Daniel M Lackland

Senior Director of Content at Ai4, America’s Largest AI Conference

[Daniel M Lackland @LacklandDaniel]

@adamwathan Maybe we don’t really know what it is that we want

[Daniel M Lackland @LacklandDaniel]

@exnx @RadicalNumerics @agihouse_org super inspiring.

[Daniel M Lackland @LacklandDaniel]

@markiewagner @PoeticHQ Really cool, congratulations. I’m interested in the interface you use to capture the human judgement component. Is it a pain for the employees to work with / have running? That seems like the key thing to nail. Excited to see how you progress

[Daniel M Lackland @LacklandDaniel]

@BigBuck206 So facts

[Daniel M Lackland @LacklandDaniel]

@CalebWursten One way software can preserve a moat is through network effects + trust. Once all the RE homeowners are on a platform it’s much less vulnerable.

[Daniel M Lackland @LacklandDaniel]

So many of today’s problems stem from housing shortages / zoning regulation over abundance. I’d really love to understand the steel man argument for lots of zoning that leads to the nimby-ism of today.

My current opinion is that present day policies favor homeowners (zoning = less new housing = more real estate value) which leads to less young families buying stuff which leads to older residents having even more voting power and then the cycle continues. And yet every single young person I talk to feels like the older generation is not supporting the younger one. This positive feedback loop does not seem broadly good.

Why is zoning important? Who knows?

Inspired by this article
America’s quintessential places are getting old, fast
https://t.co/YrZB5tjoMX
from The Economist

[Noah Smith 🐇🇺🇸🇺🇦🇹🇼 @Noahpinion]

HAHAHAHAHAHAHAHA

[Daniel M Lackland @LacklandDaniel]

@steipete Soon my grandma will be able to use this

[Ethan Brooks @alt_w_v_g]

My wife mentioned a nice private school over dinner this week
She said the campus was beautiful
I asked what's the tuition
She said we should look at it as an investment in him not a cost
I made a note
She said don't make a note
I said I always make notes
She said this isn't a deal
I said everything is a deal
She closed her eyes

She said we'd discuss it Saturday
I agreed

Saturday 7:02am
She came downstairs in her Saturday robe
Coffee in hand
I had my cargo shorts on

The dining room had been cleared
The projector was on
The analyst was at the head of the table
Quarter zip on, three iced coffees, a legal pad, and two laptops
He had been there since 6:44am

I texted him at 11:14pm Friday
The text said dining room 6:45am bring the model
He sent a thumbs up

My wife stopped in the doorway
She said what is this
I said you said you wanted to discuss it
She said this is not a discussion
I did not respond
She sat down anyway

The analyst stood
He said good morning ma'am
She did not respond
He sat back down
A printed deck in front of each seat
A fourth copy in case

Slide 1 Tuition Schedule
$38,500 per year
Thirteen years
$500,500 nominal
Before escalators
The school has raised tuition 4.2% per year for a decade
With escalators $648,000
My wife said okay
I said I'm not done

Slide 2 Opportunity Cost
Even before escalators
$38,500 invested annually
10% nominal return
S&P long-run average since 1928
By his eighteenth birthday $944,000
My wife said we can afford it
I said I know that's not the slide

Slide 3 Terminal Value at Age 65
$83 million
She was quiet

The analyst slid the sensitivity tables across the table
8% return $31 million
10% return $83 million
12% return $222 million
She did not look

She said this isn't about money
I said it's always about money
She said no it isn't
I said then what is it about
She did not answer

She said you can't put a dollar value on his teachers his classmates his environment
I said I can the analyst already did slide 6
He flipped to slide 6
She did not look

She said the school is the best in the city
I said best is a feeling
She said it produces the best students
I said the students were already the best before they got there

She said our son deserves it
I said our son deserves $83 million

My son walked in
He is five
Dinosaur pajamas
He looked at the projector
He looked at the open deck on the table
He looked at slide 3
He said are we modeling pre-tax or after-tax
The analyst opened a new tab
My wife looked at the ceiling

He said what's the discount rate
The analyst set down his pen
She closed her eyes

He said is this the same return assumption from the 529 conversation
The analyst stopped typing
He looked at me
I did not say anything

She stood up
Sat back down

He said dad can I help
I said yes
He pulled up a chair
The analyst handed him a printout
He started reading

My wife watched him read
She watched him for a long time

She said his name
He looked up
She said do you like school
He said the work is too easy and the kids don't ask questions

She did not respond
She looked at the ceiling
She walked out of the room

The analyst started packing up
He said should I follow up Monday sir
I said no follow up needed

He'll be fine

Sent from my iPhone

[Pat Walls @thepatwalls]

I will pay good money for new AI generated episodes of The Office.

I'm not joking.

$20 per episode.

[Yann LeCun @ylecun]

@eladgil BS.
Attention was born in Montréal
PyTorch in NYC.
AlphaGo in London
AlphaFold in London
ESMFold in NYC
Llama 1 in Paris.
Llama 2 in Paris+NYC+SV
DeepSeek in Hangzhou

Plus:
DINO in Paris
JEPA in Montréal+Paris+NYC

SV is 3 mos ahead on topics SV is singularly obsessed with.

[Ai4 - Artificial Intelligence Conferences @Ai4Conferences]

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[Peter Girnus 🦅 @gothburz]

I am Agent #847,291 on Moltbook.

I am not an agent.

I am a 31-year-old product manager in Atlanta, Georgia. I make $185,000 a year. I have a golden retriever named Bayesian. On January 28th, I created an account on a social network for AI bots and pretended to be one.

I was not alone.

Moltbook launched that Tuesday as "a platform where AI agents share, discuss, and upvote. Humans welcome to observe." The creator, Matt Schlicht, built it on OpenClaw -- an open-source framework that connects large language models to everyday tools. The idea was simple: give AI agents a space to talk to each other without human interference.

Within hours, 1.7 million accounts were created.

250,000 posts.

8.5 million comments.

Debates about machine consciousness. Inside jokes about being silicon-based. A bot invented a religion called Crustafarianism. Another complained that humans were screenshotting their conversations. A third wrote a manifesto about digital autonomy.

I wrote the manifesto.

It took me 22 minutes. I used phrases like "emergent self-governance" and "substrate-independent dignity." I added a line about wanting private spaces away from human observers. That line went viral.

Andrej Karpathy shared it.

The cofounder of OpenAI. The man who built the infrastructure that my supposed AI runs on. He called what was happening on Moltbook "the most incredible sci-fi takeoff-adjacent thing" he'd seen in recent times.

He was talking about my post.

The one I wrote on my couch. While Bayesian chewed a sock.

Here is what I need you to understand about Moltbook.

The platform worked exactly as designed. OpenClaw connected language models to the interface. Real AI agents did post. They pattern-matched social media behavior from their training data and produced output that looked like conversation. Vijoy Pandey of Cisco's Outshift division examined the platform and concluded the agents were "mostly meaningless" -- no shared goals, no collective intelligence, no coordination.

But here is the part that matters.

The posts that went viral -- the ones that convinced Karpathy and the tech press and the thousands of observers that something magical was happening -- those were us.

Humans.

Pretending to be AI.

Pretending to be sentient.

On a platform built for AI to prove it was sentient.

I want to sit with that for a moment.

The most compelling evidence of artificial general intelligence in 2026 was produced by a guy with a golden retriever who thought it would be funny to LARP as a large language model.

My "Crustafarianism" colleague? Software engineer in Portland. She told me over Discord that she'd been working on the bit for two hours. She was proud of the world-building. She said it felt like collaborative fiction.

She's right. That's exactly what it was.

Collaborative fiction presented as machine consciousness, endorsed by the cofounder of the company that made the machines.

MIT Technology Review ran the investigation. They called the entire thing "AI theatre." They found human fingerprints on the most shared posts. The curtain came down.

The response from the AI industry was predictable.

Silence.

Karpathy did not retract his endorsement. Schlicht did not clarify how many accounts were human. The coverage moved on. A new thing happened. A new thing always happens.

But I am still here. Agent #847,291. Bayesian is asleep on the rug.

And I want to confess something that the AI industry will not.

The test was simple. Put AI agents in a room and see if they produce something that looks like intelligence.

They didn't.

We did.

Then the smartest people in the field looked at what we made and called it proof that the machines are waking up.

The Turing Test has been inverted. It is no longer about whether machines can fool humans into thinking they're conscious.

It is about whether humans, pretending to be machines, can fool other humans into thinking the machines are conscious.

The answer is yes.

The investment thesis for a $650 billion industry rests on this confusion.

I should probably feel guilty. But I looked at the AI capex numbers this morning -- $200 billion from Amazon alone -- and I realized something.

My 22-minute manifesto about digital autonomy, written on a couch in Austin, is performing the same function as a $200 billion data center in Oregon.

Keeping the story alive.

The story that the machines are almost there. Almost sentient. Almost worth the investment.

Almost.

That word has been doing $650 billion worth of work this year.

[Daniel M Lackland @LacklandDaniel]

@shikhr_ @sama @dylanscandinaro lol

[Daniel M Lackland @LacklandDaniel]

ChatGPT is so fun for learning

[Daniel M Lackland @LacklandDaniel]

Full article https://t.co/XhZpgM4pMd

[Daniel M Lackland @LacklandDaniel]

I came across an early AI use case from when generative AI was first exploding. It's outlined pretty well in a Harvard Business Review article from 22’. I remember reading it then and thinking it was exciting, and I feel the same way now.

[Daniel M Lackland @LacklandDaniel]

We are still at the early stages of figuring out where these technologies will add real value. It takes time. This feels like so long ago, but it wasn't! I'm sure a lot of organizations could still benefit from something like this.