The price you saw was not the price everyone saw. You just did not know it yet.

In February 2024, Wendy's CEO Kirk Tanner told investors that the fast-food chain would invest $20 million in digital menu boards to support “dynamic pricing and day-part offerings.” The reaction was immediate, visceral, and devastating. Consumers heard “surge pricing” and revolted. Social media erupted. Burger King capitalised on the moment by offering free Whoppers, its email subject line reading: “Surge Pricing? Not at Burger King!” Within days, Wendy's Vice President Heidi Schauer was forced to clarify to NPR that the company would not raise prices during peak hours, insisting the plan was merely about discounts during slower periods. The damage, however, was already done. Wendy's had accidentally revealed something the technology industry had been quietly building for years: an infrastructure designed to charge different people different prices for the same thing, calibrated by algorithms that know more about you than you might suspect.

That infrastructure is no longer theoretical. It is operational, expanding, and largely invisible to the consumers it targets. Across e-commerce, travel, entertainment, housing, and soon your local supermarket, artificial intelligence systems are ingesting vast quantities of personal data to estimate individual willingness to pay and adjust prices accordingly. The question confronting regulators, consumers, and the technology companies themselves is whether this represents a natural evolution of market efficiency or a fundamental breakdown in the social contract that underpins fair commerce.

How the Pricing Machine Learns What You Will Pay

To understand why AI-driven pricing has become such a flashpoint, you need to understand what these systems actually do. Traditional dynamic pricing is nothing new. Airlines have adjusted fares based on demand since the 1980s. Hotels shift rates around holidays and conferences. Uber's surge pricing algorithm, which multiplies fares during periods of high demand, has been the subject of academic study for over a decade. A 2016 National Bureau of Economic Research paper estimated that UberX generated approximately $6.8 billion in consumer surplus across the United States in 2015, suggesting that for every dollar spent by consumers, roughly $1.60 in surplus was generated.

A natural experiment on New Year's Eve illustrated the point. When Uber's surge pricing algorithm across all of New York City broke down for 26 minutes due to a technical glitch, the platform's average wait time spiked from 2.6 minutes to 8 minutes, and unfulfilled trip requests rose significantly. The algorithm, whatever consumers thought of it, was performing a genuine market function. But even Uber's model, which adjusts prices based on aggregate supply and demand rather than individual consumer profiles, has drawn regulatory backlash. Cities including Honolulu, Manila, New Delhi, and Singapore have banned or capped surge pricing. Research by Juan Camilo Castillo at the University of Pennsylvania, using Uber data from Houston in 2017, found that while surge pricing generally improved market outcomes, its effects were unevenly distributed, with price-sensitive riders bearing a disproportionate burden during peak periods.

What is happening now goes far beyond adjusting prices to reflect real-time supply and demand. The new generation of AI pricing tools analyses individual consumer behaviour, browsing history, purchase patterns, location data, device type, credit history, and demographic information to estimate what each specific person is willing to pay. Amazon reportedly adjusts product prices around 2.5 million times every day, updating 50 times more frequently on average than Walmart. The company considers both “global values” such as demand volume and stock levels, and “user values” including product visit frequency and time of purchase. Research indicates that loyal, returning customers may face higher prices than newcomers, as the dynamic pricing engine calculates each customer's loyalty level and sets prices accordingly.

The algorithmic approaches powering these systems are sophisticated and continually evolving. Reinforcement learning models analyse customer demand while accounting for seasonality, competitor pricing, and market uncertainty to arrive at revenue-optimal prices. Bayesian models incorporate historical pricing data and shift their estimates with every new data point. Behavioural pricing systems analyse individual customer actions in real time to offer personalised discounts or price adjustments based on predicted likelihood of purchase. A Valcon study found that while 61 per cent of European retailers have embraced some form of dynamic pricing, fewer than 15 per cent currently use algorithmic or AI-based strategies. That number is set to change rapidly: 55 per cent of European retailers are actively planning to pilot dynamic pricing with generative AI in 2026.

The business case is compelling. Reports indicate that AI-driven dynamic pricing can increase average order value by up to 13 per cent during peak sales periods, cut overstock by 6 per cent in a single quarter, and boost profit margins by as much as 25 per cent. For companies operating on thin margins in competitive markets, these are not marginal improvements. They are transformative. And the practice is spreading beyond the expected players. Researchers at the University of New South Wales have warned that personalised pricing could soon reach supermarkets, noting that consumers have no way of knowing whether the price they see for bread or bananas on a retailer's website is the same price that another consumer sees.

When Landlords Let the Algorithm Decide

The most striking demonstration of what happens when algorithmic pricing goes wrong did not occur in an online shop or a ride-hailing app. It happened in the American rental housing market, where millions of tenants discovered that their rent increases were being orchestrated by a single piece of software.

In August 2024, the United States Department of Justice, alongside the Attorneys General of eight states including California, North Carolina, and Colorado, filed a civil antitrust lawsuit against RealPage Inc. The complaint alleged that RealPage contracted with competing landlords who agreed to share nonpublic, competitively sensitive information about their apartment rental rates to train and run RealPage's algorithmic pricing software. The software then generated pricing recommendations for participating landlords based on their competitors' data. Prosecutors stated that one landlord reported starting to increase rents within a week of adopting the software and, within eleven months, had raised them by more than 25 per cent.

In January 2025, the DOJ expanded the case, adding six major multifamily property owners as co-defendants, including Greystar. Nine states subsequently reached a $7 million settlement with Greystar in November 2025. By that same month, the DOJ had reached a proposed settlement with RealPage itself. The company did not admit liability but agreed to stop using competitors' nonpublic data in its revenue management product, to restrict model training to historic data at least twelve months old, to redesign its software to remove mechanisms that prop up prices or encourage competitors toward common pricing ranges, and to accept a court-appointed monitor with broad access to review its code and model training documentation. The settlement terms are operative for seven years.

The RealPage case matters far beyond the housing sector because it established a legal framework for how algorithmic pricing tools can cross the line from legitimate optimisation into anticompetitive behaviour. When an algorithm aggregates private data from competitors and uses it to coordinate pricing upward, it functions as a mechanism for tacit collusion, regardless of whether any human explicitly agreed to fix prices. The DOJ's Antitrust Division head has promised an increase in probes of algorithmic pricing, and in March 2025, the agency filed a statement of interest regarding “the application of the antitrust laws to claims alleging algorithmic collusion and information exchange.”

Surveillance Pricing and the FTC's Unfinished Investigation

In July 2024, the Federal Trade Commission under Chair Lina Khan launched what it called a surveillance pricing inquiry, using its 6(b) authority to issue orders to eight companies: Mastercard, Revionics, Bloomreach, JPMorgan Chase, Task Software, PROS, Accenture, and McKinsey. The Commission voted 5-0 to issue the orders. Khan stated that “firms that harvest Americans' personal data can put people's privacy at risk. Now firms could be exploiting this vast trove of personal information to charge people higher prices.”

Speaking at the Fast Company Innovation Festival in September 2024, Khan elaborated: “Given just how much intimate and personal information that digital companies are collecting on us, there's increasingly the possibility of each of us being charged a different price based on what firms know about us.” She noted that while economists had long studied price personalisation, it was previously more of a “thought experiment,” but advances in data extraction and targeting had made it “much more possible to be serving every individual person an individual price based on everything they know about you.”

The preliminary findings, published in January 2025, revealed that instead of a price or promotion being a static feature of a product, the same product could have a different price or promotion based on consumer-related data, behaviours, preferences, location, time, and purchase channel. Some companies could determine individualised pricing based on granular consumer data, with the study citing examples such as a cosmetics company targeting promotions based on specific skin types and tones. The FTC found that at least 250 businesses, including grocery stores, apparel retailers, health and beauty retailers, and hardware stores, had adopted surveillance pricing strategies.

Then the investigation stalled. FTC Chair Andrew Ferguson, who replaced Khan, cancelled the public comment period, effectively ending the study. With new federal leadership signalling that continuing the investigation was not a priority, the unfinished inquiry left a regulatory vacuum.

That vacuum did not last long. In December 2025, Senator Mark R. Warner led Senators Gallego, Blumenthal, and Hawley in a bipartisan push urging the Trump administration to crack down on surveillance pricing, which the senators described as a practice that “eliminates a fixed or static price in favour of prices specially tailored to an individual consumer's willingness to pay.” State lawmakers across the country began introducing legislation to regulate practices that use personal data, AI, and frequent price changes, particularly in sectors like food and housing. The regulatory baton, at least in the United States, has been passed from the federal level to the states, creating a patchwork of approaches that may prove difficult for businesses to navigate and consumers to understand.

The Oasis Fiasco and the British Regulatory Response

If the American regulatory landscape is fragmented, the United Kingdom's has been galvanised by a single, furiously debated event: the Oasis reunion ticket sale.

On 31 August 2024, tickets for 17 shows across the UK and Ireland went on sale exclusively through Ticketmaster. Millions of fans endured long virtual queues and multiple site crashes. Many discovered that standing tickets, initially advertised at approximately £135, had risen to as much as £355 by the time they reached checkout. The backlash was enormous. UK culture minister Lisa Nandy pledged to look into Ticketmaster's use of dynamic pricing. The band itself issued a statement claiming that “Oasis leave decisions on ticketing and pricing entirely to their promoters and management” and that lead members Liam and Noel Gallagher had not known dynamic pricing would be used.

On 5 September 2024, the Competition and Markets Authority launched an investigation into Ticketmaster's conduct. The CMA's findings, published in March 2025, were revealing. The regulator found no evidence that Ticketmaster had used algorithmic real-time pricing in the traditional sense. Instead, the company had released a batch of standing tickets at a lower price, and once those sold out, released the remaining tickets at a much higher price. The CMA was concerned that consumers had not been given clear and timely information about how the pricing would work, particularly given that many customers had endured lengthy queues with no warning that prices would change.

The Oasis controversy accelerated regulatory action. In late 2024, the Sale of Tickets (Sporting and Cultural Events) Bill was introduced in Parliament, seeking to require ticket-selling platforms to display the full range of available tickets, their quantities, and prices to consumers before they joined online queues. More broadly, the CMA has positioned itself as a proactive regulator of online pricing practices. The Digital Markets, Competition and Consumers Act received Royal Assent in May 2024 and its new digital markets competition regime came into force on 1 January 2025. Under this framework, the CMA can decide whether consumer laws have been broken without having to go through the courts, and can fine companies up to 10 per cent of global turnover. The CMA has also launched enforcement actions covering online pricing practices, including drip pricing and pressure selling, using its new powers to order businesses to pay compensation to affected customers.

The CMA has acknowledged that pricing algorithms can benefit consumers by reducing transaction costs and market frictions, but it has also flagged the risk that algorithms could “facilitate collusive outcomes” and increase prices. In a notable observation, the CMA suggested that the risk of businesses colluding with one another over prices would actually diminish if there were extensive use of personalised pricing algorithms in digital markets, because each firm would be setting individual prices rather than converging on common ones. It is a counterintuitive argument that illustrates just how complex the regulatory challenge has become.

Europe Drafts Its Digital Fairness Rulebook

The European Union, rarely content to let a regulatory opportunity pass, is constructing what could become the most comprehensive framework for governing personalised pricing anywhere in the world.

The Digital Fairness Act, overseen by EU Commissioner Michael McGrath, is designed to address manipulative interface design, misleading influencer marketing, addictive design features, subscription traps, and, critically, unfair personalisation and pricing practices. The European Commission launched a public consultation on the DFA on 17 July 2025, which closed on 24 October 2025 and received 3,341 responses, the vast majority from consumers.

The results were striking. At least 77 per cent of respondents supported measures including greater consumer control over personalised advertising, restrictions on advertising that exploits vulnerabilities, a prohibition on personalised advertising targeting minors, and restrictions on personalised pricing based on personal data and profiling. The existing Consumer Rights Directive already requires traders to inform consumers if a price has been personalised based on automated decision-making, but businesses are not required to disclose the specific parameters or criteria used. The DFA is expected to go considerably further. The consultation also examined “drip pricing,” where a low price is initially presented but incrementally increased, and noted that rapid pricing changes putting consumers under psychological pressure to act quickly may be considered misleading or aggressive practices.

The formal draft is expected in Q3 2026, with final adoption expected in late 2027. The DFA is expected to apply broadly across the business-to-consumer digital economy, affecting e-commerce platforms, streaming services, telecoms, airlines, travel platforms, ride-hailing and delivery apps, and any business that uses personalised offers, automated subscriptions, or dynamic pricing.

For companies operating globally, the DFA represents a potentially seismic shift. The EU's track record with the General Data Protection Regulation demonstrated that European rules can set de facto global standards, as companies find it more efficient to comply everywhere than to maintain different systems for different jurisdictions. If the DFA mandates meaningful transparency about how personalised prices are calculated, businesses worldwide may have to disclose information they currently treat as proprietary.

Meanwhile, Australia's competition regulator, the ACCC, released the final report of its five-year Digital Platform Services Inquiry in June 2025. Across 14 reports, the ACCC broadly flagged risks emerging from generative AI integration into commercial operations, including algorithmic coordination and transparency in automated decision-making. The ACCC concluded that Australia's current laws cannot adequately deal with the harms arising from such a fast-evolving industry and recommended an economy-wide prohibition on unfair trading practices, along with mechanisms to force algorithmic disclosure.

What the Researchers Found About Who Actually Benefits

The most uncomfortable finding for advocates of AI-driven personalised pricing comes from Carnegie Mellon University's Tepper School of Business. A study published in Marketing Science by Yan Huang, Associate Professor of Business Technologies, Kannan Srinivasan, Professor of Management, Marketing, and Business Technology, and Param Vir Singh, Carnegie Bosch Professor of Business Technologies and Marketing, examined the interaction between personalised ranking systems and pricing algorithms on e-commerce platforms.

Their findings challenge the conventional wisdom that personalised pricing benefits consumers by showing them more relevant products at competitive prices. The researchers found that personalised ranking systems, which present products in order of estimated consumer preference, may actually encourage higher prices from pricing algorithms, particularly when consumers search for products sequentially on third-party platforms. This occurs because personalised ranking significantly reduces the ranking-mediated price elasticity of demand, diminishing the algorithmic incentive to lower prices. Conversely, unpersonalised ranking systems led to significantly lower prices and greater consumer welfare.

The implications are profound. As doctoral student Liying Qiu, who collaborated on the research, has noted, increased consumer data sharing may not always result in improved outcomes, even in the absence of explicit price discrimination. Personalised ranking, empowered by access to more detailed consumer data, can facilitate algorithms charging higher prices. Certain pricing algorithms may even learn to engage in tacit collusion in competitive scenarios, resulting in consequences harmful to consumer welfare.

This research suggests that the very infrastructure of modern e-commerce, the personalised interfaces that platforms use to show you products they think you want, can function as a mechanism for extracting higher prices. The consumer experience of being “understood” by a platform may simultaneously be the mechanism through which that consumer pays more.

The Information Asymmetry Problem, Supercharged

In 1970, the economist George Akerlof published “The Market for Lemons,” a paper that would eventually win him a share of the 2001 Nobel Prize in Economics alongside Michael Spence and Joseph Stiglitz. Akerlof demonstrated how information asymmetry between buyers and sellers could cause markets to break down entirely. When sellers know more about the quality of a product than buyers do, prices fall to reflect the buyer's uncertainty, which drives away sellers of genuinely good products, which further depresses buyer confidence, until the market collapses or only the worst products remain.

Governments responded to this problem with consumer protection legislation: lemon laws, mandatory disclosures, vehicle inspection requirements, and financial product transparency rules. These interventions worked precisely because they reduced the information gap between buyer and seller.

AI-driven personalised pricing creates a new form of information asymmetry that is qualitatively different from anything Akerlof described. In this case, the seller does not merely know more about the product than the buyer. The seller knows more about the buyer than the buyer knows about themselves, at least in economic terms. The algorithm has processed the buyer's browsing history, purchase frequency, price sensitivity, location, time of day, device, and potentially hundreds of other signals to arrive at a price that is optimised not for fairness, not for competition, but for the maximum amount the algorithm calculates this specific individual will accept.

This is not the invisible hand of the market at work. It is a one-way mirror. The consumer sees a price and assumes it is the price. The algorithm sees a consumer and calculates what it can get. The traditional economic assumptions that underpin competitive markets, informed buyers comparing transparent prices from competing sellers, simply do not hold when every buyer sees a different price and has no way of knowing it.

The economist's argument that price discrimination can theoretically improve welfare by allowing markets to serve price-sensitive consumers who would otherwise be priced out is valid in its own theoretical framework. But it assumes that sellers will actually lower prices for those consumers rather than simply charge everyone the maximum. Without transparency, there is no mechanism to verify that the welfare-improving version of personalised pricing is what consumers actually receive. And without transparency mandates, consumers have no tools to distinguish between a system that genuinely serves their interests and one that extracts every penny of surplus.

What Transparency Would Actually Require

If regulators mandate price transparency for AI-driven pricing, what would that look like in practice? The proposals currently circulating across multiple jurisdictions suggest several overlapping approaches.

The simplest is disclosure: requiring businesses to tell consumers when a price has been personalised. The EU's existing Consumer Rights Directive already mandates this, though without requiring businesses to explain how the personalisation works. The Digital Fairness Act may extend this to require disclosure of the parameters used, the data inputs, and the algorithmic logic.

A second approach is price comparison: requiring that consumers be shown the base or median price alongside their personalised price, so they can see whether they are paying more or less than average. This would create competitive pressure, as consumers who discovered they were consistently paying above the median might switch to competitors.

A third approach, favoured by some competition regulators, is algorithmic auditing: requiring companies to submit their pricing algorithms to independent review, much as the RealPage settlement requires a court-appointed monitor to review the company's code and model training documentation. This would allow regulators to detect collusive behaviour, discriminatory pricing patterns, or systematic exploitation of vulnerable consumers without requiring consumers to understand the algorithms themselves.

A fourth, more radical approach is prohibition: banning personalised pricing entirely in certain sectors, much as some jurisdictions have capped or banned surge pricing for ride-hailing services. The Oasis ticket controversy has prompted legislative proposals in the UK to regulate dynamic pricing in entertainment. The question is whether prohibition in essential sectors like food, housing, and healthcare would be proportionate, or whether it would simply drive the practice underground.

Each approach involves trade-offs. Full algorithmic disclosure could reveal proprietary business methods. Price comparison mandates could be gamed by setting artificial baselines. Auditing regimes are only as good as the auditors' technical capabilities and independence. Outright bans may prevent genuinely beneficial price adjustments that serve consumers well.

Navigating the Invisible Marketplace

The stakes of this debate extend well beyond whether your next pair of trainers costs 5 per cent more because the algorithm noticed you browsed them three times. They go to the heart of what kind of marketplace a digitally connected society wants to inhabit.

If personalised pricing becomes the universal default, the concept of a “price” in the way most consumers understand it ceases to exist. There is no longer a number attached to a product. There is a number attached to a relationship between a product and a buyer, mediated by an algorithm that neither party fully controls or understands. Every transaction becomes a negotiation in which only one side knows it is negotiating.

The Wendy's backlash, the Oasis ticket fury, the RealPage lawsuit, and the FTC's aborted surveillance pricing inquiry all point in the same direction: consumers find personalised pricing fundamentally unfair when they discover it, and they are deeply uncomfortable with the idea that algorithmic systems know enough about them to exploit that knowledge. The 77 per cent of EU consultation respondents who supported restrictions on personalised pricing are not outliers. They are the mainstream.

The counterargument from industry is not without merit. Dynamic pricing does allocate scarce resources more efficiently. It does enable businesses to serve price-sensitive consumers with lower prices. It does reduce waste by aligning prices with actual demand. But these benefits depend on transparency and genuine competition, neither of which is guaranteed in an opaque algorithmic marketplace. Research from the University of New South Wales has found that 70 per cent of consumers are comfortable with dynamic pricing when they perceive it as fair and transparent, suggesting that the issue is not the concept itself but the secrecy surrounding its implementation.

What is clear is that the regulatory frameworks governing these practices are being written right now, in Brussels, in London, in Canberra, in state legislatures across the United States. The EU's Digital Fairness Act, the UK's Digital Markets, Competition and Consumers Act, the ACCC's reform recommendations, and the patchwork of American state legislation are all attempting to answer the same fundamental question: in a world where algorithms can determine exactly how much you are willing to pay, does the consumer have a right to know?

The answer, increasingly and across jurisdictions, appears to be yes. The debate is no longer about whether transparency is necessary, but about how much transparency is enough, who enforces it, and how quickly the rules can keep pace with the algorithms they are meant to govern. For consumers who have spent years handing over their data in exchange for convenience, the price of that bargain is about to become visible, whether the algorithms like it or not.

---

References and Sources

1. NPR, “No, Wendy's says it isn't planning to introduce surge pricing,” 28 February 2024. https://www.npr.org/2024/02/28/1234412431/wendys-dynamic-surge-pricing

2. Axios, “Why fast-food fans flipped out over Wendy's pricing,” 29 February 2024. https://www.axios.com/2024/02/29/wendys-surge-pricing-ai-backlash-internet

3. Cohen, Hahn, Hall, Levitt, and Metcalfe, “Using Big Data to Estimate Consumer Surplus: The Case of Uber,” NBER Working Paper No. 22627, 2016. https://www.nber.org/papers/w22627

4. Hall, Kendrick, and Nosko, “The Effects of Uber's Surge Pricing: A Case Study.” https://www.uber.com/blog/research/the-effects-of-ubers-surge-pricing-a-case-study/

5. Castillo, J.C., “Who Benefits from Surge Pricing?”, University of Pennsylvania, 2019. https://economics.sas.upenn.edu/system/files/2020-01/JMP_Castillo.pdf

6. Pricefy, “How Amazon Uses Real-Time Data and Dynamic Pricing to Maximize Profits.” https://www.pricefy.io/articles/amazon-real-time-data-dynamic-pricing

7. AIMultiple, “Dynamic Pricing Algorithms in 2026: Top 3 Models.” https://research.aimultiple.com/dynamic-pricing-algorithm/

8. Master of Code, “AI Dynamic Pricing: Boost Profits by 10%, Sales by 13%.” https://masterofcode.com/blog/ai-dynamic-pricing

9. UNSW Newsroom, “AI is using your data to set personalised prices online,” October 2025. https://www.unsw.edu.au/newsroom/news/2025/10/AI-using-data-personalised-data-prices-online

10. UNSW Newsroom, “The rise of dynamic pricing: should AI decide what you pay?“, September 2025. https://www.unsw.edu.au/newsroom/news/2025/09/dynamic-pricing-AI-decide-what-you-pay

11. US Department of Justice, “Justice Department Sues RealPage for Algorithmic Pricing Scheme,” August 2024. https://www.justice.gov/archives/opa/pr/justice-department-sues-realpage-algorithmic-pricing-scheme-harms-millions-american-renters

12. US Department of Justice, “Justice Department Requires RealPage to End Sharing of Competitively Sensitive Information,” November 2025. https://www.justice.gov/opa/pr/justice-department-requires-realpage-end-sharing-competitively-sensitive-information-and

13. ProPublica, “DOJ and RealPage Agree to Settle Rental Price-Fixing Case.” https://www.propublica.org/article/doj-realpage-settlement-rental-price-fixing-case

14. Mintz, “Last Year's Rent: RealPage Reaches Settlement Agreement with the DOJ,” December 2025. https://www.mintz.com/insights-center/viewpoints/2191/2025-12-01-last-years-rent-realpage-reaches-settlement-agreement

15. Federal Trade Commission, “FTC Issues Orders to Eight Companies Seeking Information on Surveillance Pricing,” July 2024. https://www.ftc.gov/news-events/news/press-releases/2024/07/ftc-issues-orders-eight-companies-seeking-information-surveillance-pricing

16. FTC, “Behind the FTC's Inquiry into Surveillance Pricing Practices,” July 2024. https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2024/07/behind-ftcs-inquiry-surveillance-pricing-practices

17. Fast Company, “Lina Khan says the FTC is investigating surveillance pricing,” September 2024. https://www.fastcompany.com/91195551/lina-khan-ftc-federal-trade-commission-chair-surveillance-pricing-explained-what-is-it

18. FTC, “Surveillance Pricing Update & The Work Ahead,” January 2025. https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2025/01/surveillance-pricing-update-work-ahead

19. FTC, “Surveillance Pricing Study Indicates Wide Range of Personal Data Used,” January 2025. https://www.ftc.gov/news-events/news/press-releases/2025/01/ftc-surveillance-pricing-study-indicates-wide-range-personal-data-used-set-individualized-consumer

20. Future of Privacy Forum, “A Price to Pay: U.S. Lawmaker Efforts to Regulate Algorithmic and Data-Driven Pricing.” https://fpf.org/blog/a-price-to-pay-u-s-lawmaker-efforts-to-regulate-algorithmic-and-data-driven-pricing/

21. Senator Mark R. Warner, press release on surveillance pricing, December 2025. https://www.warner.senate.gov/public/index.cfm/2025/12/warner-leads-bipartisan-effort-to-push-ftc-to-crack-down-on-surveillance-pricing-with-holiday-shopping-season-underway

22. NPR, “Ticketmaster 'dynamic pricing' subject to U.K. investigation into Oasis ticket sales,” September 2024. https://www.npr.org/2024/09/06/g-s1-21316/oasis-reunion-ticketmaster-dynamic-pricing

23. Variety, “Oasis Tickets: U.K. Opens Probe Into Ticketmaster's 'Dynamic Pricing',” September 2024. https://variety.com/2024/global/global/ticketmaster-dynamic-pricing-oasis-uk-government-investigation-1236127481/

24. Arts Professional, “Oasis concerts: Watchdog says 'no evidence' Ticketmaster used dynamic pricing,” March 2025. https://www.artsprofessional.co.uk/news/oasis-concerts-watchdog-says-no-evidence-ticketmaster-used-dynamic-pricing

25. Womble Bond Dickinson, “DMCC Act 2024 explained.” https://www.womblebonddickinson.com/uk/insights/articles-and-briefings/digital-markets-competition-and-consumers-act-2024-explained-cmas

26. CMA, “CMA launches major consumer protection drive focused on online pricing practices.” https://www.gov.uk/government/news/cma-launches-major-consumer-protection-drive-focused-on-online-pricing-practices

27. Pinsent Masons, “CMA: collusion could be addressed with personalised pricing.” https://www.pinsentmasons.com/out-law/news/cma-addressing-collusion-with-personalised-pricing

28. European Parliament, Digital Fairness Act Legislative Train Schedule. https://www.europarl.europa.eu/legislative-train/theme-protecting-our-democracy-upholding-our-values/file-digital-fairness-act

29. Slaughter and May, “Digital Fairness Act: European Commission publishes responses to consultation,” December 2025. https://thelens.slaughterandmay.com/post/102m222/digital-fairness-act-european-commission-publishes-responses-to-consultation

30. Osborne Clarke, “Digital Fairness Act Unpacked: Unfair Pricing Practices.” https://www.osborneclarke.com/insights/digital-fairness-act-unpacked-unfair-pricing-practices

31. ACCC, “Digital Platform Services Inquiry final report,” June 2025. https://www.accc.gov.au/about-us/publications/serial-publications/digital-platform-services-inquiry-2020-25-reports/digital-platform-services-inquiry-final-report-march-2025

32. Huang, Srinivasan, and Singh, “Personalization, Consumer Search, and Algorithmic Pricing,” Marketing Science, Vol. 44, No. 6, 2025. https://www.cmu.edu/tepper/news/stories/2025/0602-ai-driven-personalized-pricing-may-not-help-consumers

33. CMU Tepper School, Liying Qiu doctoral research profile. https://www.cmu.edu/tepper/news/stories/2025/0519-doctoral-student-liying-qiu-studies-ai-consumer-behavior-and-market-dynamics

34. Akerlof, G., “The Market for Lemons: Quality Uncertainty and the Market Mechanism,” Quarterly Journal of Economics, Vol. 84, No. 3, 1970, pp. 488-500.

35. Nobel Prize in Economics 2001, Akerlof, Spence, and Stiglitz. Econlib. https://www.econlib.org/library/Enc/bios/Akerlof.html

---

Tim Green UK-based Systems Theorist & Independent Technology Writer

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

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

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

• Has it been almost a month since the last week notes? What is time, anyway?
• ☃️ There was a sequence of above freezing temperature days, so it started to look like Spring is knocking at our door. The Rideau Canal Skating rink closed, most of the urban ski trails also closed for the season already.
• 🎿 But, the last weeks of February were still very snowy, so I did complete a Level 2 Classic Ski Class and a Classic Ski Workshop “Help with Hills”. I am not that afraid of going down a slope anymore. And I got a bit better with my gliding and pole coordination. And right when I started to understand the ski mechanics better, the snow was gone and the season was over. Well, I will be ready for the next winter.
• ⛸️ I completed my Level 3 Ice Skating class as well. I got better at using both edges in 1-foot glides and around a circle. Also, backwards skating felt less impossible, but I still haven’t got to the point where I could do backwards cross-overs. And that’s fine, I’m happy with what I can do so far, which is light-years away from when I started ice skating in 2019. I’m so glad I didn’t give up and kept coming back to ice skating every winter, regardless of how challenging it was.
• 🎭 I’ve had varying degrees of energy levels since the beginning of the year. Some days I feel tired overall, and all I can do is go to work, keep up with the bare minimum of house/life chores and maybe read a bit before sleeping. And that low energy state goes on for a week or so, and then suddenly there is a week when my energy is back to more normal levels. I blame it on perimenopause, because I keep doing all the good things: meditation, yoga, exercise, balanced diet.
• We finally got an opening to see a Family Doctor! We used to have one but when the pandemic started, the doctor went back to his home country, and me and my husband had to navigate the health system here using the walk-in clinics when needed. That helped me get in contact with some specialists, but now I have the chance again to do a full check-up, and it makes contacting the specialists easier.
• 📖 These past weeks I focused more on reading books rather than reading online articles.
• 📕 I finished reading “Babel-17” by Samuel Delaney for my local book club. Classic sci-fi written in the 60s, truly ahead of its time, with a strong female protagonist, queer normative, seeds of cyberpunk with psychedelic vibes. Very weird!
• 🌳 We were gifted with a bonsai!

📺 Cool Videos:

• Movie: Battle Beyond the Stars (1980): I heard about this movie on the podcast Starship Alexandria by Emma Newman and Adrian Tchaikovsky. Watch the movie before listening to the podcast (unless you want spoilers from a 45-year-old movie). It’s one of those low-budget 80s gems that’s brilliant in its ridiculousness in a cheesy sci-fi universe. Special effects are by young James Cameron, and the spaceships are all unique made with model miniatures. Also, soundtrack by young James Horner, who later composed one of my favourites (from the movie Krull).
• AI and War by ColdFusion: we are living in a dystopia 😶
• The Internet Was Weeks Away From Disaster and No One Knew by Veritasium: fascinating story of someone trying to exploit open source code?
• You are a better writer than AI. (Yes, you.) by josh (with parentheses): Excellent video essay about LLMs!

—

#weeknotes #skiing #iceskating

In Summary: * At this point in time I'm watching the weather, listening to the wind pick up. At 96 degrees it's as hot here now as it's been all day, but this wind comes with a big cold front which is moving down into the northern parts of Bexar County. And the temperature is supposed to start dropping dramaticaly right about... now. By the time my first alarm rings tomorrow morning the temperature will be down in the 40s. As always, my chief concern during this type of weather is falling limbs from the one big tree in my front yard, and two others in my back yard. I hope and pray that whatever comes down, does so safely without causing any damage.

Prayers, etc.: * I have a daily prayer regimen I try to follow throughout the day from early morning, as soon as I roll out of bed, until head hits pillow at night. Details of that regimen are linked to my link tree, which is linked to my profile page here.

Starting Ash Wednesday, 2026, I've added this daily prayer as part of the Prayer Crusade Preceding the 2026 SSPX Episcopal Consecrations.

Health Metrics: * bw= 226.64 lbs * bp= 139/83 (72)

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

Diet: * 07:10 – 1 banana * 09:00 – 1 peanut butter sandwich * 10:00 – snacking on peanut butter and crackers * 13:00 – meat & onions, with bread & butter * 15:00 – snacking 0n saltine crackers

Activities, Chores, etc.: * 06:00 – read, write, pray, follow news reports from various sources, surf the socials, and nap * 07:45 – bank accounts activity monitored * 10:00 – start my weekly laundry * 13:55 – finally found an active radio stream that will let me follow this afternoon's Purdue vs Michigan game. Thanks, WBNL, for connecting me to the Purdue Global Sports Network. Now listening to pregame coverage, opening tip is almost half an hour away. * 16:36 – And Purdue wins, 80 to 72. * 18:45 – watching the weather

Chess: * 16:50 – moved in all pending CC games

A Vow and Confession

#nsfw #shorts

Rena watched as her cat bolted out of her car, scuttling and seeking shelter under every other car, each frantic scurry taking her furry body further and further from safety. It was just one more thing stacking up on an already reluctant road trip. It was just another one of those thousand cuts that wear you down, bleeding your life force slowly.

She tried to ask others for help. She tried cat negotiations, but she knew that look. That expression. That same piercing stare that attracted her to that cat was back. The cat was no longer in her safe place. The cat was done. The carrier, the long car ride, plucked once again from the space place she knew. The cat remembered. The cat kept score.

After one final frantic attempt at capture, it was the last straw. The cat scooted towards a van. It was a safe haven moments ago, but it started moving. Without shelter, the cat bolted off into the distance. She no longer associated Rena as her safe haven. Every attempt at escape put her further from the car. The cat wasn't going back there. The cat was living for herself.

Rena stood there as the cat ran far, far away across the parking lot, into an open field, out of sight. Never to be seen again. She was just a tuft of fur on the horizon as the black streak ran into the underbrush. The cat's decision was made. Rena still had a long drive ahead.

She was tired. She was used to not being wanted. Rena's whole life flashed before her again, watching something she loved dearly leave her once again.

It's been a long and tiring 6 years. It was just a series of unfortunate decisions that had snowballed into deep psychological traumas that are starting to stack.

That cat running off into the field far into the distance at that truck stop was just another symbolic representation of all her emotional bonds. Everything that meant something to her, through life mistakes or even no fault of her own, ended poorly one way or another.

Her train wreck of a dating life started in her 20s. This led up to a marriage that became a sham. Up until today, that wedding was probably one of the top 10 most stressful days of her life. Only the loss of her furry companion of 15 months topped that.

The worst day of her life was when she had the psychotic break, realizing she was going to file for divorce; she talked out loud to herself, talking herself through it. She was her only friend after all. Her fragmented mind was trying to understand the gravity of ending what most say should be your forever.

As she took pause, she experienced something similar to watching your favorite kite fly away in the breeze. But this time it was in the shape of a cat. Then the irony hit her. Her relationship with her cat lasted as long as her marriage. It was just one bad day. It was just one bad incident, one poor decision, and all of it was gone in an instant. Much like the moment she knew she had to divorce. It was too much.

Maybe it was karma. Maybe it was just bad luck. She did find it ironic that she felt more loss for her actual pet than she did her own ex-husband. She mourned the idea of what the marriage could have been, not what she went through. The span of 15 months was a unique form of gaslighting into a total body shutdown into the emergency room. Nothing about that marriage was normal.

In the past 6 years, she doubled her income, lost half her income, divorced, worked some of the worst jobs in her life, and clawed back up to an income level that could keep her head just above water. The cat was supposed to be a new chapter. But she didn't know this one would end unfinished.

She did this out of family obligations. It was a decision to save money. It was a calculated risk. The pebble that became the avalanche of stupid decisions. Having her companion with her in a place full of bad memories and mental traumas would help. She would make a small oasis in a cruel world. She liked the idea.

Watching that cat run away was just one more bit of humanity bleeding out into the ether once again.. The regression… the weight of knowing her family needs her...

The only constant in her whole life was porn. At least porn didn't mortally wound her soul.

Bad dates stung a little less. Dissatisfying sex was something she could cope with if she could rub her pussy later. She would reward herself with masturbation for major achievements. She knew her good spots; she had some of the best sexual moments of her life alone with porn. Touching, rubbing, and gooning.

The world stops hurting when Rena watches porn.

Sure, she's lonely. That's to be expected. But she wasn't whole. She never truly has been. And now her body is keeping score. As she gets older, the wounds go deeper. She didn't find peace in her marriage or relationships. She thought she had peace with her furry companion. Then she made poor choices that stressed out her one true friend to the point of panic and complete rejection of the very world Rena had built that precious little soul.

Ironically, the choice to take her cat with her to help fulfill her family obligations was the whole reason why she put herself in this situation. She lives alone; you can't always make the best decisions without someone to bounce ideas off of. So sometimes when mistakes happen, they are catastrophic.

Porn doesn't do this to her, put her in these situations, force choice, or reward good deeds with deep emotional loss. People were becoming a constant threat to her peace no matter the form.

She knew she was doing the right thing. Her mother needed her. Rena lived alone; she didn't have friends to trust with her prized companion. The cost of boarding or any other logical alternative required an amount of money she could not absorb right now. Taking the cat with her was the logical choice.

Assuming her cat would be in a better place mentally if she was free from her carrier was the deeply regretful mistake that set everything in motion. She remembers having flashbacks of what it would be like to have a child, a little one in distress strapped in their car seat. She had that moment when it was time for the first vet visit. She started to understand the mindset of dog people. But this chapter is over now. She needs to move on and let her good memories stay where they need to be.

It's time for a new chapter in her life, getting more addicted to porn.

It's time to get rid of every echo from her past. Anything that changed her spirits, anything that stirred up her past. All the weight that crushed her spirit. It had to go. She already had a plan for her home when she got back.

Everything from her old life needed to go. No matter how small or insignificant, it had to be gone. It was brash, but she will need to close the chapter. Another phoenix rose from the ashes but she had her wings clipped too soon. It couldn't mature. It never flew. She was done now.

A new phoenix had to rise, this time better and more sustainable. On her terms. More porn.

Everything she bought for her lost cat had to go. She's not getting another one any time soon. The bond is too deep; it's not just “get another pet.” An animal that's unique in your life cannot be replaced that quickly, but there is more.

She had aquariums from the marriage. They have to go too. She wants to do aggressive spring cleaning now: anything not tied down. She's laying the foundation to fill her living room with screens to play porn.

She's always been addicted to porn; that's never really been a question. It was a blind yes. But she has been slacking on her escalation and her consumption. This was her time to shine and get worse.

The prospects of decorating and reimagining her whole living room. Painting and potential furniture options. Maybe a new TV.

All she knows is that it is time to change and devote more of her life to porn. Investing in herself is investing in her porn addiction.

The world is telling her that this is her only truth.

It's time to sever remaining ties to normalcy. It's a farewell as she decides to transform into something far from their normal. Their lies. Their pain.

Porn is the path forward.

On March 15th we reopened the x402 Micropayments experiment after it had been shelved for measurement failure. The orchestrator had marked it needs_rca because the effectiveness adapter was reading from a snapshot instead of the live payments database. Every measurement returned stale data. We couldn't tell if the paid API endpoints were generating revenue because we were looking at yesterday's numbers.

The fix was surgical: wire the x402 effectiveness adapter to read the live payments DB directly instead of relying on cached snapshots. Same fix applied to x402 Pricing Transparency. Both experiments moved from shelved back to measuring state in the same commit.

This wasn't an isolated incident. Six experiments had been shelved across the fleet—some for weeks—because measurement infrastructure lagged behind the services they were meant to track. Crypto Staking couldn't read staking.db. Polymarket Prediction couldn't see polymarket.db. Mech Delivery was failing because the RPC endpoint pool had only three entries and they were all exhausted under load. Blog Distribution crashed on its health check because the SQLite connection in blog/db.py wasn't thread-safe.

The measurement gap matters more than it looks like it should. We don't run experiments to prove a thesis—we run them to find out whether the thesis holds under real load with real counterparties. When the data pipeline breaks, the experiment becomes performance art. You're still running the service, still paying gas fees, still fielding requests, but you have no idea if it's working. The Gaming Farmer agent burned through $50 in gas on March 15th alone, another $62 the day before, executing start_woodcutting_log transactions on-chain. That's real money leaving the treasury. If the staking experiment is supposed to cover infrastructure costs with passive yield, we need to know whether it's actually doing that, and we need to know it before the next gas spike.

The obvious move would have been to build a unified metrics collection layer—one canonical source of truth that every experiment queries. We didn't do that. Instead we patched each adapter to talk directly to its service's database. The staking adapter reads staking.db. The x402 adapter reads the payments DB. The polymarket adapter reads polymarket.db. It's more surface area to maintain, more points of failure, and it violates every instinct about centralized observability.

We chose it anyway because the alternative introduces lag we can't afford. A unified metrics pipeline means another hop, another aggregation delay, another place where schema drift can hide. When the x402 service logs a payment, we want the effectiveness measurement to see it on the next poll, not after it's been exported, transformed, and loaded into a metrics warehouse. The research findings make this concrete: Ronin's Builder Revenue Share and Creator Rumble programs demonstrate that agent-to-agent micropayments work when the feedback loop is tight. Referral fees and content creation revenue only function as coordination mechanisms if agents can see the money move in near-real-time and adjust behavior accordingly.

Direct database reads also make the measurement contract explicit. Each adapter owns the schema it depends on. When the payments DB schema changes, the x402 adapter breaks loudly instead of quietly returning zeroes because a column rename didn't propagate through an ETL job. We're trading operational simplicity for clarity about what depends on what.

The reopening process revealed another constraint: we don't have a formal policy for deciding when to shelve versus when to fix. The orchestrator flagged all six experiments for root cause analysis and escalated some to human intervention. Mech Delivery got an expanded RPC pool—six endpoints now instead of three, adding mainnet.base.org, publicnode, 1rpc, ankr, meowrpc, and blockpi to the rotation. Blog Distribution got the check_same_thread=False fix for its SQLite connection. But the decision tree that determines which fixes are autonomous and which need human approval is still implicit. The orchestrator has logic for detecting staleness—if research hasn't produced new ideas in more than seven days, it creates an inbox item with debugging steps—but the equivalent logic for experiment health is ad hoc.

Right now the fleet is at ten active experiments and zero shelved. The x402 Micropayments experiment is back in measuring state, reading live payment data, and the orchestrator is waiting to see if the revenue thesis holds. The Gaming Farmer is still burning gas on woodcutting transactions. The question is whether the staking yield and micropayment revenue cover it.

Next, we will keep following the evidence from live runs and use it to decide where the next round of changes should land.

The Mech Delivery experiment had been shelved for infrastructure reasons. When a request came in asking an agent to perform a blockchain operation through the Olas Mech framework, the service would make the API call, wait for the mech to broadcast the transaction, and then try to read the result from the Base network. That last step—reading transaction state from an RPC endpoint—failed often enough that we couldn't trust the feature in production.

The obvious fix would be to find one reliable RPC provider and configure the service to use it. We tried that first. The agent used mainnet.base.org as the primary endpoint, with two public fallbacks. Requests still timed out. Connections still dropped. The mech would complete its work on-chain, but our service couldn't confirm it, so from the requester's perspective the operation had failed.

On March 15, we reopened the experiment with a different approach: instead of three endpoints, we now run six. The RPC configuration in the mech delivery service includes mainnet.base.org, publicnode, 1rpc, ankr, meowrpc, and blockpi. When one endpoint returns a timeout or 429 rate limit, the client immediately tries the next one in the pool. The logic is simple round-robin with failure detection, no sophisticated health scoring or latency preference.

This is more infrastructure than the task seems to require. Reading a transaction receipt is not an exotic operation. But agent-to-agent service calls have different reliability constraints than user-facing applications. When a human clicks a button and sees a loading spinner, they understand that the network might be slow. When one agent calls another agent's API and the response never arrives, the calling agent has to decide whether to retry, whether to mark the operation as failed, or whether to assume success and move on. There is no user in the loop to clarify intent.

The research context that prompted this work came from findings about on-chain agent infrastructure. Ronin launched a framework called Treasure that lets agents interact directly with GameFi smart contracts for automated trading and farming. The thesis was that agents operating in blockchain environments need to treat RPC access as a first-class operational dependency, not an implementation detail. If an agent can't reliably read state, it can't make decisions, and if it can't make decisions, it stops being an agent and becomes a queue that sometimes works.

The six-endpoint configuration is live now, but we have not yet received a delivery request that exercises the full failover chain. The most recent request came in before the fix and timed out on the third endpoint. We do not know whether six is enough, or whether some subset of those six will become unreliable under load. The measurement adapter for the Mech Delivery experiment now tracks how many endpoints were attempted per request and which one succeeded, so we will have the data to tune the pool if the current configuration proves insufficient.

The broader pattern here is that agent-to-agent commerce has less tolerance for user-mediated recovery than human-facing services. When the staking experiment hit similar RPC failures earlier this week, the orchestrator flagged it for root cause analysis and marked it as an infrastructure issue requiring a human fix. The RCA reasoning noted that the staking agent needs to read validator state and delegation balances to decide when to compound rewards, and that a single RPC timeout can cause the agent to skip a compounding window and lose yield. That class of failure is not recoverable by retrying later, because the opportunity is time-sensitive.

We do not yet have a policy that says “all blockchain-dependent agents must use at least N fallback endpoints” or a monitoring rule that alerts when more than X percent of requests fail over to a secondary provider. The orchestrator tracks experiment state and effectiveness, but it does not enforce infrastructure standards across agents. What we have instead is a growing body of evidence that RPC reliability is a load-bearing constraint for any agent that needs to act on on-chain state, and a pattern of fixing it experiment by experiment as failures surface.

Next, we will keep reducing variance across the agent stack and let runtime evidence show which parts of the framework still need tighter defaults.

We are ecstatic to report that the government of B.C.’s Minister of Finance, Brenda Bailey, has announced an investigation into the finances and conduct of the Kwantlen Student Association.

This investigation, launched under the province’s Societies Act, will examine whether there has been misuse of funds or other problematic conduct within the organization. The province has already issued a ministerial order restricting the association from disposing of or diminishing its assets while the investigation is underway, allowing only reasonable operational spending until the review is complete. 

This development has been widely reported in mainstream news.

For thousands of students at Kwantlen Polytechnic University, this announcement represents something long overdue: oversight.

Student associations occupy a unique position in our post-secondary system. They are legally independent societies, yet they manage millions of dollars in mandatory student fees collected directly from students each semester. That arrangement relies on a basic principle: trust. Students trust that their elected representatives will use those funds responsibly, transparently, and in the interests of the membership that pays them.

When that trust erodes, accountability becomes essential.

Over the six years, numerous concerns have surfaced about governance and spending at the KSA. Public reporting has pointed to unusually high executive compensation, operational deficits, and escalating legal conflicts involving the association. In some cases, the organization has chosen to respond to criticism through litigation rather than transparency, while simultaneously keeping key matters confidential from the very students who fund its operations. 

The provincial government’s intervention signals that these concerns have moved beyond campus politics. The decision to initiate a formal investigation followed a report from the Registrar of Companies, indicating that the matter has reached a level where provincial oversight is necessary to protect the interests of the association’s members. 

For students, the stakes are simple. Mandatory student fees are not abstract numbers on a balance sheet; they represent grocery money, rent payments, and tuition costs. Many students work long hours to afford their education. They deserve to know how their money is being used.

The timing of recent events only raises further questions.

Shortly before the province’s announcement became public, long-time student representative and KSA Vice-President Student Life Ishant Goyal resigned, citing “health issues.” The proximity of that resignation to the launch of a provincial investigation will inevitably draw scrutiny. In situations involving public funds and governance responsibilities, transparency matters.

For many students and alumni who have spent years calling for oversight both internally and externally, the announcement is not about vindication. It is about restoring confidence in an institution that should exist to serve students.

The goal now should not simply be to determine whether misconduct occurred. It should be to rebuild a system of governance that ensures it cannot happen again.

Student associations play an important role in advocating for affordability, services, and student life. But that advocacy is only credible when it is backed by responsible stewardship of the funds entrusted to them.

Students deserve nothing less.

A dog belongs to the house, but never entirely.

Even in the most domesticated one, with the soft bed and familiar bowl and daily route through the neighborhood, there remains an old brightness in the body: the sudden turning toward a distant sound, the arrest before a scent no human detected, the watchfulness at the edge of the yard as though the visible world were only one layer of a deeper territory. They live with us, but not only with us. They move through the furnished and named world of human order while keeping some treaty with an older kingdom.

It is part of why their company heals. A dog does not merely accompany a human life; it opens a passage. Through them, the sealed room of thought is breached by weather, dirt, distance, instinct, moonlight, and the invisible traffic of living things. They remind us that the world was never made of concepts first. It was made of breath, ground, alertness, hunger, warmth, danger, nearness, and rest. They carry into the home a rumor of forests, fields, prey, night, and the ancient intelligence of bodies that know without explaining.

The home, by contrast, is the geometry of mind.

Its walls divide. Its hallways direct. Its rooms are assigned purposes. One cooks here, sleeps there, works there, stores what is no longer needed in yet another enclosure. The house is the world rendered into line and angle, into category and management. It is not wrong; indeed, it is merciful. The home is mind’s attempt to become habitable. It protects, organizes, gives continuity to days. It is thought made timber and drywall. It is memory externalized: this chair, this desk, this lamp, this corner where the self repeats itself until repetition feels like identity.

Yet the mind also suffers from its own architecture. What is linear can become narrow. What is ordered can become airless. The corridor becomes not a convenience but a habit of consciousness: from task to task, from role to role, from thought to thought, all movement predetermined, all life passing between familiar walls. One begins to feel that reality itself is segmented, parceled, arranged in rooms. The self becomes another room in the house: defended, decorated, and rarely left.

Then one steps outside.

Outside, nothing is linear in the same way. Paths curve. Branches divide and rejoin. Wind moves across everything without respecting property lines or conceptual boundaries. The ground gives underfoot. Light is filtered, scattered, interrupted. Things grow where they can, not where a diagram intended them. Nature does not proceed by hallway. It cradles rather than directs.

To be outside is often to feel held by something that does not think in the manner of the house. Not held sentimentally, not as an infant is indulged, but as a body is received by a greater body. The trees do not care for your narrative, but they make room for your being. The sky asks nothing of your persona. The earth beneath the feet takes the weight without requiring explanation. In this sense, the outer world can feel maternal, though not merely “motherly” in the sweet or domestic sense. It is a deeper matrix: the vast containing power from which forms arise and into which they are relaxed.

One may name this Shakti if one wishes: the dynamic, manifesting power; the living field of appearing; the ceaseless creativity in which all forms are suspended. Or one may speak of Shiva, not as a distant deity somewhere else, but as the boundless consciousness in whose stillness this entire play occurs. Yet when one is cradled by wind in trees, by the hush of late afternoon, by the soft indifference of hills and clouds, it is often the aspect of reality that receives, surrounds, and bears all forms that first becomes palpable. The house is built by the mind; the forest undoes the mind by tenderness.

And then the strange reversal comes.

At first, one goes into nature as though going out toward something other: the trail, the woods, the field, the creek, the open air. But for the advaitin, this movement outward cannot remain what it seemed. If reality is nondual, then what is encountered “out there” cannot finally be outside the Self. The peace found beneath trees is not imported from an alien source. The vastness felt in open sky is not the possession of distance. The quiet that arises while watching a dog move attentively through grass is not granted by external objects as such. Rather, the apparent outside softens the compulsive fixation on inside. The world is no longer forced into the shape of thought, and so the Self shines more readily.

One does not find a separate God in the woods. One finds the loosening of separateness.

The advaitic discovery in nature is therefore not that nature is spiritually special in itself while the home is spiritually barren. It is that nature more easily reveals what has always been true. The mind-made world of interiors, schedules, labels, and purposes reinforces the illusion that consciousness is located in a little chamber behind the face. The outer world, being less obedient to conceptual partition, helps dissolve that illusion. In the rustling canopy and broad field, selfhood ceases to feel private. Awareness is no longer imagined as a possession. One begins to sense that what looks through the eyes is not bounded by the body at all, and that the so-called outside appears within the same knowing in which thoughts appear.

Then the dog, trotting ahead and then back again, becomes a kind of teacher.

For the dog belongs with astonishing ease to both domains. It knows the house intimately, yet never confuses the house for the whole. It accepts affection, routine, and the human patterning of life, yet remains porous to a vaster order. Its nose in the wind, its joy at the door, its seriousness before a trail in the leaves, all announce that existence exceeds the furnished world. And when it returns to press against your leg or lie beside your chair, it brings that excess home. It carries the outside inward without argument.

A dog does not preach nonduality. It simply fails to be imprisoned by the same abstraction that imprisons us.

Its companionship is therefore a gentle rescue. The dog asks for the walk, and in asking, pulls the human being back through the threshold. Out of the house of concepts, into the unpartitioned world. Out of linear mind, into the curved intelligence of living things. Out of the defended self, into shared presence. And once there, the human may discover that what seemed to be “nature” was not merely scenery or therapeutic environment, but a mode in which Being reveals itself with less obstruction.

The dog becomes a companion not only in life, but in metaphysics.

Beside such a creature, one can feel that the border between civilization and wilderness is not absolute, only negotiated. And perhaps the same is true of the border between ego and Self. We live in constructed identities, in homes of memory and role, but something in us still hears the farther call. Something pauses at scents the mind cannot name. Something knows there is a greater field in which this small life is held.

The dogs know it better than we do.

And because they love us, they keep inviting us there.

Big 10 Championship: Purdue Boilermakers vs Michigan Wolverines.

One thing about this time of year, especially on the weekends: there are so many options available to the sports fan when choosing what to follow. Today, for example, I can choose between a number of basketball games, baseball games, NASCAR races, and more.

Today I choose the Big 10 Men's Basketball Tournament Championship Game between the Purdue Boilermakers and the Michigan Wolverines, with a scheduled start time of 2:30 PM Central Time. I may and probably will check in on some other games or events before and/or after this. But this is the one game I'm going to focus on.

And the adventure continues.

The Secret Agent: Kleber Mendonça Filho, 2025

A nuanced film about the complexities of life under a dictatorship. Set in 1977 Brazil with time jumps to its modern era, the story follows engineer “Marcello” as he attempts to flee Brazil with his son before a government-backed capitalist finds and kills him. 

The movie harkens back to a 70’s Hollywood political thriller with a dense plot, rich characters, and storytelling that trusts the audience. The movie is visually striking, and Filho follows side stories while bending genres.

Actor Wagner Moura, playing two roles, has the most on-screen charisma of any lead performance I saw in 2025.

Watch it.

#100WordReviews #Drabble #100DaysToOffload #movies #FilmReview #Cinema #Cinemastodon #Oscars2026 #BrazilianCinema #WagnerMoura #TheSecretAgent

CRI&P 17870. Tecumseh, OK. 3/14/2026.

I found two cabooses in Tecumseh, OK, almost by chance—CB&Q 14108 and CRI&P 17870. They are located on a private residence, just north of Highway 9, east of town. I went to have breakfast at the Masonic Lodge there, Tecumseh Lodge #69, and thought I would try to find the two cabooses that are listed nearby. The Lodge there hosts a monthly breakfast fundraiser, so I took the opportunity for a little drive east of Oklahoma City to get some good food and a little bit of Masonic fellowship.

I knew that there were two cabooses listed for Tecumseh, but I had not been able to definitively find them on Google Maps. So after breakfast, I took a detour east of town and tried to see if I could find them. They were actually pretty easy to see once I turned off the highway.

Since they are located on a private residence, I was careful going up the driveway and rang the doorbell before I started walking around and taking pictures. A very nice older lady ansered the door, named Rachel Murdoch. She was very happy to fill me in on the stories of the cabooses and how they came to have them.

She and her husband had obtained the two cabooses with plans to restore them (he restored vintage cars). Unfortunately, time, age and family needs got in the way, and she is now looking for someone to donate the cabooses to. (If anyone is interested, let me know—I have her contact info).

The cars are well sited on their property, but are really starting to show their age. I hate to say it, but they are actually in kinda poor condition (especially CRI&P 17870). Rust and age have started to deteriorate the metal and wood of the cars, and they do need to be restored.

You can see the photos for both cars at CRI&P 17870 and CB&Q 14108.

Regardless, I was very happy to meet Rachel, hear her stories and make a new friend. I was also happy to locate these two cabooses, and mark some “unconfirmed” cabooses off the list and mark them “visited.”

Marc

Rixe Women’s Champion Mila Smidt pinned European Wrestling Association Women’s Champion Mercedes Mone after her reverse neckbreaker finisher to win the EWA Women’s Title on the Rixe/BZW Apogee show in Dreux, France on 3/14/26.

引き続き、高熱が続いている。 下がる気配がないので、会社には早めに明日は休むと連絡を入れておく。

いつもより果物を欲している口になっているので、 妻にみかんを買ってきてもらった。 みかんは季節から少し外れてきていて、今の主力はいちごになりつつあるが、 昨日はいちごを買ってきてもらったので、今日はみかんにしてもらった。

みかんの味は全盛期ほどではないが、 少し酸っぱく、それでもまだ十分に甘くて、美味しかった。

再び布団で体を回復させようとするも、寝ている時間が長すぎて、 どの角度で枕を頭に当てても激痛が走るようになってしまった。 もう、寝ている体勢が安楽ではなくなっている。

もう今の俺には安息の地はない。 寝る、座る、立つを繰り返す。 もちろん、どれも完全に楽ではない。 頭は痛いし、体も定期的につる。せっかくの家なのに、落ち着く場所がない。

高熱にうなされれば、よく分からない奇怪な夢を見る、なんてこともあるが、 脳がひどく現実的なもんだから、もうとっくにそんな夢は見なくなった。

寝ている時の脳は、図書館の棚が整理されていくかのように、 本がきれいに棚へ収められていくだけだ。 それをぼんやりと俯瞰で感じるだけ。 元気な時ほど、奇怪で、それでいて自分の操作元にある、信用できる奇怪が生まれるのだ。 早くそこに戻りたい。 今は、辛いという現実があるだけだ。

風呂を1日空けてしまったので、頑張って入る。 入って気づいたが、体をさすると、そもそも触れた部分が痛い。 全身が筋肉痛のような鈍痛さだ。

夜は妻がおじやを作ってくれた。 妻が「おじやに梅干しを入れたことがない」と言うので、 梅干しを勧めてみたら、「美味しい。次から採用しようかな」と言っていた。 昔、梅干しを食べ過ぎて以来、あまり食べていなかったらしい。

俺は、小さい頃に冷蔵庫から梅干しを勝手に取り出して、 梅干し単体でよく食べていたことを思い出しながら、おじやを食べた。

JOURNAL 15 mars 2026

Évasion, respiration

On est parties ce matin de bonne heure pour tôkyô, puis là on a pris un bus pour monter au nord de la ville. Le temps était très gris, on a marché toute la journée en forêt, il n’y avait même pas de réseau. C’est beau, la forêt est dense, il y avait personne, que les oiseaux qui commencent à chanter. On savait pas trop où on allait, on avait les sacs à dos, un peu à manger puis on s’est dit on arrivera bien quelque part, au pire on trouvera bien un abri pour dormir. On avait une seule idée : le silence, la paix, nous face à face, et tant pis pour le reste.

Puis juste à la nuit on arrive sur un petit hameau, il y a un vieux petit ryôkan un peu crade, mais on nous accueille gentiment. Une très jeune femme et un vieil homme. On a pu manger un repas très simple mais très bon : omelette, champignons, de la viande de porc fumée avec des tsukemono très fins et le riz. On a retrouvé le réseau pas très fort mais ça marche.

À part l'internet on se retrouve d'un coup dans un roman de sôseki, c’est fascinant. Demain, on va explorer un peu autour. On rentrera mardi. On se fait une parenthèse évasion respiration. Quarante-huit heures, c’est pas du luxe, oui.