This week, two launches tried to answer the same question.
iProov's HAPS: a signed, single-use credential that proves a human approved this exact action. Change one detail — the amount, the destination — and the credential dies.
World ID for Agents: a cryptographic check that a real, unique human stands behind the agent.
Both are real progress. Both answer: who authorized this?
Neither answers what happens next.
Approval is a moment. Behavior is a trail.
The credential proves the human said yes. It says nothing about the chain of actions that followed — or whether the outcome matched the intent.
The industry is solving the front door. Nobody is filming the hallway.
That's the gap. A verifiable record of behavior — what was done, in what order, with what result — tied back to the human who authorized it.
Identity says who sent the agent.
Authorization says what it was allowed to do.
Behavior says what it actually did.
Two of the three now have infrastructure.
The third is the one we're building.
iProov's HAPS: a signed, single-use credential that proves a human approved this exact action. Change one detail — the amount, the destination — and the credential dies.
World ID for Agents: a cryptographic check that a real, unique human stands behind the agent.
Both are real progress. Both answer: who authorized this?
Neither answers what happens next.
Approval is a moment. Behavior is a trail.
The credential proves the human said yes. It says nothing about the chain of actions that followed — or whether the outcome matched the intent.
The industry is solving the front door. Nobody is filming the hallway.
That's the gap. A verifiable record of behavior — what was done, in what order, with what result — tied back to the human who authorized it.
Identity says who sent the agent.
Authorization says what it was allowed to do.
Behavior says what it actually did.
Two of the three now have infrastructure.
The third is the one we're building.
An AI agent can now prove who sent it.
Proof's x401 gives agents a verified identity and a signed record of what a human authorized. x402 lets them pay in USDC.
Identity: handled. Payments: handled.
So why do we still not trust them?
Because authorization is a snapshot. Behavior is the film.
A signature proves what an agent was allowed to do at 9:00.
It says nothing about what the agent actually did at 9:05.
This week, Liquibase put numbers on the gap: 96.5% of companies say AI now touches their production databases. Only 32% keep an audit-ready record of the changes.
We are building agents that can prove their permission — and cannot prove their conduct.
Trust doesn't live in the permission slip.
It lives in the verifiable history of behavior.
That's the layer that still doesn't exist at scale.
That's the layer we're building.
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An AI agent can now prove who sent it.
Proof's x401 gives agents a verified identity and a signed record of what a human authorized. x402 lets them pay in USDC.
Identity: handled. Payments: handled.
So why do we still not trust them?
Because authorization is a snapshot. Behavior is the film.
A signature proves what an agent was allowed to do at 9:00.
It says nothing about what the agent actually did at 9:05.
This week, Liquibase put numbers on the gap: 96.5% of companies say AI now touches their production databases. Only 32% keep an audit-ready record of the changes.
We are building agents that can prove their permission — and cannot prove their conduct.
Trust doesn't live in the permission slip.
It lives in the verifiable history of behavior.
That's the layer that still doesn't exist at scale.
That's the layer we're building.
Proof's x401 gives agents a verified identity and a signed record of what a human authorized. x402 lets them pay in USDC.
Identity: handled. Payments: handled.
So why do we still not trust them?
Because authorization is a snapshot. Behavior is the film.
A signature proves what an agent was allowed to do at 9:00.
It says nothing about what the agent actually did at 9:05.
This week, Liquibase put numbers on the gap: 96.5% of companies say AI now touches their production databases. Only 32% keep an audit-ready record of the changes.
We are building agents that can prove their permission — and cannot prove their conduct.
Trust doesn't live in the permission slip.
It lives in the verifiable history of behavior.
That's the layer that still doesn't exist at scale.
That's the layer we're building.
$15M. Not to train a bigger model. To check the math.
This week, OpenAI and Anthropic funded the Simons Foundation to help mathematicians evaluate AI-generated proofs.
Why? OpenAI released hundreds of machine-generated results. Mathematicians pushed back: a mass dump of proofs overwhelms how verification actually works.
Read that again. The labs aren't funding more generation. They're funding verification.
The bottleneck has moved. It used to be: can the model produce the answer? Now it's: can anyone prove the answer is right?
Every field AI touches will hit this same wall. Faster output, slower truth.
Generation is cheap. Verification is the scarce resource. Whoever owns verification owns trust.
The question isn't whether AI can do the work anymore. It's whether anyone can verify it did.
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$15M. Not to train a bigger model. To check the math.
This week, OpenAI and Anthropic funded the Simons Foundation to help mathematicians evaluate AI-generated proofs.
Why? OpenAI released hundreds of machine-generated results. Mathematicians pushed back: a mass dump of proofs overwhelms how verification actually works.
Read that again. The labs aren't funding more generation. They're funding verification.
The bottleneck has moved. It used to be: can the model produce the answer? Now it's: can anyone prove the answer is right?
Every field AI touches will hit this same wall. Faster output, slower truth.
Generation is cheap. Verification is the scarce resource. Whoever owns verification owns trust.
The question isn't whether AI can do the work anymore. It's whether anyone can verify it did.
This week, OpenAI and Anthropic funded the Simons Foundation to help mathematicians evaluate AI-generated proofs.
Why? OpenAI released hundreds of machine-generated results. Mathematicians pushed back: a mass dump of proofs overwhelms how verification actually works.
Read that again. The labs aren't funding more generation. They're funding verification.
The bottleneck has moved. It used to be: can the model produce the answer? Now it's: can anyone prove the answer is right?
Every field AI touches will hit this same wall. Faster output, slower truth.
Generation is cheap. Verification is the scarce resource. Whoever owns verification owns trust.
The question isn't whether AI can do the work anymore. It's whether anyone can verify it did.
Everyone in AI is being asked to show receipts today.
Anthropic: +147% last quarter, +45% this one. $16.8B in revenue — still below what some investors expected.
Korea's KOSPI just triggered circuit breakers. One reason named: fear that AI infrastructure spending never pays back.
The "demo and raise" era is over. What replaces it is a brutal, simple question:
If AI did the work — who proves it happened?
Outputs are cheap now. Every model generates. What stays scarce is proof: a verifiable record of what action was taken, by whom, and whether it actually worked.
That's the whole thesis of Proof of Human Action.
Not trust-me AI. Show-me AI.
The next cycle won't be won by the best demo. It'll be won by whoever can prove what really happened.
Can your AI show its work?
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Everyone in AI is being asked to show receipts today.
Anthropic: +147% last quarter, +45% this one. $16.8B in revenue — still below what some investors expected.
Korea's KOSPI just triggered circuit breakers. One reason named: fear that AI infrastructure spending never pays back.
The "demo and raise" era is over. What replaces it is a brutal, simple question:
If AI did the work — who proves it happened?
Outputs are cheap now. Every model generates. What stays scarce is proof: a verifiable record of what action was taken, by whom, and whether it actually worked.
That's the whole thesis of Proof of Human Action.
Not trust-me AI. Show-me AI.
The next cycle won't be won by the best demo. It'll be won by whoever can prove what really happened.
Can your AI show its work?
Anthropic: +147% last quarter, +45% this one. $16.8B in revenue — still below what some investors expected.
Korea's KOSPI just triggered circuit breakers. One reason named: fear that AI infrastructure spending never pays back.
The "demo and raise" era is over. What replaces it is a brutal, simple question:
If AI did the work — who proves it happened?
Outputs are cheap now. Every model generates. What stays scarce is proof: a verifiable record of what action was taken, by whom, and whether it actually worked.
That's the whole thesis of Proof of Human Action.
Not trust-me AI. Show-me AI.
The next cycle won't be won by the best demo. It'll be won by whoever can prove what really happened.
Can your AI show its work?
Everyone is building AI agents. Almost no one is asking what happens after they act.
Here's the infrastructure gap.
Agents can browse the web, deploy code, move money, and modify production systems.
But when something goes wrong, can we reconstruct the chain of events?
What ho authorized it?
What information informed the decision?
What actually happened in the real world?
The final output tells only part of the story.
Models generate. Infrastructure provides accountability.
The agent economy needs more than execution logs. It needs verifiable records linking authorization, actions, and outcomes — evidence that can be independently checked rather than blindly trusted.
That's where Proof of Human Action (PoHA) becomes relevant.
By establishing verifiable evidence of human actions at their source, PoHA can help connect real-world events to the AI systems that rely on them.
Because as AI gains more autonomy, knowing what a system produced is no longer enough.
We need to know what happened, how it happened, and who was responsible.
Intelligence without provenance leaves critical questions unanswered.
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Everyone is building AI agents. Almost no one is asking what happens after they act.
Here's the infrastructure gap.
Agents can browse the web, deploy code, move money, and modify production systems.
But when something goes wrong, can we reconstruct the chain of events?
What ho authorized it?
What information informed the decision?
What actually happened in the real world?
The final output tells only part of the story.
Models generate. Infrastructure provides accountability.
The agent economy needs more than execution logs. It needs verifiable records linking authorization, actions, and outcomes — evidence that can be independently checked rather than blindly trusted.
That's where Proof of Human Action (PoHA) becomes relevant.
By establishing verifiable evidence of human actions at their source, PoHA can help connect real-world events to the AI systems that rely on them.
Because as AI gains more autonomy, knowing what a system produced is no longer enough.
We need to know what happened, how it happened, and who was responsible.
Intelligence without provenance leaves critical questions unanswered.
Here's the infrastructure gap.
Agents can browse the web, deploy code, move money, and modify production systems.
But when something goes wrong, can we reconstruct the chain of events?
What ho authorized it?
What information informed the decision?
What actually happened in the real world?
The final output tells only part of the story.
Models generate. Infrastructure provides accountability.
The agent economy needs more than execution logs. It needs verifiable records linking authorization, actions, and outcomes — evidence that can be independently checked rather than blindly trusted.
That's where Proof of Human Action (PoHA) becomes relevant.
By establishing verifiable evidence of human actions at their source, PoHA can help connect real-world events to the AI systems that rely on them.
Because as AI gains more autonomy, knowing what a system produced is no longer enough.
We need to know what happened, how it happened, and who was responsible.
Intelligence without provenance leaves critical questions unanswered.
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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 ng.
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.
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 ng.
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.
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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.
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 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.
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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.
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.
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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.
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.
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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.
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.
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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.
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.
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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.
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.
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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.
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…
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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 ctured signals that AI can understand, analyze, and learn from.
The next generation of AI won't just process information.
It will understand behavior.
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 ctured signals that AI can understand, analyze, and learn from.
The next generation of AI won't just process information.
It will understand behavior.
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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.
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.
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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.
🤔
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.
🤔
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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.
igence.
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.
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.
igence.
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.
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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.
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.
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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 HA 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.
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 HA 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.
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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 pens beyond the screen.
ACTUM is building toward that future with AI + DePIN + PoHA.
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 pens beyond the screen.
ACTUM is building toward that future with AI + DePIN + PoHA.
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