DEV Community: Khali Sollis

The Most Wonderful Time of the Year Is Also an Expensive One

Khali Sollis — Sat, 07 Nov 2026 15:00:00 +0000

Why the holiday season can intensify financial anxiety, emotional exhaustion, and the pressure to perform happiness.

It is the first week of November, and someone is sitting at the kitchen table with a budget open on a laptop. Rent is due on the first. The grocery line has crept upward all year. A credit card payment is coming, and there is a quieter worry underneath the numbers: whether the job that pays for all of it will look the same in January.

Then the phone lights up. A retailer's early holiday sale has started. An app reports that a gift on someone's wish list is almost sold out. An email promises a discount that expires at midnight. A scroll through social media delivers a living room trimmed like a catalog and a family in matching pajamas.

A new column quietly enters the spreadsheet. Gifts, travel, a hosted dinner, decorations, and one line item that never appears by name: the cost of meeting expectations.

Plenty of people love this season, and for good reason. But it promises abundance at precisely the moment some households are trying to protect whatever margin they have left. The holidays ask for two things at once: that we spend generously, and that we feel wonderful about it.

The Pressure to Feel the Season

The second request may be the harder one.

In a 2011 study, Iris Mauss and colleagues asked whether valuing happiness could backfire. Some participants read a fabricated news article praising happiness; others read a neutral one. All then watched either a happy or a sad film clip. Those primed to value happiness felt less happy afterward, but only after the happy clip, and their disappointment in their own feelings helped explain the drop. The samples were small and all women, so the study is a starting point rather than a verdict.

Brock Bastian and colleagues examined the social version of the same pressure. In a 2012 series of studies in Australia and Japan, people who believed others expected them not to feel sad or anxious evaluated themselves more harshly when those feelings arrived, and reported more frequent negative emotion. In one experiment, students told that negative emotions harm the people around them felt worse after recalling a painful memory, while students told those emotions were harmless did not.

Neither study examined Thanksgiving or Christmas. Still, the pattern is worth noticing: Mauss found the cost of valuing happiness in the happy condition, not the sad one. My interpretation, not a finding, is that a season built around expected joy may be where ordinary sadness, loneliness, or fatigue is most likely to feel like a personal failure.

The Price of Generosity

The financial side of the season starts with a forecast. On October 15, the National Retail Federation projected that November and December retail sales will grow 4.2 percent to about $1.013 trillion, after an actual $972 billion last season. Its chief economist noted that inflation and gas prices will affect income groups differently. A forecast models aggregate spending; it says nothing about who can comfortably afford their share.

The households carrying that share are not starting from zero. In a quarterly report released in August, the New York Fed measured credit card balances at $1.26 trillion in the second quarter of 2026.

Economists have long argued about what gifts are worth. Joel Waldfogel's 1993 paper "The Deadweight Loss of Christmas" surveyed Yale students and estimated that gifts destroyed between 10 percent and a third of their value. Sara Solnick and David Hemenway later found that more than half of their respondents valued gifts above their price. The argument persists because a price tag cannot capture what a gift means.

The question for households is rarely whether a gift is efficient. It is whether it is voluntary. Generosity chosen freely and spending driven by obligation can produce identical receipts and very different months afterward. The research on debt and well-being is mostly correlational. In a large U.S. sample of young adults, Elizabeth Sweet and colleagues found that those who would remain in debt even after selling everything they owned reported more stress and depressive symptoms, after accounting for earlier life circumstances.

The Labor Behind the Magic

Someone has to make the season happen. Someone remembers the teacher's gift, books the flight before prices jump, reconciles three dietary restrictions, and decides who sits next to whom.

Sociologist Arlie Hochschild coined "emotional labor" in 1983 to describe paid workers managing their feelings as part of the job, so the term should not be stretched across every family dinner. Allison Daminger's 2019 study offers a closer fit. Interviewing 35 couples, she described household cognitive labor in four parts: anticipating needs, identifying options, deciding, and monitoring. Women in her sample did more of the anticipating and monitoring, while decisions were shared more evenly. The work is often invisible even to the people doing it.

Daminger studied ordinary household management, not holidays, and families divide this work in many ways. But celebrations multiply exactly these tasks, often on top of year-end deadlines and caregiving. A gathering can look effortless to the guests precisely because someone spent weeks making it so.

The Season on Display

In 1954, Leon Festinger proposed that people evaluate themselves partly through comparison with others. Social media extends that habit across thousands of curated windows, and the holidays supply unusually photogenic material.

A 2020 review by Philippe Verduyn and colleagues concluded that comparison on social platforms can occasionally raise well-being, but more often harms it, mainly through upward comparisons, and especially for people already prone to comparing. Much of this evidence is correlational, and effects vary by person and use. Whether the holidays intensify them is my inference, not an established finding. But a season that rewards visible happiness also rewards displaying it, and the private reality behind each display remains offscreen.

The Holiday Shopping Machine

Much of the season now runs through software, which is where DEV.to readers enter the story.

Recommendation systems use browsing history, purchase records, and context to suggest products. Done well, this is genuinely useful. It also means a steady supply of well-chosen temptations at a moment of heightened obligation.

A countdown or a low-stock warning can be accurate, helpful information. But when Arunesh Mathur and colleagues crawled about 11,000 shopping sites in 2019, they found 1,818 dark-pattern instances, including timers and low-stock messages; 234 of them, on 183 sites, were deceptive. The design question is not whether urgency exists, but whether it is true.

One-click checkout and saved cards remove real annoyances, and also the pauses in which a shopper might reconsider. That is a plausible mechanism, not proof of overspending.

Buy now, pay later services show how payment design shapes the experience of a purchase. Picture the same $100 gift presented two ways. One checkout page says "Total: $100." Another leads with "4 payments of $25," in large type, with the total in smaller print below. The price is identical; only the number the eye meets first has changed. A small, repeated figure can feel easier to accept than a single larger one, and three of the four payments sit in weeks that have not arrived yet. That is a plausible effect of framing, not evidence that the layout causes overspending; many shoppers read both numbers and choose installments deliberately, often without paying interest.

The larger risk is several split purchases stacking up. The Consumer Financial Protection Bureau analyzed 2022 pay-in-four loan data from six large lenders: Affirm, Afterpay, Klarna, PayPal, Sezzle, and Zip. Among borrowers in that data, about 63 percent took out another BNPL loan within six weeks of a previous one at some point during the year, meeting the report's measure of simultaneous borrowing. They also tended to carry higher credit card balances. The figure describes borrowers at these six lenders, not every shopper or the entire U.S. market. Using banking data on 10.6 million consumers, Ed deHaan and colleagues found that new BNPL users saw rapid increases in overdraft charges and credit card interest and fees, which the authors call consistent with overborrowing, not proof of it.

Regulation remains unsettled. In May 2025, the CFPB withdrew its 2024 interpretation treating these loans like credit cards. New York moved the other way: under a 2025 state law, its Department of Financial Services formally proposed licensing, disclosure, and fee rules in July 2026, which are not yet in force.

Engineers are often asked to optimize conversion, cart completion, and average order value, all legitimate goals. But helping someone complete a purchase they intended differs from nudging one they will regret. Three considerations follow: show the full price and every future payment, not just the installment; make urgency claims match real inventory and real deadlines; and make promotional notifications easy to control. These are principles of transparent design, not proven mental-health interventions.

What a Celebration Requires

The paradox is not that celebration is bad. It is that a season of emotional abundance can press hardest on people whose financial, cognitive, and emotional resources are already stretched. Connection slides into performance, generosity into obligation, convenience into impulse, happiness into the expectation of happiness.

Part 1 of this series explored how economic uncertainty occupies the mind; Part 2, how information systems hold attention. The holidays combine both pressures, then add a deadline and a dress code.

Back at the kitchen table, the bills remain, and so do the people worth celebrating. What can change is the question. Not how much must be bought for the season to count, but what a meaningful celebration actually requires. Often the answer is smaller than the spreadsheet suggests: time, attention, presence, and a few gifts chosen freely rather than bought defensively.

Perhaps the most expensive part of the holidays is not what we buy, but what we believe we must prove.

For those of us who build the shopping experiences: what might they look like if success were measured not only by completed purchases, but by whether customers felt informed, respected, and satisfied afterward?

This is Part 3 of America Under Pressure: The Psychology of Uncertainty. Part 1 examined economic uncertainty and anticipatory stress. Part 2 explored political uncertainty and the attention economy.

References

Mauss, I. B., Tamir, M., Anderson, C. L., & Savino, N. S. (2011). Can seeking happiness make people unhappy? Paradoxical effects of valuing happiness. Emotion, 11(4), 807–815. https://doi.org/10.1037/a0022010

Bastian, B., Kuppens, P., Hornsey, M. J., Park, J., Koval, P., & Uchida, Y. (2012). Feeling bad about being sad: The role of social expectancies in amplifying negative mood. Emotion, 12(1), 69–80. https://doi.org/10.1037/a0024755

National Retail Federation. (2026, October 15). NRF forecasts 2026 holiday sales growth as consumers remain resilient [Press release]. https://nrf.com/media-center/press-releases/nrf-forecasts-2026-holiday-sales-growth-as-consumers-remain-resilient

Federal Reserve Bank of New York. (2026, August 11). Household debt balances decreased slightly; credit card delinquency transition rates remained steady. https://www.newyorkfed.org/newsevents/news/research/2026/20260811

Waldfogel, J. (1993). The deadweight loss of Christmas. American Economic Review, 83(5), 1328–1336. https://www.jstor.org/stable/2117564

Solnick, S. J., & Hemenway, D. (1996). The deadweight loss of Christmas: Comment. American Economic Review, 86(5), 1299–1305. https://www.jstor.org/stable/2118293

Sweet, E., Nandi, A., Adam, E. K., & McDade, T. W. (2013). The high price of debt: Household financial debt and its impact on mental and physical health. Social Science & Medicine, 91, 94–100. https://doi.org/10.1016/j.socscimed.2013.05.009

Hochschild, A. R. (1983). The managed heart: Commercialization of human feeling. University of California Press.

Daminger, A. (2019). The cognitive dimension of household labor. American Sociological Review, 84(4), 609–633. https://doi.org/10.1177/0003122419859007

Festinger, L. (1954). A theory of social comparison processes. Human Relations, 7(2), 117–140. https://doi.org/10.1177/001872675400700202

Verduyn, P., Gugushvili, N., Massar, K., Täht, K., & Kross, E. (2020). Social comparison on social networking sites. Current Opinion in Psychology, 36, 32–37. https://doi.org/10.1016/j.copsyc.2020.04.002

Mathur, A., Acar, G., Friedman, M. J., Lucherini, E., Mayer, J., Chetty, M., & Narayanan, A. (2019). Dark patterns at scale: Findings from a crawl of 11K shopping websites. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW). https://arxiv.org/abs/1907.07032

Consumer Financial Protection Bureau. (2025, January). Consumer use of buy now, pay later and other unsecured debt.
https://files.consumerfinance.gov/f/documents/cfpb_BNPL_Report_2025_01.pdf

deHaan, E., Kim, J., Lourie, B., & Zhu, C. (2024). Buy now pay (pain?) later. Management Science, 70(8), 5586–5598. https://doi.org/10.1287/mnsc.2022.03266

Consumer Financial Protection Bureau. (2025, May 12). Interpretive rules, policy statements, and advisory opinions; withdrawal. Federal Register, 90, 20084. https://www.federalregister.gov/d/2025-08286

Orrick InfoBytes. (2026, July 24). NYDFS proposes rule to implement state BNPL licensing and consumer protection framework. https://infobytes.orrick.com/2026-07-24/nydfs-proposes-rule-to-implement-state-bnpl-licensing-and-consumer-protection-framework/

Your Brain Was Not Designed for a Never-Ending Election

Khali Sollis — Fri, 06 Nov 2026 15:00:00 +0000

What political uncertainty, algorithmic amplification, and nonstop news exposure do to our attention and emotional regulation.

The phone is in your hand before your feet touch the floor. It is the morning of November 4, and the screen has been busy overnight. One headline declares a winner. Another asks what happens next in a race nobody has called. Notifications stack up like unopened mail. One friend is posting confetti; another has written a paragraph that begins, "I genuinely cannot believe." A commentator is already explaining the consequences of decisions that have not yet been made.

You lock the screen, decide to make coffee, and unlock it again before the kettle is warm.

Wanting to know what happened is reasonable. But somewhere between the first check and the fifth, the purpose of checking quietly changes. The first look informs. The later ones are often less about learning than about quieting an uncertainty that refuses to settle.

The election may have an official ending. The information cycle does not.

Part 1 of this series examined how economic uncertainty occupies mental bandwidth before any loss arrives. This essay follows that thread into political uncertainty and an information environment that almost never goes quiet.

The Brain on an Unresolved Question

In a 2013 review in Nature Reviews Neuroscience, Dan Grupe and Jack Nitschke described anxiety as the anticipatory changes in feeling, thinking, and behavior that arise when a possible future threat is uncertain. Uncertainty is costly, they argued, because it undermines preparation.

Their Uncertainty and Anticipation Model of Anxiety identifies five processes that, when exaggerated, characterize clinical anxiety. Two are especially relevant here: heightened vigilance toward threat, and heightened reactivity to uncertainty itself, where not knowing becomes distressing in its own right.

The model describes clinical anxiety, and the authors stress that the same circuitry serves adaptive purposes. A voter refreshing results is not displaying a disorder, but the vocabulary is useful. A ballot count is exactly the kind of unresolved, consequential, largely uncontrollable question that anticipatory vigilance is built to monitor. Scanning feels like doing something. Often, it is the only thing available to do.

When the Coverage Becomes the Stressor

The most cited evidence that media exposure carries a psychological cost comes from crisis research. After the 2013 Boston Marathon bombings, E. Alison Holman, Dana Rose Garfin, and Roxane Cohen Silver surveyed a representative national sample of 4,675 U.S. adults two to four weeks after the attack. Adjusting for pre-bombing mental health and prior trauma exposure, they found that people reporting six or more daily hours of bombing-related media in the first week reported higher acute stress than people who had been at or near the event.

The study was cross-sectional and cannot establish that media caused the stress. And an election is not a terrorist attack; the finding offers a cautious signal about repeated exposure, not an equivalence.

There is also a complication. In a three-year longitudinal study (2019), Rebecca Thompson and colleagues found that the relationship runs in both directions: distress after one collective trauma predicted heavier media consumption around a later one, which in turn predicted more distress. Anxious people seek more news; more news can sustain anxiety.

Political news has been studied more directly. Brett Ford, Matthew Feinberg, and colleagues (2023) combined daily diary studies with experiments using political news clips. Daily politics reliably evoked negative emotions, which tracked with worse psychological and physical well-being, yet also predicted greater motivation to volunteer, donate, or protest. Participants who dampened those emotions through distraction or reappraisal felt better and were less motivated to act.

Political distress is not simply a malfunction. It is partly the fuel of participation.

The Feed Meets the Anxious Brain

Imagine two posts from accounts you follow: a plain update on certified results, and an emotionally charged prediction about what happens next. A chronological feed shows them in the order they were posted. A ranked feed weighs many signals, often including predicted engagement: how likely you are to click, reply, share, or linger. If the model expects the charged post to hold your attention, it can rise above the plain one, even if it arrived later. Objectives differ across platforms and change over time, so no single description fits every system.

Does emotional content travel further? Analyzing more than 560,000 tweets on polarizing issues, William Brady and colleagues reported in 2017 that each additional moral-emotional word was associated with roughly a 20 percent increase in retweets, mostly within like-minded networks. The size of that effect has been debated (Burton et al., 2021), and a 2025 preregistered meta-analysis by Brady's group, pooling 27 studies, put it closer to 12 percent per word. But retweets are human choices. More sharing is not proof that an algorithm amplified anything.

Direct tests of ranking are more mixed. A 2025 audit by Smitha Milli and colleagues found that, compared with a chronological feed of the same accounts, Twitter's engagement-based algorithm amplified emotionally charged, out-group hostile political content, the kind users said made them feel worse about the other side. Yet when Andrew Guess and colleagues, working with Meta, moved consenting Facebook and Instagram users to chronological feeds for three months during the 2020 election, time on the platforms fell substantially while polarization, political knowledge, and other key attitudes did not measurably change. In these studies, ranking changed what people saw and how long they stayed more clearly than it changed what they believed.

Emotions themselves can spread, modestly. In the 2014 Facebook "emotional contagion" study, Adam Kramer, Jamie Guillory, and Jeffrey Hancock reduced positive or negative posts in 689,003 users' feeds for a week; those shown fewer positive posts wrote slightly fewer positive words, and vice versa. The effects were tiny, the measure was word choice rather than felt emotion, and the journal issued an Editorial Expression of Concern about consent. My interpretation, not a finding, is that a feed's emotional texture may make anger look more universal than it is.

So the answer is conditional. When a system rewards engagement and a person is searching for certainty, the two can plausibly feed each other: uncertainty drives checking, checking produces engagement signals, and those signals can favor whatever is most arousing. The evidence supports pieces of that loop, not a complete causal chain.

The Developer's Paradox

Many DEV.to readers sit on both sides of this screen: we build notification pipelines and ranking services, then pick up phones running them.

We have become extraordinarily sophisticated at engineering information delivery without becoming equally sophisticated at protecting the attention that receives it.

This is not a story of villains. Engagement is often a reasonable proxy for relevance. The tension is that the easiest metrics to measure are not always what users would choose on reflection, the very gap Milli's team documented.

A few evidence-informed starting points:

Batch rather than drip. In a two-week field experiment with 237 smartphone users in India, Nicholas Fitz and colleagues found that delivering notifications three times a day lowered self-reported stress and inattention and lifted mood, though it also raised fear of missing out. Switching notifications off entirely raised anxiety without improving attention.
Rank by stated preferences, not only behavior. In Milli's analysis, ranking by what users said they valued reduced angry and hostile content, though it risked reinforcing existing views.
Make ranking legible and optional. A plain-language "why am I seeing this" and an easy chronological mode give people control. Their mental-health effects are unestablished; frame them as respect for agency, not therapy.

The Election Ends. The Uncertainty Changes Shape.

Back to November 4. Even when a race is called, its question gives way to new ones. Which policies will change? What happens to prices and jobs? How much of the commentary is evidence, and how much is forecasting dressed as fact?

Vigilance tries to resolve uncertainty by gathering information. But here lies the quiet mismatch at the center of the morning:

Much post-election uncertainty is not informational. It is temporal.

The answers depend on decisions not yet made and events not yet arrived. They will come on the schedule of legislatures, courts, and economies, not push alerts. Checking again cannot make them arrive sooner; it can only make the waiting louder.

None of this argues for looking away; Ford's research ties political discomfort to the willingness to act. Nor is any party or group of voters uniquely susceptible. These processes are human, not partisan. Responsibility is shared. Individuals choose when to check. Newsrooms choose how often to alert and how clearly to separate reporting from speculation. Platforms choose what their systems optimize for. Only the last two operate at scale.

The distinction worth protecting is between attention that serves understanding and attention that has become reflex. One informs a vote, a conversation, a decision. The other mostly keeps the question open.

Democracy asks citizens to pay attention. The attention economy asks them never to stop. Between those two demands lies a design problem, and many people reading this are equipped to work on it.

So the question for you: if you could change one default in the products you build, so that a person searching for certainty would be better served rather than merely more engaged, what would it be?

This is Part 2 of America Under Pressure: The Psychology of Uncertainty. Part 1 examined economic insecurity and anticipatory stress. Part 3 turns to the financial and emotional pressures of the holiday season.

References

Grupe, D. W., & Nitschke, J. B. (2013). Uncertainty and anticipation in anxiety: An integrated neurobiological and psychological perspective. Nature Reviews Neuroscience, 14(7), 488–501. https://doi.org/10.1038/nrn3524

Holman, E. A., Garfin, D. R., & Silver, R. C. (2014). Media's role in broadcasting acute stress following the Boston Marathon bombings. Proceedings of the National Academy of Sciences, 111(1), 93–98. https://doi.org/10.1073/pnas.1316265110

Thompson, R. R., Jones, N. M., Holman, E. A., & Silver, R. C. (2019). Media exposure to mass violence events can fuel a cycle of distress. Science Advances, 5(4), eaav3502. https://doi.org/10.1126/sciadv.aav3502

Ford, B. Q., Feinberg, M., Lassetter, B., Thai, S., & Gatchpazian, A. (2023). The political is personal: The costs of daily politics. Journal of Personality and Social Psychology, 125(1), 1–28. https://doi.org/10.1037/pspa0000335

Brady, W. J., Wills, J. A., Jost, J. T., Tucker, J. A., & Van Bavel, J. J. (2017). Emotion shapes the diffusion of moralized content in social networks. Proceedings of the National Academy of Sciences, 114(28), 7313–7318. https://doi.org/10.1073/pnas.1618923114

Burton, J. W., Cruz, N., & Hahn, U. (2021). Reconsidering evidence of moral contagion in online social networks. Nature Human Behaviour, 5(12), 1629–1635. https://doi.org/10.1038/s41562-021-01133-5

Brady, W. J., Rathje, S., Globig, L., & Van Bavel, J. J. (2025). Estimating the effect size of moral contagion in online networks: A pre-registered replication and meta-analysis. PNAS Nexus. https://doi.org/10.1093/pnasnexus/pgaf327

Milli, S., Carroll, M., Wang, Y., Pandey, S., Zhao, S., & Dragan, A. D. (2025). Engagement, user satisfaction, and the amplification of divisive content on social media. PNAS Nexus, 4(3), pgaf062. https://doi.org/10.1093/pnasnexus/pgaf062

Guess, A. M., Malhotra, N., Pan, J., Barberá, P., Allcott, H., et al. (2023). How do social media feed algorithms affect attitudes and behavior in an election campaign? Science, 381(6656), 398–404. https://doi.org/10.1126/science.abp9364

Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788–8790. https://doi.org/10.1073/pnas.1320040111

Fitz, N., Kushlev, K., Jagannathan, R., Lewis, T., Paliwal, D., & Ariely, D. (2019). Batching smartphone notifications can improve well-being. Computers in Human Behavior, 101, 84–94. https://doi.org/10.1016/j.chb.2019.07.016

When the Economy Becomes a Psychological Threat

Khali Sollis — Thu, 05 Nov 2026 15:00:00 +0000

How recession fears, financial uncertainty, and chronic stress reshape the way Americans think, work, and make decisions.

There is a moment many Americans now know well. The number on the gas pump climbs past where it stopped last week, and a small, involuntary calculation begins. It is not only what does this cost but what does this mean. Is this temporary? Is it the start of something? Should I be doing something differently?

That second calculation, the one about meaning, is where the economy stops being a set of figures and starts behaving like a psychological threat.

By the standard measure of how households feel, 2026 has been an unusually heavy year. The University of Michigan’s Index of Consumer Sentiment fell to 44.8 in May, a record low, as supply disruptions tied to the conflict with Iran pushed gasoline prices higher. Nearly 40 percent of respondents mentioned gas prices without being asked. The final October reading was 48.0, which the survey’s director, Joanne Hsu, noted was about 15 percent below February, just before the conflict began.

The American Psychological Association’s latest Stress in America survey, fielded from late July through August among a national sample of 3,132 adults, captures the same weight from another angle. Seventy-seven percent rated the cost of living a very or somewhat significant source of stress, and 68 percent agreed, strongly or somewhat, that they had become accustomed to living with uncertainty. The report’s subtitle is The stress of uncertainty.

Surveys like these record what people report, not what happens in their bodies. But research increasingly suggests that how people expect the economy to behave can affect their bodies and minds well before the feared outcome arrives, and sometimes whether or not it arrives at all.

Uncertainty is a stressor in its own right

In 2016, a University College London team led by Archy de Berker designed an experiment to separate two things that usually travel together: bad outcomes, and not knowing whether they are coming. Forty-five participants guessed whether a snake was hidden under a rock. When a snake appeared, they received a mildly painful electric shock to the hand. A computational model estimated each participant’s uncertainty on every trial.

Stress tracked uncertainty. Self-reported stress, pupil dilation, and skin conductance peaked when the chance of a shock was near 50 percent and were lowest when a shock was either very unlikely or nearly certain. Not knowing was worse than knowing, even when knowing meant pain.

The study used a small sample, brief sessions, and a sting-like shock, not months of rising prices or rumors of layoffs, so applying it to recessions is an analogy rather than a demonstration. Still, the analogy is useful. An economy in which recession is perpetually “possible,” debated nightly but never confirmed or ruled out, resembles the experiment’s most stressful condition: a coin that never stops spinning.

The authors also pointed to an adaptive side. Participants whose stress tracked uncertainty more closely were better at predicting shocks. Stress is part of how the brain learns about danger. The trouble starts when the danger cannot be learned because the signal never resolves.

The worry that outlasts the event

If uncertainty carries its own cost, then the fear of losing a job might have consequences that do not depend on actually losing it. Sociologists Sarah Burgard, Jennie Brand, and James House tested this in a 2009 study in Social Science & Medicine, drawing on two nationally representative U.S. longitudinal datasets covering periods of roughly three to ten years. Persistent perceived job insecurity predicted poorer self-rated health in both samples, and more depressive symptoms in one.

The association held after the researchers accounted for actual job loss and unemployment, and after controls for demographic and job characteristics, earlier health, and a general tendency to report things negatively. Burgard offered several explanations: prolonged ambiguity about the future, the inability to act until the feared event happens, and the lack of the institutional supports that exist for people who have actually lost work.

This is observational research, and self-rated health is an imperfect outcome. But it points to something economic coverage tends to miss. Unemployment figures count the people who have lost jobs. They do not count the much larger group who go to work each day wondering whether they will be next.

The bandwidth tax

Financial pressure may also change how people think, not only how they feel. In a widely cited 2013 paper in Science, Anandi Mani, Sendhil Mullainathan, Eldar Shafir, and Jiaying Zhao proposed that money worries consume mental resources that would otherwise be available for other tasks.

In a New Jersey shopping mall, roughly 400 participants thought through a hypothetical expense before taking cognitive tests. When the expense was small, lower- and higher-income participants performed similarly. When it was large, lower-income participants performed worse and higher-income participants did not. In India, the team tested 464 sugarcane farmers before and after harvest, and the same farmers scored higher after being paid. The authors argued that this reflected attention capture: a pressing concern pulls the mind toward it and leaves less “bandwidth” for everything else.

The finding has been challenged. In 2016, economists Leandro Carvalho, Stephan Meier, and Stephanie Wang randomly surveyed low-income U.S. households just before or just after payday. Despite clear differences in available cash, the two groups performed similarly on cognitive tasks. The difference may reflect short payday cycles versus deeper, longer-lasting scarcity, or an effect smaller and more context-dependent than first reported. For now, the bandwidth hypothesis is best read as a plausible mechanism under active debate, not a settled law.

When your livelihood depends on your attention

For developers and other knowledge workers, these findings raise a pointed question, because attention is not incidental to the job. It is the job. Debugging a race condition, holding a system’s architecture in mind, or reviewing a pull request carefully all depend on sustained, uninterrupted focus.

None of the studies above tested software developers, and nothing in them shows that job insecurity degrades code quality. If financial or job-related worry captures attention, as Mani and colleagues proposed, then work that requires holding many details in mind at once could be among the first to feel it, while urgent, well-defined tasks keep getting done. That is a hypothesis, not a finding.

There are signs the worry is common in the field. In Devographics’ State of Devs 2026 survey, 2,578 of the 5,352 developers who answered a question about workplace issues, about 48 percent, said they had experienced job insecurity at some point in their careers. The sample is self-selected, drawn largely from people who took earlier Devographics surveys and weighted toward web development, and the question covered entire careers rather than the present moment. The figure describes that community, not the profession as a whole.

The UCL experiment invites one further, more speculative extension. In the lab, stress helped people learn when a threat was coming. Whether anything similar happens at work has not been tested, and the leap from laboratory physiology to workplace behavior is a large one. Still, a possible parallel is easy to recognize: refreshing layoff trackers, rereading a manager’s message for hidden meaning, scanning company channels for hints. If habits like these are attempts to resolve uncertainty, they may also draw on the focus the work depends on.

What can be controlled

None of this means Americans are collectively impaired. People vary widely in how they respond to uncertainty, and surveys capture mood more than capacity. The burden is also uneven. APA surveys have long found that lower-income adults, younger adults, and parents report more money-related stress, and in 2026, 59 percent of adults agreed, strongly or somewhat, that they felt they were doing everything “right” and still falling behind.

The research does suggest where relief might come from. In the UCL experiment, and in decades of earlier animal research, predictability and a sense of control reduced stress responses. That points less toward individual willpower and more toward the conditions people live in: employers who say clearly what is and is not changing, policies that soften the cost of a single financial error, and support that reaches people who are anxious before they are unemployed. Burgard’s team noted that programs built for displaced workers miss those who still have jobs but fear losing them. APA’s 2014 data adds one more factor: adults who reported having emotional support said they managed financial stress in healthier ways than those who did not.

Perhaps the larger question is what happens to a society when millions of people spend more mental energy preparing for what might go wrong than building what could go right. The economy may or may not tip into recession in the coming months. For many Americans, the uncertainty has already arrived, and the mind does not wait for the official data to respond.

This is Part 1 of America Under Pressure: The Psychology of Uncertainty. In Part 2, we’ll examine how election coverage, political uncertainty, and digital platforms compete for our attention during an already stressful period.

References

American Psychological Association. (2015, February 4). APA survey shows money stress weighing on Americans’ health nationwide [Press release]. https://www.apa.org/news/press/releases/2015/02/money-stress

American Psychological Association. (2026). Stress in America™ 2026: The stress of uncertainty. https://www.apa.org/pubs/reports/stress-in-america/2026

American Psychological Association. (2026). Stress in America™ 2026: Topline data. https://www.apa.org/pubs/reports/stress-in-america/2026/topline-data.pdf

Burgard, S. A., Brand, J. E., & House, J. S. (2009). Perceived job insecurity and worker health in the United States. Social Science & Medicine, 69(5), 777–785. https://pmc.ncbi.nlm.nih.gov/articles/PMC2757283

Carvalho, L. S., Meier, S., & Wang, S. W. (2016). Poverty and economic decision-making: Evidence from changes in financial resources at payday. American Economic Review, 106(2), 260–284. https://doi.org/10.1257/aer.20140481

de Berker, A. O., Rutledge, R. B., Mathys, C., Marshall, L., Cross, G. F., Dolan, R. J., & Bestmann, S. (2016). Computations of uncertainty mediate acute stress responses in humans. Nature Communications, 7, 10996. https://doi.org/10.1038/ncomms10996

Devographics. (2026). State of Devs 2026: Career. https://2026.stateofdevs.com/en-US/career/

Mani, A., Mullainathan, S., Shafir, E., & Zhao, J. (2013). Poverty impedes cognitive function. Science, 341(6149), 976–980. https://doi.org/10.1126/science.1238041

University of Michigan Surveys of Consumers. (2026, May 22). Consumer sentiment, final results for May 2026. https://data.sca.isr.umich.edu/fetchdoc.php?docid=81238

University of Michigan Institute for Social Research. (2026). As concerns over purchasing power dominate, consumer sentiment moves sideways. https://isr.umich.edu/news-events/news-releases/as-concerns-over-purchasing-power-dominate-consumer-sentiment-moves-sideways/

The Bug That Outlived the Event: What Neuroscience Can Teach Us About Systems That Remember

Khali Sollis — Thu, 05 Nov 2026 15:00:00 +0000

How a mouse study involving early-life pain, neural circuits, and brain stimulation raises questions about persistent state, feedback loops, and the limits of debugging analogies

The incident ended. The system didn't get the memo.

What follows is invented. If it sounds familiar, that is rather the point.

A payment service has a bad hour. A downstream dependency starts timing out, retries pile up, latency climbs, and the on-call engineer spends 50 minutes learning new words for frustration. Then the dependency recovers. Error rate: zero. Every dashboard turns a reassuring shade of green, and someone opens the postmortem template.

Two weeks later, a quieter anomaly surfaces. Conversion on one checkout path is down a few percent. No errors. Latency is fine. Eventually someone finds the residue: during the incident, an adaptive rate limiter tightened its thresholds and never fully relaxed them, and a feature flag flipped as an emergency mitigation was never flipped back. Nothing is broken. Something is different.

The incident is over. Its consequences are not.

Distributed-systems researchers have a name for the dramatic version of this pattern. In a 2021 paper, Nathan Bronson and colleagues described metastable failures: a trigger pushes a system into a bad state that persists even after the trigger is gone, sustained by a feedback loop of its own. Their point was that the real root cause is the loop, not the trigger.

Removing the original disturbance does not always restore the original state. A recent mouse study asks a version of that question in living tissue.

A mouse study with an unexpected result

In a paper published in Translational Psychiatry in July 2026, Xiangdong Wan, Tao Du, and colleagues at Xuanwu Hospital, Capital Medical University, in Beijing injected a small amount of Complete Freund's Adjuvant into one hind paw of 10-day-old mice. The substance produces local inflammation. Control pups received saline. Twenty-four hours later, the injected pups were measurably more sensitive to pressure and heat.

The researchers then waited until the mice were adults. At eight weeks of age, the paw sensitivity of the two groups was indistinguishable. By that measure, the original problem was gone.

The social behavior was not the same. In the standard three-chamber test, a mouse can spend time near a cage holding an unfamiliar mouse or near an identical empty cage. Control mice favored the stranger. Mice that had experienced the early inflammatory pain showed no preference either way. The difference appeared in males and females, and it was still present when the same animals were retested at 12 and 16 weeks.

Two details matter. First, when a second unfamiliar mouse was introduced, the pain-exposed mice still preferred the newcomer to the now-familiar one, so their ability to tell mice apart looked intact. They also showed no extra anxiety-like behavior in two standard tests. The authors interpret the deficit as reduced social motivation rather than general anxiety or memory impairment. Reasonable, but that is an inference from behavior, not a measurement of motivation.

Second, timing. Injections at postnatal days 6, 9, or 12 produced the adult deficit. Injections at days 15, 18, or 21 did not. Something about that early window made the same insult leave a different mark.

What changed was time spent near a caged stranger. That is not loneliness, empathy, or social withdrawal in any human sense, and the paper does not claim it is.

The system remembers, even when the input is gone

Engineers know this shape. A cache keeps serving a value computed under conditions that no longer apply. Configuration drifts one hotfix at a time until production is a system no one designed.

The lesson in every case is the same: a system that has returned to normal on one metric has not necessarily returned to baseline. Paw sensitivity is one metric. It recovered. Social preference is another. It did not.

Here the analogy needs a leash. A mouse brain does not store an early injury the way a server stores a stale config file. There is no hidden value waiting to be reset. Nor does the study show the kind of self-sustaining loop the metastability researchers describe: it documents a lasting difference, not the mechanism that keeps it in place. The shared idea is abstract: history can alter internal organization in ways a single recovered measurement will not reveal.

The brain has its own balancing act
The researchers focused on the anterior cingulate cortex, a region implicated in both social processing and the unpleasant, emotional side of pain. Like most cortex, it depends on a working relationship between excitatory neurons, which push connected cells toward activity, and inhibitory neurons, which hold them back.

"Accelerator and brake" is the usual shorthand, and it misleads. Inhibition in cortex does not simply slow things down. It shapes timing, sharpens which signals stand out, and gates when a circuit can change at all. Work by Takao Hensch and others in visual cortex showed that the maturation of local inhibitory circuits helps open developmental critical periods. Different region, same reminder: inhibition is architecture, not just friction.

Using fiber photometry, which records the pooled activity of a genetically labeled cell population, the team watched both cell types as mice approached a stranger. In controls, excitatory neurons became more active and inhibitory neurons quieted down. In pain-exposed mice, the excitatory response was weaker and the inhibitory dip was smaller. Sensors for the neurotransmitters glutamate and GABA showed a matching pattern, with one caveat: the glutamate signal was also weaker when pain-exposed mice approached the empty cage. The authors suggest the change may reflect a blunted response to salient stimuli in general, not to other mice specifically. Cell counts also differed: fewer excitatory neurons and more inhibitory ones.

Then came a wrinkle worth keeping. When the team measured total glutamate and GABA in dissected tissue, both were higher in the pain-exposed mice. The authors propose that stored neurotransmitter and released neurotransmitter may have come apart. A full buffer says nothing about throughput.

Correlation is not causation, so the team intervened. Boosting excitatory neurons, with light or with engineered receptors activated by a drug, restored social preference in pain-exposed mice. So did silencing inhibitory neurons. In healthy mice, activating inhibitory neurons produced the deficit. Not every manipulation was clean. Inhibiting excitatory neurons in healthy mice removed their preference for the stranger, yet the summary social score was not significantly different from controls, and drugs infused directly into the region helped only partially.

Changing one population changed the behavior of the whole circuit. That much transfers to systems thinking. The specific mechanism does not.

Can you fix a system without understanding everything that changed?

Finally, the researchers implanted electrodes for deep brain stimulation, a technique already used clinically for some human conditions. They tested three current levels. The lowest did little. The highest strongly drove neural activity and caused seizure-like reactions, head-bobbing and freezing, in some mice. The middle setting reliably raised excitatory activity while only mildly activating inhibitory neurons.

Switched on during the social test, that middle setting restored the pain-exposed mice's preference for the stranger. The authors describe the deficit as reversed.

The precise claim is narrower. Stimulation changed behavior while it was running. Its effect on neural activity appeared immediately and faded when it stopped. The study did not test whether any benefit outlasted the stimulation, and the authors themselves say they do not know whether DBS compensates for the deficit or remodels the circuit. They also report that experimenters were not blinded to group, that animals were not randomized, and that no formal power analysis was performed.

So: did the intervention correct the system, or change what was observable? This experiment cannot say. Every engineer has shipped the equivalent: a flag that masks a symptom, a retry that hides an error rate, a restart that clears a leak until Tuesday. And the seizure-like reactions at the higher setting are a reminder that tuning a parameter you do not fully understand can trade one failure mode for another.

The danger of debugging by analogy

Software state is written, addressable, and in principle inspectable. Biological state is distributed across cell numbers, connection strengths, receptor levels, and immune and hormonal signals. It is built during development rather than deployed after it. A brain has no clean separation between code and data, no restore point, and no version history. The study itself shows the mess: researchers could not yet tell whether weaker excitatory signals came from fewer neurons or from quieter ones.

The inference also stops at the species boundary. A single inflammatory injection in a mouse pup is not chronic childhood pain, and a three-chamber preference score is not a human social life. The study treated no human condition. It also cannot fully separate pain from the inflammation that produced it.

And a successful targeted intervention does not mean complex experience reduces to one circuit with a tunable gain. It means that, in this model, changing this circuit changed this measurement. That is valuable. It is also exactly as far as the evidence goes.

Analogies are good at generating questions and bad at answering them. The useful move is to borrow the question, not the mechanism.

What developers can learn from a system that remembers

The study offers no design rules, only sharper questions.

What does recovery mean? Back to the old baseline, or stable at a new one? Those are different states that can produce identical dashboards.

Which metrics did we choose because they were easy? Google's SRE guidance distinguishes symptoms from causes and black-box from white-box monitoring. Paw withdrawal was the black-box check. It passed. The internal circuit told a different story.

Did the fix restore function, or suppress a signal? If behavior improves only while an intervention runs, you have learned something important about the system. Whether you have restored it is a separate question.

What did the incident teach the adaptive parts? Rate limiters, autoscalers, and caches learn from the past. So, in their own utterly different way, do developing brains.

The bug is gone. The history isn't

Back at the payment service, someone finally resets the rate limiter and flips the forgotten flag. The postmortem gains a new section, titled something like "things that stayed broken after we said it was fixed."

The mice in this study had no postmortem. Their paws healed, their pain tests came back normal, and they still did not behave like animals that had never been hurt. Researchers could shift that behavior, for as long as the current flowed.

The question worth carrying back to your own systems is not whether the incident is over. It is: what did the system learn while it was happening, and are you measuring anything that would tell you?

Sources:

Bronson et al., Metastable Failures in Distributed Systems (HotOS ‘21, official PDF) https://sigops.org/s/conferences/hotos/2021/papers/hotos21-s11-bronson.pdf

Penn State record with DOI https://pure.psu.edu/en/publications/metastable-failures-in-distributed-systems/

Huang et al., Metastable Failures in the Wild (OSDI ‘22) https://www.usenix.org/conference/osdi22/presentation/huang-lexiang

We Built a Power Plant Beneath the Ocean. A Safety System Brought It to a Stop.

Khali Sollis — Fri, 23 Oct 2026 03:04:47 +0000

What an eight-second geothermal breakthrough reveals about software reliability, engineering, and the future of energy

An extraordinary achievement. An extraordinarily short run

On September 27, roughly 5,000 feet below the surface of the northeast Pacific, a three-ton machine sat on an active underwater volcano and did something its builders say no machine had done before: it produced more electricity than it consumed.

It did this for 8.7 seconds.

Then the battery’s overcurrent protection stopped it. About ten minutes later, during a restart attempt, a power cable feeding one of its pumps failed, and the dive was over.

The machine is Adélie, a test plant built by Seattle startup Endurance Energy. The volcano is Axial Seamount, about 300 miles off the Oregon coast. The team drilled a well into the seafloor, landed a power plant nearby, and ran it in real time from Seattle. And the run ended the way so many of our own deployments end: not with a volcanic catastrophe, but with a protection threshold and a bad cable. Humanity can now park a power plant on an underwater volcano, and it can still be stopped by the same category of problem that takes down a homelab.

It would be easy to read “8.7 seconds” as a punchline. But proof-of-concept tests exist to answer one question cheaply before you spend a fortune answering a hundred others. Endurance’s question was blunt: can a seafloor geothermal plant generate more power than it uses? Briefly, yes. Everything that went wrong afterward is data, arguably the more valuable kind.

How do you generate electricity beneath the ocean?

Axial Seamount sits on a mid-ocean ridge, where tectonic plates pull apart and magma rises close to the seafloor. Seawater seeps into the crust, gets superheated, and returns through hydrothermal vents. The heat is free. Collecting it is not.

According to Canary Media, Adélie uses an organic Rankine cycle, the approach behind many land-based “binary cycle” geothermal plants. The hot geothermal fluid never touches the turbine. Instead, it boils a separate working fluid with a lower boiling point than water inside a heat exchanger; that vapor spins a turbine, which drives a generator, and is then condensed and pumped around the loop again. Endurance mentions a refrigerant pump, which fits: something has to keep that fluid moving, and it runs on electricity.

Which brings us to “net-positive.” Every power plant spends part of its output running itself. Adélie’s 8.7 seconds were the window in which generation exceeded that internal consumption, at a reported median of about 2 kilowatts net and a peak of 16.5 kilowatts net. Canary reports that the turbine ran for about 29 seconds in total and produced roughly 60 watt-hours, with the surplus charging the battery system. For scale, that would run a 2-kilowatt electric kettle for a little under two minutes.

That is calibration, not criticism. “Net-positive” here means the plant briefly out-produced its own electrical appetite, not that the project recovered the energy spent building hardware, sailing a ship, or drilling. Nor does it say anything about cost. Even the well was a mixed result: Endurance’s 52-foot, roughly two-inch-wide well delivered fluid at 260°C but was too restrictive to run the plant, so the team connected Adélie to a natural vent at about 281°C instead. The company itself calls Adélie a test article, not a commercial plant.

The software lesson beneath the sea

Here is where developers should lean in, because the most interesting word in this story is “protection.”

Endurance says the run ended when battery overcurrent protection stopped the system. Its account doesn’t say whether that protection lives in a hardware breaker, battery-management firmware, or control software, and for this argument it doesn’t need to. The shape of the event is the same: a monitored value crossed a limit, and the system stopped rather than continued.

That is not, by itself, a bug. It may be the system doing exactly what it was designed to do.

Developers know the circuit breaker pattern: when a dependency starts failing, stop calling it so the failure doesn’t cascade. It borrows its name from the electrical device, which exists because excessive current can overheat wiring, damage cells, or start a fire. Tripping is the feature.

The questions that matter are subtler. Was the overcurrent a genuine electrical event or a measurement artifact? If genuine, was it a harmless transient a better-tuned limit could have tolerated, or a real hazard? Could the system have degraded gracefully, shedding a load rather than stopping outright? Those are questions about sensors, thresholds, power electronics, and integration, not about whether someone typed > when they meant >=. The public accounts don’t answer them, and pretending otherwise would be fiction.

It also helps to separate two verdicts that are easy to blur. The protection may have worked exactly as intended while the test as a whole fell short of its operating goal. Those statements don’t contradict each other. Anyone who has watched a health check correctly evict a failing node and still ended up with an outage knows the feeling: each component did its job, and the system still didn’t do what you needed.

This is the difference between fail-safe and fault-tolerant design. A fail-safe system, when in doubt, moves to a safe state, usually “off.” A fault-tolerant system keeps operating through a fault, often through redundancy or degraded modes. On a first end-to-end dive, leaning fail-safe is a defensible choice. You don’t get clever with a battery pack you can only reach with a ship and a robot.

This world differs from the web. When a web service misbehaves, you roll back, retry with exponential backoff, page someone. Nobody’s 500 error has ever corroded. In machinery, energy is stored in batteries, spinning turbines, and pressurized loops, and “just restart it” is itself a physical operation with its own risks.

Observability becomes literal here. Endurance’s engineers received temperature and flow data in Seattle, and sent commands back, over the Regional Cabled Array, a seafloor fiber-optic network the University of Washington operates for the National Science Foundation. That link made the prototype debuggable at all. In software we say you can’t fix what you can’t see. A mile underwater, you can’t even touch what you can see.

When the physical world refuses to cooperate

The second failure was not a decision. During the restart attempt, the cable powering Adélie’s refrigerant pump failed. Endurance hasn’t detailed the cause, but whatever the exact mechanism, the episode is a compact summary of deep-sea engineering. Seawater is conductive and corrosive, and at roughly 1,500 meters it presses on everything at about 150 times atmospheric pressure. Every connector and seal is a place where water is looking for a way in.

And nobody can swim down with electrical tape. Retrieval means a ship, a crane, and cooperative weather. Endurance says it once recovered, modified, recharged, and redeployed Adélie in under 16 hours, a genuinely fast turnaround, but a commercial plant can’t depend on that kind of intervention. A system that works is the beginning. A system that keeps working for years, at a maintenance cost its electricity can pay for, is the actual product.

The connection to AI, data centers, and our energy-hungry future

Why should anyone shipping code care about a vent field off Oregon? Because our industry has become one of the fastest-growing electricity customers around.

The International Energy Agency estimated that data centers used about 415 terawatt-hours in 2024, roughly 1.5% of global electricity, and projected this could more than double to around 945 TWh by 2030, with AI as the main driver. In the United States, a Lawrence Berkeley National Laboratory report found data centers used about 4.4% of national electricity in 2023 and could reach 6.7% to 12% by 2028.

Data centers need power continuously and predictably, which makes “firm” clean power, available regardless of weather, especially valuable. Geothermal is one of the few renewables that is firm by nature. The U.S. Department of Energy has estimated that next-generation geothermal could supply around 90 gigawatts in the United States by 2050.

That projection concerns land-based technologies. Subsea geothermal is a separate and much earlier bet. Endurance lists data centers among potential customers, but a 16.5-kilowatt peak powers no GPU cluster, and nothing in this test shows it eventually will.

The difference between possible and practical

The open questions are substantial: running for months rather than seconds, deployment and maintenance costs, getting power to shore, and the effect of years of operation on vent ecosystems. Endurance ran voluntary environmental monitoring and reported no significant changes, with samples still at independent laboratories. A responsible start, but three weeks is not a lifetime.

Skeptics exist. Speaking to Canary Media, geologist Emily Pope of the Center for Climate and Energy Solutions questioned whether offshore geothermal can pay off given the harsh environment and transmission distances, suggesting island nations could more easily adopt solar and batteries.

Endurance’s roadmap includes an upgraded unit called Gentoo this winter, deeper wells, a 2-megawatt generator, and a first commercial project in Tonga targeting power in 2027. Those are plans, not results, and they will be judged the way hardware always is: one deployment at a time, each failure shaping the next design.

Conclusion: Eight seconds can tell us something important

Endurance’s CEO compared the run to Robert Goddard’s first liquid-fueled rocket, which flew for about two and a half seconds. The comparison is flattering, but it carries a useful lesson: what mattered most was everything learned afterward to make flights routine.

Demonstrating that something works once is a milestone. The engineering lives in what follows: systems that keep working, notice when something is wrong, stop safely when they must, and come back up without breaking something else on the way.

Adélie’s first stop may well have been a protection system doing its job. Its second was a cable failing in one of the least forgiving places to have one. The interesting work sits in the narrow space between them, where code meets current and the decision to restart is also a physical event.

So the next time your system decides to stop something, it’s worth asking: are you sure “off” is the safe state, and do you know what it will cost to turn it back on?

Sources and Further Reading

Ars Technica. “Startup taps underwater volcano as energy source in proof-point test.” October 2026. https://arstechnica.com/science/2026/10/startup-taps-underwater-volcano-as-energy-source-in-proof-point-test/

Endurance Energy. “Endurance Energy Drills World’s First Subsea Geothermal Well and Runs First Net-Positive Power Plant.” PR Newswire, October 19, 2026. https://www.prnewswire.com/news-releases/endurance-energy-drills-worlds-first-subsea-geothermal-well-and-runs-first-net-positive-power-plant-302912079.html

Gallucci, Maria. “This geothermal startup produced a tiny bit of power from the seafloor.” Canary Media, October 19, 2026. https://www.canarymedia.com/articles/geothermal/geothermal-startup-power-seafloor-drilling

World Oil. “Endurance Energy drills subsea geothermal well, demonstrates net-positive power.” October 20, 2026. https://www.worldoil.com/news/2026/10/20/endurance-energy-drills-subsea-geothermal-well-demonstrates-net-positive-power/

ThinkGeoEnergy. “Endurance Energy demonstrates subsea geothermal power in Pacific Ocean.” October 2026. https://www.thinkgeoenergy.com/endurance-energy-demonstrates-subsea-geothermal-power-in-pacific-ocean/

Ocean Observatories Initiative. Regional Cabled Array. https://oceanobservatories.org/regional-cabled-array/

National Renewable Energy Laboratory. “Geothermal Electricity Production Basics.” https://www.nrel.gov/research/re-geo-elec-production.html

International Energy Agency. Energy and AI, Executive Summary. 2025. https://www.iea.org/reports/energy-and-ai/executive-summary

Lawrence Berkeley National Laboratory. “Berkeley Lab Report Evaluates Increase in Electricity Demand from Data Centers” (2024 Report on U.S. Data Center Energy Use). https://energyanalysis.lbl.gov/news/berkeley-lab-report-evaluates-increase-electricity-demand-data-centers

U.S. Department of Energy. Pathways to Commercial Liftoff: Next-Generation Geothermal Power. March 2024. https://liftoff.energy.gov/next-generation-geothermal-power/

The Attention Trap: When Good Software Design Works Against the People Using It

Khali Sollis — Wed, 14 Oct 2026 19:29:08 +0000

What behavioral science reveals about engagement loops, digital habits, and the responsibility of developers.

The software that doesn't want you to leave

You open an app to check one thing: whether a package shipped, what time the game starts, a reply from a friend. You find it in seconds. Then a post below it catches your eye, and another below that, and when you next look at the clock twenty minutes have gone somewhere you never decided to send them.

Nothing malfunctioned. The feed did precisely what it was built, tuned and measured to do, and those twenty minutes were logged somewhere as a small engineering success.

Software can be excellent at meeting its engagement objectives while producing experiences that conflict with what its users wanted from their time. What happens when software succeeds at keeping us engaged but fails to respect our original intentions?

Engagement in itself is not the problem. Reading a long essay, learning a language or a video call with family are engagement too. The useful distinction is between engagement a person would endorse on reflection and engagement they drift into and later regret.

Public conversation tends to land on the individual. In a July 2026 segment of NPR's TED Radio Hour, journalist Nayeema Raza proposed that three pre-internet habits could help people slow down and break the screen cycle. Personal habits matter, but every habit plays out in an environment, and on a screen that environment is written by developers. This essay looks at that side of the glass.

The architecture of engagement

Almost every feature associated with compulsive use began as a fix for a real problem. Infinite scrolling removed the friction of pagination on small touchscreens. Autoplay spares someone watching a series from reaching for the remote. Notifications tell you a person needs you. Recommenders find the few relevant items among millions. Streaks help people keep up practice they chose.

What these features share is that they remove a decision point. A paginated list ends, and the end is a natural moment to ask whether you want more. Each convenience shortens the distance between continuing and never having decided at all.

Reviewing 43 papers, Monge Roffarello, Lukoff and De Russis catalogued eleven types of what they call attention-capture damaging patterns, infinite scroll and autoplay among them. They distinguish deceptive designs, which mislead, from seductive ones, which deceive no one and simply make continuing the path of least resistance. Most engagement features are of the second kind, which is why intent is a poor guide to their effects.

In a survey of 120 YouTube users, Lukoff and colleagues found that autoplay and recommendations primarily undermined people's sense of agency, while search and playlists supported it. That is self-reported perception, not measured harm, but it shows that one product can contain features that hand control to the user and features that quietly take it back.

Underneath the interface sits the objective function. A ranking model trained on watch time or click-through rate learns to serve whatever produces those signals. Kleinberg, Mullainathan and Raghavan formalized the difficulty in a theoretical model in which users have an impulsive in-the-moment self and a long-term self with different preferences, while the platform observes only behavior. In their model, raising engagement improves welfare along some directions of design change and lowers it along others, and engagement data alone cannot reveal which. It is a model rather than an empirical finding, but it names the core problem: behavioral data measures what people do, not what they wanted.

What behavioral science actually tells us

The science here is more interesting and less alarming than the popular version.

Habits. Wood and Rünger's review describes habits as responses that, through repetition in stable contexts, come to be triggered by cues rather than deliberate goals. They are an efficient default mode of action, with a curious side effect: people tend to infer from how often they do something that they must have intended it. Software that sits one tap away in every idle moment is well positioned to become such a cue.

Uncertain rewards. Since Skinner's operant conditioning research, psychologists have known that behavior rewarded intermittently tends to persist longer than behavior rewarded every time. A feed mixes things you care about with things you do not, in an unpredictable order, which is why HCI researchers, Rixen and colleagues among them, have discussed partial reinforcement as a plausible explanation for scrolling's pull. It is a plausible hypothesis built on an analogy to laboratory findings, not an established mechanism of feed use.

Dopamine and the word "reward." In 1997, Schultz, Dayan and Montague showed that the activity of dopamine neurons in monkeys matched a reward prediction error, the quantity at the heart of temporal-difference learning algorithms. That paper bridged machine learning and neuroscience, and it has been stretched far beyond what it shows. The signal it describes concerns learning, the updating of expectations, rather than pleasure, and nothing in those recordings establishes that every notification releases dopamine in your brain.

The shared vocabulary causes its own confusion. In a recommender trained with reinforcement learning, "reward" is a number an engineer chose, such as a click or seconds watched, and "learning" means updating parameters. In psychology and neuroscience, the same words describe how organisms' behavior and neural activity change with experience. The mathematics overlaps; the mechanisms differ. What genuinely connects them is the behavioral loop: the system learns what you respond to while you learn what the system delivers. Two learners, coupled through an interface.

Attention. The experimental evidence on interruptions is clearer. In a laboratory study, Stothart, Mitchum and Yehnert found that merely receiving a call or text notification, without answering it, impaired performance on an attention-demanding task. In a randomized field experiment with 237 smartphone users, Fitz and colleagues found that people whose notifications were batched three times a day felt more attentive, productive and in control, and reported better mood, than controls. Hourly batching did little, and no notifications at all brought more anxiety and fear of missing out. The lesson concerns timing and dose, not abolition.

Self-regulation. In an experience-sampling study of 205 adults, Hofmann, Vohs and Baumeister found that desires for media use, along with work, brought about the most self-control failure. A randomized experiment by Allcott, Gentzkow and Song offers stronger evidence. Temporarily paying people to use social media less reduced their use even after the payments stopped, consistent with habit formation, and letting people set limits on their own future use reduced it substantially, suggesting they wanted to use less than they did. Through their economic model, the authors estimate that self-control problems account for 31 percent of social media use: a model-based estimate, but resting on an experimental pattern that is hard to dismiss.

The trouble with "addiction." None of this licenses calling ordinary use an addiction. Reviewing the literature, Panova and Carbonell found insufficient support for smartphone addiction as a disorder and proposed "problematic use" instead. Kardefelt-Winther and colleagues warned that the behavioral-addiction label is being stretched until it pathologizes ordinary leisure.

"Screen time" deserves similar caution. Across three large datasets, Orben and Przybylski found that digital technology use explained at most 0.4 percent of the variation in adolescent well-being; Twenge, Haidt and colleagues contested that reading, and the debate remains open. Meanwhile, a meta-analysis by Parry and colleagues found that self-reported media use correlates only moderately with logged use, and problematic-use scales even more weakly. Total minutes is a blunt instrument. What people do, why, and whether it crowds out the rest of their lives tell us far more.

Why willpower is only part of the equation

If behavior were purely a matter of resolve, interface design would be irrelevant. The Fitz study is a quiet rebuttal: participants did not become more disciplined; their environment changed, and their experience changed with it.

A useful way to see this is to count actions. In a continuous feed, continuing costs nothing, while stopping requires a decision nothing in the interface prompts. In a batched design, continuing costs one deliberate action and stopping costs none. Neither forces anything; they differ in which choice is the default.

A field study by Rixen and colleagues shows how rarely the app itself provides the exit. Tracking 46 participants' social media sessions for a week, they found that most reasons for ending a session had nothing to do with the app: a waiting task, sleep, food, another person. Sessions spent mainly scrolling were rated less positively than those spent mainly posting or watching stories, though individual differences mattered far more than session content.

It is tempting to conclude that friction is the answer. The evidence is more modest. A large meta-analysis of choice-architecture interventions by Mertens and colleagues, subsequently corrected by its authors, reported an average effect, but a reanalysis by Maier and colleagues found no evidence of an overall effect after correcting for publication bias, a disagreement still unresolved. In HCI, Monge Roffarello and De Russis's meta-analysis of digital self-control tools reported a small-to-medium effect on reducing time spent, but noted that the evidence comes mostly from short experiments, often without control groups, that cannot show whether the change lasts. Friction is a design variable worth testing, not a cure.

Two ways to build a content feed

Imagine two versions of the same feed, showing the same posts in the same order. The content is identical. Only the shape of the experience differs.

In Version A, continuous scrolling, new posts appear automatically as the reader nears the bottom of the page. There is no page two, no final post, no visible edge. Reaching the end of what has loaded is never an event, because more has already arrived. The feed never asks whether you want to continue; continuing is simply what happens next. If the reader stops, the decision has to come entirely from them, made against the momentum of the interface.

In Version B, intentional continuation, the feed shows a limited batch of posts, perhaps ten, followed by a visible stopping point: a short note that the batch is finished, a count of how many posts have been seen so far, and an option to load more. Nothing prevents the reader from continuing. The next batch is a single tap away. But the end of the batch becomes a moment, and continuing becomes a choice rather than a default.

That is the essential difference. Version B does not force anyone to stop, impose a time limit or hide content. It restores the kind of decision point that a paginated list or the last page of a magazine once supplied without anyone noticing. In the terms of the previous section, it changes which action costs effort: in Version A, stopping requires initiative; in Version B, continuing does, if only slightly.

Both designs offer access to exactly the same information. What changes is the behavioral environment surrounding it, and the research reviewed above suggests that cues, defaults and stopping points shape behavior in ways that willpower alone does not explain.

This comparison is an illustration, not evidence. Nothing here shows that Version B improves well-being, reduces regret or changes long-term habits. Those are empirical questions, best answered by testing designs like these with real users and measuring the outcomes that matter to them.

Can we measure success differently?

Teams optimize what they measure. Time spent, session length and daily active users are cheap to collect, hard to dispute and tied to revenue. As the Kleinberg model makes explicit, they cannot distinguish a person who stayed because the product was valuable from one who stayed because leaving took effort.

Complementary signals get closer. Task completion asks whether someone who arrived to do something did it. Satisfaction and perceived control, gathered in brief in-product surveys, capture whether the time felt worthwhile. Notification usefulness shows in how often notifications are opened deliberately rather than dismissed or disabled. Voluntary return, sessions started without a prompt, separates a pull the user generates from one the system generates. Reported regret asks directly whether a session was time well spent.

None of these is easy. Surveys suffer from low response rates and selection bias, and regret is colored by mood. Users genuinely disagree: one person's relaxing evening of scrolling is another's lost one. And any metric that becomes a target can be gamed, a well-being metric included.

The practical approach is to treat these as guardrails, not replacements. When an experiment raises session length, check task completion and reported control in the same arms. A change that adds time while lowering perceived control is a different result from one that raises both, even if the headline dashboard cannot tell them apart.

The responsibility of developers

It would be convenient to blame engineers, and wrong. Developers usually implement requirements set elsewhere, under deadlines, against targets and business models they did not choose. Most infinite feeds were built by people trying to make a product feel smooth.

But engineering responsibility has never been limited to intent. We expect developers to consider security risks nobody asked about and accessibility needs missing from the ticket. An interface's behavioral consequences belong in the same category.

In practice that means asking a slightly different question in design review: not only what a feature makes possible, but what it makes easier, more automatic, or harder to interrupt. Engineers can flag metrics that reward behavior users would not endorse. Designers can make stopping points visible. Product managers can propose guardrails. Organizations, which set the incentives, carry the largest share, because individual conscience is a poor substitute for goals that don't penalize doing the right thing.

Designing for the freedom to leave

Engagement and autonomy are not opposites; the best software earns long sessions because people choose them. The problem begins when a system becomes better at keeping people than at serving them, and our metrics cannot tell the difference.

Behavioral science offers no villain here and no simple fix, only a quieter observation: people act within environments, and much of their environment is now made of code. Those who write it are deciding, whether they intend to or not, where the exits are and how easy they are to find.

If we can design software that makes people want to stay, can we also design software that respects their decision to leave?

References and Further Reading

Allcott, H., Gentzkow, M., & Song, L. (2022). Digital addiction. American Economic Review, 112(7), 2424–2463. https://doi.org/10.1257/aer.20210867

Hofmann, W., Vohs, K. D., & Baumeister, R. F. (2012). What people desire, feel conflicted about, and try to resist in everyday life. Psychological Science, 23, 582–588. https://doi.org/10.1177/0956797612437426

Kardefelt-Winther, D., Heeren, A., Schimmenti, A., van Rooij, A., Maurage, P., Carras, M., Edman, J., Blaszczynski, A., Khazaal, Y., & Billieux, J. (2017). How can we conceptualize behavioural addiction without pathologizing common behaviours? Addiction, 112(10), 1709–1715. https://doi.org/10.1111/add.13763

Kleinberg, J., Mullainathan, S., & Raghavan, M. (2024). The challenge of understanding what users want: Inconsistent preferences and engagement optimization. Management Science, 70(9), 6336–6355. https://doi.org/10.1287/mnsc.2022.03683

Lukoff, K., Lyngs, U., Zade, H., Liao, J. V., Choi, J., Fan, K., Munson, S. A., & Hiniker, A. (2021). How the design of YouTube influences user sense of agency. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3411764.3445467

Maier, M., Bartoš, F., Stanley, T. D., Shanks, D. R., Harris, A. J. L., & Wagenmakers, E.-J. (2022). No evidence for nudging after adjusting for publication bias. Proceedings of the National Academy of Sciences, 119(31), e2200300119. https://doi.org/10.1073/pnas.2200300119

Mertens, S., Herberz, M., Hahnel, U. J. J., & Brosch, T. (2022). The effectiveness of nudging: A meta-analysis of choice architecture interventions across behavioral domains. Proceedings of the National Academy of Sciences, 119, e2107346118. https://doi.org/10.1073/pnas.2107346118

Mertens, S., Herberz, M., Hahnel, U. J. J., & Brosch, T. (2022). Correction for Mertens et al., The effectiveness of nudging: A meta-analysis of choice architecture interventions across behavioral domains. Proceedings of the National Academy of Sciences, 119(19), e2204059119. https://doi.org/10.1073/pnas.2204059119

Monge Roffarello, A., & De Russis, L. (2023). Achieving digital wellbeing through digital self-control tools: A systematic review and meta-analysis. ACM Transactions on Computer-Human Interaction, 30(4), Article 53, 1–66. https://doi.org/10.1145/3571810

Monge Roffarello, A., Lukoff, K., & De Russis, L. (2023). Defining and identifying attention capture deceptive designs in digital interfaces. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–19. https://doi.org/10.1145/3544548.3580729

NPR. (2026, July 24). Battling screen addiction with three simple habits [Audio segment, TED Radio Hour]. https://www.npr.org/2026/07/24/nx-s1-5887506/battling-screen-addiction-with-three-simple-habits

Orben, A., & Przybylski, A. K. (2019). The association between adolescent well-being and digital technology use. Nature Human Behaviour, 3(2), 173–182. https://doi.org/10.1038/s41562-018-0506-1

Orben, A., & Przybylski, A. K. (2020). Reply to: Underestimating digital media harm. Nature Human Behaviour, 4, 349–351.
https://doi.org/10.1038/s41562-020-0840-y

Panova, T., & Carbonell, X. (2018). Is smartphone addiction really an addiction? Journal of Behavioral Addictions, 7(2), 252–259. https://doi.org/10.1556/2006.7.2018.49

Parry, D. A., Davidson, B. I., Sewall, C. J. R., Fisher, J. T., Mieczkowski, H., & Quintana, D. S. (2021). A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use. Nature Human Behaviour, 5(11), 1535–1547. https://doi.org/10.1038/s41562-021-01117-5

Rixen, J. O., Meinhardt, L.-M., Glöckler, M., Ziegenbein, M.-L., Schlothauer, A., Colley, M., Rukzio, E., & Gugenheimer, J. (2023). The loop and reasons to break it: Investigating infinite scrolling behaviour in social media applications and reasons to stop. Proceedings of the ACM on Human-Computer Interaction, 7(MHCI), Article 228. https://doi.org/10.1145/3604275

Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275, 1593–1599. https://doi.org/10.1126/science.275.5306.1593

Stothart, C., Mitchum, A., & Yehnert, C. (2015). The attentional cost of receiving a cell phone notification. Journal of Experimental Psychology: Human Perception and Performance, 41(4), 893–897. https://doi.org/10.1037/xhp0000100

Twenge, J. M., Haidt, J., Joiner, T. E., & Campbell, W. K. (2020). Underestimating digital media harm. Nature Human Behaviour, 4, 346–348. https://doi.org/10.1038/s41562-020-0839-4
Wood, W., & Rünger, D. (2016). Psychology of habit. Annual Review of Psychology, 67, 289–314. https://doi.org/10.1146/annurev-psych-122414-033417

When the Reward Disappears: What Neuroscience Can Teach Us About Adaptive AI

Khali Sollis — Tue, 13 Oct 2026 19:02:00 +0000

How a mouse-brain experiment reveals a challenge shared by biological learning and reinforcement learning systems.

The Problem With Yesterday's Winning Move

Picture a simple learning agent with two buttons. Button A pays out 80% of the time; button B pays out 20%. After a few hundred trials, the agent's value estimates settle where you would expect, and it presses A almost every time. By any reasonable metric, it has learned.

Then, without announcement, the payouts swap.

Nothing in the agent's input tells it the world has changed. It still sees two buttons. Its value table still says A is worth about 0.8. So it keeps pressing A, collecting mostly nothing, and its estimate of A drifts downward only as fast as its update rule allows. Depending on how that rule is written, "as fast as it allows" can mean twenty trials or five hundred.

This is the problem hiding inside many deployed decision systems: learning what works is a different task from noticing that what worked has stopped working. A recommender tuned to last year's catalogue or a scheduler calibrated to traffic that a product launch just rewrote may not use reinforcement learning at all, but each carries its training conditions forward as assumptions.

Animals face this problem constantly, and a recent study offers an unusually detailed look at one piece of how a mammalian brain responds when a reliable reward fails to appear.

What the Mouse Experiment Actually Showed

The study, "Spatially heterogeneous acetylcholine dynamics in the striatum promote behavioral flexibility", by Gideon Sarpong, Jeffery Wickens and colleagues, led from the Okinawa Institute of Science and Technology (OIST), appeared in Nature Communications on 17 December 2025. It reached a wider audience about six months later, through an institutional press release carried by ScienceDaily in June 2026.

The task. Head-fixed mice ran on a floating ball through a virtual-reality Y-maze. One arm delivered a drop of sugar water; the other delivered a black screen. Nothing visually marked the rewarded arm, so the mice had to discover it by trial and error. Once a mouse chose correctly in at least 80% of a session's trials, the researchers reversed the contingency—again without any cue. The previously rewarded arm now paid nothing.

Behaviorally, the mice did what you would predict: they kept choosing the old arm for a while (average accuracy after the reversal fell to about 47%), then gradually shifted to the new one.

What was measured. The team expressed a fluorescent acetylcholine sensor (iAChSnFR) in the dorsal striatum of 11 mice and imaged it through an implanted lens with two-photon microscopy. Acetylcholine is a neurotransmitter with many jobs, from muscle control to attention and learning. In the striatum—a basal ganglia hub for action selection and habit formation—it comes mostly from a small population of cholinergic interneurons.

Three findings stand out:

Rewards produced dips. During initial learning, a delivered reward was followed by a brief drop in acetylcholine. Ordinary errors produced only smaller dips.
Unexpected non-reward after the reversal produced rises. When a mouse chose the formerly rewarded arm and got nothing, acetylcholine increased across most of the imaged tissue—about 85% of the small (~50 × 50 µm) patches the team analyzed.
The rise tracked what the mouse did next. Signals were larger on failed trials that preceded a switch to the other arm ("lose-shift") than on trials followed by a repeat. Across animals, larger responses correlated with a higher probability of lose-shift (r = 0.67, n = 11).

The responses were also spatially uneven. Patches showing increases sat more toward the medial striatum, with a trend toward anterior positions; patches showing decreases sat more laterally.

What the intervention showed. Correlation alone cannot show that acetylcholine mattered, so in a separate group of ten mice the researchers used a chemogenetic tool to suppress striatal cholinergic interneurons in half of them during reversal. Those mice made fewer lose-shift choices, performed worse in the first reversal session, and needed more trials to reach criterion (about 115 versus 63). Win-stay behavior and running speed were not significantly affected.

What remains interpretation. The inhibition was not time-locked to specific trials, so it shows these neurons are needed for timely adaptation in this task, not exactly when their signal acts. The authors themselves note they cannot yet separate a learning signal from an affective or motivational reaction to the missing reward. Their proposal—that spatially patchy acetylcholine might let new associations form in some striatal territory while sparing old ones elsewhere—is a hypothesis, not a result. And the whole study used a single reversal in a single task, with small groups.

What the study does give us is a striking contrast: the same outcome—no reward—was followed by opposite acetylcholine responses before and after a well-learned rule broke. The authors read this as a signal of violated expectation. Whatever its precise meaning, that distinction is the crux of the computational problem.

When Expectations Fail: The Computational View

Reinforcement learning formalizes expectation as a value estimate and surprise as a prediction error. A widely used version is the temporal-difference (TD) error from TD(0) learning, as presented in Sutton and Barto's textbook:

text
δt = r_t + γ · V(s{t+1}) − V(s_t)
δt — the temporal-difference error at time t
r_t — the immediate reward received after leaving state s_t
γ — the discount factor (0 ≤ γ ≤ 1), weighting future value
V(s_t) — the agent's estimated value of the current state
V(s{t+1}) — the estimated value of the next state

(Sutton and Barto write the reward as R_{t+1}; it is the same quantity under a different indexing convention.) This is one formulation among many. Q-learning, actor-critic methods, and policy-gradient methods use related but distinct error signals.

A worked example. Suppose an agent has learned that a choice leads to reward 90% of the time, so V(s_t) = 0.9. The trial then ends, so V(s_{t+1}) = 0. Let γ = 0.9.

If the reward arrives (r = 1): δ = 1 + 0.9 × 0 − 0.9 = +0.1
If the reward is missing (r = 0): δ = 0 + 0.9 × 0 − 0.9 = −0.9

With a learning rate of 0.1, the omission nudges the estimate from 0.9 to 0.81.

Now notice something uncomfortable. If the reward really does arrive 90% of the time, a −0.9 error appears on one trial in ten, and the errors average to zero. After a reversal, the identical −0.9 appears on the identical kind of trial. A single prediction error carries no label saying "bad luck" or "the rules changed." It drives a value update; it does not diagnose its own cause. Distinguishing noise from a genuine shift requires integrating evidence across trials and holding some model of how volatile the environment is likely to be. That is change detection, a problem related to—but separate from—prediction error.

This is where a careful reader should resist a tempting shortcut. Dopamine, not acetylcholine, is the neuromodulator classically linked to TD-style errors: Schultz, Dayan, and Montague (1997) reviewed evidence that the phasic firing of primate dopamine neurons resembles a reward prediction error. Striatal acetylcholine is not a copy of that signal; Duhne and colleagues (2024) found that, in rats, cholinergic pauses did not line up in time or location with dopamine prediction-error signals. Nothing in the mouse study shows acetylcholine computing the equation above. The equation is an educational bridge—a way of stating precisely what problem the mice were solving, not a description of how they solved it.

Exploration Versus Exploitation

Even an agent that suspects something has changed still has to decide what to do about it. Reinforcement learning frames this as a tradeoff:

Exploitation — keep taking the action that currently looks best.
Exploration — try alternatives that might reveal something better.

In a changing world, too little exploration anchors an agent to an outdated policy; too much throws away behavior that was still working.

The simplest strategy, epsilon-greedy selection, exploits most of the time and picks a random action with small probability ε. It is simple and gives every action a nonzero probability of being sampled, though no finite run guarantees that every action will be selected. But exploration alone does not make an agent adaptive; what it does with the information matters as much as gathering it.

The simulation below is the opening scenario in code: arm 0 pays out with probability 0.8 and arm 1 with 0.2, and the probabilities swap at step 500. Both agents use ε = 0.1 and differ only in how they average rewards. The first uses a sample average, so every reward in its history counts equally. The second uses a constant step size (α = 0.1), so recent rewards count more—a standard approach Sutton and Barto describe for tracking nonstationary problems.

python
"""Two-armed bandit whose reward probabilities reverse partway through.

Simulated agents only. Standard library, Python 3.8+.
Arm 0 pays 1 with p=0.8 and arm 1 with p=0.2, then they swap at step 500.
Both agents use epsilon-greedy selection (epsilon = 0.1). They differ in how
they update value estimates:
sample average : step size 1/n (every past reward weighted equally)
constant step size : alpha = 0.1 (recent rewards weighted more)
"""
import random

STEPS, SWITCH, RUNS, EPSILON = 1500, 500, 1000, 0.1
P_BEFORE, P_AFTER = [0.8, 0.2], [0.2, 0.8]

def run(rng, alpha=None):
q, n = [0.0, 0.0], [0, 0]
chose_best = []
for t in range(STEPS):
probs = P_BEFORE if t < SWITCH else P_AFTER
if rng.random() < EPSILON:
a = rng.randrange(2)
else:
a = 0 if q[0] > q[1] else 1 if q[1] > q[0] else rng.randrange(2)
r = 1.0 if rng.random() < probs[a] else 0.0
n[a] += 1
step = alpha if alpha is not None else 1 / n[a]
q[a] += step * (r - q[a]) # the bandit form of a prediction error update
chose_best.append(a == probs.index(max(probs)))
return chose_best

def summarize(label, alpha):
rng = random.Random(42) # fixed seed: identical numbers on every run
totals = [0] * STEPS
for _ in range(RUNS):
for t, ok in enumerate(run(rng, alpha)):
totals[t] += ok
rate = [x / RUNS for x in totals]
before = sum(rate[SWITCH - 100:SWITCH]) / 100
after = sum(rate[SWITCH:SWITCH + 200]) / 200
late = sum(rate[-100:]) / 100
first_crossing = next(t - SWITCH for t in range(SWITCH, STEPS) if rate[t] > 0.5)
print(f"{label:<20} best-arm rate | last 100 before switch: {before:.2f}"
f" | first 200 after: {after:.2f} | final 100: {late:.2f}"
f" | first step >50%: {first_crossing}")

summarize("sample average", None)
summarize("constant alpha=0.1", 0.1)

Simulated output (seed 42, 1,000 runs per agent). Each rate is the share of runs choosing the currently better arm, averaged over the stated window. "First step >50%" is the first post-switch step at which more than half of the runs chose the new best arm. It is an illustrative threshold crossing, not a per-agent recovery time or a measure of sustained adaptation:

Before the switch, the two agents perform almost identically by this metric. Afterward, the sample-average runs mostly stay on the old arm: only 5% of their choices in the first 200 post-switch steps go to the new best arm, and the majority does not cross over until step 519—even though every agent keeps exploring with the same probability throughout. Each agent's estimate for arm 0 is anchored by hundreds of earlier successes, so each new failure moves it only by 1/n. The constant-step runs cross the 50% line by step 21 and stay above it.

This is not a verdict that constant step sizes are better. They never fully converge, so in a truly stationary problem they keep reacting to noise. Neither agent detects the change; one simply forgets faster. More principled methods model the change explicitly—sliding-window and discounted UCB base their confidence estimates on discounted or recent observations, and Bayesian online change-point detection maintains a running probability over how long ago the last change occurred. Each embeds an assumption about how often the world shifts, and each pays for it when that assumption is wrong.

What Neuroscience Can—and Cannot—Teach AI Engineers

The legitimate connection between the mouse study and the bandit is a shared problem, not a shared mechanism. Both must answer the same questions. Was this failure noise or signal? How many failures justify reconsidering a learned strategy? When the strategy changes, should the old one be erased or kept in reserve?

The neuroscience offers useful prompts:

Context-dependent responses to failure. In the study, ordinary errors during learning and unexpected failures after a well-learned rule broke produced different acetylcholine signals. If the authors' reading is right, the response to a bad outcome depended on how established the expectation had been. A fixed ε has no such sensitivity; it explores at the same rate whether the agent is confident or confused.

Uncertainty as a control variable. Computational neuroscientists have long proposed that neuromodulators report different kinds of uncertainty. Yu and Dayan (2005) suggested that acetylcholine signals expected uncertainty and norepinephrine signals unexpected uncertainty, such as an unsignaled context switch. That theory concerned cortical circuits, and a reversal like the one in this study would arguably fall in its "unexpected" category—a reminder that mapping neuromodulators onto computational variables remains unsettled.

Adapting without erasing. The authors' hypothesis that patchy acetylcholine might protect old associations while new ones form echoes a problem continual-learning researchers know well: catastrophic forgetting. It is an intriguing parallel, but it is a hypothesis about mice, not evidence for any AI method.

The limits need stating just as plainly:

A single-reversal task in small groups of mice cannot establish how human decision-making works.
Artificial agents have no cholinergic interneurons; "neuromodulation" in a model is a design analogy.
Similar behavior does not imply similar internal processes.
The study proposes no AI algorithm.
Nothing in it shows that copying acetylcholine dynamics would improve agent performance.
Biology can inspire a computational hypothesis; only experiments on the computational system can validate it.

The Bigger Engineering Lesson

Return to the two-button agent. Before the switch, it looked excellent: 95% optimal choices. That number described its performance under the original reward structure, but it did not reveal how slowly the agent would adapt when that structure changed. Robustness to change shows up only when you test under change.

That is why optimizing against a fixed reward structure is not enough. Adaptive systems need a way to update expectations, an estimate of how much to trust those expectations, and a policy for responding when they fail. None of these settings is universally correct. Switching after every disappointment is just as maladaptive as never switching; the switching the mouse study examined followed failures that violated a well-established rule, not every missing reward.

The mice did not solve reinforcement learning. They did illustrate, with unusual precision, where the hard part lies.

Learning which decisions lead to reward is only half of intelligence. The other half is recognizing when yesterday's right decision has quietly become today's mistake.

References

Sarpong, G. A., Pass, R., Liyanagama, K., Chu, K.-Y., Kurima, K., Akamine, Y., Chouinard, J. A., Looger, L. L., & Wickens, J. R. (2025). Spatially heterogeneous acetylcholine dynamics in the striatum promote behavioral flexibility. Nature Communications, 16, 10877. https://doi.org/10.1038/s41467-025-66826-1

Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. http://incompleteideas.net/book/the-book-2nd.html

Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275(5306), 1593–1599. https://doi.org/10.1126/science.275.5306.1593

Duhne, M., Mohebi, A., Kim, K., Pelattini, L., & Berke, J. D. (2024). A mismatch between striatal cholinergic pauses and dopaminergic reward prediction errors. Proceedings of the National Academy of Sciences, 121(41), e2410828121. https://doi.org/10.1073/pnas.2410828121

Yu, A. J., & Dayan, P. (2005). Uncertainty, neuromodulation, and attention. Neuron, 46(4), 681–692. https://doi.org/10.1016/j.neuron.2005.04.026

Garivier, A., & Moulines, E. (2011). On upper-confidence bound policies for switching bandit problems. In Algorithmic Learning Theory (ALT 2011). Springer. https://doi.org/10.1007/978-3-642-24412-4_16

Adams, R. P., & MacKay, D. J. C. (2007). Bayesian online changepoint detection. arXiv:0710.3742. https://arxiv.org/abs/0710.3742

Okinawa Institute of Science and Technology Graduate University. (2026, June 8). Scientists discover the brain chemical that helps you break bad habits. ScienceDaily. https://www.sciencedaily.com/releases/2026/06/260606075901.htm

Your Wi-Fi Has No Walls: What Aluminum Foil Reveals About Wireless Security

Khali Sollis — Tue, 13 Oct 2026 18:00:00 +0000

How a 2017 experiment challenges the way we think about network boundaries, signal propagation, and defense in depth.

The Invisible Network Beyond Your Walls

A home has walls, doors, and locks. A wireless network does not necessarily stop at any of them. Its edge is set by physics rather than property lines: a 2.4 GHz Wi-Fi signal has a wavelength of roughly 12.5 cm, and how much of it survives a wall depends on the frequency, the material, its thickness, the angle of incidence, and everything else in the room. Drywall and wood let a good deal through. Concrete, brick, and metal take more. The result is a coverage footprint shaped like no floor plan.

That spillover is a fact about propagation, not automatically a vulnerability. A beacon that is detectable on the sidewalk only matters if someone on the sidewalk can do something useful with it. Still, the mismatch between the physical boundary of a home and the radio boundary of its network raises a fair engineering question: if we could shape where the signal goes, would that add a meaningful layer of protection?

The 2017 Experiment Behind the 2026 Headline

A news story circulating in 2026 revived this question, but the research behind it is nine years old. In November 2017, Xi Xiong, Justin Chan, Ethan Yu, Nisha Kumari, Ardalan Amiri Sani, Changxi Zheng, and Xia Zhou presented "Customizing Indoor Wireless Coverage via 3D-Fabricated Reflectors" at ACM BuildSys in Delft. It extended a 2015 workshop paper by Chan, Zheng, and Zhou that introduced the system's name, WiPrint.

The approach is computational, not improvised. The user supplies a coarse 3D model of the space (walls and doors, no furniture), the access point's location and antenna positions, and a set of target areas where signal should be stronger or weaker. A 3D ray tracer, accounting for reflection, transmission, and diffraction, predicts signal strength in each 1 m × 1 m cell. The reflector surface is represented as a smooth parametric NURBS surface, and a simulated-annealing search guided by gradient descent reshapes it until predicted coverage best matches the target. On a 2017 laptop this took about 23 minutes. The shape is then 3D-printed in plastic, roughly 20 × 20 cm for under $35, coated in 0.024 mm household aluminum foil, and mounted around the router's antennas.

The team tested this in two furnished indoor scenes, a 19 × 13 m workspace and a 16 × 12 m home-like layout, with people moving around. The main access point was a Netgear R7000 (802.11ac, three antennas) set to 10 mW and measured on the 2.4 GHz band; two older 2.4 GHz routers were also tested. A programmed drone hovered at each location to log signal strength, and laptops running iperf measured throughput.

The widely repeated numbers check out, with a qualifier: the paper reports that optimized reflectors weakened signals in target areas by up to 10 dB and strengthened them by up to 6 dB. These are best-case figures in specific target regions, not averages across a home. In the single-router test, 83.1% of cells moved in the intended direction; in the two-router test, 91%. Throughput changes ranged from −63.3% to +55.1% across seven of nine sampled locations in the two-router scene. A naive concave reflector, the soda-can approach, simply weakened everything behind it.

Why Aluminum Reflects Wi-Fi Signals

Wi-Fi is electromagnetic radiation. When it meets a good conductor, free electrons in the metal respond to the oscillating field and re-radiate, so most of the incident energy is reflected rather than passed through. At 2.4 GHz, current in aluminum is confined to a skin depth of under two micrometers, which is why even kitchen foil is electrically thick enough to act as a reflector. The researchers found copper and aluminum performed similarly, silver slightly better, and thicker sheets improved blocking only moderately.

Energy striking the reflector is split between reflection, absorption, and some transmission. The authors observed that their reflectors were better at weakening than strengthening, and the reason is instructive: only reflected energy can boost the signal in front of the reflector, while both reflected and absorbed energy reduce it behind. A passive reflector adds no power; it only redirects what the antennas already radiate. Sending more energy toward one area generally leaves less for others, and because the redirected waves also interfere with the room's existing multipath, the reflector can shift signal strength in places it was never aimed at, upward in some spots and downward in others. The paper documents one such side effect: weakening the hallway also slightly weakened part of the lab.

The flashlight-mirror analogy helps up to a point and then fails. A 20 cm reflector is less than two wavelengths across at 2.4 GHz, so waves diffract around its edges instead of being cleanly redirected. Indoors, signals also arrive by many paths at once, bouncing off walls, floors, and furniture. This multipath propagation means the copies combine constructively in some spots and destructively in others, so readings can change by several decibels over a few centimeters. That is why a shape optimized for one floor plan is the wrong shape for another.

What the Experiment Demonstrated—and What It Didn't

What the study supports is specific: under its conditions, an optimized, carefully placed reflector redistributed received signal strength and throughput by moderate amounts in the intended direction across most cells. The effect tolerated about 10° of orientation error and 10 cm of placement error.

What it did not establish matters as much. All signal maps were collected at 2.4 GHz, so nothing in the paper speaks directly to 5 GHz, 6 GHz, Wi-Fi 6, 6E, or 7, wide channels, or modern routers with internal antenna arrays and beamforming. The authors themselves describe the RSS and throughput changes as moderate and attribute the limit to Wi-Fi's wavelength. Nor does the paper show that a hand-shaped sheet of foil behind a router reproduces the result; its own naive reflector did not.

Outcomes will also vary with antenna configuration, room layout, building materials, client placement, moving people, and neighboring networks. And received signal strength is not throughput, reliability, or security. Even the model's predictions carried a mean error of about 3 dBm.

Signal Containment Is Not Encryption

Coverage control answers where a signal can be received. Authentication answers who may join the network. Encryption answers what someone who receives the signal can learn. A reflector addresses only the first.

The paper's security test measured packet loss at locations outside each router's intended region. At some locations that could previously reach the network, packet loss rose to 50–60%, enough that the authors treat the network as inaccessible there. But at several others, including one close to the router, packet loss barely changed, and the authors proposed adding transmit-power control to address it. That is evidence of reduced usability at specific points for ordinary laptops. It is not evidence about an attacker.

A lower received signal level, often reported through RSSI (received signal strength indicator) or a device-specific signal measurement, may make reception more difficult without making it impossible. An adversary can move closer, wait outside a different wall, or use a directional antenna with substantially more gain than a laptop's. The researchers themselves frame physical confinement as complementary to encryption, and that is the defensible framing.

The protections that actually govern access and confidentiality live higher up, and each covers a different stretch of the path:

WPA2/WPA3 authenticate devices joining the network and encrypt traffic over the air between each device and the access point. Their protection ends at the router. WPA3-Personal replaces WPA2's pre-shared key handshake with SAE, which the Wi-Fi Alliance says gives users stronger protection against password-guessing attempts; WPA3 networks also require Protected Management Frames and disallow outdated protocols.
HTTPS/TLS encrypts and authenticates the connection between an application and the specific server it talks to, across every network in between. It protects content whether or not the Wi-Fi is trustworthy, though observers can still see which hosts you contact.
VPNs wrap traffic in an encrypted tunnel from your device to a VPN server, hiding traffic routed through the tunnel from the local network and your ISP. Beyond that server, traffic is only as protected as its own encryption, and you are trusting the VPN operator. A VPN can be useful for accessing corporate networks or adding privacy against local network observers, but it does not replace HTTPS and is not automatically necessary on public or home Wi-Fi.

The NSA's home network guidance translates this into practical steps:

Use WPA3, or WPA2/WPA3 mode only while older devices still need it, and enable Protected Management Frames where available.
Choose a passphrase of at least twenty characters.
Keep router firmware updated, and replace routers that no longer receive support.
Protect the router's admin login with strong authentication, and disable remote administration and UPnP.
Segment primary, guest, and IoT devices onto separate networks.

This is defense in depth: independent layers, each with a clearly stated job, so that one failure does not become total failure. For organizations, NIST SP 800-153 applies the same thinking to WLAN configuration and monitoring.

How Developers Can Investigate Their Own Wireless Coverage

A controlled comparison at home teaches a lot about propagation. It will not validate foil as a security control.

Pick five to ten fixed measurement spots, including a few near exterior walls, and mark them.
Record a baseline: at each spot, take at least ten readings with the same device, held the same way.
Place a reflector behind the router without covering vents or touching power connections.
Repeat the same readings at the same spots.
Compare medians and spread, not single readings.

Keep the router, client device, band, channel, and orientation constant. On Linux, iw dev link reports signal in dBm; Windows' netsh wlan show interfaces reports a percentage, which is not directly comparable. RSSI scales are vendor-defined and uncalibrated, so trust differences on one device more than absolute numbers across devices.

A decibel describes a ratio: ΔP(dB) = 10 · log₁₀(P₂ / P₁). dBm is an absolute level referenced to one milliwatt, so −60 dBm is one nanowatt. Subtracting two dBm readings gives a difference in dB. A 10 dB drop means one-tenth the received power, not one-tenth the range: in free space, where power falls with the square of distance, it corresponds to roughly a 3.2× change in distance, and indoors the relationship depends on the environment. It says nothing direct about throughput or attack probability.

This script compares the median reading at each spot before and after, and reports the spread of each set. The sample data is hypothetical, ten readings per spot to match the procedure above, and two spots stand in for your full list.

python
import statistics as st
import matplotlib.pyplot as plt

HYPOTHETICAL readings in dBm — replace with your own measurements

baseline = {
"desk": [-48, -50, -49, -47, -51, -49, -48, -50, -52, -49],
"porch": [-71, -74, -72, -73, -70, -72, -75, -71, -73, -72],
}
modified = {
"desk": [-45, -46, -44, -47, -45, -46, -44, -45, -47, -46],
"porch": [-78, -80, -77, -79, -81, -78, -80, -79, -77, -82],
}

locations = list(baseline)
deltas = [st.median(modified[k]) - st.median(baseline[k]) for k in locations]

for k, d in zip(locations, deltas):
before = max(baseline[k]) - min(baseline[k])
after = max(modified[k]) - min(modified[k])
print(f"{k:>6}: median change {d:+.1f} dB (power ×{10 ** (d / 10):.2f}), "
f"spread {before} dB before, {after} dB after")

plt.bar(locations, deltas, color=["tab:green" if d > 0 else "tab:red" for d in deltas])
plt.axhline(0, color="black", linewidth=0.8)
plt.ylabel("Median change (dB)")
plt.title("Change in median RSSI per spot (hypothetical data)")
plt.show()

The bar chart shows the change in median RSSI at each named spot. It is not a coverage heatmap; for that, you would plot the same deltas at each spot's position on a floor plan.

Test only networks you own or are authorized to measure, never block router ventilation, and keep foil away from power supplies and outlets.

The Bigger Engineering Lesson

Software engineers work mostly above the physical layer, for good reason. But coverage is a design parameter, shaped by geometry and materials as much as by configuration files, and WiPrint makes that unusually visible.

The lesson is not that foil secures Wi-Fi. It is that every security control should come with an honest statement of what it protects against and what evidence supports it. Signal shaping can modestly reduce where a network is usable, under measured conditions, at 2.4 GHz. Encryption, authentication, and secure configuration protect what the network carries and who may join. The two belong in the same threat model, at different layers, and neither substitutes for the other.

We put enormous effort into protecting the information that travels through our networks. Knowing where the network itself physically travels belongs in that same engineering conversation.

References and Further Reading

Xiong, X., Chan, J., Yu, E., Kumari, N., Amiri Sani, A., Zheng, C., & Zhou, X. (2017). Customizing Indoor Wireless Coverage via 3D-Fabricated Reflectors. Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys '17). https://doi.org/10.1145/3137133.3137148 · PDF

Chan, J., Zheng, C., & Zhou, X. (2015). 3D Printing Your Wireless Coverage. ACM Workshop on Hot Topics in Wireless (HotWireless). https://cs.dartmouth.edu/~xia/papers/hotw15-wiprint.pdf

DartNets Lab, Dartmouth College. WiPrint project page. https://dartnets.cs.dartmouth.edu/wiprint

Sheth, A., Seshan, S., & Wetherall, D. (2009). Geo-fencing: Confining Wi-Fi Coverage to Physical Boundaries. Pervasive Computing (Pervasive 2009), 274–290. Springer. https://doi.org/10.1007/978-3-642-01516-8_19

Souppaya, M., & Scarfone, K. (2012). Guidelines for Securing Wireless Local Area Networks (WLANs), NIST SP 800-153. National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.800-153

Wi-Fi Alliance. Security: WPA3, Wi-Fi Enhanced Open, and Protected Management Frames. https://www.wi-fi.org/security

National Security Agency (2023). Best Practices for Securing Your Home Network, Cybersecurity Information Sheet. https://media.defense.gov/2023/Feb/22/2003165170/-1/-1/0/CSI_BEST_PRACTICS_FOR_SECURING_YOUR_HOME_NETWORK.PDF

Dependency Injection: How Some People Quietly Program Themselves Into Your Identity

Khali Sollis — Fri, 25 Sep 2026 12:00:00 +0000

How emotional dependency becomes embedded inside self-concept, why certain relationships begin influencing your decisions from the inside, and what happens when another person slowly becomes part of your psychological operating system

Some relationships affect you externally.

Others go much deeper.

They quietly integrate themselves into your internal architecture.

At first, it feels harmless:

considering their opinion
adapting to their preferences
emotionally consulting them internally
imagining their reaction before making decisions

Over time, something more subtle happens.

You stop simply loving the person.

You start organizing yourself around them.

That was the realization that changed everything for me:

Certain people do not just enter your life.

They become embedded inside your identity infrastructure.

The Bug: Dependency Injection

In software architecture, dependency injection occurs when external components become integrated into a system’s internal functioning.

The system begins relying on those components to operate normally.

Emotionally, the same thing happens when another person becomes psychologically embedded into:

decision-making
self-worth
emotional regulation
identity stability
future orientation
internal validation systems

The self slowly stops operating independently.

The Integration Happens Gradually

This process rarely feels dangerous at first.

In fact, it often feels like intimacy.

You begin:

thinking through their perspective automatically
adjusting behavior around their preferences
filtering choices through their approval
imagining life outcomes based on their presence

Eventually, their psychological presence remains active even when they are physically absent.

That is where dependency deepens.

The Person Becomes an Internal Reference Point

One uncomfortable truth:

At some point, I stopped making certain decisions based on myself.

Instead, my mind automatically evaluated:

How would they react?
Would they approve?
Would this threaten the connection?
Would they still choose me?

Without realizing it, another person had become part of my internal operating logic.

Dependency Often Masquerades as Love

This distinction became critical.

Love:

allows connection while preserving selfhood

Dependency:

merges emotional stability with another person’s presence

One expands life.

The other quietly restructures identity around emotional access.

Why Dependency Injection Feels So Powerful

Because the nervous system associates attachment with safety.

Especially when someone becomes tied to:

emotional relief
belonging
identity reinforcement
validation
nervous system regulation

The brain begins integrating the person into survival processing.

Their presence starts feeling psychologically necessary.

The Internalization Process

The progression often looks like this:

connection
→ emotional reliance
→ behavioral adaptation
→ identity integration
→ dependency stabilization

The dangerous part is that the process feels emotionally meaningful while it is happening.

Sometimes even romantic.

The Self Slowly Becomes Negotiated

Dependency changes internal behavior quietly.

You start:

abandoning preferences
minimizing needs
reshaping routines
adjusting goals
suppressing authenticity

not because someone explicitly demanded it…

but because preserving the attachment becomes psychologically prioritized.

Why Separation Feels So Disorienting

Because dependency removal feels like identity disruption.

When someone deeply embeds into your internal systems, their absence affects:

emotional regulation
daily habits
future imagination
self-perception
nervous system stability

People often think they are grieving only the relationship.

Sometimes they are grieving the collapse of the psychological structure built around it.

The Difference Between Bonding and Psychological Merging

Healthy bonding:

creates closeness while maintaining autonomy

Dependency injection:

blurs the boundary between connection and identity

One preserves individuality.

The other slowly absorbs it.

The Most Dangerous Part

The most dangerous part is not emotional pain.

It is self-disconnection.

Eventually you stop asking:

What do I want?

And start asking:

What preserves the emotional system built around them?

That shift changes your entire relationship with yourself.

The Realization

Eventually, I stopped asking:

Why does losing this connection feel like losing myself?

And started asking:

How much of my identity became structurally dependent on this person’s presence?

That question exposed the architecture underneath the attachment completely.

The Fix: Dependency Extraction

Healing required more than emotional distance.

It required rebuilding independent internal systems.

Not coldness.

Autonomy.

Rebuild Internal Decision-Making

I began practicing making choices without emotionally consulting:

their approval
their reaction
their preferences
their imagined perception

At first, it felt unnatural.

That discomfort revealed how deep the dependency had become.

Separate Love From Psychological Reliance

Old model:

If someone matters deeply to me, my identity should naturally revolve around them.

Updated model:

Healthy connection supports identity. It should not replace it.

That distinction restored clarity.

Reclaim Internal Territory

This part was critical.

I started reclaiming:

routines
goals
emotional regulation
creative focus
future planning

without organizing them around another person’s presence.

The nervous system slowly relearned self-sufficiency.

Allow Relationships to Be Additions, Not Operating Systems

This became the new framework:

People can matter deeply without becoming:

your emotional infrastructure
your identity anchor
your source of self-worth
your primary nervous system regulator

Connection is healthiest when it enhances identity instead of consuming it.

What Changed

After identifying dependency injection patterns:

emotional autonomy increased
self-trust strengthened
relationships became clearer
identity stability improved
fear of abandonment decreased significantly

And most importantly:

I stopped confusing emotional merging with emotional depth.

Reframing Attachment

Old model:

The deeper the attachment, the more central they should become to my identity.

Updated model:

Healthy attachment preserves individuality while allowing connection.

That distinction restored balance.

Closing the Series

This entire series began with one realization:

Many emotional struggles are not random.

They are systems.

Patterns.
Conditioning.
Behavioral loops.
Identity distortions.
Nervous system adaptations.

For years, I believed emotional exhaustion was simply part of being “good,” “loving,” “understanding,” or “available.”

It wasn’t.

Many of those behaviors were unexamined survival strategies running automatically underneath my personality.

This series was never about becoming cold.

It was about identifying the hidden architectures that quietly teach people to:

abandon themselves
over-function emotionally
confuse instability with love
mistake exhaustion for devotion
tolerate dynamics that slowly erase them

Awareness changes the system.

Not instantly.

But permanently.

Final Takeaway

Some people do not merely influence your emotions.

They quietly integrate themselves into your identity until your internal stability becomes partially dependent on their existence.

Healing begins when you reclaim authorship over your own psychological architecture.

Because relationships should complement your identity.

Not become the operating system running it.

Final Status
Behavioral anti-pattern audit: completed
Dependency injection vulnerabilities: identified
Self-authorship systems: restoring
Internal architecture ownership: returned
Series Complete: Behavioral Anti-Patterns
The Audit of the “Nice” Trap: A Documentation of Self-Erasure
Missing Boundary Checks: Why “Nice” Code Always Gets Exploited
Unbounded Processes: The Hidden Cost of Always Saying Yes
Deferred Execution: Why Avoiding “No” Creates Worse Outcomes
Infinite Approval Loops: Breaking the Need for External Validation
Caching Bad Data: Why We Keep Trusting Broken Patterns
Handling Exceptions That Aren’t Yours: The Trap of Emotional Over-Responsibility
Refactoring Identity: When “Being Nice” Becomes Technical Debt
No Rate Limiting: How Instant Availability Destroys Focus
Broken Access Control: Who Actually Deserves Your Time?
From Approval-Seeking to System Integrity: Shipping a New Version of Yourself
Recursive Loneliness: Why Some Attachments Feel Impossible to Exit
Emotional Cache Invalidation: Accepting That Someone Has Changed
Ghost Dependencies: The Relationships That Still Control You After They End
Phantom Notifications: Why Your Nervous System Still Waits for People Who Left
Emotional Memory Leaks: Why Old Relationships Keep Reappearing During Vulnerable Moments
Emotional Rollbacks: Why We Revert to Old Versions of Ourselves Around Certain People
Identity Drift: How We Slowly Abandon Ourselves to Keep Connections Alive
Approval Latency: Why Waiting for Validation Delays Your Entire Life
Emotional Deadlocks: When Two People Wait for Each Other to Change First
Dependency Injection: How Some People Quietly Program Themselves Into Your Identity

Emotional Deadlocks: When Two People Wait for Each Other to Change First

Khali Sollis — Fri, 18 Sep 2026 13:00:00 +0000

How unresolved relational dynamics create emotional standstills, why mutual avoidance freezes connection, and what happens when two people become trapped in passive psychological negotiations

Some relationships do not end dramatically.

They stall.

Communication weakens.
Resentment accumulates.
Distance increases.
But neither person fully leaves.

Instead, both people enter a silent negotiation:

I will change once they change first.

And the relationship slowly freezes there.

No repair.
No resolution.
No honest rupture.

Just emotional deadlock.

The Bug: Emotional Deadlocks

In computing systems, a deadlock occurs when two processes become permanently blocked because each is waiting for the other to act first.

Neither process progresses.

Human relationships can operate the same way.

Examples:

both people wait for the other to apologize
both wait for vulnerability first
both wait for emotional maturity first
both wait for effort first
both wait for accountability first

The connection becomes trapped inside mutual hesitation.

Why Deadlocks Feel So Draining

Because emotional energy remains allocated without movement.

The system never fully:

repairs
resolves
reconnects
separates

Which creates continuous psychological occupation.

Part of the mind stays stuck monitoring:

possibility
disappointment
unresolved tension
imagined future resolution

The relationship remains emotionally open without actually functioning.

Passive Waiting Masquerades as Hope

This realization was uncomfortable.

Sometimes what people call:

hope

is actually:

prolonged emotional avoidance

Both people sense the dysfunction.

But instead of confronting reality directly, they remain psychologically suspended waiting for the other person to become different first.

Mutual Fear Creates Stagnation

Deadlocks are usually powered by fear.

Fear of:

rejection
vulnerability
accountability
abandonment
emotional exposure
losing control of the dynamic

So both people unconsciously choose emotional paralysis over uncomfortable clarity.

The Emotional Negotiation Loop

The pattern often looks like this:

Person A withdraws
→ Person B becomes defensive
→ Person A waits for accountability
→ Person B waits for warmth
→ neither moves
→ resentment compounds

Over time, inactivity itself becomes the system architecture.

Why Deadlocks Often Last for Years

Because ambiguity allows emotional fantasy to survive.

As long as the relationship remains unresolved:

hope remains technically possible
emotional attachment stays partially active
accountability stays avoidable

Deadlocks preserve potential while postponing reality.

The Difference Between Patience and Stagnation

This distinction changed everything.

Patience:

supports healthy progress

Deadlock:

suspends necessary movement indefinitely

One creates space for growth.

The other delays truth.

Emotional Pride Intensifies the Freeze

Sometimes neither person wants to “lose” emotionally.

Which means:

nobody apologizes first
nobody communicates honestly first
nobody softens first
nobody leaves first

The relationship becomes a psychological standoff disguised as emotional complexity.

Why Some People Prefer Deadlocks Over Endings

Because endings create certainty.

Deadlocks preserve illusion.

An unresolved relationship still allows fantasies like:

“Maybe later.”
“Maybe they’ll realize.”
“Maybe timing is the issue.”
“Maybe things will shift naturally.”

The nervous system often prefers suspended hope over definitive grief.

Even when the suspension itself becomes painful.

The Hidden Cost of Emotional Stalemates

Deadlocks quietly consume:

attention
emotional energy
identity stability
future openness
nervous system capacity

Part of the self remains permanently allocated toward unresolved emotional processing.

That fragmentation affects everything else.

The Realization

Eventually, I stopped asking:

Why does this relationship feel impossible to resolve?

And started asking:

What truth are both people avoiding by waiting for the other to change first?

That question exposed the actual architecture underneath the stagnation.

The Fix: Breaking Emotional Deadlocks

Resolution required movement.

Not perfect mutual readiness.

Movement.

Because deadlocks survive through indefinite hesitation.

Accept That Waiting Is Also a Decision

This part was critical.

Inaction is not neutral.

Every day spent inside unresolved dynamics reinforces the structure itself.

Deadlocks continue because both people continuously participate in maintaining them.

Stop Interpreting Silence as Emotional Depth

Old model:

This unresolved tension must mean the connection is profound.

Updated model:

Unresolved tension can also indicate avoidance, fear, and emotional immobility.

That distinction restored clarity.

Move Independently of Their Timeline

One powerful realization:

Your healing cannot depend on mutual synchronization.

If:

communication is absent
accountability is absent
effort is absent
growth is absent

you still retain responsibility for your own movement.

Waiting indefinitely for another person’s transformation delays your own life.

Prioritize Clarity Over Emotional Suspension

This became the new standard.

Not:

Does this connection still feel emotionally charged?

But:

Is this dynamic actually moving toward health, honesty, and reciprocity?

That question changes everything.

What Changed

After identifying emotional deadlock patterns:

unresolved dynamics became easier to recognize
emotional stagnation lost some of its romantic appeal
decision-making improved
self-respect strengthened
emotional energy returned

And most importantly:

I stopped confusing suspended movement with meaningful connection.

Reframing Waiting

Old model:

If I wait long enough, eventually they will become who I need them to be.

Updated model:

Mutual growth requires active participation, not indefinite emotional waiting.

That distinction restored momentum.

Takeaway

Emotional deadlocks occur when two people remain psychologically connected while simultaneously waiting for the other person to change first.

The result is emotional suspension:

unresolved tension
prolonged ambiguity
stalled growth
fragmented attention

Healing begins when clarity becomes more important than preserving possibility.

Because some relationships do not fail from lack of feeling.

They fail from lack of movement.

Status
Emotional deadlock detection: active
Passive waiting behaviors: identified
Forward movement protocols: restoring
Series: Behavioral Anti-Patterns

Previous: Approval Latency: Why Waiting for Validation Delays Your Entire Life
Next: Dependency Injection: How Some People Quietly Program Themselves Into Your Identity

Approval Latency: Why Waiting for Validation Delays Your Entire Life

Khali Sollis — Fri, 11 Sep 2026 13:00:00 +0000

How external validation creates emotional bottlenecks, why approval-seeking slows personal evolution, and what happens when your decisions remain permanently queued behind other people’s opinions

One of the most expensive realizations of my life was understanding how much time I lost waiting.
Waiting for:

permission

reassurance

approval

agreement

recognition

emotional confirmation

Not always consciously.
Sometimes the waiting was subtle.
I would hesitate before decisions.
Overanalyze before speaking.
Delay action until someone else validated the direction first.
At the time, I called it:
being thoughtful
Looking back, it was something else entirely.
My life was running on approval latency.

The Bug: Approval Latency
In technology systems, latency refers to delays between request and response.
High latency slows the entire system.
Emotionally, approval latency happens when:
internal decisions
cannot fully execute until:
external validation
is received.
The result:

delayed action

delayed boundaries

delayed self-trust

delayed identity formation

delayed life progression

The system becomes externally bottlenecked.

The Hidden Dependency
This realization was uncomfortable:
Many of my decisions were not actually autonomous.
They were socially processed.
Internally, the sequence looked like this:
desire→ self-doubt→ external consultation→ emotional waiting→ permission received→ action finally begins
The problem was not reflection.
The problem was dependency.

Why Approval Feels Necessary
Because validation temporarily reduces uncertainty.
Especially for people conditioned to associate approval with:

safety

belonging

worthiness

emotional stability

acceptance

The nervous system learns:
approval=reduced emotional risk
So independent action begins feeling psychologically dangerous.

The Cost of Chronic Permission-Seeking
Approval latency quietly delays entire lives.
Not dramatically.
Incrementally.
You postpone:

projects

conversations

boundaries

decisions

reinvention

visibility

waiting for:

consensus

reassurance

emotional certainty

guaranteed acceptance

Meanwhile, life continues moving.

The Validation Queue Never Ends
One dangerous truth:
External approval systems are infinite.
No matter what you do:

someone will disagree

someone will misunderstand

someone will disapprove

someone will withdraw support

If your nervous system requires universal validation before action, execution becomes impossible.

Approval-Seeking Creates Identity Fragmentation
This part changed everything.
The more I optimized for approval, the less clear my actual preferences became.
Because constant external calibration weakens internal reference systems.
You stop asking:
What do I think?
And start asking:
What response minimizes rejection?
That shift slowly disconnects people from themselves.

Why Highly Sensitive People Often Struggle Here
Because emotionally perceptive people detect reactions quickly.
Micro-signals:

tone changes

hesitation

facial expressions

silence

disapproval cues

become emotionally significant.
The nervous system starts adapting behavior preemptively to avoid friction.
Approval latency increases.

The Emotional Economics of Delayed Action
Waiting for validation creates compounding costs.
Every delayed decision drains:

momentum

confidence

clarity

energy

self-trust

The longer action remains externally dependent, the weaker internal authority becomes.

The Difference Between Feedback and Permission
This distinction became critical.
Healthy feedback:
informs decisions
Approval dependency:
controls decisions
One expands intelligence.
The other weakens autonomy.

The Realization
Eventually, I stopped asking:
Why do I overthink everything before acting?
And started asking:
Why does my nervous system treat disapproval like danger?
That question exposed the actual architecture underneath the hesitation.

The Fix: Reducing Approval Dependency
Healing required strengthening internal execution systems.
Not becoming reckless.
Becoming self-authorized.

Shorten the Delay Between Knowing and Acting
I began noticing how often I already knew the answer internally before seeking external confirmation.
The issue was not lack of clarity.
It was lack of self-trust.
Reducing approval latency meant acting closer to initial internal knowing.
Normalize Discomfort During Independent Decisions
Old model:
If I feel anxious making this decision alone, it must be wrong.
Updated model:
Independent action often feels uncomfortable while self-trust is rebuilding.
That distinction changed execution completely.
Stop Outsourcing Identity
Some questions cannot be solved through consensus.
Especially:

values

boundaries

life direction

self-respect decisions

emotional standards

Too much external input creates internal noise.

Allow Disapproval Without System Failure This part was transformative. I started practicing:

saying things people may not like

making decisions without unanimous support

disappointing others without self-collapse

remaining stable during disagreement

The nervous system slowly learned:
disapproval≠emotional catastrophe
That lesson restored enormous freedom.

What Changed
After reducing approval latency:

decisions accelerated

overthinking decreased

self-trust strengthened

emotional dependence weakened

identity clarity improved

And most importantly:
I stopped placing my life on hold waiting for emotional authorization from other people.

Reframing Validation
Old model:
Once people approve, I can move forward confidently.
Updated model:
Confidence is built through aligned action, not universal approval.
That distinction restored momentum.

Takeaway
Approval latency delays lives quietly.
Not because people lack intelligence or capability, but because the nervous system becomes conditioned to treat external validation as a prerequisite for movement.
The result is chronic hesitation, fragmented identity, and delayed execution.
Healing begins when internal alignment becomes more important than external permission.
Because waiting for universal approval is one of the slowest ways to experience your own life.

Status
Approval dependency detection: activeExternal validation bottlenecks: reducingInternal execution systems: stabilizing

Series: Behavioral Anti-Patterns
Previous: Identity Drift: How We Slowly Abandon Ourselves to Keep Connections Alive
Next: Emotional Deadlocks: When Two People Wait for Each Other to Change First

Identity Drift: How We Slowly Abandon Ourselves to Keep Connections Alive

Khali Sollis — Fri, 04 Sep 2026 12:00:00 +0000

How self-erasure happens gradually inside relationships, why emotional adaptation can become identity loss, and what happens when maintaining connection becomes more important than maintaining yourself

Identity drift rarely happens dramatically.

Most people do not wake up one morning and consciously decide:

I am going to abandon myself for this relationship.

It happens slowly.

Quietly.

Incrementally.

A small compromise here.
A swallowed boundary there.
A personality adjustment that initially feels harmless.

Until one day you realize:

your behavior changed
your standards changed
your emotional tolerance changed
your priorities changed
your sense of self became unrecognizable

Not because you consciously chose self-erasure.

Because the connection slowly trained you to disconnect from yourself in order to preserve it.

The Bug: Identity Drift

In technology systems, identity drift occurs when a system gradually deviates from its original configuration over time.

Small unauthorized changes accumulate until the system no longer resembles its intended architecture.

Human relationships can create the same phenomenon.

Especially when emotional survival becomes tied to:

approval
acceptance
validation
emotional access
fear of abandonment

The self slowly adapts to maintain relational stability.

Even at its own expense.

The Drift Is Usually Rewarded at First

This was difficult to admit.

Many forms of self-abandonment initially receive positive reinforcement.

You become:

easier to deal with
more accommodating
more emotionally available
more forgiving
less confrontational

People often reward the version of you that requires the least resistance from them.

Which means identity drift can feel temporarily successful.

Small Self-Abandonments Compound

Identity erosion rarely begins with major sacrifices.

It starts microscopically.

Examples:

laughing at things that hurt you
tolerating disrespect to avoid conflict
pretending not to care
saying yes when you mean no
reducing your needs to remain “easy”
overexplaining your boundaries until they disappear

Each individual compromise seems manageable.

But accumulated compromises restructure identity over time.

The Nervous System Prioritizes Attachment

One uncomfortable truth:

Humans are biologically wired to preserve connection.

Especially if abandonment, rejection, or instability became emotionally significant earlier in life.

Which means the nervous system may unconsciously conclude:

maintaining attachment

maintaining authenticity

Once that equation activates, identity drift accelerates.

Adaptation Becomes Performance

At some point, I realized I was no longer relating naturally.

I was managing perception.

Monitoring:

tone
reactions
timing
emotional responses
approval signals

Parts of my personality became optimized for relational stability instead of internal truth.

That is not connection.

That is behavioral performance.

Why Identity Drift Feels Invisible

Because the changes happen gradually.

The system normalizes them incrementally.

You tell yourself:

“It’s not a big deal.”
“I’m just being understanding.”
“I’m trying to make things work.”
“Relationships require compromise.”

And they do.

But compromise becomes self-erasure when:

the relationship survives
while your authenticity slowly disappears
The Loss of Internal Reference

The most dangerous part of identity drift is not external.

It is internal disorientation.

Eventually you stop asking:

What do I actually want?

And start asking:

What response keeps this connection stable?

That shift changes everything.

Because your identity slowly becomes externally calibrated.

Emotional Shape-Shifting

Some relationships unconsciously reward emotional shape-shifting.

You become:

softer to avoid conflict
quieter to avoid rejection
more agreeable to maintain closeness
more available to avoid abandonment

Over time, adaptability becomes identity fragmentation.

You stop experiencing yourself consistently.

Why Exhaustion Appears

Maintaining distorted versions of yourself consumes enormous energy.

Because self-suppression requires continuous monitoring.

The nervous system remains hyper-alert:

adjusting
filtering
editing
minimizing
predicting reactions

The body eventually experiences this as emotional exhaustion.

Not because connection is hard.

Because performance is.

The Difference Between Growth and Self-Abandonment

This distinction became critical.

Healthy growth expands authenticity.

Identity drift suppresses it.

Growth says:

I am evolving into a more aligned version of myself.

Identity drift says:

I am becoming easier to tolerate so the connection survives.

Those are entirely different processes.

The Realization

Eventually, I stopped asking:

Why do I feel disconnected from myself lately?

And started asking:

What parts of myself have I been slowly negotiating away to maintain this connection?

That question exposed the architecture underneath the exhaustion.

The Fix: Identity Realignment

Recovery required rebuilding internal reference points.

Not through isolation.

Through honesty.

Notice Where You Self-Edit Automatically

The first signs were subtle:

hiding emotions
minimizing needs
avoiding honesty
suppressing discomfort
performing emotional neutrality

Those behaviors became indicators of drift.

Not maturity.

Stop Treating Self-Abandonment as Kindness

Old model:

Making myself smaller keeps the relationship peaceful.

Updated model:

Relationships that require self-erasure are unstable by design.

That distinction restored clarity.

Rebuild Internal Decision-Making

I started asking:

What do I actually feel?
What do I genuinely want?
What would I choose without fear of disapproval?
What response protects my integrity instead of preserving access?

The goal became internal alignment, not external management.

Allow Relationships to React to the Real Version of You

This part changes everything.

When authenticity returns:

some dynamics improve
some people adapt
some relationships collapse

But collapse is sometimes evidence that the relationship depended on distortion to survive.

What Changed

After identifying identity drift patterns:

self-awareness increased
emotional exhaustion decreased
boundaries became clearer
authenticity felt safer
internal consistency strengthened

And most importantly:

I stopped confusing self-suppression with emotional maturity.

Reframing Connection

Old model:

Keeping the relationship stable is proof of emotional success.

Updated model:

If maintaining connection requires abandoning myself, the stability is artificial.

That distinction restored self-respect.

Takeaway

Identity drift happens when preserving connection slowly becomes more important than preserving yourself.

The process is gradual enough to feel invisible until emotional exhaustion, resentment, or disorientation finally expose it.

Healing is not only about finding better relationships.

It is about refusing to disappear inside them.

Because the strongest connections are not built through self-erasure.

They are built through mutual reality.

Status
Identity drift detection: active
Self-abandonment patterns: identified
Internal alignment systems: restoring