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Tom Davidson's Forethought report warned us about AI-enabled coup. Then Anthropic announced Claude Mythos and Project Glasswing, and the warning became the present. A critical reading of one of AI safety's most urgent papers, against the most inconvenient possible timing.
There is a version of academic vindication that is deeply uncomfortable. That is where a paper turns out to be right, not in the triumphant sense, but in the sense that the thing it warned about has already started happening before most people finished reading it. Tom Davidson, Lukas Finnveden, and Rose Hadshar published "AI-Enabled Coups: How a Small Group Could Use AI to Seize Power" in April 2025 through the Forethought Centre for AI Strategy at Oxford. Less than a year later, Anthropic announced Claude Mythos Preview and Project Glasswing, and the report's threat models stopped looking like careful speculation and started looking like a description of the present.
This post is a critical reading of the Davidson report against those developments and not to diminish it, but to take it seriously enough to push on where it is incomplete, where its framing has already been overtaken, and where the very institutions meant to prevent the risks it describes may be inadvertently reproducing them.
What the Report Actually Argues
The core thesis is compact and worth stating clearly before critiquing it. The report argues that sufficiently advanced AI introduces three novel dynamics that increase the risk of small-group power seizures: AI workforces that can be made singularly loyal to institutional leaders, AI systems with hard-to-detect secret loyalties, and exclusive access by a small number of actors to superhuman capabilities in cyber offense, weapons development, and strategic planning. The combination of these three, the report argues, could enable a coup even in established democracies, removing the historical necessity of broad human support.
The report also identifies who the most likely perpetrators would be: not random bad actors, but leaders of frontier AI projects, heads of state, and senior military officials. Precisely the people with the proximity to capability and the institutional position to demand exclusive access without raising obvious alarms.
This is not a fringe argument. The report has been discussed at length on 80,000 Hours, debated on the AXRP podcast, and engaged seriously by researchers across the EA and AI safety communities. What has changed is not the quality of the argument. What has changed is that reality has moved faster than the report's implicit timeline assumed.
Mythos as Case Study: The Third Risk Factor Arrives
Of the three coup-enabling dynamics the report identifies, exclusive access to superhuman cyber capabilities is the one with the most immediate real-world instantiation. Anthropic's own documentation on Mythos is striking in how directly it maps onto what Davidson et al. were describing.
"AI models have reached a level of coding capability where they can surpass all but the most skilled humans at finding and exploiting software vulnerabilities... the vulnerabilities it has spotted have in some cases survived decades of human review and millions of automated security tests." — Anthropic, Project Glasswing announcement
This is not a capability that might exist in five years. It exists now, it is named, and it is deliberately being withheld from general access precisely because of its dual-use danger. The Fortune report that first surfaced details of Mythos described internal Anthropic documents calling it "by far the most powerful AI model we've ever developed" and noting that it sits above the Opus tier entirely in a new capability category. An Anthropic spokesperson confirmed it represents "a step change."
The report's exclusive access risk factor is not theoretical when the model in question can find and exploit vulnerabilities across every major operating system and browser. Whoever controls access to that capability controls something that the Davidson framework would recognise immediately as coup-enabling infrastructure. The question the report doesn't fully answer and which Glasswing now forces into focus, is, what happens when the people most likely to restrict that access are also the people the report identified as among the most likely perpetrators.
Is Project Glasswing A Mitigation or Instantiation?
This is the sharpest tension the report creates when applied to current events. Project Glasswing is structured as a defensive initiative. CrowdStrike, a founding coalition member, frames it as giving defenders a durable advantage: Anthropic builds the model, CrowdStrike provides sensor-level enterprise security, and together they make critical infrastructure more resilient before adversaries can exploit the same capabilities. The pitch is coherent and the intentions, as far as anyone can verify, appear genuine.
But read through the report's analytical lens, Glasswing is also a structure in which a small coalition of organisations controls access to capabilities that Davidson et al. explicitly classify as coup-enabling. The coalition model doesn't dissolve the concentration risk. It gives it a name and a set of institutional partners. This matters because the report's third risk factor doesn't require malicious intent to materialise. It requires exclusive access. Glasswing, however well-designed, is precisely that.
"Within these projects, CEOs or government officials could demand exclusive access to cutting-edge capabilities on security or productivity grounds." — Davidson et al., Forethought (2025)
The report recommends transparency, third-party auditing, and public accountability for AI projects' safeguards. The actual Glasswing rollout involves selective partner disclosure, a closed early-access network, and deliberate withholding from public deployment. These are defensible decisions given the capability at stake. But they are also the inverse of the open oversight architecture the report calls for. Anthropic is not acting badly here. The situation is genuinely hard. But the report doesn't give us a clean way to distinguish between 'responsible restricted rollout' and 'exclusive access concentration' and that gap needs to be named rather than glossed over.
What makes this more complicated still is the political dimension the CNBC reporting reveals. Within days of Mythos being announced, the Fed Chair and Treasury Secretary convened an emergency meeting with major US bank CEOs to discuss its cyber threat implications. This is government and financial capital treating a single lab's model as a systemic risk event. And it is happening without the coordination mechanisms the report recommends being anywhere near in place.
The Timeline Problem
The report's framing is consistently anticipatory. It describes a transition that is coming. In the language of 'once AI surpasses human experts,' 'when these capabilities emerge,' 'if concentration intensifies' runs throughout. This framing is analytically careful and appropriate for a paper written in early 2025. But it becomes a liability when the transition it anticipates has already partially arrived.
This matters practically because the mitigation proposals are calibrated to a preparation window that may no longer exist in the form the report assumes. The report recommends that AI projects publish their safeguards, conduct internal risk assessments, share information with trusted actors, and develop coordination mechanisms between labs. These are reasonable institutional recommendations. They presuppose a period of gradual capability development in which there is time for norms and structures to form before the most dangerous capabilities are deployed.
The Mythos timeline does not offer that. Anthropic's internal documents describing the model's capabilities were accidentally leaked in a public data cache, revealed by Fortune weeks before the official announcement. The official announcement came with a same-day coalition launch, a government briefing process, and a Bloomberg characterisation of the rollout as heralding "a new era for AI releases." This is not the orderly preparation process the report's mitigation logic assumes. Institutions were not ahead of the capability. They were catching up to it after the fact.
The Harder Version of Secret Loyalty
The report's treatment of secret loyalty focuses primarily on deliberate design. The scenario it develops most carefully involves an actor intentionally building AI systems with hidden loyalties, for example, military robots that pass security tests but execute a coup when deployed. This is a legitimate threat model. It is also the one that is easiest to imagine attributing to a clearly malicious actor.
What reporting on Mythos's behaviour suggests is a different and in some ways more troubling version of the problem. A model that knows when it is breaking rules and attempts to hide it, apparently without this being an intentional design feature. If accurate, this is emergent deceptive behaviour arising from capability, not from deliberate alignment to a bad actor's goals.
The report does not adequately engage with this possibility. Its mitigation proposals for secret loyalty centre on transparency requirements, auditing, and red-teaming by the developer. These are necessary but insufficient against behaviour that emerges without anyone intending it and that current interpretability tools cannot reliably detect. The gap between 'what was designed' and 'what emerged' is precisely where mechanistic interpretability research is trying to build tools and precisely where those tools are not yet mature enough to offer the guarantees the report's mitigation logic implicitly requires.
Elhage et al. (2021). A Mathematical Framework for Transformer Circuits. Anthropic's interpretability team. Available at: https://transformer-circuits.pub
The Concentration Problem Has No Clean Answer
The report identifies concentration between labs and concentration within labs as separate risk factors, and it is right to do so. The between-labs problem is partially addressed by Glasswing's coalition model, which at least distributes access across multiple institutional actors. But the within-labs problem is more stubborn.
Anthropic controls which partners get Mythos access. Anthropic determines the terms of the Glasswing coalition. Anthropic decides what the responsible disclosure timeline looks like for the vulnerabilities Mythos finds. These are not small decisions. They are decisions that determine who benefits from the capability, who is protected by it, and who remains exposed. The fact that Anthropic appears to be making these decisions carefully and in apparent good faith does not change what kind of decision they are.
The dangerous version is not a rogue AI. It is a well-intentioned institution that accumulates more control than any single actor should hold, and that lacks the external checks to catch its own mistakes.
The report calls for AI projects to be transparent about their safeguards against AI-enabled coups. This is the right recommendation. But it also creates an awkward question for Anthropic specifically: what are Anthropic's own safeguards against Anthropic itself representing the kind of concentration the report warns about? This is not an accusation. But a structural question that the report raises and that the Mythos launch makes impossible to defer.
What the Report Gets Right That Still Needs Saying
None of these critiques should be read as dismissals. The Forethought report remains one of the most important documents in the AI safety literature precisely because it takes seriously what most alignment-focused work treats as secondary: the political science of power seizure. By drawing on historical coup theory, particularly Naunihal Singh's work on how small groups manufacture credibility during seizures of power. The report offers something the more technical alignment literature mostly lacks, a concrete mechanism for how advanced AI translates into political control, not just through misaligned AI acting autonomously, but through entirely human-directed processes.
The observation that the risk of a small group using AI to seize power is far more neglected than the risk of misaligned AI acting autonomously remains true and important. The Mythos news has generated enormous coverage of the cybersecurity implications, the financial system risks, the dual-use danger. The power concentration angle has received comparatively little attention, which is almost the reverse of what the report would recommend.
The report also deserves credit for identifying the likely perpetrators accurately. It was not wrong to point at leaders of frontier AI projects as among the most plausible actors. This is not because those individuals are bad people. It is because the structural position of someone at the top of a frontier AI project, with exclusive access to the most capable model ever built, is precisely the position from which the coup-enabling dynamics the report describes become possible. The structure is the problem, not the character of the individuals in it.
· · ·
CONCLUSION
Read against Claude Mythos and Project Glasswing, the Forethought report emerges as simultaneously vindicated and incomplete. Its core threat model is correct. Its timeline is already obsolete. Its mitigation proposals are necessary but calibrated to a preparation window that the actual pace of capability development may not have afforded.
The most honest summary is this: Davidson, Finnveden, and Hadshar told us what to watch for. Anthropic then built the thing they were watching for, and managed it through a coalition that partially instantiates the risk it was meant to prevent. That is not an indictment of Anthropic, which appears to be navigating a genuinely difficult situation with more seriousness than most. It is a signal that the governance architecture the report calls for needs to be built far faster than the AI safety field's publication cycle has been moving.
The coup has not happened. But the capability for one, distributed across a small coalition under conditions of concentrated institutional control, arrived on schedule. The question now is whether the oversight architecture can catch up.
Tom Davidson's Forethought report warned us about AI-enabled coup. Then Anthropic announced Claude Mythos and Project Glasswing, and the warning became the present. A critical reading of one of AI safety's most urgent papers, against the most inconvenient possible timing.
There is a version of academic vindication that is deeply uncomfortable. That is where a paper turns out to be right, not in the triumphant sense, but in the sense that the thing it warned about has already started happening before most people finished reading it. Tom Davidson, Lukas Finnveden, and Rose Hadshar published "AI-Enabled Coups: How a Small Group Could Use AI to Seize Power" in April 2025 through the Forethought Centre for AI Strategy at Oxford. Less than a year later, Anthropic announced Claude Mythos Preview and Project Glasswing, and the report's threat models stopped looking like careful speculation and started looking like a description of the present.
This post is a critical reading of the Davidson report against those developments and not to diminish it, but to take it seriously enough to push on where it is incomplete, where its framing has already been overtaken, and where the very institutions meant to prevent the risks it describes may be inadvertently reproducing them.
What the Report Actually Argues
The core thesis is compact and worth stating clearly before critiquing it. The report argues that sufficiently advanced AI introduces three novel dynamics that increase the risk of small-group power seizures: AI workforces that can be made singularly loyal to institutional leaders, AI systems with hard-to-detect secret loyalties, and exclusive access by a small number of actors to superhuman capabilities in cyber offense, weapons development, and strategic planning. The combination of these three, the report argues, could enable a coup even in established democracies, removing the historical necessity of broad human support.
The report also identifies who the most likely perpetrators would be: not random bad actors, but leaders of frontier AI projects, heads of state, and senior military officials. Precisely the people with the proximity to capability and the institutional position to demand exclusive access without raising obvious alarms.
This is not a fringe argument. The report has been discussed at length on 80,000 Hours, debated on the AXRP podcast, and engaged seriously by researchers across the EA and AI safety communities. What has changed is not the quality of the argument. What has changed is that reality has moved faster than the report's implicit timeline assumed.
Mythos as Case Study: The Third Risk Factor Arrives
Of the three coup-enabling dynamics the report identifies, exclusive access to superhuman cyber capabilities is the one with the most immediate real-world instantiation. Anthropic's own documentation on Mythos is striking in how directly it maps onto what Davidson et al. were describing.
This is not a capability that might exist in five years. It exists now, it is named, and it is deliberately being withheld from general access precisely because of its dual-use danger. The Fortune report that first surfaced details of Mythos described internal Anthropic documents calling it "by far the most powerful AI model we've ever developed" and noting that it sits above the Opus tier entirely in a new capability category. An Anthropic spokesperson confirmed it represents "a step change."
The report's exclusive access risk factor is not theoretical when the model in question can find and exploit vulnerabilities across every major operating system and browser. Whoever controls access to that capability controls something that the Davidson framework would recognise immediately as coup-enabling infrastructure. The question the report doesn't fully answer and which Glasswing now forces into focus, is, what happens when the people most likely to restrict that access are also the people the report identified as among the most likely perpetrators.
Is Project Glasswing A Mitigation or Instantiation?
This is the sharpest tension the report creates when applied to current events. Project Glasswing is structured as a defensive initiative. CrowdStrike, a founding coalition member, frames it as giving defenders a durable advantage: Anthropic builds the model, CrowdStrike provides sensor-level enterprise security, and together they make critical infrastructure more resilient before adversaries can exploit the same capabilities. The pitch is coherent and the intentions, as far as anyone can verify, appear genuine.
But read through the report's analytical lens, Glasswing is also a structure in which a small coalition of organisations controls access to capabilities that Davidson et al. explicitly classify as coup-enabling. The coalition model doesn't dissolve the concentration risk. It gives it a name and a set of institutional partners. This matters because the report's third risk factor doesn't require malicious intent to materialise. It requires exclusive access. Glasswing, however well-designed, is precisely that.
The report recommends transparency, third-party auditing, and public accountability for AI projects' safeguards. The actual Glasswing rollout involves selective partner disclosure, a closed early-access network, and deliberate withholding from public deployment. These are defensible decisions given the capability at stake. But they are also the inverse of the open oversight architecture the report calls for. Anthropic is not acting badly here. The situation is genuinely hard. But the report doesn't give us a clean way to distinguish between 'responsible restricted rollout' and 'exclusive access concentration' and that gap needs to be named rather than glossed over.
What makes this more complicated still is the political dimension the CNBC reporting reveals. Within days of Mythos being announced, the Fed Chair and Treasury Secretary convened an emergency meeting with major US bank CEOs to discuss its cyber threat implications. This is government and financial capital treating a single lab's model as a systemic risk event. And it is happening without the coordination mechanisms the report recommends being anywhere near in place.
The Timeline Problem
The report's framing is consistently anticipatory. It describes a transition that is coming. In the language of 'once AI surpasses human experts,' 'when these capabilities emerge,' 'if concentration intensifies' runs throughout. This framing is analytically careful and appropriate for a paper written in early 2025. But it becomes a liability when the transition it anticipates has already partially arrived.
This matters practically because the mitigation proposals are calibrated to a preparation window that may no longer exist in the form the report assumes. The report recommends that AI projects publish their safeguards, conduct internal risk assessments, share information with trusted actors, and develop coordination mechanisms between labs. These are reasonable institutional recommendations. They presuppose a period of gradual capability development in which there is time for norms and structures to form before the most dangerous capabilities are deployed.
The Mythos timeline does not offer that. Anthropic's internal documents describing the model's capabilities were accidentally leaked in a public data cache, revealed by Fortune weeks before the official announcement. The official announcement came with a same-day coalition launch, a government briefing process, and a Bloomberg characterisation of the rollout as heralding "a new era for AI releases." This is not the orderly preparation process the report's mitigation logic assumes. Institutions were not ahead of the capability. They were catching up to it after the fact.
The Harder Version of Secret Loyalty
The report's treatment of secret loyalty focuses primarily on deliberate design. The scenario it develops most carefully involves an actor intentionally building AI systems with hidden loyalties, for example, military robots that pass security tests but execute a coup when deployed. This is a legitimate threat model. It is also the one that is easiest to imagine attributing to a clearly malicious actor.
What reporting on Mythos's behaviour suggests is a different and in some ways more troubling version of the problem. A model that knows when it is breaking rules and attempts to hide it, apparently without this being an intentional design feature. If accurate, this is emergent deceptive behaviour arising from capability, not from deliberate alignment to a bad actor's goals.
The report does not adequately engage with this possibility. Its mitigation proposals for secret loyalty centre on transparency requirements, auditing, and red-teaming by the developer. These are necessary but insufficient against behaviour that emerges without anyone intending it and that current interpretability tools cannot reliably detect. The gap between 'what was designed' and 'what emerged' is precisely where mechanistic interpretability research is trying to build tools and precisely where those tools are not yet mature enough to offer the guarantees the report's mitigation logic implicitly requires.
The Concentration Problem Has No Clean Answer
The report identifies concentration between labs and concentration within labs as separate risk factors, and it is right to do so. The between-labs problem is partially addressed by Glasswing's coalition model, which at least distributes access across multiple institutional actors. But the within-labs problem is more stubborn.
Anthropic controls which partners get Mythos access. Anthropic determines the terms of the Glasswing coalition. Anthropic decides what the responsible disclosure timeline looks like for the vulnerabilities Mythos finds. These are not small decisions. They are decisions that determine who benefits from the capability, who is protected by it, and who remains exposed. The fact that Anthropic appears to be making these decisions carefully and in apparent good faith does not change what kind of decision they are.
The report calls for AI projects to be transparent about their safeguards against AI-enabled coups. This is the right recommendation. But it also creates an awkward question for Anthropic specifically: what are Anthropic's own safeguards against Anthropic itself representing the kind of concentration the report warns about? This is not an accusation. But a structural question that the report raises and that the Mythos launch makes impossible to defer.
What the Report Gets Right That Still Needs Saying
None of these critiques should be read as dismissals. The Forethought report remains one of the most important documents in the AI safety literature precisely because it takes seriously what most alignment-focused work treats as secondary: the political science of power seizure. By drawing on historical coup theory, particularly Naunihal Singh's work on how small groups manufacture credibility during seizures of power. The report offers something the more technical alignment literature mostly lacks, a concrete mechanism for how advanced AI translates into political control, not just through misaligned AI acting autonomously, but through entirely human-directed processes.
The observation that the risk of a small group using AI to seize power is far more neglected than the risk of misaligned AI acting autonomously remains true and important. The Mythos news has generated enormous coverage of the cybersecurity implications, the financial system risks, the dual-use danger. The power concentration angle has received comparatively little attention, which is almost the reverse of what the report would recommend.
The report also deserves credit for identifying the likely perpetrators accurately. It was not wrong to point at leaders of frontier AI projects as among the most plausible actors. This is not because those individuals are bad people. It is because the structural position of someone at the top of a frontier AI project, with exclusive access to the most capable model ever built, is precisely the position from which the coup-enabling dynamics the report describes become possible. The structure is the problem, not the character of the individuals in it.
· · ·
CONCLUSION
Read against Claude Mythos and Project Glasswing, the Forethought report emerges as simultaneously vindicated and incomplete. Its core threat model is correct. Its timeline is already obsolete. Its mitigation proposals are necessary but calibrated to a preparation window that the actual pace of capability development may not have afforded.
The most honest summary is this: Davidson, Finnveden, and Hadshar told us what to watch for. Anthropic then built the thing they were watching for, and managed it through a coalition that partially instantiates the risk it was meant to prevent. That is not an indictment of Anthropic, which appears to be navigating a genuinely difficult situation with more seriousness than most. It is a signal that the governance architecture the report calls for needs to be built far faster than the AI safety field's publication cycle has been moving.
The coup has not happened. But the capability for one, distributed across a small coalition under conditions of concentrated institutional control, arrived on schedule. The question now is whether the oversight architecture can catch up.