Sixteen point two per cent. That is how often people in one 2026 experiment caught a machine's mistake once it had explained itself in fluent, confident prose. Swap the explanation for a bare number, the model's own estimate of how likely its answer was to be right, and the rate more than doubled, to 37.5 per cent.

The figures come from controlled studies by Ruth Cohen and Sarit Kraus of Bar-Ilan University, Lu Feng of the University of Virginia and Ayala Bloch of Ariel University, posted to arXiv as paper 2604.03237. The first version had a title built for the conference circuit: “The Persuasion Paradox: When LLM Explanations Fail to Improve Human-AI Team Performance”. Its abstract did not hedge: “We identify a Persuasion Paradox: fluent explanations systematically increase user confidence and reliance on AI”. The authors argued that interfaces should put calibrated reliance and recovery from errors ahead of persuasive fluency.

Then something unusual happened. On 18 August 2026 the authors posted a revision with a new title, “Supporting Calibrated Reliance in Human-AI Collaboration: Different Strategies for Different Tasks”. The word “persuasion” has vanished from the text. The 16.2 and 37.5 per cent figures remain, but the revised statistics report that differences in error recovery across the four support conditions did not reach significance (p = .055). The new abstract closes on a far less quotable line: “no single support strategy is universally effective.”

Hold both versions in your head, because between them they contain the whole argument. The first is the story everyone wants to tell: explanations are a trap, and the explainable AI movement has been building persuasion machines. The second is the story the evidence supports: explanations reliably raise confidence, do not reliably raise accuracy, and help or hurt depending on whether the reader can check what they say. A paper about the dangers of persuasive fluency was, in its first draft, a little too persuasive. Its authors noticed and pulled it back. That is calibration in action, and it is exactly the behaviour designers should be trying to produce in everyone else.

So the useful question is not whether explanations are good or bad. It is narrower and harder. When should a system withhold its explanation, or restructure it? And what would an interface look like if its job were not to help people understand the AI, but to help them know when to ignore it?

Sixteen Per Cent and a Change of Title

Cohen and colleagues ran three experiments with participants recruited through university mailing lists, mostly people with STEM backgrounds, who were paid a bonus for each correct answer. Two tasks carried the weight: RAVEN matrices, abstract visual puzzles in which you choose the shape that completes a pattern, and logical reasoning questions from the LSAT, the American law school admission test.

The cleverest design was the multi-stage reveal. Twenty-seven participants answered each RAVEN puzzle three times: first alone, then after seeing the AI's predicted answer, then after reading an explanation, either a written rationale from a large language model or a heatmap showing which parts of the image the model weighted. Alone, they were right 37.0 per cent of the time. With the AI's answer, 49.8 per cent. With the explanation on top, 48.8 per cent. The explanation bought nothing in accuracy.

It did buy something else. In the paper's words, “confidence remained largely unchanged after the prediction stage and increased only after explanations were presented”. The prediction made people more accurate without making them feel surer. The explanation made them feel surer without making them more accurate. If you wanted one result to sum up the worry about fluent AI, that is it.

The between-subjects RAVEN study, with 100 participants across five conditions, is where the famous numbers live. Every form of AI support beat working alone by a wide margin: unaided participants managed 24.6 per cent, while the four AI-assisted groups landed between 53.5 and 60.5 per cent, with predicted probabilities at the top. On overall accuracy, though, the AI-supported conditions were statistically indistinguishable. The difference appeared in how people handled the AI's mistakes. “LLM explanations showed the lowest recovery rate (16.2%) despite high agreement (84.2%),” the authors write. The probability condition showed the highest recovery, 37.5 per cent, with 75.8 per cent agreement on correct predictions.

Read those pairs together. The explanation group agreed with the AI more when it was right (a gap that, like the recovery gap, fell short of significance) and caught it less when it was wrong. That is the signature of reliance that has stopped discriminating: trust that rises evenly across good and bad advice instead of tracking which is which.

The caveats matter, and the revised paper is admirably blunt about them. There was no power analysis in advance, and twenty people per condition is a small sample. The RAVEN probabilities were raw, uncalibrated softmax outputs. A “selective automation” strategy, in which the AI decides confident cases and hands uncertain ones to the human, is now presented only as a benchmark derived after the fact from the probability group's data. The pattern is consistent and striking. It is not yet proof.

The Same Explanation Wins at Logic

The LSAT study rarely makes it into social media summaries, and it is the part that turns a scare story into a design problem.

Eighty participants worked through ten LSAT questions with help from an AI whose answers were right 60 per cent of the time. One group saw only the AI's answer, one saw the answer plus a stated probability, one saw an expert-written explanation adapted from earlier research, and one saw an explanation generated by a large language model.

The language model's explanation won outright. That group reached 72.5 per cent accuracy, against 55.5 per cent for expert explanations, 48.5 per cent for the bare prediction and 47.0 per cent for probabilities. It was the only group to beat the AI's own 60 per cent, and it recovered best from errors, at 47.5 per cent against 35.0 per cent for probabilities. It even beat the human experts' explanations, which rules out the comforting idea that machine prose is simply worse prose. Here the fluent explanation was not a sedative. It was a tool.

Why would the same kind of support hurt in one task and help in another? The authors are careful not to overclaim, noting that task, model and explanation style all changed between studies. But the contrast lines up with an idea that runs through the field. An LSAT explanation lives in the same medium as the problem. If a model says an argument assumes that correlation implies causation, you can reread the passage and see whether it does. The explanation offers a claim the reader can test against evidence already in front of them.

A verbal rationale for a visual puzzle is different. It translates a pattern into words, and the reader has to translate it back. That is laborious, so most people do not bother. They register that the paragraph sounds like reasoning and move on. The explanation becomes a vibe rather than an argument.

In 2023, Helena Vasconcelos and colleagues at Stanford University and the University of Washington, including Michael Bernstein, Tobias Gerstenberg and Ranjay Krishna, gave this intuition a formal shape. Across five studies with 731 participants navigating a maze with a simulated AI, they argued that people make a strategic cost-benefit choice about whether to engage with an explanation at all. Task difficulty, explanation difficulty and money all shifted how much people over-relied on the AI. Earlier studies that found explanations useless, they suggested, may simply have used explanations that did not make checking the AI any cheaper. Their title made the counterintuitive point: “Explanations Can Reduce Overreliance on AI Systems During Decision-Making”.

Put Cohen's LSAT result beside that framework and a first principle falls out. An explanation earns its place when it lowers the cost of verification. When it raises that cost, or supplies the feeling of verification without the substance, it should be withheld or rebuilt.

Editing the Machine's Mind Feels Like Power

If reading an AI's reasoning can make you overconfident, perhaps the fix is to let users get their hands on it. Interactive reasoning, where people inspect and change a model's chain of thought, has become a fashionable idea in human-computer interaction. A short paper at CHI 2026, the field's flagship conference, held in Barcelona in April, suggests the fashion deserves scrutiny.

Jiwon Moon of the University of Chicago, with Chien-Ming Huang and Ziang Xiao of Johns Hopkins University, published “Understanding the Affordances of Control in AI Reasoning for Human-AI Decision-Making” in the conference's Extended Abstracts, the venue for shorter, often preliminary work. They compared three interfaces in a decision task: no chain of thought, a read-only chain of thought, and an editable one that let users alter the AI's displayed reasoning.

Their abstract captures the gap between how an interface feels and what it does. “Editability significantly increases perceived power, control and satisfaction, but does not improve decision accuracy relative to a no-rationale baseline.” The read-only version was worse: it “increases agreement with the AI and induces inappropriate reliance when the AI is wrong”, reducing accuracy compared with editing and trending below the baseline in which people saw no reasoning at all.

The mechanism is the interesting part. Mediation analyses, the authors report, suggest that editability raises perceived power, which in turn predicts higher confidence and greater over-reliance on incorrect advice, a pattern “consistent with illusion of control.”

The phrase comes from a 1975 paper by the psychologist Ellen Langer in the Journal of Personality and Social Psychology. Across experiments built on games of pure chance, Langer argued that features associated with skill, such as choice, familiarity, involvement and competition, lead people to expect success beyond what the odds justify. Her best-known demonstration, in which people who picked their own lottery ticket asked far more to sell it than people who were handed one, has not aged well: in 2021 Joowon Klusowski, Deborah Small and Joseph Simmons reported in Psychological Science that experiments with more than 10,000 participants found choice alone did not make people think they were more likely to win.

That wrinkle is instructive rather than fatal. Moon, Huang and Xiao measured perceived power directly and linked it to confidence and over-reliance in their own data. But it is a reminder that a vivid label can travel further than the evidence beneath it, which is the subject of this article. Because the CHI paper is an extended abstract, its effect sizes should be treated as provisional. The direction of the result is still a warning to anyone who treats “user control” as an automatic good. Control that is not connected to better outcomes is a feeling, and feelings are what persuasion is made of.

Thirty Years of Leaning on Machines

None of this is new in kind. It is new in fluency.

In 1997, Raja Parasuraman and Victor Riley published “Humans and Automation: Use, Misuse, Disuse, Abuse” in the journal Human Factors. Misuse, in their taxonomy, is over-reliance on automation, producing monitoring failures and decision biases. Disuse is neglect of automation that works, often because it has cried wolf too often. Abuse is automating without regard for the consequences for human performance. All three map onto modern AI assistants, but misuse is the one fluent explanations feed.

In 2004, John Lee and Katrina See gave the field its central goal in the title of their review in the same journal: “Trust in Automation: Designing for Appropriate Reliance”. The key idea is calibration. Trust should match a system's actual trustworthiness, neither overshooting nor undershooting. And it has to work at a fine grain: trusting a system the right amount on average is not enough. You need to trust it more where it is good and less where it is bad.

Medicine has the clearest data on what happens when that fails. A 2012 systematic review in the Journal of the American Medical Informatics Association by Kate Goddard, Abdul Roudsari and Jeremy Wyatt drew on 74 studies of automation bias, which they defined as “the tendency to over-rely on automation”. Pooling four healthcare studies in which decision support systems gave wrong advice, they found the systems increased the risk of an incorrect decision by 26 per cent. The accelerants were familiar: high trust, low self-confidence, workload, complexity, time pressure. Among the design mitigations, they listed attaching updated confidence levels to the system's output, and offering information rather than direct recommendations.

A review published years before large language models went mainstream recommended, in effect, what the RAVEN probability condition did: show people how sure the system is, case by case.

The idea is now law. Article 14 of the European Union's AI Act, on human oversight of high-risk systems, requires that overseers be enabled “to remain aware of the possible tendency of automatically relying or over-relying on the output produced by a high-risk AI system (automation bias)”, particularly where systems provide information or recommendations for decisions taken by people. Those obligations do not bite yet, either: the Digital Omnibus on AI, in force since July 2026, pushed the high-risk deadlines back to December 2027 for standalone systems and August 2028 for AI built into regulated products. The regulation names the problem. It does not specify the interface that solves it. That is the designer's job, and the evidence suggests a fluent paragraph of reasoning is not, by default, the answer.

Explanations Help Most When You Need Them Least

The most cited study on this question predates the language model boom and predicted much of what followed. In 2021, Gagan Bansal and colleagues from the University of Washington and Microsoft Research, among them Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro and Daniel Weld, presented “Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance” at CHI.

Their target was complementary performance: a human-AI team that beats both the human alone and the AI alone. Earlier studies showing that explanations helped had mostly used AI far more accurate than people, so teams improved simply by deferring to a better decision-maker. Bansal's group used AI roughly as accurate as humans, across three tasks (sentiment in beer reviews and book reviews, plus LSAT questions), with 1,626 participants.

Teams did achieve complementary performance, but explanations did not improve on simply showing the AI's confidence. The mechanism was an asymmetry. “Explanations often increased accuracy when the AI system was correct but, worryingly, decreased it when the AI erred, resulting in a minimal net change.” Explanations made people more likely to accept the AI's recommendation whether or not it was right.

Bansal's team tried a fix. Their “adaptive explanation” explained only the top answer when the AI was confident, but laid out the top two alternatives when confidence fell below a threshold, inspired by an efficient assistant that divulges more, “confessing doubts and arguing for alternatives”, only when unsure. It did not significantly improve team performance, though the authors saw suggestive evidence that it nudged agreement in the right direction. Keep it in mind: it is the seed of a better idea.

In 2023, Max Schemmer and colleagues at the Karlsruhe Institute of Technology gave the asymmetry two names. At the Intelligent User Interfaces conference, they proposed measuring “appropriateness of reliance” on separate axes. Relative AI reliance captures how often people switch to correct AI advice after starting with a wrong answer. Relative self-reliance captures how often people keep their own correct answer when the AI is wrong. In their 200-person experiment, explanations raised relative AI reliance from 29.59 to 38.87 per cent. Relative self-reliance did not significantly change.

That is the pattern in miniature. Explanations help people accept the AI when it is right. They do far less to help people resist it when it is wrong. And resistance is the whole point of a human in the loop. If the machine were never wrong, you would not need the person.

Why Fluency Reads as Truth

Large language models sharpen the problem because their explanations are not heatmaps or feature weights. They are prose, and prose is the native format of human persuasion.

In 2025, Mark Steyvers and colleagues at the University of California, Irvine published “What large language models know and what people think they know” in Nature Machine Intelligence. They measured the calibration gap: the difference between how confident people are in a model's answers and how confident the model itself is. Shown default explanations, people overestimated the model's accuracy, and longer explanations raised their confidence even when the extra length did nothing for accuracy. “There's a disconnect between what LLMs know and what people think they know,” Steyvers said in the university's announcement. “We call this the calibration gap.” When the team rewrote explanations so their language reflected the model's internal confidence, that gap narrowed, and people got better at telling right answers from wrong ones.

A year earlier, Sunnie Kim of Princeton University, with Q. Vera Liao, Mihaela Vorvoreanu, Stephanie Ballard and Jennifer Wortman Vaughan of Microsoft, tested something similar. In a pre-registered experiment with 404 participants answering medical questions with a simulated LLM-powered search engine, first-person expressions of uncertainty, along the lines of “I'm not sure, but...”, reduced people's confidence in the system and their agreement with it, and improved their accuracy. Impersonal hedges had weaker effects that did not reach significance. Wording matters enough, the authors concluded, that it needs user testing before deployment.

Back in 2020, Yunfeng Zhang, Q. Vera Liao and Rachel Bellamy of IBM Research found that confidence scores helped people calibrate their trust, while explanations of individual predictions did not do the same job. Their caveat still applies: calibrated trust does not guarantee better decisions unless the human brings knowledge that can compensate for the model's errors. Calibration tells you when to be suspicious. It does not tell you the right answer.

Fluency, in other words, is not a neutral property. Length, confident tone and smooth reasoning all signal reliability without being connected to it. A well-designed system should make those signals honest, or remove them.

Friction Works and Nobody Likes It

If people skim explanations and defer, the obvious response is to make deferral harder. Zana Buçinca, Maja Barbara Malaya and Krzysztof Gajos of Harvard University tested exactly this in “To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making”, published in 2021 in the Proceedings of the ACM on Human-Computer Interaction.

Borrowing from research on diagnostic error in medicine, they built three interventions into a task in which 199 participants judged how to cut the carbohydrate content of meals. In the “on demand” condition, the AI's suggestion stayed hidden until people clicked to see it. In “update”, people decided first and could revise after seeing the AI's advice. In “wait”, a message saying the AI was processing the image appeared for 30 seconds before the suggestion arrived.

All three significantly reduced over-reliance compared with simple explainable AI interfaces. None eliminated it: the interventions “reduced, but not yet eliminated, overreliance on the AI”, and the human-AI teams still underperformed the AI alone. Then the twist. When the AI was wrong, “people performed best in conditions that they preferred and trusted the least”, which they also rated most difficult. The interventions also helped people high in “need for cognition”, a measure of how much someone enjoys effortful thinking, more than others, raising the question of who friction actually protects.

This is the commercial problem in one finding. Every metric a product team watches, from satisfaction scores to stated trust, rewards the smooth, confident explanation. The metric that matters, whether people catch the machine's errors, rewards friction. Moon, Huang and Xiao found the same split from the other side. And a 2026 CHI review of reliance research by Muhammad Raees, Vassilis-Javed Khan, Ioanna Lykourentzou and Konstantinos Papangelis made the general point: user engagement is valuable for appropriate reliance, but it comes at a cost.

When to Withhold the Explanation

So when should a designer leave the explanation out? The evidence supports no blanket rule, and the revised Cohen paper is explicit that no single strategy wins everywhere. But it does point to conditions under which a default explanation is more likely to hurt than help.

The first is when the explanation cannot be checked against evidence the user can see. The RAVEN rationale asked people to verify a verbal account of a visual pattern; the LSAT rationale asked them to verify a verbal account of a verbal argument. Where explanation and evidence live in different media, or the evidence is hidden, the explanation tends to function as a confidence signal rather than an argument. Vasconcelos's cost-benefit framing predicts exactly this.

The second is when fluency is decoupled from reliability. A language model can write an equally polished rationale for a right answer and a wrong one, and Steyvers's results show that people read polish and length as accuracy. If a system cannot make the style of its explanation track its actual confidence, the explanation is broadcasting a false signal.

The third is when the user cannot bring independent knowledge. Zhang, Liao and Bellamy's caveat applies: suspicion helps only if the human can act on it. Goddard's review identified time pressure, workload and complexity as amplifiers of automation bias, precisely when people are least able to scrutinise a paragraph of reasoning. In those conditions, a persuasive explanation mostly accelerates deference.

The fourth is before the user has formed a view. Buçinca's update condition and Cohen's multi-stage design both suggest timing matters. An explanation shown before someone has thought for themselves anchors them. Shown afterwards, as a challenge rather than a verdict, it does a different job.

None of this means explanations should vanish. Withholding an explanation from the moment of decision is not the same as withholding it from the record. Engineers debugging a model, auditors checking it and people contesting its verdicts may all need a full account. The question here is narrower: what to put in front of someone in the seconds when they decide whether to accept the machine's answer. The default, an explanation attached to every answer and displayed immediately, is the wrong default for a large class of tasks.

How to Rebuild an Explanation

Withholding is the blunt option. Restructuring is more interesting, and the literature offers several patterns.

Lead with uncertainty rather than reasoning. In Cohen's RAVEN study, the probability display recovered best from errors and was rated clearest by participants, despite explaining nothing. Goddard's review flagged confidence displays more than a decade ago, and Kim's work shows uncertainty can be expressed in natural language, if the phrasing is tested. A number or a well-chosen hedge is a cue to look harder, cheap to read and hard to mistake for an argument.

Make explanations conditional, in the spirit of Bansal's adaptive design. When the model is confident and historically reliable on this kind of case, a short answer may suffice. When it is uncertain, it should do the opposite of what fluent assistants usually do: argue against itself, present the strongest alternative, and show what evidence would change its answer.

Stop recommending and start evaluating. In a 2023 paper at the ACM Conference on Fairness, Accountability and Transparency, titled “Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support”, Tim Miller, then at the University of Melbourne, argued that recommendations plus explanations undermine human agency and do not match how people reason. He proposed “evaluative AI”, tools that “provide evidence for and against decisions made by people” rather than handing them a decision to accept or reject. The human proposes; the machine stress-tests. There is nothing to be persuaded of, because the machine is not selling an answer.

Sequence the interaction. Ask for the human's judgement first, reveal the AI's view second, and explain only where they disagree. That combines Buçinca's update intervention with adaptive explanation, concentrating scarce attention where it could change the outcome.

Route the cases. Machine learning researchers have long studied “learning to defer”; a 2020 paper at the International Conference on Machine Learning by Hussein Mozannar and David Sontag, for instance, developed methods for training a model that either predicts or hands the decision to a human expert. Cohen's selective-automation benchmark gestures at the same idea, and the authors note that humans can keep final authority under such schemes. Human scepticism is a scarce resource and should be spent where the model is weakest.

A Framework Built Around Distrust

Pull these threads together and a framework for calibrated reliance emerges. Its goal is not understanding but discrimination: helping people tell the machine's good answers from its bad ones, case by case. What follows is a synthesis of the research above, not a standard anyone has yet adopted.

Measure the right thing. Evaluate a system separately on cases where the AI is right and cases where it is wrong, which is the first recommendation in the revised Cohen paper. Schemmer's two axes offer one method; error recovery offers another. Overall accuracy and trust ratings on their own are close to useless, because the Cohen, Bansal, Buçinca and Moon studies all show satisfaction and confidence moving independently of decision quality.

Make every confidence signal honest. If explanations sound equally sure whether the model is right or wrong, their style is lying. Either calibrate the language to the model's real confidence, as Steyvers's team did, or strip out the persuasive styling and show a calibrated number. One caveat: Cohen's RAVEN probabilities were not carefully calibrated, and the authors warn that uncertainty alone can miss errors the model makes confidently. Probability displays are a starting point, not a solution.

Match support to the task's evidence. Where users can verify an explanation against material in front of them, as with the LSAT, rich explanations can pay their way. Where they cannot, prefer uncertainty cues, contrastive alternatives or nothing at all. This is the core lesson of the revised Cohen paper, and a direct rejection of one-size-fits-all explainability.

Protect independent judgement. Elicit the human's view before revealing the machine's wherever time allows, and present explanations as challenges to disagreement rather than justifications for agreement.

Budget friction. Buçinca's results show friction works and people dislike it. Applied everywhere, it wears people down and pushes them back towards blind acceptance. Applied where the model is uncertain, the stakes are high and the human might know better, it earns its cost.

Distrust the feeling of control. Moon, Huang and Xiao's work warns that editing, steering and customising can raise users' sense of power without improving outcomes. Every control affordance should be tested against accuracy on cases where the AI is wrong, not just against satisfaction.

Treat oversight law as a design brief. Article 14 asks that overseers “remain aware” of automation bias. Awareness is not a pop-up warning. It is an interface that makes the machine's fallibility visible at the moment of decision.

The Researchers Who Took Their Own Advice

The revision of the Cohen paper is worth dwelling on. The first version made a vivid, general claim with a memorable name. The second narrowed it, flagged its statistical limits, demoted its most appealing benchmark to a reference point, and concluded that the right answer depends on the task. The authors noticed that their framing was more confident than their evidence, and they recalibrated. The Langer replication story is the same lesson at a fifty-year scale.

That is precisely the behaviour a calibrated-reliance interface tries to produce, and it is hard. Product teams have every commercial reason to keep the fluent, confident explanation, which tests well, demos well and makes users feel smart. The evidence from Bansal in 2021 to Moon in 2026 says that feeling is not the same as being right, and the two can pull in opposite directions.

The explainable AI movement rested on an appealing premise: if people understand why a machine says what it says, they will use it better. The premise is not wrong so much as incomplete. Understanding helps when it gives people something to check. When it gives them only something to believe, it becomes another form of persuasion, and a more effective one than a bare answer, because it looks like reasoning.

The goal, then, is not more explanation or less, but better-aimed explanation: cheap to verify, honest about uncertainty, sequenced so it does not pre-empt human judgement, and most forthcoming when the machine is least sure. Judge it on one question above all: when the model was wrong, did the person notice?

In the RAVEN study, people reading fluent explanations caught roughly one AI error in six. Shown a number instead, they caught more than one in three. Neither is good enough, and the gap between them is statistically fragile. But it points towards where better designs will come from: interfaces built not to win the user's trust, but to earn it one case at a time, and to surrender it gracefully when it has not been earned.

References and Sources

All URLs accessed and verified on 29 October 2026.

1. Cohen, Ruth; Feng, Lu; Bloch, Ayala; Kraus, Sarit. “The Persuasion Paradox: When LLM Explanations Fail to Improve Human-AI Team Performance.” arXiv:2604.03237v1, first version, 2026 (superseded). https://arxiv.org/abs/2604.03237v1 (full text: https://arxiv.org/html/2604.03237v1)
2. Cohen, Ruth; Feng, Lu; Bloch, Ayala; Kraus, Sarit. “Supporting Calibrated Reliance in Human-AI Collaboration: Different Strategies for Different Tasks.” arXiv:2604.03237v2, revised 18 August 2026. https://arxiv.org/abs/2604.03237 (full text: https://arxiv.org/html/2604.03237v2)
3. alphaXiv listing for arXiv:2604.03237, “The Persuasion Paradox: When LLM Explanations Fail to Improve Human-AI Team Performance.” https://www.alphaxiv.org/abs/2604.03237v1
4. Moon, Jiwon; Huang, Chien-Ming; Xiao, Ziang. “Understanding the Affordances of Control in AI Reasoning for Human-AI Decision-Making.” In Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA '26), Barcelona, 13 to 17 April 2026, pp. 1-9. ACM. DOI: 10.1145/3772363.3798555. https://doi.org/10.1145/3772363.3798555
5. Semantic Scholar record and abstract for Moon, Huang and Xiao (2026), Corpus ID 287443795. https://api.semanticscholar.org/graph/v1/paper/DOI:10.1145/3772363.3798555?fields=title,abstract,authors,venue,year
6. Langer, Ellen J. “The illusion of control.” Journal of Personality and Social Psychology, 32(2), 311-328, August 1975. DOI: 10.1037/0022-3514.32.2.311. https://doi.org/10.1037/0022-3514.32.2.311
7. Klusowski, Joowon; Small, Deborah A.; Simmons, Joseph P. “Does Choice Cause an Illusion of Control?” Psychological Science, 32(2), 159-172, 2021. DOI: 10.1177/0956797620958009. https://faculty.wharton.upenn.edu/wp-content/uploads/2016/11/36-Klusowski-Small-Simmons-2021.pdf
8. Yale School of Management, Yale Insights. “Does Having Choice Provide an Illusion of Control?” https://insights.som.yale.edu/insights/does-having-choice-provide-an-illusion-of-control
9. Bansal, Gagan; Wu, Tongshuang; Zhou, Joyce; Fok, Raymond; Nushi, Besmira; Kamar, Ece; Ribeiro, Marco Tulio; Weld, Daniel S. “Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance.” In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama, May 2021, pp. 1-16. ACM. DOI: 10.1145/3411764.3445717. arXiv:2006.14779. https://arxiv.org/abs/2006.14779
10. Buçinca, Zana; Malaya, Maja Barbara; Gajos, Krzysztof Z. “To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making.” Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), Article 188, pp. 1-21, April 2021. DOI: 10.1145/3449287. https://kgajos.seas.harvard.edu/papers/bucinca21trust.pdf
11. Vasconcelos, Helena; Jörke, Matthew; Grunde-McLaughlin, Madeleine; Gerstenberg, Tobias; Bernstein, Michael S.; Krishna, Ranjay. “Explanations Can Reduce Overreliance on AI Systems During Decision-Making.” Proceedings of the ACM on Human-Computer Interaction, 7(CSCW1), pp. 1-38, April 2023. DOI: 10.1145/3579605. arXiv:2212.06823. https://arxiv.org/abs/2212.06823
12. Schemmer, Max; Kühl, Niklas; Benz, Carina; Bartos, Andrea; Satzger, Gerhard. “Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations.” In Proceedings of the 28th International Conference on Intelligent User Interfaces (IUI '23), Sydney, March 2023, pp. 410-422. ACM. DOI: 10.1145/3581641.3584066. arXiv:2302.02187. https://arxiv.org/abs/2302.02187
13. Zhang, Yunfeng; Liao, Q. Vera; Bellamy, Rachel K. E. “Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making.” In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20), January 2020, pp. 295-305. ACM. DOI: 10.1145/3351095.3372852. arXiv:2001.02114. https://arxiv.org/abs/2001.02114
14. Kim, Sunnie S. Y.; Liao, Q. Vera; Vorvoreanu, Mihaela; Ballard, Stephanie; Vaughan, Jennifer Wortman. “'I'm Not Sure, But...': Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust.” In The 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT '24), June 2024, pp. 822-835. DOI: 10.1145/3630106.3658941. arXiv:2405.00623. https://arxiv.org/abs/2405.00623
15. Steyvers, Mark; Tejeda, Heliodoro; Kumar, Aakriti; Belem, Catarina; Karny, Sheer; Hu, Xinyue; Mayer, Lukas W.; Smyth, Padhraic. “What large language models know and what people think they know.” Nature Machine Intelligence, 7(2), 221-231, 2025. DOI: 10.1038/s42256-024-00976-7. https://www.nature.com/articles/s42256-024-00976-7 (preprint: https://arxiv.org/abs/2401.13835)
16. University of California, Irvine, School of Social Sciences. “UC Irvine study finds mismatch between human perception and reliability of AI-assisted language tools.” 22 January 2025. https://www.socsci.uci.edu/newsevents/news/2025/2025-01-22-steyvers-uci-study-finds-mismatch-between-human-perception-and-reliability-of-ai-assisted-language-tools.php
17. Parasuraman, Raja; Riley, Victor. “Humans and Automation: Use, Misuse, Disuse, Abuse.” Human Factors, 39(2), 230-253, June 1997. DOI: 10.1518/001872097778543886. https://journals.sagepub.com/doi/10.1518/001872097778543886
18. Lee, John D.; See, Katrina A. “Trust in Automation: Designing for Appropriate Reliance.” Human Factors, 46(1), 50-80, 2004. DOI: 10.1518/hfes.46.1.50_30392. https://pubmed.ncbi.nlm.nih.gov/15151155/
19. Goddard, Kate; Roudsari, Abdul; Wyatt, Jeremy C. “Automation bias: a systematic review of frequency, effect mediators, and mitigators.” Journal of the American Medical Informatics Association, 19(1), 121-127, January 2012. DOI: 10.1136/amiajnl-2011-000089. https://pmc.ncbi.nlm.nih.gov/articles/PMC3240751/
20. Miller, Tim. “Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support.” In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT '23), June 2023, pp. 333-342. DOI: 10.1145/3593013.3594001. arXiv:2302.12389. https://arxiv.org/abs/2302.12389
21. Mozannar, Hussein; Sontag, David. “Consistent Estimators for Learning to Defer to an Expert.” In Proceedings of the 37th International Conference on Machine Learning (ICML 2020), PMLR 119, pp. 7076-7087. arXiv:2006.01862. https://arxiv.org/abs/2006.01862
22. Raees, Muhammad; Khan, Vassilis-Javed; Lykourentzou, Ioanna; Papangelis, Konstantinos. “Do People Appropriately Rely on AI-Advice? An Analytical Review of HCI Research on Human-AI Decision-Making.” In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, Barcelona, April 2026, Article 805. ACM. DOI: 10.1145/3772318.3791467. https://research-portal.uu.nl/en/publications/do-people-appropriately-rely-on-ai-advice-an-analytical-review-of/
23. Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 (Artificial Intelligence Act), Article 14 (Human oversight). Official Journal L, 12 July 2024. https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-14 ; https://artificialintelligenceact.eu/article/14/
24. Regulation (EU) 2026/1744 of the European Parliament and of the Council of 8 July 2026 amending Regulations (EU) 2024/1689, (EU) 2018/1139 and (EU) 2023/1230 as regards the simplification of the implementation of harmonised rules on artificial intelligence (Digital Omnibus on AI). Official Journal L, 24 July 2026; in force 27 July 2026. https://eur-lex.europa.eu/eli/reg/2026/1744/oj

---

Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

Listen to the free weekly SmarterArticles Podcast

Support What We Do

One of the side projects I run next to readalikes.ai is a hockey romance books site, hockeyromancebooks.com. It's one of several exact-match domains I'm building, each one focused on a single, very specific search.

The genre is bigger than most people think. The site now lists 4,092 books, sorted by trope, heat level and pairing.

The search numbers

This is why the niche caught my eye. According to US keyword data, “hockey romance books” averages about 27,100 searches a month, and “hockey romance novels” gets about the same.

The growth is the wild part:

• Early 2019: about 390 searches a month.
• January 2022: about 1,900.
• January 2025: about 12,100.
• December 2025: a peak of 135,000 in a single month.

It has cooled since, landing around 6,600 in August 2026. That's still roughly 17 times where it was in 2019. Keyword difficulty is listed at 0, which is rare for a search that size.

How it works

Readers rarely search for “a good book.” They search for the exact thing they want. So the site is built around tropes:

• Enemies to lovers for rival players and teams who can't stand each other.
• Grumpy sunshine for the moody captain and the person who melts him.
• Fake dating for the “it's just for the cameras” setup.
• The full trope list covers everything else, from figure skaters to second chances.

How it ties into readalikes

Readalikes.ai is the broad version: find your next book with AI, picks from well-known people and community recommendations. The niche sites go deep on one corner of reading. Same goal, different entry points. If someone finishes a hockey romance and wants something outside the genre, readalikes.ai is the next stop.

Why exact-match domains

My read: a site named after the exact search has a clear focus, and that helps both readers and search engines know what it's about. The catch is that it has to be genuinely useful, not a thin page with a keyword in the URL. So each one gets real depth on its topic.

More of these are coming. I'll share what works and what doesn't.

I'm building a mobile app for readalikes.ai. The goal is simple: make it the best place to find your next book.

Most book apps lean on star ratings. Readalikes skips those and mixes three things instead.

• AI search. Name a book you loved and the AI book recommender finds similar ones, with filters to narrow it down. You can import your Goodreads library too.
• Influential people. Book picks from well-known people, pulled from celebrity podcast episodes, so you can read what the people you follow are reading.
• Community. Everyone who signs up shares one book they'd recommend. One pick each, no ratings. You can browse them on the community page.

How I'm growing it

SEO is a big part of the plan. I publish “books like” lists for popular titles, so people searching for their next read land on the site. A few examples:

• Books like A Gentleman in Moscow
• Books like Shoe Dog
• Books like The Covenant of Water
• Books like Can't Hurt Me
• Books like Yellowface

What's next

The website just launched. The app is next, so the same picks fit in your pocket when you're standing in a bookstore wondering what to grab. More updates soon as it comes together.

This is my main focus right now. But I have almost 20 other projects on the go right now. The plan is to build a powerful system using Grokbot to grow these. Stay tuned 👀

In Summary: * Have moved into the night meds and night prayers phase of my evening while listening to the Notre Dame Radio Network ahead of tonight's game between the Fightin' Irish and the Miami Hurricanes. Opening Kickoff is less than 30 minutes away, and I fully expect to listen to the whole game before heading to bed.

Today has been a Saturday that has felt like a Sunday to me almost all day long. I don't know why.

By the time the game ends I'll have finished taking all my night meds and will be ready to finish the night prayers. Then I'll be ready to slide into bed.

Prayers, etc.: * I have a daily prayer regimen I try to follow throughout the day from early morning, as soon as I roll out of bed, until head hits pillow at night.

Health Metrics: * bw= 219.58 lbs. * bp= 151/67 (63)

Exercise: * morning stretches, balance exercises, kegel pelvic floor exercises, half squats, calf raises, toe raises, wall push-ups, BP breathing exercises, pilates

Diet: * 05:20 – 1 banana, 4 little cookies * 06:45 – 1 ham & cheese sandwich * 07:45 – snacking on chocolate covered raisins * 11:45 – big buffet meal at Golden Corral * 15:15 – snacking on chocolate covered raisins

Activities, Chores, etc.: * 04:00 – wake * 05:00 – bank accounts activity monitored * 05:30 – read, write, pray, follow news reports from various sources, surf the socials, take daily meds, unpack, organize, and nap * 11:45 – brunch with Sylvia at Golden Corral * 13:00 – tuned into Texas A&M Sports Network ahead of this afternoon's A&M Aggies vs the S. Carolina Gamecocks * 17:30 – tuned into the Notre Dame Radio Network ahead of tonight's game between the Fightin' Irish and the Miami Hurricanes

Chess: * 13:30 – moved in all pending CC games, winning one by checkmate

Someone had stuck a sticker On the back of a toilet door.

“Pray to God” It said in big letters.

Someone else had crossed out the word “Pray” with a black pen.

Then above it they wrote the word “listen”.

Then they’d crossed out the word God

Above it they wrote the word “Love”.

“Listen to Love”

The toilet door said as I sat down to take a big shit

□

...

When the emptiness fills you.

□

Kyoto, Japan. ​The city is a postcard, but today it just feels like glass. ​I got the dream. Three months, an impossible language, temples and rainy streets. Exactly what I swore I wanted. And yet, sitting right here in the middle of it, the chest feels tight and the weight is too real. ​I don't want to be here today. ​Maybe next week the magic will break back through, maybe the ramen will taste better or the streets will feel less wide. But today? Today just sucks. ​And that's allowed. Even in paradise, you're allowed to want to pull the covers over your head and just... pause.

Meeting: Remere & Alexis

And so the hunter became the hunted.

He’d been scouting for the entire afternoon, but to no avail. Alexis’ boots now drag wearily against the grass as he makes his way from the bridge, scythe rested lazily over a shoulder like it’s entirely weightless despite its tremendous size.

There’s a sudden rustle of leaves nearby—not his own footsteps—but that’s not what snaps him fully alert. It’s the restless stomp of a nearby pegasus that gives him pause; the creature lifts its head from where it grazes and exhales a nervous breath.

Alexis moves before he even sees the person in the ruins nearby, weapon drawn as he sprints forward. With a flurry of dexterous movements, he poises himself behind the unfamiliar person, facing their back, the blade of his scythe poised just shy of their throat. The implication is obvious—a single sharp pull of the weapon would leave Remere lacking a head.

“I don’t suggest moving,” Alexis says dryly. “But I do suggest telling me who you are, and what you’re doing wandering out here this late.”

Remere had been doing what they often did late in the evenings: searching for suitable quarry. Subtlety did not matter at this hour, having grown accustomed to blending in with the creatures who preyed upon the naiveté of others. Provided they had iron on their person, and did not step into a fairy ring in error, Remere was as much a danger as the natural inhabitants of this realm.

Or, they would be. If they didn’t have a blade to their neck.

They remain still, the bump on their throat that protrudes from their collar pushes against the blade as they swallow in disinterest. When their lips part the thing that escapes them is not a plea for their life, but a melodic laugh. It cuts through the stranger's threat like mockery, smooth and alluring all the same.

“That’s no way to greet someone here. Has no one taught you manners?” They remain still, and continue to speak.

“Remere, Witch and Scholar. I study the things that go bump in the night. Would you be a doll and lower that, for me? My back will start to ache if I stand like this for too long.” They lick their lips, a glimmer of gold shining beneath in the dim evening light.

A smirk twitches the corner of Alexis’ mouth, but with their back to him, Remere would not see it. He relents much too easily; with a short breath of laughter, he takes a half step forward to create distance between blade and throat, and then draws the weapon away with a showy, graceful twirl of motion.

“You’re right: wherever are my manners?” he answers in kind, but it's unclear if the mockery is playful or cautious on his part; it seems practiced all the same. Alexis finally strolls forward, brushing nonchalantly past Remere to move to stand in front of him.

As he looks the curious individual over one or two times, he processes the greeting offered to him. “Remere,” he repeats softly, restlessly tapping gloved fingers against the haft of his scythe. “Oh! You mean... the famed vampire researcher I’ve heard mention of?” Alexis sounds doubtful, a brow raised, like he had expected something entirely different of Remere.

The short laugh does something to Remere, softens them too easily. They roll their shoulders in place of audibly sighing in relief, brushing a stray hair from their face—as though the action made a difference. They bend down, then, and retrieve a single rose from where it had fallen during their scuffle, holding it close. They have no regard for thorns or otherwise, or that the petals seem trapped in a flawless state of existence.

Sorcery, no doubt.

“I wouldn’t say I have a reputation, but your recognition flatters me. Yes. Vampire researcher.” Their eyes drift lower, towards the rosary upon Alexis’ chest. Their lips curl upward, in softness.

“You arm yourself with trinkets worn without faith, and a blade that you lift with too much ease. Tell me your name, fair hunter.” A pause. “Do not worry. I am not one of the creatures here who will take it from you. I’m asking as a courtesy.”

Alexis leans too comfortably against his weapon, making it plainly obvious that he’s disgustingly easy to sway with flattery and a sweet voice. He’d certainly never been accused of a sharp wit or a strong will.

Rather than question Remere’s perception, blue eyes drop to his own conveniently-exposed chest and settle upon the rosary in question. Gloved fingers lazily lift it to examine passively as he speaks. “Mm, I suppose you must be as wise as I’ve heard people say.” He does not precisely confirm if Rem speaks true of his lack of faith. “The fairest hunter, actually,” he then answers with a wink. “Alexis Ashford. I've been recently traveling these parts following claims of a vampire stirring trouble nearby—not that I’ve found much luck.”

He pauses and a faint smile softens his features. “Until now, I suppose.”

Remere watches in fascination as the rosary is lifted between Alexis’ gloved fingers, head tilted. Curiosity gets the better of them, and they reach towards it in interest. They feel no discomfort as their fingers inch closer, and closer still. Before their gloved fingers can touch it, they halt and withdraw their hand, fingers curling inwards and arm hovering towards their own chest.

Best not to touch the hunter with a weapon, they must have decided.

“Ashford.” They repeat, and their lips flatten into a straight line as they digest the implications of the name. “I have heard of the family’s accomplishments, and I have heard of their young prodigy hunter with a more... altruistic disposition. That must be you.”

They extend their hand again, this time to shake. “A pleasure, fairest Alexis.” It comes out with more familiarity than it ought to, characteristic of a Witch. “I shall endeavor to help you find this trouble.”

Alexis stills very visibly at Rem’s description of him, barely shielding against the impulse to flinch reflexively—not because he is unaccustomed to the biting criticism of his various unconventional virtues, by any means. He’s heard it all, for better or worse, and likewise sees less and less of his father as a consequence of his practices.

What shocks him is Rem’s complete lack of judgment.

His surprise is short-lived, hopefully short enough that his momentary falter in expression escapes Remere’s notice. He promptly but delicately accepts Rem’s handshake, and yes—he goes there, in fact. Remere’s hand is raised as he elegantly bows forward to press a light kiss to knuckles, and then their hand is carefully released.

“A pleasure as well,” he purrs, poising himself upright. “And your aid is most welcome, Remere. I will gladly do whatever I can for you in turn.”

Remere loses composure for the first time during their meeting, the scandal of having a hunter’s lips upon their unholy hand nearly enough to send them into a frenzy. It masks the giddiness threatening to betray its murmuring heart as best they can, the hitch of breath swiftly followed by a small cough as their hand is subsequently released.

“Yes. Of course.” They take a moment to consider their next words. Traditional expressions of gratitude were a danger in these lands. Many things were, themselves included.

“I will offer you information. Our quarry seems to feed primarily on cattle, wildlife, and the occasional... unfortunate. Said unfortunates tend to be ne’er-do-wells, no-goodniks, or otherwise unsavory folk. Nonetheless, people do not take kindly to other people going missing, regardless of their character.”

Remere allows Alexis to draw their own conclusions. It is likely they already have this information, but offering it voluntarily at least allowed them to test the waters of what this altruistic hunter deemed acceptable.

For Alexis, the charming display is as mundane as anything else in his day. It’s practiced and necessary for a favorable relationship of any kind, and Rem had criticized his lack of manners besides—fairly so, of course, considering the blade that had been aimed at his throat. Alexis did not blame him, by any means, but he also had pride and a point to prove.

When he leans against his scythe once more, he looks meaningfully contemplative. “Interesting,” he says softly, tapping the side of a curled finger to his chin in thought. “I was aware of the drastic increase in the deaths of local fauna—bodies drained partly or completely of blood, all the rather obvious signs.” He peeks up at Rem and pauses his tapping in favor of lightly gesturing toward Rem—not in accusation, but in speculation.

“But the particulars of the backgrounds of any human victims—that, I had not known.” He huffs a small laugh. “I... admittedly have yet to do all my research. I wanted to immediately venture to the last known area a body had been sighted, on the small chance I could catch them in the act.”

He shrugs a shoulder and exhales a brief sigh, looking almost shy when he speaks again. “I suppose gathering information first would be more beneficial to me…”

Whatever lack of manners that was perceived by a blade or otherwise had apparently been replaced by goodwill, or as close to goodwill one might receive from a vampire scholar—definitely not a vampire scholar who was seeking a potential meal, companionship, or a way to occupy their time in what was otherwise a meaningless existence.

When they speak again, there is something distant in their voice.

“It helps to understand them. How they think. How they feel. To see through their eyes... taste with their tongue.” The latter remark has a bitterness to it, a distaste. “I’ve taken samples from cattle, spoken to slaughterers, processors... the proprietors of businesses where targets were last sighted. I keep records, and I can give you access to my residence in these parts, should you need it. Conjure temporary lodging.”

It sounds more like a statement than an offer.

“I have a study that can be accessed from the premises. You may come and go as you see fit. Does this sound acceptable to you?”

“Moving me in on the first date?”

It’s spoken with no hesitation, not a beat missed following Rem’s very accommodating and trusting offer. Alexis punctuates his teasing question with a smirk. There is no other remark made about Rem’s description of their own investigative efforts, which must imply that Alexis believes Remere completely.

“I’d consider ‘acceptable’ to be an understatement,” he then answers plainly. “That’s quite generous, in fact. I ought to strive to match your kindness.” For the first time in their conversation, there is something authentic about the gentleness of his tone and the soft way he smiles. “My travels lead me to various rare magical artifacts and materials. I can bring you anything of interest I find, or I can keep an eye out for something specific.”

A small pause follows, but he doesn’t allow Remere to answer. He smiles and speaks promptly. “Actually, I accept no opposition to this arrangement. It’s a deal.” He casually lifts his scythe then, only to rest it lazily over his shoulder. Blue eyes glance up toward the sky quickly to assess the time. “Speaking of lodging, I ought to get myself somewhere to rest.” His eyes fall on Remere again. “I’m grateful we had such an auspicious chance encounter. I do hope I was able to make up for my rather lacking first impression.”

There is no goodbye to follow. Alexis simply winks at Remere once again before calmly walking past him, toward the bridge from where he originally wandered, to take his leave.

---

The above was conjured in the Realm of Awenia, owned by the lovely Gwen Enchanted. Thanks for letting us play vampire barbie in your home!

Three Twisted Knots • Gwen's Primfeed

There was a little girl stuck somewhere within.

Whenever I tried to be a man.

She’d make such a din.

I locked her in cupboard.

Where she couldn't be seen.

Til I fell asleep.

Then

...she'd begin

Her song

My Heroin.

□

The Medicine

Within

□

Of all the currencies...

Giving

The more you give

The richer you become

The desert in storm grows green

Abundant

Rulers. Redundant.

□

I'm planning to follow two games this College Football Saturday.

First on my list finds the (4-4) Texas A&M Aggies traveling to play the (5-3) South Carolina Gamecocks. This game is scheduled to start at 2:00 PM CT.

Then later I'll have the (7-1) Notre Dame Fightin' Irish hosting the (8-0) Miami Hurricanes. This game is scheduled to start at 6:30 PM CT.

And the adventure continues.

---

“Montana Travel, Green Glacial Valley” by moonjazz is licensed under CC BY-NC 2.0.

Nov. 7, 2026

Wait a difference a day made... One of the great jazz standards says it all. I went into yesterday still dealing with my fears and anxieties about owning a home with my partner, but by the end, I felt inspired and invigorated to walk this path together with her into the unknown.

Moving past all of these difficult feelings began with the phone conversation with my parents, in which they lovingly told me that they wanted to give us this money. That they saw how far I'd come in recovery and this gift was a part of their way of saying, we see your growth, and we want to support you. There are no words to express my gratitude for their generosity.

But then, when our lawyer reached out to set up the closing date, I called my partner, and we both had the jitters at first. Then M, my partner, said, “It feels like we're holding hands about to jump off a cliff together.” That image gave me a feeling of confidence and excitement that I hadn't felt before in regards to the house. There's no one I would rather jump off a cliff with than M. She is such a good partner and friend, and the fact that that image made me feel better about things rather than more fearful I think is a pretty good indication that we're ready to take the plunge.

Wish us luck!

PS Another thought I had was that Higher Power, Benevolent Spirits, Ancestors, Animals, and the land are all here to help us, guide us, and protect us. How lucky are we?! Amen!