{"id":347,"date":"2026-07-22T14:14:36","date_gmt":"2026-07-22T14:14:36","guid":{"rendered":"https:\/\/nationalconsumerreportss.com\/?p=347"},"modified":"2026-07-22T14:14:36","modified_gmt":"2026-07-22T14:14:36","slug":"the-next-mythos-moments","status":"publish","type":"post","link":"https:\/\/nationalconsumerreportss.com\/?p=347","title":{"rendered":"The Next Mythos Moments"},"content":{"rendered":"
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The Trump administration appears to be struggling to craft its new artificial intelligence (AI) policy. In April, the frontier AI developer Anthropic announced a powerful new cybersecurity model, Mythos. Following warnings from major companies that had received early access to Mythos that the model could undermine the security of critical U.S. infrastructure, administration officials signaled interest in clamping down on AI risks. Then, in early June, after pushback from Silicon Valley-aligned figures, the White House released an executive order that kick-started a voluntary process for developers to share their models with the government before release.<\/p>\n

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Less than two weeks later, the Trump administration switched course again, banning worldwide access to Mythos and barring the public release of OpenAI\u2019s latest model for fear of similar cyber risks. At the end of June, officials lifted the restrictions on both companies.<\/p>\n

Wherever the administration\u2019s reaction to Mythos goes next, this won\u2019t be the last time it has to react quickly to novel AI threats. Developers have predicted rapid advances in a host of dangerous capabilities, some of which may present trickier challenges than Mythos. The administration has a window to build its capacity to respond now by boosting technical talent in government, building resilience against novel ways to misuse AI systems, and standardizing processes to evaluate open-source models in particular.<\/p>\n

If the Trump administration misses that opportunity, future Mythos-like jumps in capabilities will come with two distinct risks. Such developments could spark dangerous overreactions, in which the only option available to policymakers is a crackdown. Or in some cases, they could leave policymakers with no option but to accept the model\u2019s role in society. The administration should take advantage of the Mythos warning shot to avoid both possibilities.<\/p>\n

Of all the future Mythos moments that AI policy may have to confront, three are especially notable. First, biological threats: Just as Mythos demonstrates that when AI models are trained as software engineers, advanced cybersecurity capabilities emerge, advances in the use of AI for biological research\u2014a top goal of the leading labs\u2014will likely let the models help criminals, non-state actors, and U.S. adversaries design chemical and biological weapons.<\/p>\n

Second, dangerous capabilities will once again raise questions around open-source AI. If the administration vets a proprietary model, it will do so in part based on safeguards built into the model itself. But with open-source models, attackers can remove those safeguards later\u2014and so the threshold to clear an open-source model for public release may be prohibitively high.<\/p>\n

Finally, rising demand for AI is pushing the world into a compute crunch. When the White House reportedly stalled the rollout of Mythos to a wider range of firms, officials cited compute constraints as one important reason. Today, the chip shortage is mostly a commercial concern, but if compute remains scarce once AI becomes essential to the U.S. military and intelligence services, policymakers may come to regret the sale of advanced chips to China and other nonallied countries.<\/p>\n

Those risks could arise suddenly\u2014through a capable new model, for example, or a Taiwan conflict that shuts off chip supplies\u2014and U.S. policymakers won\u2019t have simple solutions. The AI industry moves fast, competition is fierce, transparency into model development is limited, and new capabilities often emerge unexpectedly. No wonder, then, that frontier labs have floated the possibility of a coordinated slowdown in development, while acknowledging that none of them can individually reach such an outcome.<\/p>\n

What\u2019s more, Mythos was, in some ways, easy to deal with. Defenders in control of important software, such as Mozilla and Palo Alto Networks, used a private version of the tool to identify and patch vulnerabilities before they could be exploited. A few weeks\u2019 lead time might have been all that defenders needed. But responses to biological risks\u2014developing vaccines, building resilient infrastructure, stockpiling equipment\u2014have long lead times. And both open-source deployments and compute exports share an element of irreversibility: Once the open model is deployed or the chips are out of the country, there might be no way for policymakers to fix things.<\/p>\n

In addition to being harder to solve with an ad hoc response, future Mythos moments also risk provoking counterproductive political reactions. Even with Mythos, the administration rapidly risked overreaching. Once officials realized that the current regime of unrestricted releases carried security risks, some members of the administration floated highly restrictive pre-deployment licensing regimes. Kevin Hassett, the director of the White House National Economic Council, went so far as to call for an Food and Drug Administration-style oversight model that would require models to be proved safe before release in the same way that new drugs and vaccines have to go through safety trials before being used to treat patients. And before long, the government had shut down public access to Mythos entirely in response to a vulnerability Anthropic claimed was common across many openly available models.<\/p>\n

As even pro-regulation voices point out, such licensing regimes risk stifling innovation and damaging the U.S. AI industry as it competes with China. While some form of government review will likely be necessary, an arbitrary process with unknown standards, obscure rules, and contested legal authority is unlikely to produce reliable safety benefits. Based on reporting on frequent personality clashes between Anthropic and the U.S. government, it seems like passing this arbitrary process might be about good government relations at least as much as about substantive safety standards; more generally, a deployment-focused approach fails to address some of the most serious future risks, which might come from internal company deployments instead.<\/p>\n

Future crises could go wrong in predictable ways. For one, intelligence agencies or the military could emerge as an easy fallback to the lack of robust capacity elsewhere. If civilian agencies can\u2019t respond to AI-driven threats, heavy-handed restrictions might be the only alternative. National security agencies have expertise in their own domains, but they also have a bias toward secrecy and centralized control, struggle to adapt to technology developed by commercial actors, and have traditionally had limited roles in domestic regulation.<\/p>\n

Take open-source models, which remain widely accessible and largely uncontrollable once released: The security state has little sympathy for Silicon Valley\u2019s open-source-friendly logic that has so far motivated the administration\u2019s fairly supportive position. If there are no mechanisms in place to deal with potentially dangerous open-source releases\u2014evaluations to verify the releases are safe, predeveloped standards for assessment, societal defenses to deal with their effects\u2014it will be straightforward for national security agencies to persuade political leaders that their only choice is to clamp down. It is easy to imagine a Mythos-like ban applied to open-source models developed in the U.S., or even deployed on U.S. servers. Evaluations may be conducted in secret, far from civilian oversight, with few pathways for civil society or industry to challenge them.<\/p>\n

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Spur-of-the-moment decisions are also more likely to be based on thin evidence and political happenstance. The risk is governance by streetlight: Whatever the administration becomes aware of receives attention, and whatever the developers successfully conceal remains unregulated. Given that precedent, if an AI developer were to spot dangerous biological capabilities in its next model and no mechanism existed to compel the company to inform the government, executives might think twice about approaching the administration, and policymakers would miss out on an important window to prepare defenses.<\/p>\n

Finally, U.S. policymakers may fail to meet some risks not because they choose to ignore them, but because they lack the tools to respond. Compute may be the clearest example: If the United States is headed for a crunch that forces the government to trade off national security capabilities against allied access or private-sector use, the relevant decisions will have been made months or years before\u2014when chips were exported outside of its reach. Choices to sell chips to adversaries or partners of convenience that look commercially wise today might prove strategic blunders down the road.<\/p>\n

Of course, many influential Republicans do not believe the government will ever need to answer these questions. Laissez-faire voices such as venture capitalist turned megadonor Marc Andreessen or former AI Czar David Sacks dismiss most of these concerns as hypotheticals pitched by ideological \u201cdoomers.\u201d These proponents are right that regulatory restraint can be the best response to technological progress. But even Sacks has conceded that the cyber risks posed by Mythos are \u201cmore on the real side.\u201d In the face of imminent national security risks, the so-called accelerationists aren\u2019t always going to win the argument. Libertarian voices should develop government expertise and policy options now for surgical interventions in situations where the maximalist deregulatory instinct isn\u2019t politically viable.<\/p>\n

So in reality, all sides of the AI policy debate should want to proactively and deliberately meet these challenges. The AI industry and its allies have no interest in the security state seizing control of AI regulation or a capricious White House process that may backfire on them now or in three years. Democrats seeking to curtail the influence of the Trump White House should welcome a more formal structure to govern pre-deployment decisions. And populist forces on both sides have long called for stricter AI oversight.<\/p>\n

For many of these risks, a realistic solution will involve executive action before more comprehensive legislation. When it comes to pre-deployment reviews of AI models for cyber risks, the right place to start, as Dean Ball and Kevin Frazier argue, is with voluntary reviews through the Center for AI Standards and Innovation (CAISI). The institute is both the U.S. government\u2019s deepest repository of AI expertise\u2014expertise that most other agencies sorely lack\u2014and the administration\u2019s best bet to maintain a cooperative relationship with the frontier labs.<\/p>\n

But the risk profiles on the horizon will require preparation beyond pre-deployment reviews. Open-source models will face some of the greatest scrutiny from a more securitized AI policy paradigm. If the default reaction is to stop their deployment outright, the government will need dedicated evaluation infrastructure and benchmarks for open-source models that account for the likely failure of built-in safeguards in the wild. Standards and evaluation protocols cannot be spun up at the eleventh hour. To prepare for a future when officials are spooked by the prospect of highly advanced open-source models, CAISI should expand its team and work on open-source-focused evaluations today to ensure that crackdowns are not the default.<\/p>\n

Resilience to biological threats is similar enough to cyber threats that many pre-deployment mechanisms will help there, too. But defensive measures will need longer lead times. If the private-sector response to Mythos works, it will be because defenders can quickly patch their codebases, but the U.S. will not be able to create societal resilience to biological weapons after developing the models that accelerate them: Compared to cyber resilience, building up perfect resilience to pathogens is costly, time intensive, and nearly impossible. If the U.S. attempts to address biological risks only through pre-deployment approvals, long lead times will be the default. In addition to model safeguards, governments may need to set up synthesis screening, establish pathogen surveillance, and take other steps such as stockpiling protective equipment ahead of time.<\/p>\n

Ensuring the U.S. has enough compute will also take planning. Policymakers could lean on U.S. labs and major cloud providers to build the bulk of their AI compute in the United States and closely allied countries. The United States should also encourage allied governments to ease the way for their industries to develop compute capacity. Foreign partnerships could involve technical assistance to support major projects and build relationships between U.S. companies and partners in reliable locations. At the more coercive end of the scale, the administration could bar large exports to countries, especially in Southeast Asia, where the end users are likely to be Chinese developers or restrict the total volume of exports to the nonallied world.<\/p>\n

The administration has reportedly considered such steps in the past, and rising constraints on compute may give those efforts new life. For now, critics argue that there are enough chips to go around\u2014that energy to power data centers has become the real bottleneck\u2014so there is no need to interfere with the free market. But if the script flips and chips become the limiting factor, as they have been in the past, then where they go will grow more important. The trick here will be getting the balance right: If officials panic, they risk clamping down too hard and keeping more chips in the United States than the country has the land or power to run, alienating allies and hurting industry. Setting up a more flexible approach in advance\u2014with few restraints on buildouts by allies and U.S.-based firms and harder limits for competing ecosystems\u2014can help ensure the problem never materializes.<\/p>\n

Ultimately, all of this should\u2014and seems likely to\u2014result in legislation. Congress will need to provide money and hiring authorities, set reporting rules, create new safety standards, and authorize other responses to future AI risks. But passing a major AI bill in 2026 looks unlikely, and waiting until Congress acts would be a mistake\u2014both because risks could unfold well before a law could be implemented and because effective legislation requires preparation. Neither government capacity nor the present third-party ecosystem is ready to conduct pre-deployment evaluations at scale, and policymakers shouldn\u2019t squander months before preparing for compute crunches or emerging biological risks. They should not wait for the next Mythos moment to build resilience and capacity to grapple with biological risks, potentially dangerous open-source models, or immediate shortages in compute supply.<\/p>\n

When the Trump administration took office, many of its members likely assumed that the biggest risks would come from acting too quickly to regulate AI. There are still plenty of areas in AI policy, from the deployment of AI systems to the potential societal or economic impacts of their diffusion, where that\u2019s often true. But as AI progress starts to show its teeth, the administration now faces the choice of moving fast to get ahead of the starkest risks or moving too slowly and scrambling for second-best solutions when it\u2019s too late.<\/p>\n

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Future AI risks will be harder to address ad hoc.<\/p>\n","protected":false},"author":1,"featured_media":346,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-347","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-interesting"],"yoast_head":"\nThe Next Mythos Moments - National Consumer Reports<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/nationalconsumerreportss.com\/?p=347\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Next Mythos Moments - National Consumer Reports\" \/>\n<meta property=\"og:description\" content=\"Future AI risks will be harder to address ad hoc.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/nationalconsumerreportss.com\/?p=347\" \/>\n<meta property=\"og:site_name\" content=\"National Consumer Reports\" \/>\n<meta property=\"article:published_time\" content=\"2026-07-22T14:14:36+00:00\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"12 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/nationalconsumerreportss.com\\\/?p=347#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/nationalconsumerreportss.com\\\/?p=347\"},\"author\":{\"name\":\"admin\",\"@id\":\"https:\\\/\\\/nationalconsumerreportss.com\\\/#\\\/schema\\\/person\\\/ef6e8820a5e2e961e9b8cda481436ac0\"},\"headline\":\"The Next Mythos Moments\",\"datePublished\":\"2026-07-22T14:14:36+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/nationalconsumerreportss.com\\\/?p=347\"},\"wordCount\":2407,\"commentCount\":0,\"image\":{\"@id\":\"https:\\\/\\\/nationalconsumerreportss.com\\\/?p=347#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/nationalconsumerreportss.com\\\/wp-content\\\/uploads\\\/2026\\\/07\\\/30f764179c77a5cf4a86917554f2bb23-scaled.jpg\",\"articleSection\":[\"Interesting\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/nationalconsumerreportss.com\\\/?p=347#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/nationalconsumerreportss.com\\\/?p=347\",\"url\":\"https:\\\/\\\/nationalconsumerreportss.com\\\/?p=347\",\"name\":\"The Next Mythos Moments - 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