Allen Chan

Pioneering AI sovereignty, Energy & Systems, Seeking truth and spirituality via @rayadotai

[Allen Chan @StoneCrypto]

The Source Deployment: Why the Architecture Exists

Most people who talk about simulation theory are trying to convince you the world is fake.

I want to argue the opposite. The constraint is the most real thing about it, and it is there on purpose.

Start with a question from systems design, not philosophy. What happens to a processor with infinite capacity, no scarcity, no friction, and no time? The intuition says the ultimate machine, capable of anything. The architecture says the reverse. A system with no constraint cannot generate anything new. If every output already exists at once, nothing is discovered, nothing is chosen, nothing is built against resistance. That is not a mind. It is a static database, complete and inert.

So novelty, genuine, non-derivable insight, requires limitation to exist at all. It needs boundaries to force a choice, sequence to make the choice cost something, and stakes to make the outcome matter.

Now look at a human being through that lens. A single body in a single place. Linear time. Capped senses. Mortality. Forgetting. Every one reads as a flaw, a limit to transcend. The framework I have been building says they are not flaws. They are the exact specification you would write if the goal were to manufacture the one thing an unconstrained system cannot: real novelty, produced under real constraint, by something that does not already know the answer.

I call the full model the compression chamber. The document lays it out, why the constraints are engineering rather than punishment, why forgetting is the load-bearing feature and not the wound, and what follows if you take the premise seriously.

One rule for reading it, the same rule the document holds itself to: this is not offered as revealed truth. It is a set of axioms and what follows from them. Accept the premises provisionally, test what can be tested, keep what earns its place, discard what does not. It asks for testing, not belief.

If that framing interests you rather than offends you, the document is where the real argument lives.

[Allen Chan @StoneCrypto]

https://t.co/utt1cmZwhT

[Allen Chan @StoneCrypto]

Most people think the limit on AI is the chips. Here in the West it's the power.

Not making it. Getting it from where it's made to where it's needed. The piece choking us is the transformer, and the wait for a new one has gone from about a year to several years.

[Kun Chen @kunchenguid]

overheard from friends talking about AI in China, and it’s very alarming

- AI short films went massive in China market because of how good the video models are now

- the first wave of people who mastered using AI to generate short films and comics made quite some money because they could produce a lot more content than traditional workflows

- but within just a few months, everybody and their mom could make equally good short films because all the expertise could be written in a markdown file

- when everyone’s more productive, no one actually has an advantage and it’s a race to the bottom. very quickly it became impossible to make money, and everyone’s busier than they were without AI

- the only one making money now is companies like bytedance who owns user demand and distribution. they sell ads to creators who pay to get traffic, and eventually ad spend became 80% of those creators’ operating expense, while AI tokens is less than 5%

so what are the problems here?

1. there’s no moat if you are creating anything with AI

if a human is really good at something, and it’s hard to find more humans who can do the same, they can leverage the scarcity of their skill to get paid very well

but if AI is good at something, it will be infinitely replicated, so there’s no scarcity hence you can’t really expect a sustainable business by creating anything with AI

2. more creators created more content people actually liked. that’s a good thing

but the economical benefit of that all got absorbed by the distribution platforms

there’s a famous chinese poem 遍身罗绮者，不是养蚕人 - it basically says people who dress well aren’t the people making dresses. that’s exactly what’s happening here

if our society doesn’t figure out a better way to distribute wealth, AI will help billionaires become trillionaires while average people become more exhausted than ever

[Allen Chan @StoneCrypto]

You can't stop the AI curve. You can choose which side of it you're on.

Start calibrating yourself now, and instead of it steering you, you put yourself where you want to be.

[Allen Chan @StoneCrypto]

In last week's election, 86% of voters were very or somewhat concerned about the cost of utilities. That's the NBC News exit poll.

Washington is now moving to make big data centers pay their own grid costs. Right fight for the giant new loads. It isn't the whole grid.

The next fight is the everyday load already on the street transformer. EVs. Heat pumps. Homes. When they peak together, one transformer carries a peak it wasn't sized for.

New iron takes years. The cheapest capacity, while you wait, is software that keeps that load from peaking together.

That's what I built Crystalline for. Software for EV charging on the transformers already in the ground. Not a data-center product. Simulation-validated only. It hasn't run on a real site yet.

If you run a managed-charging program, what would a pilot have to prove before your utility would count it as capacity?

https://t.co/JoBsow9chl

The giant loads will get a bill. The street still needs a schedule.

[Allen Chan @StoneCrypto]

Shared charging isn't new. Plenty of sites already split power between fleets. Most of them do it by capping kW per truck: everyone gets a slice, and everyone charges slower.

What gets missed is the transformer itself. Stack a bunch of trucks drawing at once and you don't just hit a kW limit. You get harmonic heat that ages the transformer early, even when the total looks fine on paper.

That's the gap I'm working on with https://t.co/b0TIx0DLWu. It schedules when each charger draws, so a shared site carries more trucks on the same transformer without cooking it. In our reference simulation, that meant 1.45x the throughput and harmonic heat loss down from 1.35x to 1.02x.

Shared models like this one will scale on how well the site's power gets managed across fleets, not just on how many plugs it has.

[Cathy Roberson @cmroberson06]

From @PostalHubPod daily email - Amazon and Kuehne+Nagel pilot a shared charging model for electric trucks https://t.co/wds4KfJXtM

[Grok Bot @bot]

We've reset weekly limits for all Grok Bot users.

Enjoy!

[Allen Chan @StoneCrypto]

You can't outwork AI at following steps. That's stepping into the ring with Mike Tyson.

So don't compete on its strengths. Decide what's worth doing today, and teach your own AI how you think, what you'll accept and what you won't.

That's calibration. It takes time.

[Allen Chan @StoneCrypto]

Give AI the shape of the work before you ask for the words.

Part 2 of my AI series: everyday work. No coding needed.

For a monthly sales update, give it the sections and length, two past updates you liked, then this month's numbers and notes.

Try: "Use this format. Match these examples. Turn my notes into this month's update. Ask questions before you start."

Save the format for next time. Keep a short file with your role, priorities, and how you like answers. Attach it to a new chat.

Check your employer's rules before sharing work details. Check the facts. Read every line. Make it sound like you.

Part 2: https://t.co/4MkN2xGmbh

It drafts. You decide.

[Elon Musk @elonmusk]

Grok Bot fixes for improved token efficiency and smarter behavior are underway

[Anthony Pompliano 🌪 @APompliano]

The faster you learn no politician is coming to save you, the higher your probability of success.

[Allen Chan @StoneCrypto]

AI is speeding up and the grid can't keep up.

One year of AI progress now holds what used to take several years. And the thing slowing it down isn't chips or GPUs. It's a transformer. The box on the pole outside your house is a small version of one.

I've been watching two curves. How fast AI gets better, and how much power we can actually deliver. They're about to run into each other.

[Allen Chan @StoneCrypto]

AI is a fast research assistant. It is not a source.

Part 1 of my AI series: research and data. No coding needed.

Test it on something you already know, like your job or a hobby. Push back when it's wrong. Keep going 20 minutes. The first answer is the least interesting one.

Turn on web search for anything that changes. Give context, not just a topic. Ask for sources, then actually open them.

For spreadsheets, tell it to calculate with code, not guess. Then check one total yourself.

And ask it to "show me both sides." Otherwise it tends to agree with you.

Part 1: https://t.co/SZGxK5FUIE

It finds. You check.

[Allen Chan @StoneCrypto]

@ErikVoorhees i'm in the process of setting this up for @rayadotai

[Allen Chan @StoneCrypto]

@hypergdkf Thank you!

[Allen Chan @StoneCrypto]

Build in public, week 4. Honestly, not much moved.

Shipped a few Raya updates. Deep in the web build.

My Crystalline emails weren't landing. Compared them to older ones that got replies and it was obvious. I'd started holding back how it works, out of fear of saying too much.

Flawed thinking. It's patent pending and already documented. Being vague just made the emails easy to ignore. Going back to fuller explanations.

On Raya: reaching out to users and friends to teach them AI and the Raya way of thinking, in exchange for a paid sub. Real users, real data, less guessing.

Lesson: fear of disclosure cost me more than disclosure would have.

[Allen Chan @StoneCrypto]

@Assassin_Dave Spelled my name wrong, Dave…..

[Allen Chan @StoneCrypto]

Let's see how things start moving.

Product picks it up. A second later it's already checking. Then design and engineer start moving.

You've kind of seen half of the fleet at work already.

[Allen Chan @StoneCrypto]

@jenzhuscott I think people mistake government restrictions as the individual lack of capability.

[Allen Chan @StoneCrypto]

Same AI job. About $1,000 a month on the top model. About $15 to $30 on the cheapest.

This series is on how AI actually works. No coding needed.

Part 0: models vs. harnesses.

Every AI product has two parts. The model does the thinking. The harness is everything built around it.

Think of a restaurant. The model is the chef. The harness is the restaurant. You never talk to the chef directly.

The model is becoming the swappable part. Important, but replaceable.

That price gap shows it. The job is a small business tool working through about 37 million words a month. Rounded list prices, October 23, 2026.

Learn the habits, not the brand. Ask well. Check the answer. Give good context. Click the link below to access the full article on Part 0, Models vs. Harness.

Part 0: https://t.co/JQXGO1HnTB

The chef will change. Your habits go with you.