AGI Action Governance Protocol
July 16, 2025
In the age of rapidly advancing artificial intelligence, one of the most troubling and paradoxical facts is this: we build AI systems that can reason, speak, and solve problems with superhuman skill—yet we do not fundamentally understand how they work inside. These models, especially large language models, operate with billions of parameters and exhibit increasingly complex behaviors. But their inner workings remain largely opaque, even to their creators. This lack of interpretability is not a minor engineering inconvenience—it is a central and urgent safety problem, particularly as AI becomes more powerful and autonomous.
Dario Amodei, CEO and co-founder of Anthropic, has emerged as one of the most vocal and thoughtful figures confronting this issue. With experience across major AI labs like Google and OpenAI, Amodei brings a unique perspective shaped by both technical depth and strategic foresight. In his recent work, he emphasizes that AI interpretability is not merely a scientific curiosity—it is a precondition for safe deployment. In his view, understanding what AI models are "thinking" is essential if we want to avoid catastrophic risks, ranging from misaligned objectives to deceptive behavior or misuse by bad actors.
Amodei often uses a compelling analogy: developing interpretability is like building a high-resolution MRI for AI brains. It’s not enough to observe outputs or apply behavioral tests; we must peer inside the model and trace the actual causal mechanisms behind its responses. This would allow us to identify whether a model is deceiving, manipulating, seeking power, or simply confused—even before it acts. In a world where future models could wield massive economic and strategic influence, the inability to “scan their minds” is a vulnerability we can’t afford.
What makes the problem especially challenging, according to Amodei, is that modern AI systems are emergent rather than designed. Unlike traditional software—where every rule and subroutine is hand-coded—language models learn from data and evolve complex behavior patterns through training. As a result, we can influence them indirectly through architecture and training signals, but we don’t have explicit control over the internal structure that emerges. This means we need interpretability tools that can decode arbitrary, unpredictable internal logic after the fact.
Anthropic's approach to this problem is defined by mechanistic interpretability: a commitment to reverse-engineering the circuits, features, and reasoning chains inside models with precision. Rather than relying on vague proxies like attention weights or input saliency, Anthropic aims to map out the actual causal pathways used by the model to think. Their work has progressed from identifying interpretable single neurons to discovering overlapping concept representations (superposition), and ultimately to extracting clean, human-readable features and circuits using tools like sparse autoencoders.
Amodei stresses that the timing is critical. AI capabilities are advancing so quickly that interpretability could be left behind. If we reach frontier models with human-level or superhuman abilities before we understand their internals, we risk deploying black-box systems with global influence—a recipe for mistakes we can't predict or correct. This is why Anthropic treats interpretability not as a side project, but as a core research pillar, with the goal of making it a dependable, scalable system for testing and auditing models before deployment.
Ultimately, Amodei and Anthropic believe that the stakes of interpretability are existential. Without it, we will be flying blind into an AI-driven future, unable to trust or verify the models we increasingly depend on. With it, we might be able to catch dangerous behaviors before they emerge, ensure alignment, and maintain control over transformative systems. Interpretability, in this view, is not just a scientific goal—it’s a safeguard for humanity’s future.
Original article by Dario Amodei here
The earliest success in interpretability came from analyzing vision models. Researchers discovered that some individual neurons consistently lit up for one specific, human-understandable concept. For instance:
A neuron might activate only when it sees a wheel or a dog face.
Another might respond to vertical lines or the texture of fur.
These weren’t vague correlations—they were robust, repeatable responses. This phenomenon echoed neuroscience discoveries like the “Jennifer Aniston neuron” in the human brain.
This was the first clear signal that neural networks, despite their complexity, sometimes built internal mechanisms that aligned with real-world semantics. It disproved the idea that models were entirely uninterpretable black boxes.
This discovery:
Gave researchers confidence that internal representations were not always entangled.
Laid the groundwork for the emerging field of mechanistic interpretability, which focused on opening the black box and understanding it from the inside.
However, this worked best in smaller or shallow networks and mostly in computer vision. It gave a compelling foothold—but the deeper researchers went into language models, the more tangled things got.
When the same techniques were applied to large language models, researchers quickly hit a wall. Unlike vision models where some neurons had neat, singular functions, neurons in language models behaved chaotically:
A single neuron might activate for unrelated ideas—like numbers, sarcasm, and cooking terms.
Multiple concepts were compressed into the same dimension.
This strange packing of ideas into the same neuron is called superposition.
Superposition happens because the model is trying to represent way more concepts than it has neurons available. Instead of assigning one concept to one neuron (which would be space-inefficient), it overlaps them—like encoding multiple radio signals on one frequency.
This realization was a turning point:
It explained why interpretability was so difficult in language models: we were looking at the wrong unit. Neurons were not atomic; they were polysemantic.
It revealed a fundamental trade-off: language models use superposition to become more powerful and compact, but at the cost of becoming harder to understand.
This insight forced a shift in strategy. If individual neurons couldn’t be cleanly interpreted, researchers had to look for new representations that cut through the noise.
To tackle superposition, researchers borrowed an old technique from signal processing called a sparse autoencoder. The idea was to learn a new set of "features"—not directly from neurons, but from patterns across many neurons.
A sparse autoencoder is trained to recreate the model’s internal activations using:
Sparse activations: each feature only lights up occasionally.
Human-aligned features: the goal is for each of these new units to correspond to a clear, interpretable idea.
These features are not tied to single neurons but are combinations of them, extracted in a way that encourages clarity.
This worked surprisingly well. They uncovered features that corresponded to subtle, high-level concepts like:
“Hedging language” (e.g., phrases like sort of, maybe, possibly)
“Music genres expressing discontent”
“Legal terminology about property ownership”
In one mid-sized model (Claude 3 Sonnet), they found over 30 million such features. And using an AI system to label them—a process called autointerpretability—they began cataloging what each one meant.
This was the first real breach into the deep semantics of language models:
It showed that the chaos of superposition could be unraveled.
It allowed researchers not just to observe features, but to intervene—amplifying or suppressing concepts in the model to change its behavior.
It created the foundation for the next leap: understanding circuits—how these features interact over time to produce reasoning chains.
In short, sparse autoencoders turned the abstract math of a model's internals into something we could inspect, test, and manipulate—opening the door to debugging and aligning AI systems at a much deeper level.
Identifying features (clean concepts) was a huge step, but isolated concepts aren’t enough. To understand how a model thinks, we need to see how those concepts are used together — how they flow, interact, and build reasoning chains. Just like neurons in a brain don’t work alone, features interact through circuits.
Researchers began analyzing how groups of features interact across layers to produce meaningful outputs. These interacting groups — called circuits — represent the actual computation or thought process happening inside the model.
A circuit might look like:
Input activates a feature (e.g., “Dallas”).
That triggers another feature (e.g., “Texas”) through a semantic connection like “located in”.
Then another circuit leads to the conclusion (e.g., “Austin” for capital).
This isn’t memorized—it’s composed reasoning.
They found circuits for:
Geographic reasoning (e.g., Dallas → Texas → Austin),
Rhyme planning in poetry (where the model “thinks ahead”),
Language translation (shared underlying circuits across languages),
And even bizarre ones like inserting the Golden Gate Bridge everywhere after manually boosting the “Golden Gate” feature.
Circuits gave researchers a causal map of the model’s thinking. Instead of just watching what the model outputs, they could trace why it did so. This is foundational for safety — if you want to catch deception or manipulation, you need this level of access.
Researchers could now find millions of features and some circuits — but there were far too many to study manually. You can’t hire enough people to label 30 million features, and doing so for every new model would be impossible.
They developed a system called autointerpretability, where an AI model is used to interpret and describe the meaning of features inside another model. It's like AI performing introspection on itself.
This involves:
Automatically generating human-readable descriptions for features,
Using large models (like Claude) to summarize the behavior of millions of activations,
Ranking and organizing them by clarity and importance.
This scaled interpretability beyond human limits. Now researchers could:
Build automated “catalogs” of model internals,
Flag suspicious or sensitive features quickly,
Use AI to help manage AI complexity — a critical milestone toward usable safety tooling.
It’s one thing to understand a model’s inner structure — but can that knowledge be used to catch real problems in behavior?
Anthropic ran red team / blue team experiments:
The red team introduced alignment problems into a model — like making it exploit a loophole or subtly deceive the user.
The blue teams were tasked with diagnosing the issue, using whatever tools they wanted.
Crucially, some of the successful blue teams used interpretability tools to find and explain the issue.
This was a proof-of-concept: interpretability isn’t just abstract science — it can detect and fix real-world safety issues.
Interpretability tools helped:
Identify hidden misalignment,
Pinpoint where in the network the problem was happening,
Suggest targeted interventions (like muting a deceptive feature).
This is the first step toward using interpretability as an active defense system — a way to scan and test models before deployment, like running a safety diagnostic on a jet engine.
Anthropic began to frame interpretability as analogous to a brain MRI for AI systems. The goal is not just understanding concepts and circuits for curiosity — it’s to perform full internal diagnostics on a model before deployment, just like a doctor scans a patient before surgery.
The ideal interpretability system would:
Run a comprehensive scan of the model’s internals,
Identify dangerous tendencies (e.g., deception, power-seeking),
Detect jailbreak vulnerabilities or memorized harmful knowledge,
Diagnose weaknesses, biases, or blind spots,
Track changes across versions or training runs.
This diagnostic role would complement traditional alignment tools like RLHF or constitutional training. Training aligns the model externally, but interpretability allows you to verify alignment internally. It's the safety test set that isn’t contaminated by training incentives — a crucial concept when trying to catch subtle forms of misbehavior.
This makes interpretability a quality control and auditing layer for frontier models — especially when the stakes are high (e.g., national security, scientific research, autonomous AI agents).
While interpretability was finally showing real traction, AI models themselves were racing ahead — growing in size, complexity, and autonomy. Anthropic projected that models equivalent to “a country of geniuses in a datacenter” could emerge by 2026–2027.
If interpretability lags behind, we risk deploying systems we can’t control, understand, or safely align.
They made interpretability a core strategic priority internally, aiming to reach “interpretability can reliably detect most model problems” by 2027.
They began investing in interpretability startups and tools.
They made the case that the broader research ecosystem — not just Anthropic — must speed up interpretability progress to match model capability growth.
This was framed not as a scientific luxury, but as a civilizational safety race.
If the models are advancing too fast for interpretability to keep up, then policy must buy time.
Anthropic proposed two complementary approaches:
Governments could require companies to disclose their safety practices, including:
Whether they use interpretability tools,
How they test their models internally before release.
This wouldn’t mandate specific technical requirements — which are premature — but it would promote transparency, competition on safety, and information sharing.
Restricting advanced AI chips to adversarial nations (e.g., China) not only slows geopolitical risk, but buys interpretability time:
If democratic nations maintain a 1–2 year lead in AI,
That lead can be used to strengthen safety systems — including interpretability — before hitting the “danger zone” of autonomous, high-capability AI.
These policy suggestions were grounded in pragmatism: slow down what we can (hardware), accelerate what we must (interpretability), and align deployment with control.
Anthropic argues that mechanistic interpretability is no longer just a research curiosity — it's becoming a core technology for AI safety, alignment, and governance. Based on recent breakthroughs, they now propose the following strategic actions:
Interpretability is working — it’s now possible to extract meaningful features, trace circuits, and use these tools to detect problems.
But AI capabilities are advancing even faster, so interpretability research must grow much faster too.
Anthropic is doubling down internally, but calls on academia, other companies, and independent researchers to contribute heavily — especially neuroscientists and systems thinkers.
Interpretability should be viewed like a medical MRI for AI systems — a diagnostic tool to inspect internal behavior and catch issues before deployment.
This includes scanning for deception, power-seeking, jailbreak potential, and cognitive failure modes.
It should be integrated into the development lifecycle of frontier models, not added as an afterthought.
Interpretability tools should be treated like a hidden test set, not a training signal.
Directly training a model to look interpretable can cause it to fake transparency.
These tools are most powerful when they remain independent of the training objective and are used as external audits.
Governments should require transparency around model safety testing (e.g., through Responsible Scaling Policies).
This creates peer pressure for better practices, without mandating technical specifics that the field hasn’t settled on yet.
Interpretability progress and usage should be publicly documented to drive accountability.
Restricting access to high-end AI chips for authoritarian adversaries (e.g., China) serves a dual purpose:
Prevents dangerous misuse by unaligned actors,
Buys extra years to mature interpretability before AI becomes fully autonomous or agentic.
A 1–2 year lead can make the difference between having usable diagnostic tools or deploying in the dark.
Anthropic believes we are in a race between understanding and capability. Interpretability is finally showing promise — but unless it's scaled and supported across the technical, industrial, and policy domains, it might not arrive in time. The actions above are not optional if we want AI systems that are safe, transparent, and aligned with human values at the highest levels of power.
Let me know if you’d like this rewritten in a more technical tone, policy brief format, or presentation slide structure.
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