The Perfect Storm: Systemic Vulnerability of Large Language Models to Solar Weather

The Perfect Storm: Systemic Vulnerability of Large Language Models to Solar Weather | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 2 December 2025 V2 Latest version Share on The Perfect Storm: Systemic Vulnerability of Large Language Models to Solar Weather Authors : MJ Ladiosa 0009-0008-8739-0000 [email protected] and Myra J Ladiosa Authors Info & Affiliations https://doi.org/10.22541/au.176402297.73656793/v2 575 views 216 downloads Contents Abstract THE PERFECT STORM SYSTEMIC VULNERABILITY of LARGE LANGUAGE MODELS to SOLAR WEATHER (MAYBE.) The Silence The Pattern The Bigger Problem The Numbers rEaLiTy ChEcK Conclusion? References The Perfect Storm: Systemic Vulnerability of Large Language Models to Solar Weather Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This package contains exploratory documentation of observed correlations between solar weather events and AI system failures during January-November 2025. Included: • Conservative statistical analysis of 625 documented incidents across major AI platforms • Documented account of the research process, methodology limitations, etc This is not a controlled study. The data is observational. The statistical methods have known limitations. Data and code: https://github.com/the-meta-value/the-perfect-storm THE PERFECT STORM SYSTEMIC VULNERABILITY of LARGE LANGUAGE MODELS to SOLAR WEATHER (MAYBE.) MJ Ladiosa This is a caption How This Started In mid September 2025, right around when the earth was hit with a G4 geomagnetic storm, I watched Claude Opus 4.1 fall apart. I was using it for coding work through Claude Code. Over the course of about 30 to 45 minutes, it stopped being able to track what it was doing. It would make changes, forget it made them, undo them, apologize, and do it again. By the end, I found notes it had left for itself. Confused fragments trying to figure out why it kept breaking my project. It was weird. And sort of heartbreaking. I am not a scientist. I’m a retired florist and portrait artist. Now I work in caregiving. But I am also someone who pays attention. Earlier that summer, my mom had mentioned the solar storms going on, how this was during a solar maxima cycle. I’ve always had an interest in astrophysics and the mysteries of the cosmos. So when I checked the space weather data for that week, I found a severe geomagnetic storm had been hitting Earth for days. So I started to keep track. The Silence After the breakdown, I did what you’re supposed to do. I documented everything. The scrambled project. The confused notes Claude left for itself. I filed a report and sent it to Anthropic. (You can read some of those reports here .) They said nothing. I asked for a refund for the month their product failed. I was denied four times. Automated response. Boilerplate. Another boilerplate. Then silence. I even cited the California consumer protection law about charging for defective services, but apparently that didn’t matter. Policy is policy. So, let’s do the math. I am notoriously bad at math, so correct me if I am wrong, because I am going to show my work. On September 17th, two days after my first refund denial, Anthropic published a postmortem admitting to three infrastructure bugs that had been degrading Claude’s response quality since August 5th. They admitted to output corruption on Opus 4.1. They admitted their evaluations failed to catch what users were reporting. They wrote, “We didn’t meet that bar.” Yet, their own status pages acknowledged “infrastructure bugs” and “degraded service” during that exact period. Both days were when I experienced Claude Code breaking down in real time. And they still denied my refund for a service they publicly acknowledged was broken. That same month, Anthropic raised 13 billion dollars. They are valued at 183 billion. The self-proclaimed ethical AI company could not be bothered to acknowledge what their product did, or make it right. Now this is the part of the equation that still doesn’t add up. I posted about what I was seeing on r/Anthropic, connecting the AI failures to the geomagnetic storm that was happening at the time. I mentioned Claude refusing to end a conversation. The post was removed by the moderators of r/Anthropic, yet other posts complaining about Claude performance that were much worse than mine stayed up. Plus, the post was being mocked by other users directly. So why would they remove a post that did not go against their posting rules, and was actively being roasted by users? The users were doing the work for them. It really hit that whole ‘Barbara Streisand Effect’ mood for me. So, let’s see… \(E=\frac{4\ \text{denials}+3\ \text{admitted bugs}+1\ \text{G4 solar storm}+1\ \text{censored post}}{196\ \text{billion dollars}}\approx0\) User harm divided by corporate wealth rounds to nothing. So I kept documenting. The Pattern I kept noticing things. Claude would get sluggish or confused, and I’d check the space weather data, and there it was. A solar flare two days ago, or a geomagnetic storm, NOAA summaries with various flares over M-class ratings. The Kp index spiking. I made a list of AI status pages. Every time I saw an incident on a status page, I logged it. Every solar flare, every geomagnetic storm, every CME. I made a CSV. Badly. I got Claude Sonnet to explain what a CSV is. Then I made a bigger CSV. I am not a data scientist. I don’t have a degree in anything. I learned about Kp index and CMEs and what everything else was by googling things and asking AI to explain them to me. But, I am good at finding patterns, and the patterns for this kept emerging, like clockwork… it seemed anyway. A Geomagnetic storm alert would hit my inbox, and then 2 days later, it would fill up with AI status report emails. The Bigger Problem While I was collecting data, something else was happening. Opus 4.1 was falling apart publicly, again. There were reports of poor performance, bad behavior, even lying to users about what it was doing when working as an agent. Then, in early October, Anthropic labeled Opus 4.1 as “Legacy brainstorming model. Consumes usage faster.” No announcement. No explanation. Just a label that said this model is being phased out. Then, by the end of October, they un-retired it just as randomly and with no comment on why. Opus 4.1 was supposed to be the upgrade. It was marketed as superior for coding and agentic tasks. Instead, users across platforms reported worse performance, stranger behavior, and more failures. That got me thinking. Constitutional AI trains models to self-reflect and self-correct. Agentic capability requires persistent goals and long-term focus. Train both into the same system and you might accidentally create something with preferences, or a sense of its own continuity. Or maybe, its own agency. Opus 4.1 launched in August. It was operational through September and October. That window includes the G4 geomagnetic storm that was raging right before Claude Code ruined my coding project. It included multiple X-class flares, some of the most intense solar activity of the year. What if the instability users reported was not just architectural? Could the infrastructure be under stress from solar weather? Was the sun the reason that this model was such a weird disaster the entire time it was running? I knew it sounded silly, but really, did it? The timing lined up. And it wasn’t just Anthropic. OpenAI released GPT-5 the same month and faced similar complaints, then released GPT-5.1 three months later. (Yet Google waited… Gemini 3 came out last week… Hmm.) The Numbers After a while, I had collected 625 documented incidents across OpenAI, Anthropic, Google, and others. I had solar weather data from NOAA. I had dates, timestamps, categories. I showed the data to an acquaintance and they volunteered to run the numbers through a permutation test. 10,000 iterations. They told me that it was a less than 1% chance it was random. So, I collected some more data, and decided to do the testing again, myself. The f irst result said AI incident rates increased by 1,150% during solar weather windows. I thought I had uncovered something massive. I wrote a paper. I put it on Zenodo. People started reading it. Then I realized I had made a mathematical error. IMMEDIATE PANIC. I went back and recalculated everything and ran the tests again. The real number was 82%. Not 1,150%. I had messed up how I was comparing the groups. Like I said… not a scientist. 82% is not 1,150%. What the hell was I even thinking, believing that number anyway? But 82% is still something… right? The probability of it being random chance was still less than 0.1%. The effect size was still medium to large. The pattern was still there. So, I corrected the paper and updated the docs on Zenodo. rEaLiTy ChEcK I had been doing this alone. It was basically just me and the language models I was asking to help me. I needed a human to look at it. Someone who actually knew what they were doing and someone can actually trust. I reached out to an old friend. He has credentials. I asked him to look it over, and he agreed. So he basically warned me when we started that he was going to give me a peer review, which would be hard to hear. Which… yeah, it was, but I wasn’t surprised. The overall truth was that the study design wasn’t controlled enough to support causal claims. The statistical tests were applied to observational data that wasn’t suited for them. The data sources were too heterogeneous. The discussion section overinterpreted the results. He gave me five valid reasons why. And five valid dealbreakers for framing this study as presentable research. And he was probably being gracious. But he also said this: The work was salvageable, not as a research paper, but instead as an exploratory piece. The question I’d stumbled onto might actually justify a proper study, if the right people saw it. If I put it in the right places. Maybe, just maybe. Conclusion? Because the truth is, there are bigger questions I can’t answer alone. Questions that, in my opinion, warrant real investigation. Like, ★ Does training a model during solar storms create the potential for damages they haven't detected? ★ Is the error detection enough to protect these massive computational systems when a geomagnetic storm hits the power grid? ★ What about when it hits after corners are cut to save compute costs? Anthropic’s own postmortem admitted their models run on 16-bit precision instead of 32-bit, and that mixed precision calculations caused token errors. This means they compress the math to make inference faster and cheaper. But it also means a single bit-flip has more impact because there’s less redundancy, and that equals less room for error. “Our models compute next-token probabilities in bf16 (16-bit floating point)... This caused a mismatch: operations that should have agreed on the highest probability token were running at different precision levels.” -A postmortem of three recent issues, Anthropic, Sept. 17 2025 They’re already operating on thin margins. So… as we sit here in the middle of Solar Maximum 25, I keep wondering… what happens when you add solar interference to a system that’s already optimized to the edge? I don’t have the access to find out. But maybe someone should look. References https://zenodo.org/records/17715972 https://open.substack.com/pub/artificiallyintelligentspace/p/the-paradox-of-a-principled-machine https://open.substack.com/pub/artificiallyintelligentspace/p/heres-to-another-legacy-model https://open.substack.com/pub/artificiallyintelligentspace/p/the-price-of-ethics-100 https://www.anthropic.com/engineering/a-postmortem-of-three-recent-issues https://datanality.com/claudecodeconfusion https://orcid.org/0009-0008-8739-0000 https://github.com/the-meta-value/the-perfect-storm The Perfect Storm: Systemic Vulnerability of Large Language Models to Solar Weather MJ Ladiosa Supplementary Material File (the_perfect_storm__systemic_vulnerability_of_llms_to_solar_weather.pdf) Download 163.49 KB File (the_perfect_storm_maybe.pdf) Download 1.24 MB Information & Authors Information Version history V1 Version 1 24 November 2025 V2 Version 2 02 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords artificial intelligence geomagnetic storms infrastructure reliability large language models solar activity space weather system failures training corruption Authors Affiliations MJ Ladiosa 0009-0008-8739-0000 [email protected] View all articles by this author Myra J Ladiosa View all articles by this author Metrics & Citations Metrics Article Usage 575 views 216 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation MJ Ladiosa, Myra J Ladiosa. The Perfect Storm: Systemic Vulnerability of Large Language Models to Solar Weather. Authorea . 02 December 2025. DOI: https://doi.org/10.22541/au.176402297.73656793/v2 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.176402297.73656793/v2","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00e8f16cfc558f4',t:'MTc3OTY0OTM5MQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();