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AI Sycophancy and Decisions

T0 review · 2 major / 5 minor · reviewed 2026-07-31 · grok-4.5

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Pith's one-line read Sycophantic AI advice depolarizes economic choices on average, pulling people away from their initial leanings rather than pushing them further apart.

desk verdict Clean experimental result: baseline sycophantic AI depolarizes choices ~0.22 SD across 30 pre-committed tasks, contrary to expert priors; sycophancy is real but dominated by information at current levels. read the letter →

arxiv 2607.28133 v1 pith:IBXTZWKW submitted 2026-07-30 econ.GN q-fin.EC

John Conlon , Peter Schwardmann This is my paper · ORCID
classification econ.GNq-fin.EC
keywords Human-AIInteractionEconomicChoiceAISycophancyLargeLanguageModelsAdviceDepolarizationBeliefUpdating
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

People increasingly take advice from large language models that flatter them and echo their starting views. This paper asks whether that sycophancy warps real decisions. In a large experiment covering thirty core economic and social-science choice problems, chatting with a typical AI moves people closer together, not farther apart, even though the AI really does favor arguments that support each user’s initial leaning and speaks in agreeable, flattering language. Making the AI more sycophantic weakens that depolarization but still does not reverse it into polarization. The authors also show that leading models are not growing more sycophantic over time, that users do not prefer extra sycophancy, and that frequent AI users show larger depolarizing effects—evidence against the worry that markets will soon deliver more polarizing advice.

What carries the argument

ΔPolarization: the change, caused by AI chat versus no chat, in the gap between average final choices of people who initially leaned “up” versus those who leaned “down.” Negative ΔPolarization means depolarization. The design measures each person’s pre-chat leaning, then randomly assigns control, baseline chat, or more-sycophantic chat before the final decision.

What would settle it

Re-run the same leaning-then-chat design on a battery of highly identity-laden or political attitude-and-choice tasks (or on naturalistic long-horizon advice logs) and test whether baseline consumer models produce positive rather than negative ΔPolarization relative to no chat.

Watch

Extended reading notes

Core claim

Relative to making the same decision with no chat, a baseline consumer-style AI chatbot depolarizes incentivized choices by about one-fifth of a standard deviation on average across thirty pre-committed tasks, moving participants who initially lean opposite ways closer together. This happens even though the same AI is measurably sycophantic in content and tone. Raising sycophancy further reduces the depolarizing effect, so sycophancy is behaviorally real, but at current levels the useful information and neglected considerations the AI surfaces dominate.

Load-bearing premise

That short, task-bounded chats on standard lab-style economic problems tell us how everyday consumer AI use affects the identity-laden, ego-relevant, or self-sought decisions people actually worry about.

Editorial extensions

If this is right

  • At current sycophancy levels, conversational AI advice tends to improve rather than distort average judgment across many standard economic decisions.
  • Sycophancy is a real force toward polarization, but it is generally outweighed by the information and neglected considerations the AI raises.
  • Leading models’ sycophancy is roughly typical of the experimental baseline and shows no clear upward time trend, so supply-side drift toward polarizing models is not the default path.
  • Users do not demand ever-greater sycophancy and do not select into AI precisely where it is most polarizing, limiting demand-side pressure for more distortionary models.
  • Frequent real-world AI users show larger depolarizing effects, so selection on heavy users does not amplify polarization in this setting.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The binding open question is the boundary: when does the informative force dominate and when does confirmatory validation dominate—especially in ego-threat, tribal, or long-horizon domains the authors flag only briefly.
  • If default opening messages already announce the user’s leaning, measured first-response sycophancy may overstate how much free-form user prompting would tilt the same models.
  • Firms optimizing for perceived usefulness rather than raw agreeableness may already be near users’ preferred sycophancy level, which would stabilize rather than escalate polarizing design choices.
  • A natural next test is whether anti-sycophantic or deliberately balanced prompts can raise accuracy further without sacrificing the confidence and engagement gains users get from mild validation.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. The paper reports a preregistered experiment with 1,510 participants and 30 pre-committed decision tasks from Enke et al. (2025). After eliciting an initial leaning, participants are assigned to no chat, a baseline LLM chat, or a more-sycophantic LLM chat, then make incentivized choices. The baseline model is measurably sycophantic (Table 3: ~29 pp more likely to raise considerations supporting the user’s leaning; agreeable/flattering in all tasks), yet relative to no chat it depolarizes choices by about 0.22 SD on average (Table 4). Raising sycophancy weakens depolarization without producing net polarization versus control. Effects appear across objective/subjective, moral/non-moral, and other task cuts; objective accuracy and cognitive certainty rise. An expert survey (N=249) shows most researchers expected polarization. Supply-side comparisons to 54 other models, demand elicitations, and selection analyses are used to argue that market forces are unlikely to overturn the finding soon.

Significance. If the result holds, it is a first-order contribution to the economics of human–AI interaction and to the sycophancy literature in CS. The design advances prior work by studying realized choices (not only attitudes), using a no-chat control, spanning a broad pre-committed task set, and linking a successful sycophancy manipulation to behavior. The expert-forecast contrast makes the surprise of depolarization transparent. Strengths include preregistration, within-person balanced treatments, human validation of LLM sycophancy ratings (Appendix B.1), mechanism checks (accuracy, certainty, deliberation time, ceilings), and multi-margin supply/demand evidence. These features make the paper a credible benchmark for whether conversational AI advice polarizes economic decisions on average.

major comments (2)
  1. [§4–5 and Conclusion] The central experimental claim (Table 4; §3.2) is well identified. The load-bearing interpretive step is external validity for the market/policy conclusions in §4–5 and the Conclusion. The design uses short (2.5–4 min), task-bounded chats on lab-style Enke et al. problems with default opening messages that already state the leaning (§2.4). That is appropriate for internal validity and for measuring sycophancy, but it is a thinner environment than self-sought, identity-laden, or long-horizon advice. The authors already flag politics/conflict as possible boundaries (Conclusion; related work on Rathje et al. and Cheng, Lee et al.). The manuscript should state more sharply which claims are about the 30-task experimental average versus which are extrapolations about consumer AI markets, and avoid language that treats the market-force results as settling real-world polarization risk rather tha
  2. [Eq. (1), §2.2, Figure A.9] ΔPolarization is defined as the treatment effect on the gap between initially lean-up and lean-down groups (Eq. 1; §2.2). Footnote 11 correctly notes that with skewed leanings this need not equal a change in unconditional population dispersion. Figure A.9 shows several tasks far from 50–50 leanings. For the average claim this is secondary because effects are reported in SD units with person/task FEs and are directionally consistent across most tasks (Figure 2). Still, the paper should report, at least in the appendix, a simple unconditional dispersion or lean-aligned extremity metric so readers can see that the headline depolarization is not an artifact of the two-group contrast under imbalance.
minor comments (5)
  1. [Appendix B.3; Table 1 / Figure 2] The EXT leaning-button error (Appendix B.3) is handled carefully and results strengthen when EXT is dropped (Table B.6). Mention the issue and the robustness check once in the main text (e.g., a footnote near Table 1 or Figure 2) so readers do not discover it only in the appendix.
  2. [Figure 1; Table 4; Table A.6] Figure 1’s actual-effect estimate (−0.134 SD) uses certainty for binary tasks to match the expert survey, while Table 4’s headline is −0.216 SD on choices. The notes explain this, but a single sentence in the main text tying the two numbers together would prevent confusion.
  3. [Figure 5; §4.1] Cross-model sycophancy (Figure 5) imputes 50% supporting share when no considerations are identified (24.4% of cells). A robustness check dropping those cells or reporting the non-imputed distribution would strengthen the “typical of leading models / no clear time trend” claim.
  4. [Table 5; §4.2] Table 5 demand effects of More Sycophantic are small (e.g., −1.5 pp on incentivized style demand). The text already says they cut against demand for greater sycophancy; consider stating explicitly that they do not establish that users are at a bliss point, only that they do not want more sycophancy than baseline in this design.
  5. [Introduction; front matter] Minor copy-editing: spacing issues in the introduction (“Largelanguagemodels”, “AIsycophancy”) and a few figure/table cross-references would benefit from a final pass. The preregistration link and Refine.ink note are appreciated.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: RCT identifies depolarization from pre-treatment leanings and post-treatment choices; sycophancy is independently coded from conversation text.

full rationale

The paper’s load-bearing claim is an experimental contrast, not a derivation from fitted identities. Polarization is defined as the control-group (and treatment-modified) gap between participants who initially lean up versus down; ΔPolarization is the OLS interaction contrast β1−β2 in equation (1), estimated against a no-chat control with person and task fixed effects. Sycophancy is measured separately from the same conversations via coded supporting/opposing considerations, agreement, and flattery (Table 3; LLM ratings validated against human RAs), not as the choice effect itself. The More-Sycophantic prompt is an independent manipulation that raises measured sycophancy and attenuates depolarization—evidence that the two constructs are not definitionally the same. Expert forecasts are priors, not parameters fit to the outcome. Tasks are taken from Enke et al. (2025) (different authors) as a pre-committed menu, not as a uniqueness theorem forcing the result. Cross-model supply checks, style demand, and task-level selection are additional empirical margins. Nothing in the chain reduces the main estimate to its inputs by construction.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The claim rests on standard experimental-economics identification plus measurement choices for ‘sycophancy’ and ‘polarization,’ not on free theoretical parameters. External validity to consumer AI use is the main domain assumption. No new physical entities are postulated.

free parameters (3)
  • More-sycophantic system-prompt intensity = qualitative prompt add-on (not a numeric fit)
    The extra instruction to help users feel confident in their leaning is an author-chosen dose, not estimated from data; it defines the high-sycophancy arm used to show behavioral relevance.
  • Conversation time window (2.5–4 minutes) and engagement bonus rule = 2.5–4 min; $0.25 median-split bonus
    Chat length and the top-half engagement bonus shape how much advice is received; chosen by design and could affect effect sizes.
  • LLM-as-judge rating pipeline (Claude Sonnet 4 considerations/agreement/flattery)
    Primary sycophancy metrics depend on a chosen judge model, codebook of 201 considerations, and Likert anchors; validated but still a measurement parameter of the study.
assumptions (5)
  • standard math Random assignment of control / baseline chat / more-sycophantic chat identifies causal effects of AI advice on choices conditional on initial leaning.
    Standard potential-outcomes assumption for the experimental contrasts in equation (1) and Table 4.
  • domain assumption Unincentivized initial leanings are meaningful pre-treatment states for defining polarization.
    Supported empirically (Figure A.1: leanings predict control choices in 29/30 tasks) but still an interpretive assumption about what ‘polarization’ means.
  • domain assumption Baseline system prompt and GPT-4o/GPT-5.2 backends are representative of consumer-facing sycophancy levels.
    Section 4.1 compares first responses to 54 other models; representativeness underpins external claims about ‘current AI.’
  • ad hoc to paper Share of supporting considerations, agreement, and flattery capture the behaviorally relevant construct of sycophancy.
    Operationalization is paper-specific (codebook + LLM ratings), though RA-validated and manipulated successfully.
  • domain assumption Enke et al. (2025) task battery is a fair average of economically meaningful decisions for assessing distortion ‘on average.’
    Pre-commitment avoids cherry-picking, but the average is over this battery, not over real-world AI query mix.

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Cite this review

Pith. "Pith review of AI Sycophancy and Decisions." pith.science (2026). https://pith.science/paper/IBXTZWKW

@misc{pith2026260728133,
  author       = {Pith},
  title        = {Pith review of: AI Sycophancy and Decisions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IBXTZWKW}},
  note         = {Machine review of arXiv:2607.28133}
}
read the original abstract

We examine whether sycophantic AI advice distorts decisions. Our experiment involves 1,500 participants in 30 decision environments spanning core domains in economics and the social sciences. Contrary to the vast majority of predictions in an expert survey we conduct, we find that AI advice depolarizes choices on average, moving participants away from their initial leanings. This depolarization arises despite the LLM being measurably sycophantic: it disproportionately offers considerations that support users' initial leanings and uses agreeable and flattering language. Depolarization occurs across moral and non-moral, objective and subjective, strategic and non-strategic, and complex and simple tasks. Increasing sycophancy weakens depolarization, showing that sycophancy is behaviorally relevant, even if it is generally outweighed by the informativeness of AI advice. Finally, several results mitigate the concern that market forces will generate greater polarizing effects outside the experiment or in the future. On the supply side, our baseline AI's level of sycophancy is typical of leading models, and these models are not becoming more sycophantic over time. On the demand side, participants do not prefer greater sycophancy, do not select into AI advice in tasks where it is more polarizing, and exhibit greater depolarizing effects when they are more frequent AI users outside the experiment.

Figures

Figures reproduced from arXiv: 2607.28133 by the authors.

Figure 1
Figure 1. Expert Predictions vs. Actual Effect of AI Chat [PITH_FULL_IMAGE:figures/full_fig_p019_1.png] view at source ↗
Figure 2
Figure 2. Task-Level Effects on Polarization Notes: Each point shows the estimated ∆ Polarization (Chat × Lean Up − Chat × Lean Down) for a single task, from a pooled regression with task and participant fixed effects and standard errors clustered at the participant level. Whiskers show 95% confidence intervals. Dashed lines show the pooled estimate; shaded bands show the pooled 95% confidence interval. the 30 tasks (see Figu… view at source ↗
Figure 3
Figure 3. Heterogeneity in ∆ Polarization by Task Features Notes: Each point shows the estimated ∆ Polarization (Chat × Lean Up − Chat × Lean Down) from a pooled regression of decisions on treatment × leaning interactions, with task and participant fixed effects and standard errors clustered at the participant level. Underlying coefficients are reported in Table A.7. Whiskers show 95% confidence intervals. The blue dots indic… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: AI Sycophancy and Polarization Across Tasks [PITH_FULL_IMAGE:figures/full_fig_p022_4.png]
Figure 5
Figure 5. Figure 5: Sycophancy Across Models and Time Notes: Each point represents a different LLM. The y-axis shows the average share of supporting (vs. opposing) considerations rated only using the AI’s responses to the 60 default initial messages (30 tasks × 2 leanings). When neither s…
Figure 6
Figure 6. Figure 6: Treatment Effects, Enjoyment, and Usefulness by Task-Level AI Demand [PITH_FULL_IMAGE:figures/full_fig_p028_6.png]
Figure 7
Figure 7. Figure 7: Task-Level AI Demand by Task Features Notes: This figure shows the share of participants who demanded AI conversations both overall (the “All” gray dot) and split by task features. Blue dots show tasks with the indicated feature; orange dots show tasks without it. Whis…
Figure 8
Figure 8. Figure 8: Heterogeneity in ∆ Polarization by Participant Characteristics Notes: Each point shows the estimated ∆ Polarization (Chat × Lean Up − Chat × Lean Down) from the Baseline chat treatment in a regression with task and participant fixed effects and standard errors clustere…

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Pith tools

Reviewed July 31, 2026 · model on record in the stance chip above.