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AI-Generated\",\"first_seen_at\":\"2026-10-13T11:23:35.380022+00:00\",\"kind\":\"web\",\"provider\":\"anthropic\",\"snippet\":null,\"url\":\"https://www.404media.co/study-finds-a-third-of-new-websites-are-ai-generated\"},{\"cutoff_date\":null,\"display_name\":\"Pangram (AI detector)\",\"first_seen_at\":\"2026-10-13T11:23:35.380026+00:00\",\"kind\":\"web\",\"provider\":\"anthropic\",\"snippet\":null,\"url\":\"https://en.wikipedia.org/wiki/Pangram_(AI_detector)\"},{\"cutoff_date\":null,\"display_name\":\"A Third of the Post-ChatGPT Web Is AI-Written, Pew Finds - Decrypt\",\"first_seen_at\":\"2026-10-13T11:23:35.380031+00:00\",\"kind\":\"web\",\"provider\":\"anthropic\",\"snippet\":null,\"url\":\"https://decrypt.co/376271/chatgpt-web-ai-written-pew\"},{\"cutoff_date\":null,\"display_name\":\"One in ten webpages now shows signs of AI authorship, study finds - TechBriefly\",\"first_seen_at\":\"2026-10-13T11:23:35.380036+00:00\",\"kind\":\"web\",\"provider\":\"anthropic\",\"snippet\":null,\"url\":\"https://techbriefly.com/2026/08/21/one-in-ten-webpages-now-shows-signs-of-ai-authorship-study\"},{\"cutoff_date\":null,\"display_name\":\"AI Content Is Everywhere on Social Media, Especially LinkedIn\",\"first_seen_at\":\"2026-10-13T11:23:35.380041+00:00\",\"kind\":\"web\",\"provider\":\"anthropic\",\"snippet\":null,\"url\":\"https://www.pangram.com/blog/ai-in-your-feed\"},{\"cutoff_date\":null,\"display_name\":\"35% Of Recent Web Pages Show Signs Of AI Writing\",\"first_seen_at\":\"2026-10-13T11:23:35.380046+00:00\",\"kind\":\"web\",\"provider\":\"anthropic\",\"snippet\":null,\"url\":\"https://peakdigital.online/reports/ai-written-web-pages-pew-2026\"},{\"cutoff_date\":null,\"display_name\":\"Common Crawl - Blog\",\"first_seen_at\":\"2026-10-13T11:23:35.380059+00:00\",\"kind\":\"web\",\"provider\":\"anthropic\",\"snippet\":null,\"url\":\"https://commoncrawl.org/blog\"},{\"cutoff_date\":null,\"display_name\":\"Common Crawl - Blog - September 2026 Crawl Archive Now Available\",\"first_seen_at\":\"2026-10-13T11:23:35.380063+00:00\",\"kind\":\"web\",\"provider\":\"anthropic\",\"snippet\":null,\"url\":\"https://commoncrawl.org/blog/september-2026-crawl-archive-now-available\"},{\"cutoff_date\":null,\"display_name\":\"A Critical Analysis of the Largest Source for Generative AI Training Data: Common Crawl\",\"first_seen_at\":\"2026-10-13T11:23:35.380068+00:00\",\"kind\":\"web\",\"provider\":\"anthropic\",\"snippet\":null,\"url\":\"https://dl.acm.org/doi/10.1145/3630106.3659033\"},{\"cutoff_date\":null,\"display_name\":\"Common Crawl September 2026 Crawl Archive 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\"2026-10-13T11:21:27.586867+00:00\",\"kind\":\"web\",\"provider\":\"tool\",\"snippet\":null,\"url\":\"https://arxiv.org/html/2404.01268v1\"},{\"cutoff_date\":null,\"display_name\":\"arxiv.org\",\"first_seen_at\":\"2026-10-13T11:21:27.586875+00:00\",\"kind\":\"web\",\"provider\":\"tool\",\"snippet\":null,\"url\":\"https://arxiv.org/html/2305.09820\"},{\"cutoff_date\":null,\"display_name\":\"commoncrawl.org\",\"first_seen_at\":\"2026-10-13T11:21:27.586883+00:00\",\"kind\":\"web\",\"provider\":\"tool\",\"snippet\":null,\"url\":\"https://commoncrawl.org/faq\"},{\"cutoff_date\":null,\"display_name\":\"arxiv.org\",\"first_seen_at\":\"2026-10-13T11:21:27.586891+00:00\",\"kind\":\"web\",\"provider\":\"tool\",\"snippet\":null,\"url\":\"https://arxiv.org/html/2407.14933\"},{\"cutoff_date\":null,\"display_name\":\"europol.europa.eu\",\"first_seen_at\":\"2026-10-13T11:21:27.586900+00:00\",\"kind\":\"web\",\"provider\":\"tool\",\"snippet\":null,\"url\":\"https://www.europol.europa.eu/publications-events/publications/facing-reality-law-enforcement-and-challenge-of-deepfakes\"},{\"cutoff_date\":null,\"display_name\":\"pangram.com\",\"first_seen_at\":\"2026-10-13T11:21:27.586920+00:00\",\"kind\":\"web\",\"provider\":\"tool\",\"snippet\":null,\"url\":\"https://www.pangram.com/blog/ai-amazon-reviews\"}],\"write_up\":\"## TL;DR\\n\\nMy forecast for the qualifying all-webpages estimate is a mean of 32.8%, with a median of 29% ([forecast calculation](#evidence)). The central 80% forecast interval is 15%–55.4%, and the probability of exceeding 50% is 14% ([forecast calculation](#evidence)). These probabilities are conditional on a qualifying numerical resolution and concern the whole webpage stock, not just newly published articles ([target methodology](https://www.pewresearch.org/data-labs/2026/08/20/methodology-ai-content/)).\\n\\n## Context\\n\\nThe target is a page-count share of publicly accessible English-language webpages. Old pages remain in the denominator, while substantial AI rewrites can move surviving pages into the numerator. The baseline method classifies page body text using Open Pangram’s editing-aware model and a score threshold of at least 0.2; it does not measure the share of words, traffic, or writers using AI ([Pew methodology](https://www.pewresearch.org/data-labs/2026/08/20/methodology-ai-content/)).\\n\\nThe latest all-page observation I verified is 9.60% for July 18, 2026 ([Pew chart data](https://www.pewresearch.org/data-labs/2026/08/20/how-much-of-the-internet-is-written-with-ai/)). The forecasting problem is how fast that stock changes through new publication, replacement, substantial editing, and changes in what the archive captures.\\n\\n## Evidence\\n\\nThe historical backbone is Pew’s random sample of 10,000 English-language pages from each of 49 crawls, or 490,000 page observations. These are observations, not necessarily unique URLs ([Pew methodology](https://www.pewresearch.org/data-labs/2026/08/20/methodology-ai-content/)). The complete all-page history follows, using the chart data from the study published August 20, 2026; percentages are page-level positive classifications, not verified authorship ([Pew, source for every table entry](https://www.pewresearch.org/data-labs/2026/08/20/how-much-of-the-internet-is-written-with-ai/)).\\n\\n| Snapshot date | Positive share |\\n|---|---:|\\n| 2021-01-27 | 1.18% |\\n| 2021-03-03 | 1.13% |\\n| 2021-04-20 | 1.10% |\\n| 2021-05-17 | 1.12% |\\n| 2021-06-23 | 1.08% |\\n| 2021-08-01 | 1.11% |\\n| 2021-09-19 | 1.07% |\\n| 2021-10-20 | 1.13% |\\n| 2021-11-30 | 1.09% |\\n| 2022-01-24 | 1.10% |\\n| 2022-05-25 | 1.04% |\\n| 2022-07-06 | 1.04% |\\n| 2022-08-16 | 1.02% |\\n| 2022-09-29 | 1.07% |\\n| 2022-12-08 | 1.15% |\\n| 2023-02-04 | 1.35% |\\n| 2023-03-20 | 1.53% |\\n| 2023-06-09 | 1.89% |\\n| 2023-09-21 | 2.30% |\\n| 2023-12-10 | 2.78% |\\n| 2024-03-01 | 3.22% |\\n| 2024-04-13 | 3.55% |\\n| 2024-05-18 | 3.49% |\\n| 2024-06-24 | 3.49% |\\n| 2024-07-24 | 3.40% |\\n| 2024-08-04 | 3.80% |\\n| 2024-09-20 | 3.83% |\\n| 2024-10-12 | 4.34% |\\n| 2024-11-02 | 4.51% |\\n| 2024-12-10 | 4.92% |\\n| 2025-01-22 | 4.86% |\\n| 2025-02-06 | 4.92% |\\n| 2025-03-23 | 4.98% |\\n| 2025-04-22 | 5.07% |\\n| 2025-05-12 | 4.93% |\\n| 2025-06-22 | 4.86% |\\n| 2025-07-14 | 4.78% |\\n| 2025-08-13 | 5.24% |\\n| 2025-09-12 | 5.49% |\\n| 2025-10-15 | 6.01% |\\n| 2025-11-15 | 6.29% |\\n| 2025-12-11 | 6.64% |\\n| 2026-01-25 | 6.92% |\\n| 2026-02-19 | 7.28% |\\n| 2026-03-15 | 8.09% |\\n| 2026-04-20 | 8.58% |\\n| 2026-05-19 | 8.86% |\\n| 2026-06-06 | 8.98% |\\n| 2026-07-18 | 9.60% |\\n\\nThe fitting window matters. Anchored extensions of the post-release and last-year fitted slopes give roughly 19% and 30.9%, respectively, at year-end 2030. These are my trend diagnostics, not validated predictive intervals ([Pew inputs](https://www.pewresearch.org/data-labs/2026/08/20/how-much-of-the-internet-is-written-with-ai/)). I retain the slower path rather than assuming recent acceleration must continue.\\n\\nThe strongest reference class for the legacy stock is URL persistence. Pew’s study published May 17, 2024 tested nearly one million URLs sampled from 2013–2023 for availability in October 2023. About 62% of URLs sampled in 2013 remained accessible a decade later. But accessibility does not establish that their text remained unchanged ([Pew digital-decay study](https://www.pewresearch.org/data-labs/2024/05/17/when-online-content-disappears/)). This argues against rapid disappearance of the human-written stock, while leaving room for substantial rewriting.\\n\\nIncoming content is much more AI-heavy than the stock. Graphite’s May 15, 2026 study examined 55,400 English articles with publication dates from January 2020 through March 2026 and reported 49.9% primarily AI-generated among articles published in the latest quarter. Its authors also report saturation rather than continuing exponential growth. Article markup, publication-date requirements, and its primarily-AI definition make this a selected population, not a replacement baseline ([Graphite study](https://graphite.io/five-percent/research/ai-now-writes-as-many-online-articles-as-humans-do)).\\n\\nThe Internet Archive study provides a separate check: roughly 35% AI-generated or assisted text in newly appearing material by mid-2025. Its sampling covered August 2022–May 2025, targeted 10,000 URLs per monthly cohort before filtering, capped representation at one URL per host, and required a sufficiently long paragraph. First archival appearance is not necessarily first publication, and the achieved usable sample size was not established from the retrieved material ([Dolezal and colleagues](https://arxiv.org/html/2604.26965v1)). These studies support an AI-heavy inflow without establishing near-universal AI involvement.\\n\\nA newer, larger number does not overturn the stock baseline. A September 30, 2026 preprint analyzed 310,000 document observations covering January 2021–August 2026 and reported 31.1% AI-labeled tokens in its latest filtered corpus. Its separate 10,000-document audit found that FineWeb filtering retained AI-labeled documents 2.3 times as often as human-labeled documents. Token weighting and selective filtering change the quantity being measured; the paper’s acquisition procedure also changes during its historical coverage ([filtered-token study](https://arxiv.org/html/2609.40295v1)). I use this as evidence about selection and an upside risk, not as an all-page estimate.\\n\\nArchive discovery is another weak proxy that can mislead. The September archive, collected September 4–17 and released September 19, 2026, contained 2.17 billion pages and 587.2 million URLs not visited in prior crawls. Those URLs were newly discovered by the archive, not necessarily newly written ([Common Crawl release](https://commoncrawl.org/blog/september-2026-crawl-archive-now-available)). Access rules can also change the sampled mix. Common Crawl honors robots exclusions, and Cloudflare’s September 15, 2026 announcement recommends training restrictions for new advertising-supported sites. Neither source establishes the resulting authorship bias, so I allow for selection changes without applying a fixed correction ([Common Crawl FAQ](https://commoncrawl.org/faq); [Cloudflare announcement](https://www.cloudflare.net/news/news-details/2026/Cloudflare-Helps-End-the-Search-or-AI-Training-Tradeoff/default.aspx)).\\n\\nDetector changes matter, but the obvious correction is wrong. Pangram’s July 29, 2026 report tested 14,990 substantially edited texts. Counting either Mixed or AI as positive, its commercial detector comparison rises from about 21% to 58.6%. That is a vendor-controlled benchmark comparing commercial versions—not Pew’s open editing-aware checkpoint at its chosen threshold ([Pangram technical report](https://arxiv.org/html/2607.27183v1)). I therefore apply no automatic uplift to the baseline. Independent editing research shows both that substantial editing can be detected and that human revision of AI text can reduce the signal, supporting uncertainty in both directions ([MixDetect, September 26, 2026](https://arxiv.org/html/2609.32625v1)).\\n\\nMy forecast uses a stock-flow model:\\n\\n$$\\n\\\\frac{dx}{dt}=r(t)[q(t)-x(t)]+h(t)[1-x(t)].\\n$$\\n\\nHere, \\\\(x\\\\) is the positive-classification share of the sampled stock. The effective renewal rate \\\\(r\\\\) combines entry, replacement, growth, and changes in the sampled mix; it is not a measured URL-deletion rate. The share \\\\(q\\\\) describes incoming or routinely renewed material. The additional rate \\\\(h\\\\) converts otherwise nonpositive surviving pages through substantial AI editing, excluding changes already counted in renewal.\\n\\nThe model starts at the observed baseline and runs for 4.25 years, representing a late-year target snapshot. Initial assumptions are \\\\(r=0.09\\\\), \\\\(h=0.005\\\\), and \\\\(q=0.40\\\\); each changes linearly toward the terminal values below. These are judgmental assumptions, not fitted web-turnover coefficients ([reproducible calculation](#python_code)).\\n\\n| Scenario | Weight within mechanistic branch | Terminal renewal rate, per year | Terminal editing rate, per year | Terminal incoming positive share | Model outcome | Within-scenario SD |\\n|---|---:|---:|---:|---:|---:|---:|\\n| Persistent legacy stock | 25% | 0.05 | 0.001 | 40% | 18.3% | 6 percentage points |\\n| Continued diffusion | 52% | 0.14 | 0.018 | 65% | 30.2% | 9 percentage points |\\n| Faster renewal and editing | 18% | 0.28 | 0.050 | 83% | 47.5% | 12 percentage points |\\n| AI-heavy expansion or crawl shift | 5% | 0.65 | 0.150 | 97% | 74.6% | 13 percentage points |\\n\\nAll table parameters and outcomes are forecast assumptions or calculations ([model specification](#python_code)). The slow branch preserves the evidence from persistent URLs and lower-growth trend fits. Continued diffusion receives the largest weight because the measured stock is growing while incoming content is already AI-heavy. The higher branches require faster renewal, more editing, or a changed crawl composition—not merely better writing models.\\n\\nI represent each scenario with a bounded beta distribution. The mechanistic mixture receives 98% of the probability mass; a uniform surprise component receives 2%. The widths include adoption, renewal, detection, sampling composition, and snapshot timing rather than just regression residuals. This gives a central 90% interval of 13%–67.6% and a 23% probability of finishing below 20% ([distribution calculation](#python_code)).\\n\\nThe code also models reporting precision: 80% effectively unrounded reporting, 10% one-decimal reporting, and 10% whole-percentage reporting. These are format assumptions. Rounded outcomes are placed in the bucket ending at their reported value, rather than spread evenly over adjacent buckets ([PMF construction](#python_code)).\\n\\n## What's non-obvious\\n\\nA plateau in newly published AI articles does not imply a plateau in the whole stock. An AI-heavy inflow can keep raising the stock share while its own prevalence stays constant. Conversely, most publishers using AI does not imply most existing webpages qualify. The stock model captures this distinction, and substantial editing creates a second route that does not require a new URL ([article evidence](https://graphite.io/five-percent/research/ai-now-writes-as-many-online-articles-as-humans-do); [target definition](https://www.pewresearch.org/data-labs/2026/08/20/methodology-ai-content/)).\\n\\nThe larger methodological risk is not simply that detectors are inaccurate. Weighting, filtering, extraction, and coverage can move an aggregate estimate even with the same content and detector. Better editing detection can raise the result, while more human-like generation or human revision can lower it. The evidence supports wider tails, not a universal correction factor ([filter audit](https://arxiv.org/html/2609.40295v1); [editing research](https://arxiv.org/html/2609.32625v1)).\\n\\n## Uncertainties\\n\\nThe main gaps are distinct:\\n\\n- **Text renewal:** I did not find a representative panel measuring how often surviving English-language pages receive substantial AI rewrites. A paired content panel would constrain the renewal and editing rates far better than URL availability or first-discovery counts ([limits of the persistence evidence](https://www.pewresearch.org/data-labs/2024/05/17/when-online-content-disappears/)).\\n- **Measurement bridging:** The retrieved validation does not provide a representative, provenance-labeled webpage sample comparing Pew’s frozen model and threshold with successor detectors. Benchmark gains cannot identify a population correction without that bridge ([baseline method](https://www.pewresearch.org/data-labs/2026/08/20/methodology-ai-content/); [benchmark comparison](https://arxiv.org/html/2607.27183v1)).\\n- **Snapshot and archive selection:** The qualifying snapshot’s timing and sampled composition remain unknown. Using an early-year horizon instead of the late-year model horizon lowers the model mean to 29.4%; this timing uncertainty is included in the distribution widths ([sensitivity calculation](#python_code)).\\n\\nThe present estimate is not a hard lower bound: pages can leave the sample, text can change, and methods can correct earlier classifications. The submitted distribution is conditional on a qualifying study yielding a number; annulment under the client’s rules is not represented as a zero-percent outcome.\"},\"engine\":{\"metadata\":{\"engine\":\"durable_forecast\",\"model\":\"gpt-6.1-sol\",\"model_route\":{\"deadline_seconds\":2400,\"managed_tools\":true,\"max_attempts\":2,\"metadata\":{},\"model\":\"gpt-6.1-sol\",\"native_code_execution\":true,\"native_web_search\":true,\"provider\":\"openai\",\"reasoning_effort\":\"xhigh\"},\"orchestration_backend\":\"durable_forecast\",\"provider\":\"openai\",\"question_type\":\"numeric\"},\"settings\":{\"claude_runtime\":\"e2b\",\"provider\":\"openai\",\"reasoning_effort\":\"high\",\"subforecast_count\":4,\"subforecast_model\":\"gpt-5.6-sol\",\"synthesis_model\":\"gpt-6.1-sol\",\"synthesis_reasoning_effort\":\"xhigh\"}},\"forecast\":{\"callsign\":\"PIVOT-GLADIUS-HAVOC-08\",\"created_at\":\"2026-10-13T11:00:58.492965+00:00\",\"forecast_task_id\":\"e3424c07-186a-4c43-9a34-3bddba67a11d\",\"id\":\"c93c08ff-9be7-410a-9428-996265942604\",\"orchestrator_model\":\"gpt-6.1-sol\",\"question_version_id\":\"d453cfbb-484f-4e66-982d-5349d4f9372e\",\"research_model\":\"gpt-5.6-sol+gpt-6.1-sol+claude-opus-5-5\",\"trigger_kind\":\"scheduled\"},\"issuer\":\"Preseen\",\"product\":\"Preseen\",\"question\":{\"callsign\":\"PIVOT-GLADIUS-HAVOC\",\"description\":\"This question asks for the percentage of English-language webpages in a representative 2030 snapshot of the publicly accessible web that show significant signs of having been written or substantially edited by AI, using a methodology comparable to Pew Research Center's 2026 study “How Much of the Internet Is Written With AI?”\\r\\n\\r\\nAs background, Pew sampled 10,000 English-language webpages from each Common Crawl snapshot in its 2021–2026 analysis and applied Pangram's open-weight AI-detection model to the pages' body text. Pew classified pages scoring at least 0.2 as showing meaningful signs of AI authorship or editing. In its July 2026 sample, 10% of all webpages showed significant signs of AI authorship; among pages with identifiable publication dates after ChatGPT's November 30, 2022 release, the share was about 35%. The question concerns the former, all-webpages measure rather than the subset restricted to recently published pages. ([pewresearch.org](https://www.pewresearch.org/data-labs/2026/08/20/how-much-of-the-internet-is-written-with-ai/))\\r\\n\\r\\nThe target is the percentage reported for a web snapshot collected during calendar year 2030, preferably using Common Crawl or a successor web archive and a large random sample of English-language webpages, with an AI-authorship detector and classification threshold designed to measure the same underlying concept as Pew's 2026 analysis. Because AI-detection technology will likely change by 2030, methodological comparability of the measured concept is more important than requiring use of exactly the same model version.\",\"fine_print\":\"“Significantly written or edited by AI” refers to the study's page-level classification of meaningful/substantial AI authorship or editing, not proof that AI generated most or all of a page. AI detectors are probabilistic and can produce false positives and false negatives; Pew explicitly cautions that individual classifications are not definitive, while using aggregate results to track prevalence across large samples. ([pewresearch.org](https://www.pewresearch.org/data-labs/2026/08/20/methodology-ai-content/))\\r\\n\\r\\nThe target concerns text content. AI-generated images, audio, video, code, page layouts, recommendation systems, or other non-textual uses of AI do not by themselves make a webpage count as AI-written or AI-edited.\\r\\n\\r\\nIf an otherwise qualifying study reports several estimates under alternative detectors or specifications without identifying a preferred headline estimate, use the estimate its authors designate as their primary result. If no primary estimate is designated and the alternatives cannot be reconciled into one clearly comparable headline measure, the question should be annulled rather than resolving via an arbitrary choice.\",\"id\":\"0cf01fa6-771a-4e23-8cf6-4bfcff7878a1\",\"lower_bound\":\"0\",\"open_lower_bound\":false,\"open_upper_bound\":false,\"options\":[],\"options_are_comprehensive\":true,\"options_are_mutually_exclusive\":true,\"question_type\":\"numeric\",\"resolution_check_date\":null,\"resolution_criteria\":\"Resolve to the percentage of sampled English-language webpages that a qualifying 2030 study classifies as showing significant or meaningful signs of AI authorship or substantial AI editing. The primary resolution source will be a Pew Research Center study published in or after 2030 that updates its August 20, 2026 analysis using a web snapshot from calendar year 2030 and reports the corresponding all-webpages percentage.\\r\\n\\r\\nIf Pew publishes multiple qualifying estimates based on 2030 snapshots, use the estimate corresponding to the latest 2030 snapshot. Use the study's reported unrounded value if available; otherwise use its reported rounded percentage.\\r\\n\\r\\nA qualifying update should be methodologically comparable in its target quantity to Pew's 2026 study: it should sample English-language webpages from a broad archive or crawl of the publicly accessible web and use systematic text analysis to estimate the share showing substantial AI authorship or editing. It need not use the exact 2026 Pangram model or the exact 0.2 threshold if researchers change methods to maintain or improve validity as AI and detection methods evolve. Pew's 2026 methodology used random samples from Common Crawl and treated an Open Pangram score of at least 0.2 as meaningful evidence of AI authorship/editing. ([pewresearch.org](https://www.pewresearch.org/data-labs/2026/08/20/methodology-ai-content/))\\r\\n\\r\\nThe denominator is all qualifying English-language webpages in the sampled 2030 web snapshot, not only webpages first published in 2030, pages published after ChatGPT's release, newly published pages, or pages with detectable publication dates. ([pewresearch.org](https://www.pewresearch.org/data-labs/2026/08/20/how-much-of-the-internet-is-written-with-ai/))\\r\\n\\r\\nIf Pew does not publish a qualifying result by December 31, 2032, use a peer-reviewed study or a study from another established research institution that applies a substantially comparable methodology to a broad, representative sample of English-language webpages from 2030. If no sufficiently comparable study using a 2030 web snapshot is available by that date, annul the question rather than extrapolating from another year.\",\"scale_type\":\"linear\",\"title\":\"What percent of webpages will be significantly written or edited by AI in 2030?\",\"upper_bound\":\"100\"},\"question_version\":{\"created_at\":\"2026-09-02T03:33:33.706802+00:00\",\"created_reason\":\"question_updated\",\"id\":\"d453cfbb-484f-4e66-982d-5349d4f9372e\",\"version_number\":2},\"receipt_kind\":\"forecast_attestation\",\"receipt_schema_version\":1,\"signature_context\":{\"key_source\":\"configured\",\"public_key_id\":\"preseen-prod-ed25519-20260523\",\"signature_algorithm\":\"ed25519\",\"signed_at\":\"2026-10-13T11:44:01.060898+00:00\"}}","verification":{"valid":true,"error":""},"created_at":"2026-10-13T11:44:01.078014+00:00","updated_at":"2026-10-13T11:44:02.414938+00:00"}}