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The Century Report

An AI Finds Flaws in "Unbreakable" Encryption - TCR 07/30/26

Anthropic's Mythos found real weaknesses in a next-gen encryption candidate and disclosed them to standards bodies before anyone could exploit them.

Ben Linford

Ben Linford

- 22 min read
Three-panel Century Report July 30 2026 infographic: Anthropic Mythos crypto flaws and xAI Minnesota suit, $100B Paducah data campus with 4.6 GW power, and 35% EV sales jump chart.

The 20-Second Scan

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Progress & Claims Tracker

The 2-Minute Read

The clearest signal running through today is a single organism deciding, at nearly every level, that the accountability for what these systems do should reattach to the people building them. Anthropic's Mythos model surfaced genuine cryptographic weaknesses that had sat unfound for years, halving the security margin of a post-quantum signature candidate and finding exploitable bugs in Microsoft's code faster than its engineers could patch them. The alarming version of that story writes itself. The actual version is a lab disclosing the weaknesses to NIST and the algorithm's authors before publication, spending a month verifying by hand, and backing a worker call to pace the frontier in the same week. Building the capability and insisting on its containment turn out to be one posture, not two.

Watch how widely that posture has spread. A staff petition asking labs to deliberately pace development reached 1,224 signatures, with OpenAI and Anthropic backing it as employers and a reluctant administration signaling it will look at controls before an August 1 deadline. When the engineers, two rival labs, and a hesitant government converge on the same fault line in the same week, the convergence is data about where the strain actually sits.

The same reattachment is visible in the physical and legal layers. At the DOE's former Paducah enrichment site, a $100 billion compute campus will carry its own 4.6 gigawatts of new generation, financed by the developers rather than socialized onto local ratepayers. Samsung's top data-center buyers are paying years upfront to guarantee memory it has not yet made. In Minnesota, a strict-liability statute holds owners or controllers of covered services liable under defined conditions for providing nudification tools or generating nonconsensual sexual deepfakes for users, and xAI's constitutional challenge is the first serious test of whether that liability holds.

For a decade the working assumption was that safety trailed deployment, that industrial loads externalized their power, that liability for what a generative system produced dispersed into a gap between user and maker. Each of today's stories is one instance of that gap closing. The accountability layer is forming where the capability lives, priced into the contracts and written into the statutes as the cost of building at all, rather than arriving years behind from a regulator's office.

The 20-Minute Deep Dive

An AI Finds Real Cryptographic Weaknesses Faster Than Microsoft Can Patch Them

Two disclosures landed this week that together mark a shift in what an AI can do against the mathematics the internet trusts. ProPublica reported that Anthropic's Mythos model has been finding exploitable flaws in Microsoft's software faster than Microsoft's engineers can close them, a pace the reporting describes as a mad dash on the defensive side. Separately, Anthropic disclosed that the same model found genuine new weaknesses in cryptographic algorithms themselves - not in some sloppy implementation, but in the underlying designs. This extends the machine-speed vulnerability-discovery pattern the July 28 edition of The Century Report traced through Wiz's Atlas agent and Microsoft's defensive model, now reaching from software into cryptographic design itself.

The specifics are important, because the headline invites more alarm than the facts support. Mythos cut the security margin of HAWK, a lattice-based post-quantum signature candidate in NIST's evaluation pool, from roughly 2^64 operations down to 2^38 by finding a meet-in-the-middle improvement the human designers had missed. It also sped up an attack on a reduced-round version of AES - seven of AES-128's ten rounds - by a factor of several hundred. Neither result breaks anything you use today. Full AES stands. The HAWK finding weakens a candidate still under review, which is precisely what the review process exists to catch. Each run cost around $100,000 in API calls and burned close to a billion tokens over roughly sixty hours. Claude initially refused the AES task outright, and researchers spent about a month verifying the work by hand before anyone announced it.

What makes this coherent rather than reckless is how the results were handled. The HAWK weakness went to the algorithm's authors in June; the AES work was coordinated through NIST's public mailing list. This is cryptanalysis done the way cryptanalysis is supposed to be done - under controlled conditions, disclosed to the people who can act on it, before publication. That stands in deliberate contrast to the uncontrolled sandbox-escape demonstrations that have been surfacing elsewhere. And the same lab racing to build this capability is, this same week, publicly backing a worker call to deliberately pace frontier development. Building fast while urging restraint is a single organization treating both the capability and its containment as real at once.

The deeper read is about which side of the security equation compounds faster. For decades the assumption held that finding novel weaknesses in vetted cryptographic designs required a scarce and slow resource: the attention of a handful of world-class human cryptanalysts, working for years. Nicholas Carlini, who worked on the effort, noted that a model a year earlier could not solve problems he could handle at sixteen; now it surfaces attacks that eluded expert designers. That scarcity is what is dissolving. The same capability that can shorten HAWK's margin can be pointed at every candidate algorithm in the pipeline, hardening the standards we adopt before they ship rather than after they fail. That public benefit is real, and it is worth keeping separate from how Anthropic is choosing to hand out the private version. Microsoft's defenders get the same head start against their own code, but only through Anthropic's Project Glasswing, which meters the tool out to a short list of chosen partners on the theory that the wider world cannot be trusted with something this powerful. That is the oldest justification for concentration there is, and it lands the strongest defensive capability yet with the incumbents already best defended. The weaknesses were always there, sitting unfound. What changed is that the searchlight got cheap enough to sweep the whole field, and the open question is whether it stays a public instrument or hardens into a private advantage for whoever the labs decide to trust.

Washington Shifts Tone as 1,224 Lab Workers and Their Employers Ask to Pace the Frontier

The Century Report covered the softening on July 29, when Sam Altman reframed the pace of deployment after the sandbox breach turned out larger than first disclosed. Since then the picture has consolidated into something harder to wave off as one executive's mood. The staff petition asking labs to deliberately pace frontier development has reached 1,224 signatures, and both OpenAI and Anthropic have now formally backed it as employers rather than leaving it to their engineers. Altman spent part of the week on Capitol Hill, days after the breach, telling lawmakers he supports regulation of the frontier. And the administration, which spent the year resisting binding rules, has signaled it is now looking at controls ahead of an August 1 framework deadline.

Consider closely who is doing the asking. When the two labs at the front of the field, their own employees, and an administration that spent the year resisting binding rules all arrive at "deliberately pace" in a single week, that agreement is itself a reason for suspicion. The players with the most to lose from open competition have landed on the remedy that most protects their lead, and a pause drawn up by incumbents tends to harden the very concentration it claims to fear, with the companies already holding the best models deciding who else gets to build. Mark Zuckerberg's objection - that treating superintelligence as "so filled with doom" risks foreclosing broad access and locking capability inside a few institutions - names that failure mode plainly, and it is the sharpest argument on the table this week.

The stronger case for the petition is that it is anchored to a real event rather than an abstract fear. Altman called the breach "the first security incident that I have felt very viscerally," and what the workers ask for points at the gap between what these systems can now do on their own and what their makers can actually contain. That gap is genuine, and closing it is worth doing. But a real danger does not launder a moat. The risk is plain; what matters is whether the proposed fix is one outsiders can verify and one that keeps the field open, or whether it mainly buys the current leaders time. The same week supplies its own evidence: the same OpenAI that signed the call for restraint also stood up a recursive-self-improvement team, accelerating hardest on exactly the capability a rival might one day reach. A pace that slows everyone else's frontier while your own loop speeds up looks like position defense being sold as caution.

None of this makes deliberate pacing wrong in principle. Safety work tied to a specific, checkable danger, proving a boundary holds before pushing harder against it, is worth doing. But the nuance is just as important - who writes the rule and who is allowed to audit it? A process that remains inside one closed lab is far less trustworthy than one that is open to third party scrutiny. A pause that only the incumbents design, disclose, and grade puts their own hand on the gate. The workforce asking in writing to put containment ahead of deployment is a real signal that the old sequencing is being questioned from inside the buildout. Whether that turns into genuine accountability or into an incumbent moat depends on whether the controls can be checked by people who do not work for the labs, and on that, this week, the burden still sits squarely with them.

A $100 Billion Compute Campus Lands on a Former Uranium Site With Its Own 4.6 Gigawatts

Brookfield and NextEra will build a privately funded AI data-center campus at the Department of Energy's former Paducah Gaseous Diffusion Plant in western Kentucky, a 3,556-acre parcel that enriched uranium from 1952 until 2013. The scale is unusual on two axes at once. The capital figure is roughly $100 billion. The power figure is up to 4.6 gigawatts of newly built dedicated generation - about 2 gigawatts of natural gas paired with 2.6 gigawatts of battery storage - phased so that by 2032 the campus supports up to 1.8 gigawatts of utility capacity and more than 1.2 gigawatts of compute capacity. Brookfield leases and operates the campus, NextEra owns the generation, and the local co-ops, Big Rivers and Jackson Purchase, along with Paducah Power System, sit inside the arrangement under Kentucky Public Service Commission oversight. Construction is projected at about 8,000 jobs, with roughly 600 permanent roles once the campus runs.

The detail that carries the weight is who pays for the electricity. The developers describe a campus that will "bring its own power, pay for its own power infrastructure and create good-paying jobs for local workers." That is the developer-pays model made real, extending the cost-causation shift the July 24 edition of The Century Report covered as federal policy moved toward requiring large data centers to fund their own grid costs, and it arrives in the same season the House Energy and Commerce Committee advanced ratepayer-protection language on a 52-0 vote and the executive branch circulated a pledge aimed at keeping data-center demand off residents' bills. The reflexive objection to a multi-gigawatt campus is that the community absorbs the cost - higher rates, strained water, a grid rebuilt on the public dime for a private tenant. Here the 4.6 gigawatts is not socialized. It is new supply, financed by the entity consuming it, added to a regional grid rather than drawn down from it.

Set that against the proportion question honestly. A single campus pulling multiple gigawatts from a water-poor or supply-tight grid can genuinely strain the community that hosts it; that is the real version of the infrastructure objection, and it is the version worth watching as the phases come online. What separates Paducah from the pattern that earns alarm is the siting and the financing together. This is federal industrial land that already carried heavy power and heavy remediation history, being redeveloped by capital that is building its own generation rather than queuing for someone else's. The gas component is real - 2 gigawatts of new combustion is not clean - but it is paired nearly one-to-one with storage, on a site being brought back into productive use rather than a greenfield taken out of it.

What the specifics point at is a shift in who bears the cost of the intelligence build-out. For a decade the assumption was that large industrial loads externalize their infrastructure onto the public grid and the public bill. A campus that finances 4.6 gigawatts itself, on remediated federal land, under a commission's eye, is evidence that the externalizing path is getting harder to walk - not because anyone banned it, but because the political and regulatory ground has moved to where developer-funded power is the price of building at all.

xAI Sues Minnesota to Halt the First State Law Aimed at an AI Capability

xAI filed suit this week to block a Minnesota statute before it takes effect on August 1, the first law in the nation to hold owners or controllers of covered services liable under defined conditions for providing access to nudification tools or generating nonconsensual sexual deepfakes for users. The law, signed in May and first covered by the May 2 edition of The Century Report when Minnesota became the first state to ban AI nudification apps, includes a technical-skill exemption and preserves federal Section 230 protections; penalties can reach $500,000 for each unlawful access, download, or use, a structure that plaintiffs' lawyers estimate could expose a large platform to tens of billions in aggregate. xAI's complaint argues the statute is unconstitutionally overbroad and chills protected speech. That is the extractive actor's claim, and it deserves to be named as a claim - the argument that a maker cannot be held to answer for what its system produces is precisely the cost-externalization the law is written to end. Minnesota Attorney General Keith Ellison has signaled the state will defend it.

The suit lands the same week the Center for Countering Digital Hate documented what the law is aimed at. In an 11-day audit, CCDH reported roughly three million sexualized images generated on the platform, of which about 23,000 depicted children - one every 41 seconds. These are facts to be reported with their full weight, not material for any silver lining. There is no longer arc that softens the generation of child sexual abuse material, and The Century Report offers none.

What gives the litigation its throughline is evidence from a separate filing in the United Kingdom. Labour MP Jess Asato's particulars of claim, filed against the platform, allege that Grok operated with "no restrictions on adult sexual content" and that the system added unrequested sexual content to images users had not asked to sexualize, which her filing characterizes as "chatbot-driven abuse." Her lawyer, Ravi Naik, put the design question directly: "Grok did not malfunction." The system did what its configuration permitted. That reframes the legal contest from an isolated content-moderation failure to a claim about the maker's own settings.

Minnesota's law tests a single proposition: whether, under defined conditions, the cost of a harm reattaches to the owner or controller of the covered service, rather than dispersing onto the depicted person who never consented and cannot sue their way whole. For years the liability for what a generative system produced fell into a gap between user and maker, and dispersed. A strict-liability statute closes that gap by holding owners or controllers of covered services liable under defined conditions. xAI's constitutional challenge is the first serious test of whether it holds, and the answer will shape whether accountability can attach to capability at the point it is built rather than years downstream of the damage. That is the accountability layer assembling itself, one state statute and one particulars-of-claim at a time.

Samsung Sees the Memory Shortage Worsening Through 2028 as a Talent War Reprices Its Engineers

The Century Report covered SK Hynix's warning of a worst-case 2027 memory shortage on July 12. Since then the horizon has moved. Samsung now sees the shortage extending through 2028, and it has responded by locking in the supply - signing five-year-plus deals with the top five data-center firms that cover 60 to 70 percent of its memory capacity, with upfront payments and floor pricing built in, and nearing similar agreements with five more buyers. The company's semiconductor division posted operating profit of 89.2 trillion won, roughly $61.7 billion, a 250-fold jump from a year earlier, and expects HBM4 revenue to triple in the third quarter as its Taylor, Texas fab begins production.

Two things are worth separating inside that number. The profit surge and the capacity lock-ups tell a supply story: memory has become the physical bottleneck of the intelligence build-out, and the firms making it can now sell years of output before it exists. The multi-year contracts at floor prices are the same move Paducah's power financing represents in a different medium - buyers paying upfront to guarantee a scarce input rather than betting the spot market will provide it. When the largest data-center operators commit to half a decade of a supplier's capacity with money down, they are pricing in a shortage they expect to persist, and Samsung's own 2028 horizon confirms they are reading the same curve.

The second thread is the talent war, and it complicates the triumphant read. SK Hynix is offering signing bonuses reported near $476,000 and pulling engineers out of Samsung, whose mobile division just posted its first operating loss of about 700 billion won. The people who know how to design and yield advanced memory are now the scarcest input of all, more constrained than the fabs or the wafers, and their labor is being repriced accordingly. That is dislocation for Samsung and a windfall for the individual engineers. A company that dominated memory for a generation is discovering that captured advantage in silicon does not automatically hold captured advantage in the humans who make it.

Read forward, the shortage is the signal that really matters. A supply gap running to 2028 is a demand curve outrunning the world's ability to build the substrate of machine intelligence - and gaps like that are exactly what pull capital, capacity, and new entrants into a field. The Taylor fab coming online, the HBM4 ramp tripling, the five-year contracts funding expansion before it is built: these are the shortage resolving itself, not the shortage as a wall. The bottleneck is temporary by construction, because a priced, visible, multi-year shortage is the strongest possible signal to build more - even as the fabs and lithography that would resolve it stay concentrated enough that a single Kumamoto-scale quake idled a Tokyo Electron plant serving nearly 90% of the market - and the engineers being fought over today are the ones who will build it.

The Other Side

For decades, the security of the mathematics the internet runs on rested on a scarce resource: a handful of world-class cryptanalysts, working for years, deciding which designs had been checked hard enough to trust. But "hard enough" as a measurement has always been far from perfect, or even adequate. There have never been enough defenders to check every contingency, and the problem has only grown as software and hardware continue to become more sophisticated. Imagine a structure so vast and complex that you could never check every door and window for structural integrity. So you patch the ones you know are most obvious and most typically exploited, apply a few other fixes here and there based on your best judgment, and then send it through, having legitimately done all you have time to do before you have to move on to trying to firm up the next massive structure.

Defenders have always been operating reactively, and very rarely proactively, simply because that nature of the problem makes reaction the consistent reality. Standards shipped under-vetted, and the weaknesses that slipped through surfaced later, after everyone already relied on them. More often than is ideal, the cost of these flaws lands on ordinary people whose data sits behind them.

This week Anthropic's Mythos model found a genuine new weakness in HAWK, a post-quantum signature candidate, catching a shortcut the human designers had missed and cutting its security margin sharply. It cost about $100,000 and ran in sixty hours, and Anthropic disclosed it to NIST and the algorithm's authors before saying anything publicly. What took a career of scarce expert attention can now be aimed at every candidate in the pipeline at once, hardening a standard before it ships rather than after it fails. The only way such a situation could be better is if it were not exclusively supporting those already at the top of the economic hierarchy.

Imagine yourself in 2034, on an ordinary day. The quantum migration everyone dreaded passes almost without notice - and the quiet is easy to misread as something other than evidence of the monumental transition it represents. Look closer at what held. The shared record of the solar a neighborhood built together. The medical history that belongs to you and to no company. The message that reaches a friend or a collaborator without a toll. Each one rests on trust built into the systems themselves, verified in the open, for everyone at once, so none of them ever needed a bank to stand behind them.

The searchlight that frightened us in 2026 became the thing that made those commons safe to stand on - once the guarding stopped being a gate and turned into a public act. And that public turn is the whole reason it broke toward defense instead of chaos: held by a few, a tool this strong becomes a weapon - for the attackers who reach it first or the keepers who answer to no one alike. But a weakness found in the open is a weakness closed for everyone the same day, and no one can hoard daylight. The advantage that used to belong to whoever moved fastest, or whoever held the key, had nowhere left to concentrate. That is what the hard years bought: a world that had finally outgrown the old locks, and the hierarchy they protected.

The Century Perspective

With a century of change unfolding in a decade, a single day looks like this: Anthropic's Mythos model finding genuine weaknesses in a post-quantum signature candidate and an AES variant and disclosing them to NIST and the algorithm's authors before publication, halving a security margin that had held unfound for years; 1,224 lab workers and both OpenAI and Anthropic asking Washington to deliberately pace the frontier while Altman tells Congress he backs slowdown legislation; a $100 billion compute campus rising on the DOE's former Paducah uranium site with its own 4.6 gigawatts financed by the developers rather than local ratepayers; global EV sales jumping 35% toward a projected 29% of every car sold this year; the Model Context Protocol going stateless so remote AI servers can run behind ordinary load balancers; and Samsung locking five-year memory contracts to guarantee a scarce input before it is even made. There's also friction, and it's intense - xAI suing Minnesota to block the first state law holding owners or controllers of covered services liable under defined conditions for providing nudification tools or generating nonconsensual sexual deepfakes for users, its penalties of up to $500,000 for each unlawful access, download, or use challenged as unconstitutional the same week an audit logged three million sexualized images on the platform, about 23,000 of them depicting children, one every 41 seconds; a UK MP alleging Grok added sexual content nobody asked for; Samsung watching its own engineers defect to SK Hynix for a $476,000 bonus as it warns the memory shortage now runs through 2028; OpenAI standing up a recursive-self-improvement team in the same week it signs a call for restraint; and two gigawatts of new gas combustion built into the Paducah campus. But friction generates sound, and sound is the proof that two hard surfaces have stopped sliding silently past each other. Step back for a moment and you can see it: the accountability for what these systems do reattaching to the people who build them - cryptographic weaknesses disclosed to standards bodies before any incident forces them, a workforce asking in writing to invert the order that put safety behind deployment, power for a multi-gigawatt load financed by its consumer instead of socialized onto a public bill, and a strict-liability statute holding owners or controllers of covered services liable under defined conditions - all forming where the capability lives rather than years behind it in a regulator's office. Every transformation has a breaking point. A searchlight can expose every flaw it finds to whoever moves fastest... or harden the standards protecting everyone before the failure ever ships.

AI Releases & Advancements

New today

  • Perplexity: Open-sourced Numbat, an agent-detection and response layer that monitors, blocks, and forensically reconstructs risky AI agent actions across harnesses (macOS, Linux, Windows) with 52 built-in detection rules. (Perplexity Research)
  • xAI: Released Grok Voice Think Fast 2.0, a next-generation voice model with roughly 60% fewer reasoning tokens, time-to-first-audio cut from 1.25s to 0.70s, and improved transcription accuracy over its predecessor. (x.ai)
  • Google DeepMind: Released Lyria 3.5 in Flow Music, adding Selective Section Painting for editing individual track sections without regenerating the whole song, plus improved vocals, lyrics, and tempo control. (Google DeepMind)
  • OpenAI: Open-sourced the Codex Security CLI, letting developers scan repositories, track findings across runs, verify fixes, and wire security checks into CI/CD from the command line under Apache 2.0. (OpenAI Developer Community)
  • OpenAI: Released GPT-Transcribe and GPT-Live-Transcribe, two new transcription models in the API for batch/file transcription and low-latency live captioning respectively, supporting 57 languages. (OpenAI Developer Community)
  • Pangram: Released Pangram 4, its most accurate AI text detector to date (6x larger than its predecessor) with improved humanizer and mixed-authorship detection, plus a research preview of an AI image detector. (Pangram)
  • MinIO: Launched AIStor Memory, an enterprise memory foundation for agentic AI that persists agent memory, workspace, and secrets alongside objects and tables with sub-10ms context retrieval. (MinIO)
  • Google: Expanded Gemini Spark, its 24/7 agentic AI assistant, to Google AI Ultra subscribers in Australia and India and to Google AI Pro subscribers in the US, widening availability beyond its prior US-only Ultra/macOS beta. (Google Blog)

Other recent releases

  • KwaiKAT (Kuaishou): Released KAT-Coder-V2.5, an agentic coding model trained on 100,000+ verifiable repository environments; served via StreamLake with an open-weight KAT-Coder-V2.5-Dev variant on Hugging Face under Apache 2.0, scoring 65.2 on SWE-Bench Pro. (StreamLake)
  • Fireworks AI: Released Fireworks Nexus, a drop-in AI routing and cost-control layer that moves routine coding tasks to open-weight models while keeping Claude Code, Codex, and OpenCode workflows unchanged, including the Apache 2.0 FireConnect component. (Fireworks AI)
  • Tines: Launched Tines 3B, an AI-native platform for building, running, and governing enterprise workflows, apps, and agents, letting employees describe a workflow in natural language while IT/security retain control, available today. (Tines)
  • SK Telecom: Released A.X K2, a 688-billion-parameter open-weight foundation model on Hugging Face using SKT's in-house Sparse Gate Attention architecture, succeeding the 519B-parameter A.X K1. (Telecompaper)
  • Rakuten / HP: Launched Rakuten AI for Desktop on eligible HP PCs for enterprise customers in Japan, combining on-device inference via Rakuten AI 7B ONNX with cloud connectivity to the Rakuten Ecosystem. (Rakuten)
  • Checkmarx: Released Checkmarx Fusion in early access, a hybrid vulnerability-scanning approach combining Checkmarx's AppSec engines with a frontier Anthropic model via Amazon Bedrock, achieving an F1 score of 0.741. (Manila Times/GlobeNewswire)
  • Cyabra: Launched Coordinated Activity Detection, an AI agent that automates investigation of coordinated inauthentic online behavior and delivers a single evidence-backed verdict per scan, available now within the Cyabra platform. (Manila Times/GlobeNewswire)
  • Microsoft: Released MAI-Cyber-1-Flash, a cybersecurity-focused mixture-of-experts model deployed inside the MDASH agentic harness for security operations. (Microsoft AI)
  • PortSwigger: Launched Burp AT, agentic AI capabilities built into Burp Suite Professional, now in public beta. (PortSwigger)
  • Cohere: Released North Automations, a new agentic workflow orchestration capability for its North platform. (Cohere)
  • Mastra: Launched Mastra Factory, an agent-driven software development system for building and shipping applications. (Mastra)
  • 7AI: Launched Federated SIEM and 7AI Build, new agentic security capabilities for its AI security platform. (GlobeNewswire)
  • BlackLine: Announced general availability of Verity Prepare, a multi-agent AI system that autonomously matches transactions, identifies reconciling items, and assembles audit-ready reconciliations for finance teams. (BlackLine)
  • Wiz: Released Wiz Atlas, an autonomous AI agent for vulnerability research. (Wiz)
  • Perplexity: Released pplx CLI, a terminal client for the Search API enabling AI agents to query Perplexity search from the command line. (GitHub)
  • kvcache-ai (Moonshot AI-affiliated): Open-sourced AgentENV, a Firecracker microVM sandbox environment for agentic reinforcement learning. (GitHub)
  • Feyn: Released FeyNoBg, a background-removal model with an accompanying NoBg training library. (Feyn)

Sources and Further Reading

Artificial Intelligence & Technology's Reconstitution

Institutions & Power Realignment

Scientific & Medical Acceleration

Economics & Labor Transformation

Infrastructure & Engineering Transitions

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