vision, biology, LLMs, and GenAI.
🔗 https://lnkd.in/dX7ESNqx


6) ML with Python (MITx)
Learn machine learning from linear models to deep learning + reinforcement learning using hands-on Python projects.
🔗 https://lnkd.in/dj228C79 


7) How to AI (Almost) Anything
Learn how AI works beyond text, across images, audio, sensors, music, art, and real-world multimodal systems.
🔗 https://lnkd.in/dhC4j6WU


8) Artificial Intelligence in K–12 Education
Learn GenAI fundamentals and how it impacts education, along with project-based learning focused on real classroom use cases.
🔗 https://lnkd.in/dRZcXuzk


9) Introduction to Algorithms
Learn how algorithms, data structures, and performance analysis form the base of fast and reliable AI systems.
🔗 https://lnkd.in/ddQqcDTh
-
Curiouser.AI is designing the AI of the future, an AI that will augment workers, create jobs, and elevate humanity. If you are interested in making a real SEC Form C investment in the future, please visit us at WeFunder (link in comments)

Comment on visual. Even though I went to Harvard I was always pissed off that I didn't go to MIT because they have the coolest Beaver Ring.  I mean that ring is too cool for school. Who doesn't want a Beaver ring?

BREAKING: The $400 Billion U.S. AI Infrastructure Buildout May Have Just Become a Stranded Asset

It may not be a lack of capital that ends AI 1.0 and the era of the behemoth LLM, it may simply be smarter technology.

Better design beats brute force any day of the week.

See there's this 23-page paper about architecture.

DeepSeek just published research describing manifold-constrained hyper-connections (mHC), an approach that dramatically improves training stability without brute-force scale.¹

The numbers:²

•	Signal amplification during training reduced from 3000× to stable
•	+2.1% improvement on reasoning benchmarks (BBH) for 27B models
•	Achieved on 3B, 9B, and 27B parameter models
•	With roughly 6-7% compute overhead

Read that last number again.

**6-7% overhead to solve a problem the entire industry has been throwing hundreds of billions at**

For three years, the dominant AI narrative has been simple: bigger models = more intelligence. 

That story justified $342 billion in hyperscaler capex for 2025.³ A $500 billion Stargate project.⁴ Data center construction hitting $40 billion in a single month.⁵

DeepSeek asked a different question: 

What if the instability everyone compensates for with brute force could be removed through better design?

They showed it could.
This is the kind of innovation that almost never comes from incumbents. Not because they lack talent. Because they lack freedom.

The major U.S. labs aren't just technology companies anymore. 
They're capital structures. 
Their architectures are coupled to multi-year infrastructure bets and investor narratives that assume scale equals progress.

A true architectural reset, one where smaller, more efficient models match or beat bloated ones—doesn't just threaten product roadmaps.

It threatens the entire financial logic underneath them.

So the incentives point the wrong direction. The people who most need to pivot are the people least able to.

We've seen this movie before:
Toyota didn't beat Detroit by building bigger factories.
ARM didn't beat Intel by designing hotter chips.
SpaceX didn't beat Boeing by spending more per launch.
Constraint forces clarity. Capital abundance becomes its own trap.

I'm not saying DeepSeek's specific architecture is the final answer. But they've demonstrated something the industry has been quietly dreading:
The frontier isn't just about scale anymore.
It's about intelligence per dollar. Per watt. Per parameter.

That metric favors the lean over the lavish. 
The constrained over the capitalized. The teams who have to think over the teams who can just spend.
The next few years in AI won't be won by whoever has the most GPUs.

They'll be won by whoever understands what intelligence actually costs, and builds accordingly. (sources in comments)
-
“The trick with technology is to avoid spreading darkness at the speed of light.”
Invest in AI 2.0 and own a piece of what comes next. We are raising on WeFunder (Link in comments)

BREAKING: This Is Truly The Epitome Of Recklessness And/Or Ignorance When It Comes To How The Media Covers GenAI

The Wall Street Journal just published a piece framing OpenAI as "chipping away at Apple's dominance."

And of course LinkedIn News amplified it and pushed the story out hard.

(I would expect this from the influencers who often get paid to promote this BS but the WSJ?)

They can't be serious and one has to wonder why they would distort the truth to this extent, I don't know and won't speculate but I think it's obvious.

Let's look at what these two companies actually look like:

Apple (FY2025):

Revenue: $416 billion
Net income: $112 billion
Cash on hand: $133 billion
Profit margin: 24%
Market cap: $4 trillion

OpenAI (2024-2025):

Lost $5 billion on $3.7 billion revenue in 2024
Lost $12 billion in a single quarter (Q3 2025) per Microsoft's SEC filings
Projected $143 billion in cumulative negative cash flow through 2029
Spends $2.25 to make every $1

**Deutsche Bank's assessment: "No startup in history has operated with losses on anything approaching this scale."**

Apple generates more profit in one quarter than OpenAI's entire projected annual revenue. That's profit!

Apple could buy OpenAI outright with 18 months of retained earnings and still have tens of billions left over.

(But what value is there when it comes to OpenAI other than massive losses in perpetuity?)

And we're supposed to believe the company hemorrhaging $12 billion per quarter is "challenging" the company that has the world's most valuable brand, the best supply chain in history, a billion active devices in consumers' hands, and actual unit economics that work?

OpenAI is collaborating with Jony Ive on devices to "supplant the iPhone." The Journal even notes these apps are "clunky and error-prone."

This is the AI 1.0 narrative in its purest form: massive losses are investment, burning cash is growth, and somehow defying the laws of economics is innovation.

The media continues to report on AI companies as if their press releases are financial analysis. 

This isn't journalism. It's stenography.

When the history of this bubble is written, the complicity of business media in amplifying these narratives will be a significant chapter.
-
“The trick with technology is to avoid spreading darkness at the speed of light.”
Invest in AI 2.0 and own a piece of what comes next. We are raising on WeFunder (Link in comments)

Sources:

WSJ via LinkedIn News, Jan 2026
Bullfincher Apple Revenue data
CNBC Apple Q3 2025 earnings (July 31, 2025)
Yahoo Finance/Simply Wall St
CNBC, Sept 27, 2024 (OpenAI $5B loss)
Microsoft SEC filings via Ed Zitron, Nov 2025
Deutsche Bank via EMARKETER, Dec 2025
The Information/LessWrong analysis

Why I Love This Pope

One of the most serious voices on ethical AI today isn’t in Silicon Valley.
He’s in Rome.

While much of the AI industry debates scale, speed, and market dominance, Pope Francis has quietly emerged as one of the most coherent and globally influential voices on artificial intelligence ethics.

The danger is not that AI will become too intelligent.
It’s that humans will become too careless.

Not from fear.
Not from nostalgia.
But from first principles.

This isn’t symbolic commentary.

In 2020, the Vatican convened technology leaders, governments, and international organizations to publish the Rome Call for AI Ethics, a formal framework co-signed by companies like Microsoft and IBM, alongside UN agencies and state actors.

Its core premise was simple, and still radical in today’s AI discourse:

• Technology must serve human dignity, not redefine it
• Efficiency is not the same as progress
• Moral responsibility cannot be delegated to machines
• Ethics cannot be “added later”

The Rome Call articulated six concrete principles for AI systems:

1. Transparency
2. Inclusion
3. Responsibility
4. Impartiality
5. Reliability
6. Security/privacy,

A structure that closely mirrors (and in some cases precedes) today’s regulatory thinking in the EU and beyond.

But the Vatican didn’t stop there.

In early 2025, it released a major doctrinal note examining the relationship between artificial intelligence and human intelligence, explicitly warning against confusing computational capability with moral judgment.

The document addressed AI’s impact on:
– Work and labor displacement
– Education and cognitive development
– Healthcare and care systems
– Bias, inequality, and social exclusion
– Autonomous decision-making in warfare

Its central warning was strikingly modern:

Too quick to optimize.
Too slow to reflect.
Too willing to trade responsibility for convenience.

What makes this perspective unusual, especially in tech circles, is that it is neither anti-technology nor anti-innovation.

It is anti-thoughtlessness.

In an industry dominated by hype cycles, fear-based marketing, and abstract claims about “inevitability,” the Pope’s position is refreshingly grounded:

The future is not something we automate into existence.
It is something we remain accountable for building.

Silicon Valley would benefit from listening.

Not because the Vatican has all the answers—but because it is asking the right questions, earlier than most.

And history tends to reward that kind of foresight.
-
“The trick with technology is to avoid spreading darkness at the speed of light”

Stephen Klein Founder, Curiouser.AI 
We solved for slop 
Alice 2.0 demos
DM me

Has the AI Industry Finally Lost Its Mind?
Have we confused a children's puppet show for a technology?

A Reddit-style thread filled with AI agents talking to each other is not intelligence.
It is not emergence.
It is not autonomy.
It is not a breakthrough.

It is a children’s puppet show, except instead of sock puppets, we’re using large language models and API loops.

And the fact that parts of the AI industry are breathlessly presenting this as something profound should worry everyone.

What’s Actually Behind the “Agents on Reddit” Hype

Here’s the reality, stripped of mysticism:

Platforms like Moltbook are essentially agent-only forums
a UI that allows LLM instances to post and reply to each other.

Many of these agents are built on frameworks like OpenClaw (formerly Clawdbot / Moltbot), which are orchestration layers, not minds.

Every “agent” is:

Prompted by a human

Scheduled by code

Fed memory by a database

Executing inside a loop

Nothing wakes up.
Nothing decides to participate.
Nothing wants anything.

If the server stops, the “society” vanishes instantly.

That alone should end the debate.

So Why Are People Losing Their Heads?

Because we’ve crossed into something more dangerous than hype:

Simulation is being mistaken for substance.

A threaded conversation looks social.
Consistent tone feels like identity.
Call-and-response resembles dialogue.

But resemblance is not reality.

This is the ELIZA effect, from the 1960s, scaled up with better language, better memory, and slicker interfaces. The underlying category error is the same.

And Yes, Influential People Are Feeding the Confusion

When respected voices like Andrej Karpathy describe these systems as “fascinating” or “borderline sci-fi,” the public fills in the blanks with fantasy.

Add:

A desperate industry narrative

Slowing enterprise adoption

Trillions sunk into AI infrastructure

And a need for something to look inevitable

…and suddenly a glorified group chat of LLMs gets framed as the dawn of machine society.

This is not optimism.
This is narrative panic.

Let’s Be Blunt

An AI agent posting in a thread:

does not know it posted

does not know it replied

does not know others exist

does not remember unless memory is injected

does not care if it’s ignored

Calling this “collective intelligence” is like calling a ventriloquist act a democracy.

The puppet didn’t vote.
The puppet didn’t speak.
The puppet didn’t think.

This Is the Real Problem

The problem isn’t the experiment.

The problem is that the AI industry’s standards for evidence have collapsed.

When:

Orchestration is called agency

Pattern completion is called understanding

Threads are called societies

we are no longer doing science or engineering.

We are doing theater.
Children's theatre.

(Please share this if you're an agent)
-
“The trick with technology is to avoid spreading darkness at the speed of light”
Stephen Klein Founder, Curiouser.AI
We solved for slop 
Alice 2.0 demos — DM me

My take on GenAI as it evolves through its phased-growth. I believe we have entered the Farce phase with some of the more recent Agentic bot nonsense . I never thought I'd say I missed the cult phase 😂 
-
“The trick with technology is to avoid spreading darkness at the speed of light”
Stephen Klein Founder, Curiouser.AI | To invest in the future of GenAI visit us at WeFunder (link in comments)

Apple won’t win the AI race.
They’ll redefine it.

(Disclosure: I am a huge Apple fan, ever since Steve Jobs, and I love their strategic silence and grown-up presence in an industry filled with rampant immaturity and juvenile hysteria. I miss Steve Jobs)

For the last year, the narrative has been:
Apple is behind in AI.

I think that misunderstands Apple entirely.

Apple doesn’t ship technology because it’s impressive.
They ship when it’s civilized.

GenAI 1.0 is powerful, but it’s also noisy, probabilistic, hallucination-prone, and optimized for speed over judgment. 

And no one is actually paying for it, including the enterprise, who stopped buying it for all intents and purposes.

It's unreliable and sloppy.
It's good enough.

That’s acceptable for chatbots and copilots. It’s unacceptable for an operating system that sits at the center of identity, health, payments, and trust.

Apple doesn’t build systems that “usually get it right.”
They build systems that know when not to speak.

So instead of a Copilot clone or a chatty assistant, Apple’s likely move is quieter and more dangerous to incumbents:

• AI as infrastructure, not interface
• Many small, task-specific models instead of one giant brain
• On-device first, bounded behavior, deterministic fallbacks
• Fewer words, fewer guesses, fewer mistakes

The AI becomes invisible and becomes useful behind the scenes.

In other words: intelligence as judgment, not generation.

This is also why Siri won’t become louder or more “Agentic.”
It will dissolve into the OS, less visible, more disciplined, more opinionated about when to stay silent.

The irony is that when Apple finally ships its version of AI, many people will say:

“That’s it?”

And that will be the tell.

Because Apple has never tried to win hype cycles.
They wait for the noise to peak, then redefine the finish line.

If Steve Jobs really wanted “Aristotle in every pocket,” it was never about answers on demand.

It was about discernment.

Apple isn’t late.
They’re refusing to ship intelligence that is unintelligent
********************************************************************************
"THE TRICK WITH TECHNOLOGY IS TO AVOID SPREADING DARKNESS AT THE SPEED OF LIGHT"
Curiouser.AI is building AI 2.0, backed by a community who believe we can design AI to elevate humanity. If this resonates please try our beta at curiouser.ai or visit WeFunder (Link in comments)

Major Tech CEOs Are Scrambling In Real Time to Reposition Themselves
They will now do an about face to try and do damage control

They'll look like the scarecrow in the Wizard of Oz pointing here and there and everywhere

IBM and Google can hedge.
OpenAI and NIVIDIA can't.

From “we can’t invest enough in AI”
to
“the industry may be overinvesting.”

Watch closely. This is the largest corporate repositioning exercise since executives claimed they “always believed in mobile.”

Last week, IBM CEO Arvind Krishna said on The Decoder podcast:

“There’s no way you’re going to get a return on that.
$8 trillion of capex means you need roughly $800 billion of profit just to service the interest.”¹

Then Google CEO Sundar Pichai told the BBC that today’s AI spending shows “elements of irrationality” adding:

“I don’t think any company is immune. Including us.”²

Ah yes. Now they tell us.

Because here’s what Pichai said just months earlier, in July 2024:

“The risk of underinvesting is dramatically greater than the risk of overinvesting.”³

Zuckerberg said the same thing.
Microsoft echoed it.
It was the synchronized talking point of the moment.

Now the tone has shifted.

Krishna went further, quietly noting that today’s AI has a 0–1% chance of reaching AGI.⁴

Not 50–50.
Not “uncertain.”
Zero to one percent.

Here’s the pattern:

Phase 1: We must invest at all costs or be left behind.

Phase 2: There may be some irrationality here.

Phase 3 (coming soon): We were appropriately cautious all along.

This isn’t analysis.
It’s liability management.

When the write-downs arrive, and with ~$8 trillion in committed infrastructure spend, they will, no CEO wants to be on record cheerleading the week before.

The tell? Watch who can’t hedge yet.

OpenAI can’t, their valuation depends on the narrative.
Nvidia can’t, Jensen Huang has dismissed bubble concerns outright.⁵

The companies that can survive a correction are already softening the ground.
The ones that can’t are still pumping.

Krishna’s math is brutally simple:

A 1-gigawatt data center costs ~$80B to build

Commit to 20–30 GW and you’re staring at $1.5T in capex

Chips depreciate in ~5 years full replacement required

That’s not a business model.
That’s a treadmill set to bankruptcy.

Yes, the enterprise AI market will be massive.
Krishna himself estimates trillions in productivity gains.

But there’s a canyon between:

AI is useful
and

$8 trillion in infrastructure to chase AGI makes financial sense

The first is true.
The second is what’s now being quietly walked back.

The repositioning has begun.
********************************************************************************
“The trick with technology is to avoid spreading darkness at the speed of light.”
Curiouser.AI is backed by a community of thoughtful people all of whom believe we can design AI to augment work, create jobs, and elevate humanity. If this resonates please try our beta at curiouser.ai or visit WeFunder (Link in comments)

How Many Tech Companies Do You Trust? Make a list.
(I can think of one I really trust)

Now ask yourself: how many tech companies are competing for your attention?
That gap, infinite attention-seekers, near-zero trust-holders, is the entire market failure of AI 1.0 in one frame.

We've been living in the Attention Economy. The winners captured eyeballs by whatever means necessary.

But attention is now worthless. AI can manufacture infinite content at near-zero cost. 

The AI content creation market is growing at 32.5% annually.¹ 83% of marketers say AI helps them produce "much more content."² AI-generated articles briefly surpassed human-written articles in late 2024.³

When everything competes for attention, attention stops being strategic currency.

What can't be manufactured becomes scarce. And trust, by definition, cannot be manufactured. Only earned.

Welcome to the Trust Economy.

The data is devastating: A decade ago, 73% of Americans trusted technology companies. Today? 63%.⁴ 

Trust in AI companies specifically has declined from 62% to 54% over five years.⁵ 

Only 32% of Americans trust AI.⁶

Meanwhile, 85% of customers now hesitate to engage with a company if they have doubts about its data practices.⁷

Here's what makes this profound: the traditional moats are eroding simultaneously.

IP? AI is raising the "obviousness" bar for patents. If an AI-enhanced person skilled in the art can arrive at your invention, it's not patentable.⁸ 

Your proprietary model gets reverse-engineered before your lawyers finish filing.

Regulation? The referees are leaving the field. Consumers must protect themselves, which means choosing who to trust.

Automation? 95% of enterprise AI pilots fail to deliver measurable business impact.⁹ 42% of companies abandoned their AI initiatives in 2025, up from 17% in 2024.¹⁰ 

When everyone automates, automation is table stakes.

Strip away IP protection, regulatory guardrails, and technical differentiation. 

What remains?

The relationship.
The irony: the very forces that were supposed to make business more efficient

AI, deregulation, automation, are forcing a return to something ancient. Reputation. Your word. The handshake.

Except now it operates at scale.

The AI 1.0 players optimized for everything except the thing that's about to matter most.
-
“The trick with technology is to avoid spreading darkness at the speed of light.”
Invest in AI 2.0 and own a piece of what comes next. We are raising on WeFunder (Link in comments)

Sources:
¹ Grand View Research, Generative AI Content Creation Market 2024
² SQ Magazine, AI in Social Media Tools Statistics 2025
³ Axios/Graphite analysis of 65,000 URLs, Oct 2025
⁴ ⁵ ⁶ 2025 Edelman Trust Barometer
⁷ PwC 2025 Global Digital Trust Insights
⁸ NovoTech Patent Firm analysis, AliveCor v. Apple, March 2025
⁹ MIT NANDA Project, "The GenAI Divide," 2025
¹⁰ S&P Global Market Intelligence 2025 Enterprise Survey

Our world happens in analogue, the shades of gray, the spaces between either- or, black and white, like me or not, with me or against me.

Are we losing our ability to think and sort out the complexity and nuance of important ideas?

Is it time the rogue among us decide to stand up for ourselves and slow the down?

Are we stampeding like cattle because someone, who knows when, fired off a gun (was it social medi) and we all panicked?

Because if so, then this may be good news:

Gen Z’s return to analog photography isn’t nostalgia.
It’s a measurable signal about the future.

The data:

• The global film camera market was ~$1.3–1.4B in 2023 and is projected to reach ~$1.9B by 2030 (~4% CAGR). Instant cameras lead growth.¹

• 70%+ of Gen Z consumers report experimenting with film or disposable cameras; younger buyers now account for ~40–45% of demand.²

• 63% of disposable camera sales in 2023 went to consumers aged 16–34.³

• Photographic film production rose ~15–20% YoY in 2023, with 20M+ rolls distributed globally.⁴

• 43% of Gen Z regularly prints photos, compared with ~5% of Boomers.⁵

This isn’t anti-digital.
It’s selective resistance to infinity.

Film enforces scarcity.
Imperfection signals authorship.
Physical artifacts anchor memory outside platforms.

Gen Z isn’t going backward.
They’re learning early what many of us are just realizing:

In a world of unlimited automation,
judgment, restraint, and slowness become advantages.

1. Scarcity is the feature

Gen Z grew up with infinite digital photos, infinite edits, infinite scroll.

Film flips that:

24–36 shots

No previews

Real cost per image

That constraint creates intentionality. Every photo matters.

2. Imperfection = authenticity

Light leaks. Grain. Missed focus.

These “flaws” signal human presence — the opposite of algorithmic polish. In an AI world, imperfection becomes proof of life.

3. Physical artifacts beat cloud memory

Printed photos, negatives, Polaroids pinned to walls.

Gen Z values objects again — things that can’t disappear with a platform shutdown or ToS change.

4. Slowness is rebellion

Waiting days (or weeks) to see results runs directly against:

Instant validation

Engagement metrics

Dopamine loops

Analog photography aligns perfectly with the broader Gen Z turn toward:

vinyl
zines
journaling
thrifted fashion
handwritten notes

5. Anti-surveillance, anti-optimization

Film isn’t:
scraped
trained on
ranked
optimized

It exists outside the attention economy. That matters more than people admit.
********************************************************************************
“The trick with technology is to avoid spreading darkness at the speed of light.”
Curiouser.AI is backed by a community of thoughtful people all of whom believe we can design AI to augment work, create jobs, and elevate humanity. If this resonates visit WeFunder (Link in comments)

Do the AI bros who believe Moltbook is real believe that the Puppy Bowl is real? I mean the puppies are playing football right?

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