@Actum_AI
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Building the Behavioral Trust Layer for AI. Verifiable Human Behavior. Immutable Data. 🌐 https://nitter.cf/t.co/q4sfOu18fx | 💬 https://nitter.cf/t.co/TP7PljG2rb
Joined June 2011
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The next AI advantage may not be a bigger model.
It may be a better way to learn from reality.
Two models can have similar reasoning capabilities.
But if one continuously receives feedback from millions of real-world interactions while the other only sees static datasets, they won't stay equal for long.
The difference is the learning loop.
Human decisions create actions.
Actions create outcomes.
Outcomes create feedback.
Feedback improves the system.
The companies that can close this loop may have an advantage that can't simply be downloaded from a model release.
The future of AI isn't just about training smarter models.
It's about building systems that can keep learning from reality.
The next AI breakthrough may not be a smarter answer.
It may be a longer chain of actions.
With million-token context and increasingly capable reasoning, AI is moving beyond isolated prompts into workflows that can run for hours and involve dozens of decisions.
That changes the problem.
When an AI works continuously, the important question isn't just whether its final answer is correct.
It's whether we can understand everything that happened along the way.
Every decision.
Every intervention.
Every human action.
Longer-running AI needs more than context.
It needs memory, feedback, and a verifiable history of actions.
That's where PoHA becomes interesting.
Announcing Gemini 4 Argon, our new frontier model.
Argon is built to sustain deep reasoning across complex, long-horizon workflows and delivers frontier performance in complex workflows across real-world software engineering, enterprise knowledge work like legal and finance, and cybersecurity defense.
We’re also expanding the model’s output token limit to an industry-leading 1M tokens.
Argon is currently rolling out to a set of trusted cyber defenders in the Fairwind Program, with broader availability as soon as possible.
AI safety used to be about what a model could say.
Now we're entering a world where AI can act.
Browse the internet.
Access systems.
Write code.
Operate machines.
Make decisions.
And that's a fundamentally different problem.
When an AI takes an action, the important question isn't only why did it do it?
It's also:
What actually happened?
What triggered the action?
Who was responsible for it?
As AI moves from generating answers to executing tasks, the history of actions becomes as important as the intelligence behind them.
PoHA starts from this exact idea: actions should leave evidence.
The more autonomous AI becomes, the more important it is to know what happened in the real world.
AI learns from what we give it.
So what happens when the world changes faster than its training data?
A model can be trained on years of information and still fail when reality moves in an unexpected direction.
A machine behaves differently.
A workflow changes.
A human finds a better way to solve a problem.
These moments rarely look like traditional datasets.
They are feedback.
The next generation of AI may be defined less by how much it can remember, and more by how effectively it can learn from what is happening right now.
PoHA is one way to turn human actions into a continuous feedback signal for intelligent systems.
AI can explain a decision.
But can it prove what happened before the decision?
A model can process millions of records and identify patterns humans would miss.
But the records themselves are only representations of reality.
Someone inspected the machine.
Someone changed the system.
Someone made the decision.
Someone took the action.
If that layer is missing, AI may be reasoning over a version of reality that was never properly verified.
This is the part of AI infrastructure we rarely talk about:
not intelligence, but provenance.
Proof of Human Action gives real-world events a human-verifiable origin — creating a stronger foundation for systems that increasingly make decisions from them.
What happens when AI starts learning from the real world?
Today, most AI learns from information that has already been recorded.
But the physical world is different.
A factory changes.
A machine fails.
A technician makes a decision.
A process succeeds or fails.
These aren't just “data points.”
They are experiences.
And whoever controls how those experiences are captured, verified, and fed back into AI may ultimately influence how intelligent systems evolve.
That's why Proof of Human Action matters.
The future AI advantage may not come from owning more data.
It may come from owning better feedback loops between humans, machines, and reality.
Building superintelligence is not just a race to make AI more powerful.
It's a question of who controls it, who benefits from it, and how its decisions affect the real world.
AI should remain aligned with human interests.
Organizations should retain control of their own knowledge.
And the ecosystem should not depend on a handful of gatekeepers.
But there's another layer we shouldn't overlook:
AI needs to understand human actions, not just human instructions.
As AI moves from generating information to influencing the physical world, proving what humans actually did becomes increasingly important.
That's the idea behind Proof of Human Action at ACTUM.
AI can become more autonomous.
But humans should remain accountable.
Any pursuit of superintelligence has to be grounded in the core principle that if the AI we build is not helping humanity and under human control, it's not worth pursuing.
We also need to accelerate and spread the benefits of AI, such that they are diffused broadly across countries, communities, and companies. This requires a frontier ecosystem in which both closed and open-source models can thrive.
And for firms, it’s imperative that they retain full control over their unique and tacit knowledge. Every organization should be able to build its own continuous learning loop/hill climbing machine, without becoming dependent on any one model provider, and have the ability to embed its own knowledge into models and weights they control.
So, in this context, we welcome the research, focus, and deliberate pacing needed to get alignment right as the design goal. We also welcome ideas like "embedded evaluators" and the broader efforts to develop the mechanisms to make this more than just talk.
The key is that this cannot be controlled by a handful of entities, but must have broad representation across the ecosystem, countries, and fields, including academia.
This is the approach we are taking: broad access and choice at every layer of the AI stack; enterprise control of learning loops and models; and the “Code of Conduct” that underlies our own first party MAI models that we’ll publish tomorrow for public consultation.
The more powerful AI becomes, the more dangerous misuse can become.
Cyberattacks. Surveillance. Influence operations. Biological research. Even weapons.
The latest threat intelligence from Anthropic is a reminder that AI safety isn't just about building smarter models.
It's also about understanding what happens outside the model.
Who is acting?
What actually happened?
Can that action be verified?
As AI becomes increasingly capable of influencing the physical world, we need better ways to connect intelligence with accountable human behavior.
That's one of the problems ACTUM is working on with Proof of Human Action (PoHA).
Smarter AI needs stronger accountability.
We're publishing our most detailed threat intelligence report to date.
It covers how people tried to misuse Claude—for cyberattacks, influence operations, surveillance, biology, and building weapons—and how we found and stopped them.
We disrupted every operation in the report, and used the lessons from them to strengthen our safeguards. Where appropriate, we also shared what we found with authorities and other AI companies.
These cases are not typical: we’re highlighting some of the most sophisticated misuse we’ve seen. But they’re especially important to discuss, because they show us where AI misuse is headed, where our safeguards work, and where they need to improve.
We’re publishing this report so others can spot the same activity on their own platforms, and so we can give the public a clearer view of how emerging threats develop.
Read the report: anthropic.com/threat-intelli…
AI has plenty of information.
What it lacks is context.
A machine can report an event.
A model can analyze the data.
But understanding why something happened often requires knowing the human action behind it.
That's where PoHA becomes interesting.
ACTUM is turning real-world human actions into structured signals that AI can understand, analyze, and learn from.
The next generation of AI won't just process information.
It will understand behavior.
AI can predict what happens next.
But prediction is only useful when AI can understand what happened before.
Real-world actions create valuable signals that traditional AI systems often can't verify or contextualize.
PoHA changes that.
By turning human activity into structured, verifiable evidence, ACTUM is creating a new source of intelligence for AI.
Better context.
Better intelligence.
Smarter AI.
AI has learned almost everything the internet can teach it.
Now it needs to learn from the real world.
Machines operate.
People perform tasks.
Infrastructure changes every second.
That activity is becoming a new source of intelligence.
With AI + DePIN + PoHA, ACTUM is building the infrastructure to turn real-world activity into structured, verifiable data for AI.
The next frontier of AI isn't online.
It's physical.
🤔
AI is moving from generating information to taking action.
That changes everything.
When AI starts coordinating machines, infrastructure, and real-world operations, the connection between digital intelligence and physical execution becomes critical.
DePIN provides the network.
AI provides the intelligence.
ACTUM is building the layer that connects the two—bringing verified real-world activity into the AI economy.
Intelligence shouldn't stop at the screen.
AI can generate answers in seconds.
But understanding the real world is a different challenge.
Machines, infrastructure, and people are constantly creating signals that never make it into usable intelligence.
The next generation of AI will need more than compute.
It will need a reliable connection to real-world activity.
That's where AI × DePIN becomes powerful.
ACTUM is building the infrastructure to turn physical activity into intelligence that AI can actually use.
AI becomes more powerful when it can learn from the real world.
But real-world intelligence needs a continuous data loop:
Physical activity → Verified data → AI intelligence → Better decisions → Real-world action
DePIN provides the infrastructure to connect the physical and digital worlds.
PoHA adds a way to verify the human actions within that loop.
ACTUM is building toward an ecosystem where real-world activity becomes intelligence AI can rely on.
The next AI breakthrough may not come from a bigger model.
It may come from connecting AI to the physical world.
More machines.
More infrastructure.
More real-world activity.
The challenge is turning all of that activity into reliable intelligence.
AI needs infrastructure that can understand what happens beyond the screen.
ACTUM is building toward that future with AI + DePIN + PoHA.
AI agents are becoming more autonomous.
But autonomy creates a new challenge:
How does an AI agent know what is actually happening in the physical world?
Sensors provide signals.
Machines generate data.
Humans perform the work.
The missing piece is a reliable layer connecting real-world activity with machine intelligence.
AI needs eyes on the physical world.
It also needs proof.
ACTUM is building that layer with PoHA.
AI is moving into the physical world.
Factories. Logistics. Energy. Infrastructure.
But AI cannot build reliable intelligence from disconnected or unverifiable events.
It needs a continuous stream of trusted real-world data.
That's where ACTUM comes in.
Through Proof of Human Action (PoHA), physical operations can become structured, verifiable data for the next generation of AI and DePIN infrastructure.
From physical activity to machine intelligence.
AI is moving beyond the screen.
It is entering factories, logistics, infrastructure, and machines.
But intelligence alone isn't enough.
AI needs reliable connections to the physical world—and trusted data from every real-world action.
AI + DePIN can bridge that gap.
At ACTUM, we're building the infrastructure to make those actions verifiable, traceable, and useful for AI.
The next generation of AI will be physical.
Industrial infrastructure is moving from “Trust us.” to “Prove it.”
Supply chains, maintenance, and operations increasingly need data that is traceable, verifiable, and auditable.
As AI moves deeper into the physical world, trusted operational evidence becomes essential.
With Proof of Human Action (PoHA), ACTUM is building a foundation for turning verified real-world actions into auditable on-chain evidence.
The future of industrial AI starts with proof.
AI is only as trustworthy as the evidence behind it.
As industrial systems become more intelligent, every operation, maintenance event, and inspection should be verifiable—not just recorded.
With Proof of Human Action (PoHA), ACTUM is building the trust layer that connects real-world actions with reliable AI.
The future of industrial AI starts with verifiable trust. 🚀
