I help founders get ready and connect with investors • Seasoned entrepreneur • Building tech startups for 25yrs (zero→£mil ARR, twice)

With 30 years in startups, I now run the Gipity AI Dev Studio: Expert help to launch your MVP in 30 days. www.gipity.com

𝐌𝐲 𝐡𝐢𝐬𝐭𝐨𝐫𝐲... 👇

1968 BC (Before Computers): Born into analogue world

1982-86: Discovered computers! Vic 20, BBC micro, ICL 2900 mainframe & more

1986-89: Computation degree, UMIST (University of Manchester, Institute of Science & Technology)

1989-99: IT consultant/project manager: Logica, IBM, Reuters, Bankers Trust & Deutsche Bank

1994-97: first side hustle: scuba diving instructor & own London dive school

1996: published first website & first ever disk-mounted website on a UK magazine cover

1999: developer/project manager at 2 early London digital agencies

1999: vision/architect/investor. created first UK under-a-tenner domain name business, Easily

1999: built+launched early/successful dot-com: domain/email/web hosting ISP

1999-2001: ceo. managed growth from 0 to 2,000 daily domain sales & 30+ staff in under 2yrs

2000: invented the web-based DNS dashboard for domains

2000: created early no-code solution for one-page websites: £100k ARR

2000: highly commended "Entrepreneur of the Year": European E-commerce Association E-business 2000 awards

2001: 64th in Sunday Times "Top 100 pre-IPO e-businesses in Europe" E-League

2001: ran one of the first successful web affiliate schemes, generating 40% of rev

2004: hit 400,000+ domain registrations, 30,000+ websites & 40,000+ email accounts

2004: 3,500 commission earning affiliates & word of mouth to 100,000+ customers

2004: negotiated successful multi-million pound dot-com exit/sale of Easily

2002: vision/investor. launched one of the first ever B2B mobile text marketing services: SMS Expert

2007-11: early digital nomad: B2B Saas startup from beach/desert

2007-19: ceo. ran leading text marketing B2B Saas: zero to multi-million turnover

2007-19: cto/architect. developed 850k lines of code & supported 1mil daily transactions/API calls

2020... consultancy/advice/support for early-stage tech startup founders

2022... helping hand-picked founders find the right investors

• 2018: vision/architect/investor/cto...
Clickacall: instant video calls from anywhere with single click
Nearevo: proximity discovery for internet of things
Scan+Click: contactless business cards
Secly: personal online security

• 2021 l2o: supporting next generation of startups
• 2022 Venture Cards: all startups, in one place • connecting the right founders and investors

• 2024 The Gipity AI Dev Studio: Expert help to launch your MVP in 30 days

Steve Procter

Can someone tell me why so many founders still think investment is for building version one of their idea?

Fundraising is for scaling your idea after proving you can convince customers and the market.

Luckily with AI you no longer need to raise to launch a startup. So all that energy-zapping, pitch-decking nonsense can wait until much later.

Just build and get selling. That's all that matters.

How do we get this across to founders who still think angel investors hang out on street corners throwing money at passers-by?

Launch your MVP in 30 days | 30yrs in startups (exited. 0→£mil * 2) | Replit ambassador + expert AI builder

Why is the startup circus still spouting this sort of guff? "As a founder, chase velocity and go crazy for the scale fast mentality. Chase down investors like your life depends on it. If you see one in the street, rugby tackle them."

Founders need to feel free to ignore it, and realise they can be a very profitable and successful business without jumping on the investment treadmill and pretending they're the next Lovable.

Let's support them with more realism. Not ridiculous scare mongering that puts off a lot of people from ever starting a business, because they think anything less than £100m ARR is failure, or hear that not raising is wrong.

Let's encourage founders to build MVPs fast, get out there and start talking to customers and selling.

And then cheer them when they reach revenues that a VC may turn their nose up at, but are life altering for many people.

I'm re-watching Billions, and a line hit me like a thunderbolt yesterday:

"Audience is moat". That's it. Let it sink in. Nothing else matters.

If you want to spend 12 months building what you think is the best app ever, but never show it to anyone, then you are wasting your time.

Put together v1 of your MVP - fast! Then get out there and show people.

Spend 100% of your time growing and talking to your audience.

Stop worrying your idea isn't completely new. Let's be honest, very little is truly new any more. The whole world is a wrapper. You can sit and listen to armchair advisors who blindly preach "wrappers bad". Or you can JFDI.

If your offering has the potential to improve people's experiences, make processes and operations more efficient, create access to new audiences, lower prices, or bring other benefits, then you've got a thing people will buy.

Or you may have just figured a way to reach a particular market that others haven't spotted, or can't be bothered with because it is too niche for them.

Get started ASAP, build v1 fast, and spend all your time demonstrating it and talking to potential customers. This is the only way you will learn if you're onto something with potential.

As a potential founder, if you spend all your time looking for an elusive magic startup idea that is 100% unique, you will still be waiting for the right opportunity 100 years from now.

The only moat left is speed. So just get on with it already!

The UK has some incredible tech entrepreneurs and visionaries, and we seem to have no problem starting things. But something is off when it comes to turning our ideas into businesses that dominate globally. Matthew Syed in The Times recently outlined a bigger theory which, in my mind at least, sums up much of what is wrong with tech and innovation in the UK:

"Sir Alex Ferguson, the Man U manager said that the secret of his success was instructing his players not to go all defensive after scoring.

The psychology is significant: teams are bold, risk-taking and creative when seeking to win a match, but they sit back and worry about mistakes when trying to protect a lead. This creates the right conditions for the thing they most fear.

Ferguson told his players the best way to gain three points from a position of 1-0 up is to go out and score again.

This is doubly true in the ferociously competitive world of geopolitics. I look at Vietnam, America and Poland and see bold nations seeking to win, to drive forward, to try new things and fail, and to learn along the way.

Then I look at the UK (and, for that matter, much of the EU) and perceive an old nation that was once the envy of the world but is no longer striving to win but desperately trying not to lose, a very different thing.

Process has replaced progress, the classic symptom of what Weber called the bureaucratisation of a polity. This is action designed not to maximise innovation but to minimise mistakes."

Launch your MVP in 30 days | 30yrs in startups (exited. 0→£mil * 2)

Come on founders! Stop thinking about growth as a bolt-on for your startup's shiny new app, with its "perfect" lines of code hand-polished by unicorns. Distribution isn't a later problem, it's the whole flippin' game.

The best product is the one your audience can't stop talking about, and will help you shape if you get it in front of them early.

As an example, look at the new Clawdbot AI platform. The founder pushed it hard inside X, and then watched the world ignite. People are going nuts for it, and you can't open social media without being bombarded by "how I use ClawdBot" posts with thousands of comments.

But here's the thing - ClawdBot is far from finished. It's really only a proof of concept and a big set of tools for techies to try things out.

But the guy who created it is now getting feedback from tens of thousands of real users about what works and what doesn't.

Many of them are huge fans and ambassadors for life. You simply can't buy that kind of audience and following early on.

If you have an idea, build version one of the MVP - fast! Then spend 100% of your time out there, telling the world, and getting feedback.

Tech is easy to replicate these days. So maybe the new startup play is...

Build audience → sell your service → grow clientbase → sell your business FAST

If you're planning a new startup in 2026, don't try to guess the next trend. Everything is now happening, all the time, to everyone, everywhere. The signal hasn't disappear, it's drowned. So as a founder, just go with the vibes.

Do what you know, in a space you understand and are known.

It has as much chance of being the next trend as anything.

What matters is speed and having an audience.

Vibe coding for serious business ideas is over. A 2025 phenomenon. Sure, continue to have fun creating hobby apps - everyone should try this. But for real startups, this is the year AI-assisted dev tools move back into the hands of experienced builders, and without the silly price tags of traditional development agencies. As a founder, this will let you put your focus back where it should be - on sales and growth. Welcome to 2026.

In 2026 there will be two types of people using AI to help build your app:

1. smoke & mirror snake-oil developers who still charge 5 and 6 figure sums, convincing you they are doing traditional development. They don't realise you saw behind the curtain in 2025 and know the truth

2. amazing experienced developers, who bring their expertise in the whole software lifecycle (architecting, planning, design,...). They will show you a new world where solid MVPs and other apps are affordable for everyone

Make sure you choose wisely, my founder friends. And welcome to 2026.

“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?

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

Build Different
(Update on Curiouser.AI Progress)

Many of us are beginning to reaffirm something we almost forgot.
Thinking still matters.
Most of us knew that.
Some forgot.

It's seductive to get sucked into relying 
on something that thinks for you.

I get asked a lot, "But how do I know I'm thinking less?"
And my response is always the same,
"If thinking is getting easier and faster then you are thinking less."

Thinking is hard.
It's supposed to be hard.
That's the point.

Excellence only emerges from struggle.
There are no shortcuts.

A tool never made any one talented or even brilliant.
It magnified what was already there.

Steve Jobs said build a bicycle for the mind.
The industry has built a wheel chair.

Steve Jobs said put Aristotle in everyone's pocket.
The industry has created a cocky know-it-all.

At the beginning of last year, we had no product.
We ended it with something far more meaningful: real users, real revenue, real momentum, and a growing amazing community of people who believe AI should amplify human judgment, not replace it.

Here's some of what happened in one year:

Went from no product → beta → revenue
(We will have an updated product in weeks)
Grew from 0 to 70,000 followers here on LinkedIn (in under a year)
Built a community of hundreds of deeply engaged customers
Are approaching $10,000 in monthly recurring revenue
Achieved ~80% gross margins
Converted ~60% of trial users into paying subscribers
(That compared to an industry average of 1-2%)
Sustained ~16% month-over-month growth
Did all of this with $0 in customer acquisition costs
Raised just under $500,000 in total

And we are doing it differently.
Instead of building an AI to think for you
we built an AI to think with you.

Our AI, Alice, is designed around curiosity, not commands. 
She works the way a great co-founder or partner does
taking the time to understand you, asking better questions, 
and helping you go deeper until real insight emerges.

Through a Socratic approach to thinking, she doesn't rush you to answers. 
She helps you slow down, trace problems to their root, and discover perspectives you couldn't reach by prompting alone.

Alice is still a beta and we'll keep listening. 
And we'll keep building AI that helps people ask better questions and think more deeply.

We Owe Our Customers, Investors, and Community Our Gratitude

None of this exists without you. 
Thank you for believing early.

In the end, it comes down to building AI for people 
who refuse to be replaced.
********************************************************************************
THE TRICK WITH TECHNOLOGY IS TO AVOID SPREADING DARKNESS AT THE SPEED OF LIGHT

Stephen Klein is Founder and CEO of Curiouser.AI. He teaches Marketing in the Age of AI and AI Ethics at UC Berkeley. He is working with Georgetown University Press on a book scheduled to be published in Q2 2026 on business strategy in a post AI world.

We are raising money from our community on WeFunder (link in comments)

-
Stephen Klein
Founder & CEO, Curiouser.AI | Berkeley Instructor | Thought-Leadership Advisor | Working on an upcoming book with Georgetown University Press: The Truth Economy: Competitive Strategy in a Post Automation World

“The trick with technology is to avoid spreading darkness at the speed of light.”

Link for more information in comments

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)

BREAKING: Yesterday, Oracle announced plans to raise $45–$50 billion to expand AI infrastructure capacity.

But we have a tale of two Oracles.
One kind of hidden.
One in your face.

Legacy software company doing some AI PR?

But the company lives in two contradictory worlds emblematic of the GenAI industry

In one universe, Oracle announces plans to raise $45–$50 billion in 2026 to expand AI data-center capacity.¹

The stock rises.

In the other universe, Oracle’s credit profile has weakened, with multiple negative actions and outlook revisions from major rating agencies over the past year.²³

Same company.
Same moment.
Two very different signals.

What’s going on here?

Universe #1: The Equity Narrative

From an equity perspective, Oracle’s announcement checks every box the AI market currently rewards:

• Large-scale AI infrastructure exposure
• “Contracted demand” tied to AI leaders like NVIDIA, Meta, OpenAI, and **xAI**¹
• Massive capex interpreted as inevitability and strategic relevance
 
**Reframing Oracle as an AI infrastructure provider rather than a legacy software company**

In today’s market, capacity expansion is treated as proof of future dominance, even when profitability is deferred.

Equity markets reward participation in the AI narrative, not capital discipline.

Universe #2: The Credit Reality

Credit markets operate under a different logic.
Rating agencies and bond investors focus on:

• Rising leverage tied to AI infrastructure build-outs
• Long payback periods for hyperscale data centers
• Margin uncertainty in cloud infrastructure (already a low-margin, capital-intensive business)
• Counterparty concentration risk when “customers” are themselves cash-burning AI firms
• Exposure to power costs, utilization risk, and build-out overruns

This is why Oracle’s debt profile has drawn increasing scrutiny, even as its stock benefits from AI optimism.²³

**To bondholders, this looks less like clean growth, and more like risk being layered onto the balance sheet.**

What’s being described as “AI demand” here is not end-market revenue demand in the traditional sense.

It is largely intra-ecosystem spending:

• AI companies funded by Big Tech, venture capital, or strategic investors
• That capital is spent on GPUs, cloud compute, and data-center capacity
• Cloud providers borrow heavily to build infrastructure
• That capacity is sold back to the same capital-funded AI ecosystem

Revenue is recognized.
Cash flow is delayed.

Stock prices respond to stories (fake)
Credit ratings respond to cash flow (real)

When those two signals diverge sharply, it’s rarely because credit markets “don’t understand innovation.”

It's because they understand the math before the narrative breaks.

(sources in comments)
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“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)

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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