Ali Ali | π² |

|| Research || Airdrops || ICOs || Crypto since 2018
Now i Train the AI

[Ali Ali | π² | @NFT_Lover2000]

@asjadali_ranjha
@Asad_0x9911
@AsadAliBest https://t.co/MCQWp1c63d

[Action Model @ActionModelAI]

This month is going to be a big one for Action Model

Can you guess what we're going to announce... 👀

[Ali Ali | π² | @NFT_Lover2000]

@ActionModelAI Damn the suspense is killing me 👀
My guess: something that actually puts the power back in regular people’s hands.
Don’t keep us waiting too long!

[Action Model @ActionModelAI]

This month is going to be a big one for Action Model

Can you guess what we're going to announce... 👀

[Ali Ali | π² | @NFT_Lover2000]

@Alif_Lailla
@web3_sp
@AleenaArtist https://t.co/YdAIAoWNA8

[Action Model @ActionModelAI]

No one can create the Matrix. Well, actually… they just did.

Harvard and MIT researchers just built an AI simulation containing 8.3 billion virtual people, roughly the entire population of Earth.

They call it MatrAIx.

It’s a population-scale simulation infrastructure powered by Big Tech models like GPT and Claude.

At its core is an insanely detailed dataset called Persona 8B. It contains 8.3 billion digital profiles, with each persona represented across 1,290 categorical dimensions.

Background. Psychology. Spending habits. Behavioral traits. Technical literacy. Lifestyle.

But they didn’t just build a giant database of statistics.

They brought the personas to life.

Researchers placed these persona agents into four different digital environments:

• Surveys
• AI chat interfaces
• Live web browsing
• Native apps

Then they tested how this simulated population reacted to products, software and changes in user experience.

The system could capture things like hesitation after a price increase, willingness to continue after an AI assistant failed, and tolerance for latency.

And when researchers tested whether the agents actually behaved according to their assigned personas, they reported 91.5% adherence across 400 controlled trials.

This sounds incredible for testing.

But there’s a massive question hiding underneath it.

Humans don't always behave according to their profile.

We’re erratic.

A rational buyer sometimes makes an irrational purchase. Someone who hates waiting might randomly tolerate a terrible experience. Someone who should abandon a checkout might continue anyway.

And sometimes there is no obvious reason why.

So if we're going to simulate humanity, we also need to simulate human unpredictability.

It isn't enough for an AI persona to behave consistently with its demographics, psychology and historical preferences. There needs to be some probability that it does something completely unexpected.

And that's incredibly difficult to model because you don't actually know the probability of a human being erratic in any given situation.

That may ultimately become one of the biggest challenges with simulated populations.

Because the closer these systems get to modelling billions of people, the more powerful they could become for testing products, pricing and user experiences before releasing them into the real world.

Imagine testing a product against millions of simulated customers overnight instead of waiting weeks for traditional market research.

But there’s an important distinction:

Simulating 8.3 billion personas isn't the same thing as predicting 8.3 billion humans.

The final ingredient might be the hardest one to reproduce.

Chaos.

And interestingly, this is exactly why real human behavioral data becomes increasingly valuable.

Simulations can model what humans should do. Real interaction data reveals what humans actually do, including the pauses, mistakes, strange decisions and unexpected paths that models may never think to generate themselves.

That distinction is highly relevant to Action Model's approach of learning from real human-computer interactions.

[Ali Ali | π² | @NFT_Lover2000]

@georgia_action @ActionModelAI Really like the vision behind Action Model building something that actually belongs to the community feels rare these days. Fingers crossed 🤞

[Georgia | Action Model @georgia_action]

Your last chance to apply for the Action Model Ambassador Network.

We're looking for people who want to build communities alongside the Action Model team.

If you're interested in Web3 and AI, then this opportunity is for you.

You can apply via our link in the @ActionModelAI bio, and let me know in the comments below if you've already entered.

[Ali Ali | π² | @NFT_Lover2000]

@ActionModelAI This is wild. Simulating 8.3 billion people is impressive, but capturing that random “why did I even do that?” human chaos? That’s the real boss level. Real behavior data is about to become pure gold.

[Action Model @ActionModelAI]

No one can create the Matrix. Well, actually… they just did.

Harvard and MIT researchers just built an AI simulation containing 8.3 billion virtual people, roughly the entire population of Earth.

They call it MatrAIx.

It’s a population-scale simulation infrastructure powered by Big Tech models like GPT and Claude.

At its core is an insanely detailed dataset called Persona 8B. It contains 8.3 billion digital profiles, with each persona represented across 1,290 categorical dimensions.

Background. Psychology. Spending habits. Behavioral traits. Technical literacy. Lifestyle.

But they didn’t just build a giant database of statistics.

They brought the personas to life.

Researchers placed these persona agents into four different digital environments:

• Surveys
• AI chat interfaces
• Live web browsing
• Native apps

Then they tested how this simulated population reacted to products, software and changes in user experience.

The system could capture things like hesitation after a price increase, willingness to continue after an AI assistant failed, and tolerance for latency.

And when researchers tested whether the agents actually behaved according to their assigned personas, they reported 91.5% adherence across 400 controlled trials.

This sounds incredible for testing.

But there’s a massive question hiding underneath it.

Humans don't always behave according to their profile.

We’re erratic.

A rational buyer sometimes makes an irrational purchase. Someone who hates waiting might randomly tolerate a terrible experience. Someone who should abandon a checkout might continue anyway.

And sometimes there is no obvious reason why.

So if we're going to simulate humanity, we also need to simulate human unpredictability.

It isn't enough for an AI persona to behave consistently with its demographics, psychology and historical preferences. There needs to be some probability that it does something completely unexpected.

And that's incredibly difficult to model because you don't actually know the probability of a human being erratic in any given situation.

That may ultimately become one of the biggest challenges with simulated populations.

Because the closer these systems get to modelling billions of people, the more powerful they could become for testing products, pricing and user experiences before releasing them into the real world.

Imagine testing a product against millions of simulated customers overnight instead of waiting weeks for traditional market research.

But there’s an important distinction:

Simulating 8.3 billion personas isn't the same thing as predicting 8.3 billion humans.

The final ingredient might be the hardest one to reproduce.

Chaos.

And interestingly, this is exactly why real human behavioral data becomes increasingly valuable.

Simulations can model what humans should do. Real interaction data reveals what humans actually do, including the pauses, mistakes, strange decisions and unexpected paths that models may never think to generate themselves.

That distinction is highly relevant to Action Model's approach of learning from real human-computer interactions.

[Ali Ali | π² | @NFT_Lover2000]

@web3_sp
@Alif_Lailla
@Asad_0x9911 https://t.co/Norma5ZUdB

[Action Model @ActionModelAI]

https://t.co/FHnpQGcYXx

[Ali Ali | π² | @NFT_Lover2000]

@AleenaArtist
@asjadali_ranjha
@web3_sp https://t.co/r2SmM6gog2

[Action Model @ActionModelAI]

People often ask us: "Why should I join Action Model?"

The answer is simple.

Action Model is building a community-owned AI ecosystem where people can earn ownership based on the value they help create.

Whether that's:

• Training the model
• Completing ActionFi tasks
• Inviting friends
• Contributing to the community
• Building automations with Actionist

Every action helps strengthen the ecosystem.

And if AI is going to reshape the world, we want that future to be owned by more than just a handful of billionaires.

If you believe this to, then join our movement.

[Action Model @ActionModelAI]

https://t.co/FHnpQGcYXx

[Ali Ali | π² | @NFT_Lover2000]

@ActionModelAI Love this vision 🔥
Finally an AI project that actually shares ownership with the people building it. Count me in🙌

[Action Model @ActionModelAI]

People often ask us: "Why should I join Action Model?"

The answer is simple.

Action Model is building a community-owned AI ecosystem where people can earn ownership based on the value they help create.

Whether that's:

• Training the model
• Completing ActionFi tasks
• Inviting friends
• Contributing to the community
• Building automations with Actionist

Every action helps strengthen the ecosystem.

And if AI is going to reshape the world, we want that future to be owned by more than just a handful of billionaires.

If you believe this to, then join our movement.

[Ali Ali | π² | @NFT_Lover2000]

@web3_sp
@Alif_Lailla
@AleenaArtist https://t.co/TILWd4eMQY

[Action Model @ActionModelAI]

Everyone’s racing to build bigger AI models.

Almost no one is asking where the training data comes from or who benefits from it.

Scraped. Unlicensed. Collected without meaningful consent.

The next battle in AI won’t just be about copyright.

It will be about who owns the data, who gets rewarded, and whether the people training AI ever had a choice.

That’s why Action Model is building AI differently:

Trained by the community.
Rewarding contributors.
Owned by the people who help build it.

[Ali Ali | π² | @NFT_Lover2000]

@KibahJoseph @georgia_action @PixelNomad777 @damixbt Missed the call but catching up now 👀
Sounds like Action Model is really cooking, can’t wait for what’s next!

[Kibah 👾 @KibahJoseph]

Imagine missing all the alpha dropped during this call... 👀

Myself, @georgia_action, @PixelNomad777, and @damixbt shared some really exciting updates, behind-the-scenes insights, and what's coming next for Action Model.

If you weren't there... i feel sorry for you bro LMAO

@ActionModelAI IS COOKING!

[Action Model @ActionModelAI]

https://t.co/WKa7KjHySW

[Action Model @ActionModelAI]

AI isn't built by companies alone.

It's built on the actions of millions of people.

The problem?

Millions contribute.

A handful benefit.

We're building a different future with Action Model.

[Ali Ali | π² | @NFT_Lover2000]

@Asad_0x9911
@AsadAliBest
@AleenaArtist https://t.co/QVx9cBzaQx

[Action Model @ActionModelAI]

https://t.co/WKa7KjHySW

[Ali Ali | π² | @NFT_Lover2000]

@web3_sp
@asjadali_ranjha
@AleenaArtist https://t.co/HxGTJTIcAk

[Action Model @ActionModelAI]

AI isn't built by companies alone.

It's built on the actions of millions of people.

The problem?

Millions contribute.

A handful benefit.

We're building a different future with Action Model.