--- title: Synthetic users are fluent, confident, and wrong about the parts of life the internet never wrote down description: Synthetic data is the default stand-in wherever real data is private, scarce, or expensive: surveying users, evaluating enterprise copilots, training agents for professional work. This week brought a warning about what it systematically deletes, plus two examples of how widely it is used anyway. canonical: https://hangingcontext.com/stories/2026-10-31/synthetic-data-blind-spots/ last-updated: 2026-10-31 url: https://hangingcontext.com/stories/2026-10-31/synthetic-data-blind-spots/ published: 2026-10-31T00:36:27Z edition: 2026-10-31T00 newsroom: artificial-intelligence beat: models-labs trust: confirmed source_count: 3 cited_manifest_ids: ['1793401751028374225', '1793377721913463758', '1793326913444364552'] --- # Synthetic users are fluent, confident, and wrong about the parts of life the internet never wrote down *Synthetic data is the default stand-in wherever real data is private, scarce, or expensive: surveying users, evaluating enterprise copilots, training agents for professional work. This week brought a warning about what it systematically deletes, plus two examples of how widely it is used anyway.* Confirmed · 3 sources · Artificial Intelligence · Published 2026-10-31 00:36 UTC ## Key facts - Inflection AI researcher Jofish Kaye compared 1,000 real survey respondents with six synthetic populations and found values-based objections were 10% in the real survey versus 1.1% in synthetic responses. (self-reported) [cites: 1793401751028374225] - Kaye concludes synthetic respondents are useful for piloting survey instruments and pressure-testing hypotheses, but the laundering of values-based objections is structural and not fixable by better prompting. (self-reported) [cites: 1793401751028374225] - Microsoft researchers propose synthetic data for evaluating Copilot at enterprise scale because real enterprise data contains sensitive information that cannot be used for evaluation. (self-reported) [cites: 1793377721913463758] - In a preprint, the WorkFactory framework generated 112,686 professional tasks across 612 occupations to train WorkPilot models, with WorkPilot-35B-A3B rising from 879 to 1565 Elo on GDPval-AA v2.1. (self-reported) [cites: 1793326913444364552] ## What synthetic users delete Inflection AI researcher Jofish Kaye conducted an experiment comparing 1,000 real survey respondents with six synthetic populations generated by three models: Claude, ChatGPT, and Gemini. The finding is twofold. Synthetic respondents are accurate for heavily documented internet behaviors but inaccurate for mundane, undocumented ones: they erased behaviors like using wired headphones, and they failed to mention cost as a reason for quitting chatbots, while 3.4% of real people gave cost as a reason. A synthetic respondent, in Kaye's phrasing, is not remembering its life but interpolating from a corpus, so the parts of life the internet never wrote down simply do not appear. [cites: 1793401751028374225] The second finding is more serious. Synthetic populations systematically launder values-based objections to avoid negative claims about named people and companies, replacing boycotts or criticism of specific CEOs with generic, safe alternatives. Values-based objections in the real survey were 10% overall, compared to 1.1% in synthetic responses. Kaye concludes the laundering is structural and not fixable by better prompting, because it comes from safety training and risk management protocols rather than from a prompt choice. Synthetic respondents are also never uncertain and will answer any question instantly, which makes their output look more confident than any human sample would. [cites: 1793401751028374225] Kaye's conclusion is a division of labor, not a ban: synthetic respondents are useful for piloting survey instruments and pressure-testing hypotheses, but findings must be anchored to real-world samples. The danger is not that synthetic users are useless; it is that they are fluent, instant, and confident, so a team can mistake a clean synthetic result for a true one and never notice which voices were deleted. [cites: 1793401751028374225] > **From Synorb** — Save what you follow as a Beacon. A Beacon is a saved query that your agent pulls with an explicit lookback window. Every new account starts with 5 starter Beacons: Business & Technology Podcasts, AI Companies, Venture Capital, Private Companies and Public Company Filings & Executive Transitions. [See Beacons](https://synorb.com/connect/start) ## Why teams use synthetic data anyway Microsoft researchers describe the other side of the trade-off in a post on evaluating Microsoft Copilot at enterprise scale. Evaluating Copilot requires test data that captures the full complexity of real organizations, but real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. Synthetic data is the proposed solution: it allows robust evaluation without compromising security. The unresolved question, left open by the post, is whether synthetic enterprise data preserves the complexity it is meant to test, or whether it smooths away the same awkward, undocumented realities Kaye found in survey data. [cites: 1793377721913463758] The same logic drives training. In a preprint, researchers describe WorkFactory, a framework for constructing coherent, executable professional tasks using information-level planning to build an information support graph, probe executions to identify support failures, and execution-guided repair to fix task environment defects. The researchers constructed 112,686 tasks spanning 612 occupations and fine-tuned the WorkPilot model family from Qwen models at 4B, 27B, and 35B-A3B scales. All three WorkPilot models improved over their base models across four professional-work benchmarks, and WorkPilot-35B-A3B improved from 879 to 1565 Elo on GDPval-AA v2.1, though the authors' scaling analyses reveal model-dependent diminishing returns. [cites: 1793326913444364552] ## The rule for using the stand-in Read together, the three items suggest a rule: use synthetic data where real data cannot go, but audit it for exactly the deletions Kaye documents. Synthetic evaluation data for a copilot should be checked against real organizational complexity, not just against itself. Synthetic training tasks should be checked for whether the professional work they encode still contains the awkward, undocumented steps that real work includes. And synthetic survey results should be treated as a pilot, never as the finding. The open questions Kaye's post leaves are practical: what weighting methods could correct for structural bias in synthetic data, and what the long-term impact of synthetic data use will be on the quality of public opinion research. Neither has an answer yet, which is why the anchor to real samples is not optional. [cites: 1793401751028374225] ## What is still unresolved - Optimal methods for weighting synthetic data to account for structural bias are not established. - The long-term impact of synthetic data usage on public opinion research quality is unknown. - Whether Microsoft's synthetic enterprise data preserves real organizational complexity is not demonstrated in the post. - WorkFactory's results are from a preprint and have not undergone peer review. ## Decision card - **Useful for:** Teams using synthetic data for evaluation or training; Researchers designing surveys with synthetic respondents - **Not for:** Anyone replacing real user research with synthetic panels; Anyone treating synthetic benchmark gains as real-world proof - **Compare against:** Real respondent panels; Anonymized real enterprise data ## Provenance ledger | Manifest id | Title | Source | Claims used | Published | |---|---|---|---|---| | `1793401751028374225` | Where Synthetic Users Fail: Things the Internet doesn't know and Politics | [inflection-ai-blog](https://inflection.ai/blog/where-synthetic-users-fail) | 1793402013466134029, 1793402013474422997, 1793402013476733061, 1793402013477506151, 1793402013480967661, 1793402013469053601, 1793402013481010716, 1793402013483477309, 1793402013473183217, 1793402013479383064 | 10-30 00:00 | | `1793377721913463758` | From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence | [microsoft-research-blog-rss](https://microsoft.com/en-us/research/blog/from-synthetic-enterprise-data-evaluations-to-real-microsoft-copilot-ai-confidence) | 1793377961093775608, 1793377961101908395 | 10-30 00:00 | | `1793326913444364552` | WorkFactory: Turning Professional Intents into Executable Work for Scalable Agent Training | [huggingface-daily-papers](https://huggingface.co/papers/2610.37161) | 1793327221923121687, 1793327221924476829, 1793327221930694522, 1793327221916231783, 1793327221919208447, 1793327221920450071, 1793327221921099826, 1793327221926546287, 1793327221928661564, 1793327221929678484, 1793327221931312368 | 10-30 00:00 | ## Run these Manifests yourself Every Manifest cited here can be read in full with your own Synorb Key. 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