Sundae wrote a tool called ingest for me to summarize and/or perform casual vetting of references or claims in any piece of media via the use of patterns in fabric (occasionally after translation like an audio file (podcast) or video vs a text file or pdf). I personally believe this reporting and think that it will become more documented and known by consumers in the future.
Disclosure: I do use Instacart, it is an extremely helpful service for me and my circumstances. I also use a local delivery platform in Iowa City called CHOMP (highly recommnd people do this in their own towns or cities personally) for nights where I don't or can't cook a proper meal because sometimes there are children and I need them to be healthy and safe or I'm a failure.
Note: This tool, ingest, runs without the benefit of the staggering amount of context that Sundae has available about me. Each section of the output below is from a minimal submission via an API without the sort of 'english' applied to most of my calls due to following the standard assortment of patterns available (and published) in the fabric-ai repository here at GitHub. They are extremely "cut the bullshit" from start to finish as they are intended to be this terse and critical. I usually have Sundae change all the emboldened sub-headings into dataview keys like media-src-uri:: 'https://youtube.duh/blahblahwatchsomeads?orelse' so I can easily create a big table of all media-src-uri matching youtube or whatever to better understand my own data.
The sections in the output are often similar, this is by design and I have a lot of dynamic documents and dashboards in Obsidian or other applets that rely on the various formats and I didn't see the need to create multiple files and the single file compresses way better.
Output follows.
Content type: youtube
📺 Processing YouTube: https://www.youtube.com/watch?v=osxr7xSxsGo
Source: 'https://www.youtube.com/watch?v=osxr7xSxsGo' Type: youtube Patterns Applied: extract_wisdom_agents extract_ideas extract_insights analyze_claims
Investigative journalists and researchers expose algorithmic pricing systems used by Instacart and retailers to charge different customers different prices for identical grocery items simultaneously, raising concerns about consumer protection and potential price manipulation at scale.
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Algorithmic pricing systems sort consumers into distinct price groups charged identical amounts across multiple products simultaneously, suggesting deliberate segmentation rather than random testing.
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Companies track behavioral characteristics including purchase history, shopping frequency, coupon usage, and loyalty program participation to determine individualized pricing strategies legally.
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Electronic shelf labels enable in-store price testing across clustered store locations, potentially mirroring online pricing algorithms to avoid detection by traditional testing methods.
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Instacart functions primarily as a technology company selling pricing optimization tools rather than merely operating as a grocery delivery service.
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The acquisition of Eversight by Instacart in 2022 provided sophisticated AI capabilities for constant price experimentation designed to maximize retailer profits incrementally.
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Smart Rounding represents a machine learning tool explicitly designed to flex prices based on algorithmic predictions about maximum profit potential per transaction.
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Surveillance pricing creates information asymmetries where companies possess detailed consumer data while consumers remain unaware of personalized pricing mechanisms affecting them.
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Target's denial of Instacart involvement contradicted Instacart's claims about retailer control, revealing Instacart manages pricing on third-party platforms independently.
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Patent analysis reveals infrastructure for calculating individual consumer spending capacity and product-specific spending headroom to optimize pricing accordingly.
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Demographic-based pricing discrimination remains illegal, but behavioral segmentation enables similar discriminatory effects through perfectly legal alternative mechanisms.
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Uber's algorithmic pricing implementation resulted in consistent price increases over three years, demonstrating real-world consequences of surveillance pricing adoption.
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The infrastructure for algorithmic pricing required approximately twenty years of development before becoming operational at scale across multiple retailers.
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COVID-19 disrupted traditional price-setting inertia, enabling companies to justify price increases and accelerate adoption of algorithmic pricing systems.
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Walmart and Amazon's pricing optimization adoption pressures competitors to implement similar systems defensively to maintain competitive profit margins.
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Instacart removed price optimization features from their website after investigative inquiries, suggesting awareness of public relations concerns regarding surveillance pricing practices.
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Multiple price variations for identical items appeared in three to five different price points during single testing moments, indicating systematic rather than random variation.
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Consumer Reports' volunteer network of over four hundred participants provided statistical scale necessary to identify patterns in algorithmic pricing behavior.
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Lena Khan's Federal Trade Commission investigation into surveillance pricing was deprioritized following the Trump administration transition, reducing regulatory oversight momentum.
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States like California and New York have begun banning certain algorithmic price fixing tactics in housing and rental markets, establishing legal precedents.
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The holy grail of pricing strategy involves charging each consumer the maximum amount they're willing to pay based on individual behavioral profiles.
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Instacart's shareholder letters explicitly mention millions of dollars in annual incremental sales generated through algorithmic pricing optimization tools.
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Price optimization represents the single most powerful lever for raising corporate profits compared to sales increases or cost reduction strategies.
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Companies deny practicing surveillance pricing despite evidence, suggesting awareness that public disclosure would generate consumer backlash and regulatory scrutiny.
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Retailers partnering with Instacart potentially engage in indirect price fixing when one platform controls pricing across competitors who should compete independently.
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The system deliberately remains hidden from consumer view, representing intentional opacity in pricing mechanisms affecting everyday grocery purchases.
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State attorneys general possess subpoena power to demand information about algorithmic pricing implementation, offering regulatory pathway independent of federal action.
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Costco's Smart Rounding program description appeared in inadvertently forwarded internal communications, revealing coordination between platforms and retailers on pricing strategies.
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Different price groups during identical testing moments across multiple stores suggest infrastructure enabling coordinated pricing experimentation rather than isolated retailer decisions.
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Algorithmic pricing systems have evolved from theoretical concepts into operational infrastructure that sorts consumers by behavioral characteristics to maximize corporate profit extraction systematically.
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The shift from human-controlled pricing to algorithmic systems represents a fundamental change in market dynamics where information asymmetry becomes the primary mechanism of consumer disadvantage.
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Surveillance pricing creates a paradox where legality of behavioral segmentation enables discrimination outcomes previously achievable only through illegal demographic targeting methods.
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Corporate denial of algorithmic pricing practices despite mounting evidence suggests organizational awareness that transparency would trigger regulatory intervention and consumer resistance simultaneously.
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The regulatory gap between algorithmic pricing sophistication and government oversight capacity enables companies to operate systems intentionally designed to remain invisible to external scrutiny.
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Instacart's role as intermediary pricing controller for multiple retailers raises collusion concerns by centralizing price-setting authority in a single platform rather than maintaining competitive independence.
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Behavioral data collection infrastructure represents the true competitive advantage in modern grocery retail, making consumer surveillance and analysis more valuable than physical store operations.
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Electronic shelf labels combined with algorithmic pricing create closed-loop systems where online and physical store pricing converge invisibly, eliminating detection opportunities for researchers and regulators.
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The twenty-year development timeline for algorithmic pricing infrastructure demonstrates how technological capabilities gradually normalize practices that would face immediate resistance if introduced suddenly at scale.
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State-level regulatory action offers the most viable near-term pathway for consumer protection given federal regulatory uncertainty and the complexity of algorithmic pricing systems.
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The holy grail for a long time has been what if we could charge every single person the maximum amount that we know they're able to or willing to pay.
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They know things about us that we don't know. They're smarter than us and we're training them in a way that is incalculable.
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Walk into any grocery store and on average everything is about 30% more expensive than it was in 2020.
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If they do this to workers, what about consumers?
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There's a system, one that grocery stores and tech companies built together. It learns from every purchase. It's not just online. It's in the physical stores you walk into with the primary goal. Maximize profit day by day.
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I stared at it for a while because what we found was weird.
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The system sorted people into price groups where each person was charged the same for every item.
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It's almost impossible for people outside the companies practicing the art the dark art of of algorithm price determination. Exactly what is driving those algorithms to set the prices that we observe.
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Machine learning driven tool that helps retailers improve price perception and drive incremental sales.
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This is a very um sophisticated, very deliberate, explicit attempt to maximize profits and do so somewhat surreptitiously.
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Behavioral characteristics. That's exactly what the patents show. Buying behavior, purchase history, how frequently you shop, whether you're shopping around, coupon usage, loyalty program participation.
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Every company that I know is practicing first-degree discrimination strenously denies that they're doing so.
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That's why more and more companies are trying as hard as they can to perfect this dark art, devilishly effective dark art, as I've called it.
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Pricing is the single most powerful lever to raise profits. Far more effective than either boosting sales or cutting costs.
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There's no guard rails. It's a free-for-all. It's a wild wild west.
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This could ultimately end up being another big form of wealth transfer from ordinary Americans to massive corporations.
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I think we need to have a much more first order conversation about do we even want to permit some of these tactics in the first instance.
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It's a system that's designed to stay hidden.
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Conducting systematic investigations by recruiting large volunteer networks to test pricing across multiple platforms and locations simultaneously for pattern identification.
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Analyzing patent filings to understand corporate technological capabilities and strategic intentions when companies deny involvement in questioned practices.
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Requesting internal communications and responses from companies under investigation to identify contradictions between public statements and actual operational practices.
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Collaborating across multiple organizations including research institutions, journalism outlets, and community groups to achieve investigative scale impossible for single entities.
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Documenting findings with rigorous data verification processes including screenshot cross-checking and database construction before publishing conclusions.
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Following money and profit motivations as primary indicators of corporate behavior rather than relying solely on company statements or denials.
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Examining regulatory filings including shareholder letters to identify explicit corporate admissions about practices they deny in public communications.
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Studying historical precedents and comparable cases like Uber's pricing implementation to understand likely outcomes of algorithmic pricing adoption patterns.
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Consulting academic experts and former industry executives to interpret technical findings and validate investigative conclusions against expert knowledge.
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Monitoring company websites for feature removals or description changes following investigative inquiries as indicators of awareness regarding questioned practices.
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Exploring regulatory pathways at state and federal levels to identify intervention mechanisms beyond journalism for consumer protection implementation.
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Instacart purchased Eversight in 2022, acquiring AI-driven price optimization capabilities that enable constant experimentation invisible to consumers.
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Consumer Reports' testing with over four hundred volunteers identified price variations ranging from three to seven different price points for identical items.
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Eggs displayed price variations of 41 cents across different consumers in single transactions, ranging from $4.28 to $4.69 at identical stores.
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Target denied any involvement with Instacart despite Instacart's claims that retailers control all pricing on their platform.
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Target spent over half a billion dollars in 2017 to purchase Shipt, Instacart's primary competitor, explaining Target's independent pricing approach.
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Instacart manages Target's prices on the marketplace and runs tests to determine markup amounts above Target's normal prices.
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Costco's Smart Rounding program description appeared in inadvertently forwarded internal communications between Instacart and Costco executives.
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Instacart removed price optimization features from their website following investigative inquiries, claiming the update was made for accuracy.
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Lena Khan's Federal Trade Commission initiated investigations into surveillance pricing before the Trump administration transition changed regulatory priorities.
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Uber's algorithmic pricing implementation resulted in consistent average rider price increases per mile over three consecutive years.
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Instacart claims approximately ten clients utilize their pricing optimization technology across their platform operations.
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Electronic shelf labels enable instant price changes in physical stores, potentially mirroring online algorithmic pricing mechanisms.
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Patents reveal Instacart's infrastructure enables calculation of individual consumer spending capacity and product-specific spending headroom for each customer.
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Grocery prices increased approximately thirty percent on average between 2020 and the investigation period.
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States like California and New York have begun implementing bans on certain algorithmic price fixing tactics in housing and rental markets.
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Walmart and Amazon's adoption of algorithmic pricing created defensive pressure for competitors to implement similar systems.
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Smart Rounding explicitly promises two to five percent profit increases for participating retailers according to company promotional materials.
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The investigation required five months of research across three organizations to identify and document algorithmic pricing patterns.
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Consumer Reports' volunteer network and testing methodology for identifying algorithmic pricing variations across platforms.
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Columbia Business School research on algorithmic pricing conducted by Len Sherman examining Uber's pricing implementation outcomes.
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Eversight's AI technology for grocery price optimization acquired by Instacart in 2022.
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Patent filings describing infrastructure for consumer segmentation, spending capacity calculation, and behavioral characteristic analysis.
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Lena Khan's Federal Trade Commission investigation into surveillance pricing and algorithmic price fixing mechanisms.
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Groundwork Collaborative's research on Instacart's Washington DC workforce wage discrimination that prompted consumer pricing investigation.
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More Perfect Union's investigative journalism work exposing algorithmic pricing systems in grocery retail.
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Costco's Smart Rounding program described in internal communications with Instacart regarding price optimization strategies.
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State regulatory efforts in California and New York banning algorithmic price fixing in housing and rental markets.
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Shareholder letters and marketing materials from Instacart describing millions of dollars in incremental sales from algorithmic pricing.
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Walmart's former VP of pricing commentary on competitive pressure to adopt algorithmic pricing systems.
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Whole Foods' former VP of grocery Earl Schweiser's analysis of industry-wide algorithmic pricing adoption patterns.
Algorithmic pricing systems enable corporations to charge different customers different prices based on behavioral surveillance, requiring immediate regulatory intervention.
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Demand federal legislation explicitly prohibiting algorithmic pricing discrimination based on behavioral characteristics and consumer purchase history data.
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Empower state attorneys general to investigate and subpoena information about algorithmic pricing implementations from retailers and technology platforms.
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Require companies to disclose algorithmic pricing mechanisms, test results, and consumer segmentation criteria in transparent public reports annually.
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Establish regulatory frameworks requiring consumer consent before companies collect behavioral data for pricing determination purposes.
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Implement price transparency requirements mandating that consumers see identical prices regardless of device, account history, or behavioral profile.
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Ban electronic shelf label systems that enable dynamic pricing without consumer awareness or regulatory oversight mechanisms.
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Investigate potential collusion when single platforms like Instacart control pricing for multiple competing retailers simultaneously.
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Create consumer protection agencies specifically tasked with monitoring algorithmic pricing practices and investigating pricing discrimination complaints.
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Support academic research institutions in studying algorithmic pricing outcomes and publishing findings to inform regulatory policymaking processes.
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Establish minimum pricing standards preventing companies from charging excessive markups during periods of supply constraint or inflation.
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Require algorithmic pricing systems to undergo independent audits verifying they don't discriminate based on protected characteristics.
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Mandate that companies disclose when algorithmic pricing is being used and provide consumers with options to opt out.
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Develop international regulatory standards for algorithmic pricing to prevent companies from exploiting regulatory gaps across jurisdictions.
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Support investigative journalism efforts examining corporate pricing practices through funding mechanisms and legal protections.
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Implement penalties including fines and executive liability for companies caught engaging in undisclosed algorithmic pricing discrimination.
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Require technology platforms to maintain separate pricing systems preventing centralized control over competing retailers' price-setting decisions.
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Psychologist identified behavioral manipulation through surveillance while economist noted profit maximization as primary corporate motivation driver.
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Technology expert highlighted algorithmic sophistication and infrastructure requirements while ethicist emphasized inf ormation asymmetry creating unfair consumer disadvantage systematically.
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Legal analyst noted collusion risks and regulatory gaps while policy expert emphasized state-level intervention as viable pathway forward.
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Historian contextualized twenty-year infrastructure development timeline while systems analyst described closed-loop pricing mechanisms combining physical and digital systems.
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Consumer advocate highlighted wealth transfer implications while journalist emphasized investigative methodology and documentation requirements for establishing evidence.
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Regulatory expert noted FTC surveillance pricing investigation while business analyst explained competitive pressure driving industry-wide algorithmic pricing adoption.
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Patent analyst identified behavioral segmentation infrastructure through technical filings while market researcher noted Instacart's intermediary role enabling coordination.
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Data scientist emphasized statistical significance of testing methodology while transparency advocate stressed intentional system opacity as design feature.
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Competition economist warned about collusion mechanisms while innovation analyst noted electronic shelf label technology enabling invisible price adjustments.
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Academic researcher highlighted real-world Uber pricing outcomes while generalist synthesized findings into comprehensive algorithmic pricing system overview.
- Grocery stores and tech companies built systems together to maximize profits daily through algorithmic pricing.
- Instacart tested charging different customers different prices for identical items at same stores.
- Over 400 volunteers discovered nearly three of four products had different prices during experiments.
- Customers were sorted into price groups where everyone paid identical prices across multiple items.
- Surveillance pricing tracks customer behavior, purchase history, and charges based on that data.
- Instacart acquired Eversight in 2022, a company specializing in optimizing grocery prices algorithmically.
- Eversight runs constant experiments invisible to consumers to determine maximum willingness to pay.
- Smart Rounding is a machine learning tool designed to flex prices up or down maximally.
- Electronic shelf labels enable in-store price testing across different store clusters simultaneously.
- Instacart removed price optimization features from their website after investigation began.
- Patents reveal segmentation criteria including buying behavior, purchase history, and coupon usage patterns.
- Behavioral characteristics like shopping frequency and loyalty program participation determine price groups.
- Retailers control prices officially, but Instacart manages pricing for at least some partners.
- Target denied working with Instacart, contradicting Instacart's claim about retailer price control.
- Instacart admitted managing Target prices and running tests to charge more than normal.
- Demographic-based pricing is illegal, but behavioral-based pricing appears perfectly legal currently.
- Companies can calculate individual consumer headroom showing how much more they could spend.
- Uber's algorithmic pricing increased average rider price per mile consistently over three years.
- Entire industry exists dedicated to optimizing grocery prices using technology and algorithms.
- Amazon and Walmart already use algorithmic pricing, forcing competitors to adopt similar systems.
- Instacart provides smaller competitors access to consumer data and tools matching big companies.
- Systems learn from every purchase and continuously test to find maximum profit points.
- Inflation is real but algorithmic pricing has been pushing grocery prices higher simultaneously.
- Federal Trade Commission launched investigation into AI-driven rapid price changing under Lena Khan.
- Trump administration replaced Khan and halted public comments about pricing investigation progress.
- States like California and New York banned certain algorithmic price fixing in housing.
- Price optimization is more powerful than boosting sales or cutting costs for profits.
- Companies invest millions into systems designed to outsmart consumers through continuous testing.
- Algorithmic pricing sometimes offers lower prices, making total impact on grocery prices unmeasurable.
- Instacart claims ten clients with Eversight, but other companies likely using similar systems.
- Inertia and fear of raising prices kept systems in check before COVID changed dynamics.
- Current regulatory environment lacks guardrails, creating free-for-all conditions for algorithmic pricing adoption.
- Instacart could facilitate price fixing by setting prices for retailers who should compete.
- Information asymmetries between consumers and firms benefit massive corporations through algorithmic pricing systems.
- Surveillance pricing represents wealth transfer from ordinary Americans to massive corporations ultimately.
- State attorneys general have subpoena power to demand information about algorithmic pricing operations.
- Systems designed to stay hidden make it impossible for consumers to see inside.
- Federal Trade Commission needs to investigate whether these tactics should be permitted initially.
- Twenty years of infrastructure building enabled current algorithmic pricing systems to operate today.
- COVID removed inertia and fear, allowing companies to accelerate algorithmic pricing implementations widely.
- Researchers and community organizers collaborated to uncover grocery pricing manipulation through scientific testing.
- Consumer Reports joined investigation providing volunteer network and resources for large-scale price testing.
- Database verification and cross-checking of screenshots ensured accuracy of pricing variation findings.
- Patents show companies calculate how much more consumers could spend per product overall.
- Price gaps reached fifteen percent on single items like bags of chips tested.
- Costco executive inadvertently forwarded internal Instacart communication revealing Smart Rounding program details.
- Instacart's shareholder letter defined Smart Rounding as tool to improve price perception deliberately.
- Smart Rounding generated millions of dollars in annual incremental sales for major grocery partners.
- Shnooks regional grocer showed no price variation, suggesting in-store testing already operational there.
- Patent infrastructure describes constant in-store testing across dozens of clustered stores simultaneously.
- Electronic shelf labels can change prices instantly, enabling sophisticated in-store testing apparatus.
- Surge pricing fears may be misdirection from more permanent subtle price increases happening.
- Charging twenty cents more daily generates more profit than surge pricing during emergencies.
- Instacart denied in-store testing then removed price optimization from website after investigation.
- Instacart claimed website update was for accuracy, but feature appeared in past materials.
- Online testing varies prices across individuals while in-store testing varies prices across locations.
- Segmentation criteria in patents include demographics, buying behavior, shopping frequency, and coupon usage.
- Instacart denied using behavioral data for segmentation despite patent evidence suggesting otherwise.
- Every company practicing first-degree price discrimination strenuously denies doing so publicly always.
- Uber's algorithmic pricing transitioned company from burning money to operating as cash machine.
- Len Sherman studies algorithmic pricing effects at Columbia Business School extensively.
- Earl Schweiser, former Whole Foods VP, confirmed everyone racing to adopt algorithmic pricing.
- Walmart's former VP of pricing acknowledged defensive nature of algorithmic pricing adoption.
- Infrastructure tracks everything consumers do and uses data to decide what to charge.
- Investigation revealed system exists for surveillance pricing but operational status remains unknown.
- Intentional system design keeps algorithmic pricing hidden from consumer view and understanding.
• Algorithms now sort consumers into invisible price groups silently.
• Companies track your behavior to charge you maximum willingness.
• Retailers blame each other while algorithms quietly optimize profits.
• Electronic shelf labels enable in-store price testing at scale.
• Surveillance pricing generates millions in incremental profit annually.
• Information asymmetry between corporations and consumers has become extreme.
• Algorithmic pricing may facilitate collusion between supposedly competing retailers.
• Technology infrastructure for price discrimination took twenty years building.
• Regulatory capture and political transitions leave consumers largely unprotected.
• Most people remain unaware they're sorted into pricing buckets.
Grocery retailers and tech companies use algorithmic pricing systems to charge different customers different prices for identical items, maximizing profits through surveillance-based price discrimination.
CLAIM 1: Different customers are charged different prices for identical items at the same store simultaneously on Instacart.
CLAIM SUPPORT EVIDENCE:
- Consumer Reports and More Perfect Union conducted controlled experiments with 400+ volunteers ordering identical items from the same store at the same time, finding price variations (e.g., eggs ranging from $4.28 to $4.69, a 41-cent spread) (Consumer Reports/More Perfect Union investigation, 2024).
- In one test, nearly three of four products had different prices across shoppers; some paid $114 for 20 items while others paid nearly $124 for identical items (Investigation findings, 2024).
- Costco showed 15% price gaps on identical products in Consumer Reports follow-up experiments (Consumer Reports, November 2024).
CLAIM REFUTATION EVIDENCE:
- Instacart stated retailers control all pricing, not Instacart (Instacart response to investigation).
- Price variations could result from legitimate factors: active promotions, user error, app glitches, or timing differences in when prices update (acknowledged in investigation).
- Single experiments with small sample sizes in limited locations cannot definitively prove systematic discrimination across all transactions (methodological limitation).
- No comprehensive audit of millions of transactions has been independently verified by third parties or regulatory agencies.
LOGICAL FALLACIES:
- Appeal to authority: "Len Sherman studies this at Columbia's business school" doesn't establish his findings apply to Instacart specifically.
- Hasty generalization: "Nearly three out of four products had different prices" in limited tests extrapolated to systemic practice.
- Post hoc ergo propter hoc: "Uber rolled out algorithmic pricing... average rider price went up" doesn't prove causation without controlling for other variables.
- Circumstantial evidence: Removed website features and patents suggest intent but don't prove operational implementation.
CLAIM RATING: C (Medium)
LABELS: Investigative journalism, evidence-based but limited sample size, speculative regarding intent, defensive corporate responses, concerning but unproven at scale
CLAIM 2: Instacart uses behavioral data (purchase history, shopping frequency, coupon usage) to segment customers into price groups.
CLAIM SUPPORT EVIDENCE:
- Multiple Instacart patents describe segmentation criteria including "buying behavior, purchase history, how frequently you shop, whether you're shopping around, coupon usage, loyalty program participation" (Patent analysis in investigation).
- Instacart's 2023 shareholder letter defined "Smart Rounding" as a "machine learning driven tool" to "flex prices up or down based on what the algorithm says will maximize profit," resulting in "millions of dollars in annual incremental sales" for major grocery partners (Instacart shareholder letter, 2023).
- Internal Costco email accidentally forwarded to investigators referenced Smart Rounding program (Internal communication, inadvertently disclosed).
CLAIM REFUTATION EVIDENCE:
- Instacart denied segmenting people by personal or behavioral data, calling the 2015 patent "one old patent...before we purchased Eversight" and stating "Patents are always broad. This doesn't prove we're actually using this technology" (Instacart response).
- No direct evidence shows these patented systems are currently operational or used at scale across Instacart's customer base.
- Katie Wells' analysis found "no correlation" between demographic characteristics and price variation, suggesting behavioral rather than demographic targeting, but behavioral targeting itself wasn't directly proven (Investigation findings).
- No independent regulatory audit has confirmed actual implementation of these patented systems.
LOGICAL FALLACIES:
- Guilt by association: Patents exist, therefore the company must be using them.
- Inference from silence: Instacart's denial and website changes interpreted as admission of guilt rather than neutral explanations.
- Argument from authority: FTC Chair Lena Khan's concerns treated as validation of specific claims rather than general regulatory concerns.
CLAIM RATING: C (Medium)
LABELS: Patent-based inference, speculative implementation, corporate denial, concerning but circumstantial, defensive framing
CLAIM SUPPORT EVIDENCE:
- Derek found a patent "describing an infrastructure for constant instore price testing" with different prices in different store clusters to "find out the most profitable price point" (Patent analysis).
- Instacart's website previously listed "price optimization with Eversight via electronic shelf labels" as a feature, which was later removed (Website documentation change, pre-investigation).
CLAIM REFUTATION EVIDENCE:
- Instacart and Schnucks both denied using this system (Corporate responses).
- Instacart claimed the website feature removal was "made for accuracy" and "it was never a feature," though investigators found it in "past marketing materials and press releases" (Instacart explanation vs. investigator findings).
- No direct evidence of electronic shelf labels actually implementing differential pricing for consumers based on personal data.
- Schnooks showed "no price variation at all" in the experiment, contradicting the theory that electronic shelf labels are actively implementing discrimination (Investigation findings).
LOGICAL FALLACIES:
- Modus tollens error: Website feature removal interpreted as proof of guilt rather than administrative correction.
- Argument from design: Patent describes capability; therefore, capability must be operational.
- Inconsistent evidence: Schnooks' lack of price variation contradicts the electronic shelf label theory.
CLAIM RATING: D (Low)
LABELS: Highly speculative, circumstantial evidence only, directly contradicted by test results, defensive interpretation of corporate actions
CLAIM 4: Inflation since 2020 is partially driven by intentional algorithmic price increases beyond legitimate supply chain factors.
CLAIM SUPPORT EVIDENCE:
- "Everything is about 30% more expensive than it was in 2020" on average in grocery stores (Assertion in investigation, unattributed).
- Eversight promises "2 to 5% profit increases" through price optimization (Company marketing material).
- Instacart reported "millions of dollars in annual incremental sales" from Smart Rounding for major grocery partners (Shareholder letter, 2023).
CLAIM REFUTATION EVIDENCE:
- The investigation acknowledges "inflation is real" and identifies legitimate causes: "inflation, supply chains, tariffs" (Investigation concession).
- 30% figure lacks attribution to verified sources; actual grocery inflation from 2020-2024 averaged approximately 20-25% according to USDA and BLS data (U.S. Bureau of Labor Statistics, 2024).
- Eversight's 2-5% profit increases represent marginal gains, not the primary driver of 20%+ inflation observed industry-wide.
- No quantification provided of how much of total inflation is attributable to algorithmic pricing vs. documented supply chain disruptions, labor costs, and commodity prices.
- The investigation states "the total impact on grocery prices impossible to measure" (Investigation acknowledgment).
LOGICAL FALLACIES:
- False dichotomy: Presents inflation as either legitimate supply chain issues OR intentional price fixing, when both can coexist.
- Correlation implies causation: Price increases coincide with algorithmic pricing adoption; causation not established.
- Incomplete evidence: Acknowledges measurement impossibility while still implying algorithmic pricing is a major inflation driver.
CLAIM RATING: D (Low)
LABELS: Speculative attribution, incomplete quantification, legitimate causes acknowledged, overreaching conclusion, defensive framing
CLAIM 5: Instacart may be facilitating price-fixing or collusion by setting prices for multiple competing retailers.
CLAIM SUPPORT EVIDENCE:
- Instacart initially claimed retailers control prices, but later admitted "they actually do manage Target's prices on the marketplace" (Investigation finding/Instacart admission).
- Instacart sets prices for multiple major retailers through its technology platform (Operational fact established).
- FTC Chair Lena Khan stated: "When you have companies like Instacart that could be facilitating...it could also ultimately allow firms that really should be competing against one another...to actually engage more in something that looks like price fixing or collusion" (FTC Chair statement, 2024).
CLAIM REFUTATION EVIDENCE:
- Retailers contractually choose to use Instacart's pricing technology; they are not forced to participate (Business relationship structure).
- No evidence of explicit coordination between competing retailers to fix prices through Instacart.
- Khan used conditional language ("could be," "looks like") indicating theoretical concern rather than proven violation.
- Retailers retain ability to override algorithmic prices and set their own pricing independently.
- No antitrust charges or regulatory findings of actual price-fixing have been filed (As of investigation date).
LOGICAL FALLACIES:
- Slippery slope: Algorithmic pricing infrastructure "could" lead to collusion; therefore, it likely is.
- Argument from authority: FTC Chair's theoretical concern treated as validation of actual collusion.
- Speculation presented as fact: "May be facilitating" framed as established behavior.
CLAIM RATING: C (Medium)
LABELS: Theoretically concerning, speculative collusion, regulatory attention warranted, unproven violation, defensive framing
CLAIM 6: The FTC investigation into surveillance pricing has been halted or deprioritized under the Trump administration.
CLAIM SUPPORT EVIDENCE:
- "Under Khan's watch, the FTC started an investigation into surveillance pricing" (Investigation statement).
- "The Trump administration claims it is ongoing, but Khan's replacement stopped public comments" (Investigation reporting).
CLAIM REFUTATION EVIDENCE:
- The Trump administration stated the investigation "is ongoing" (Official statement).
- "Stopped public comments" is not equivalent to halting the investigation itself.
- No official announcement of investigation closure or deprioritization has been made.
- Regulatory investigations routinely reduce public communications during certain phases.
LOGICAL FALLACIES:
- Inference from silence: Lack of public comments interpreted as investigation closure.
- Hasty generalization: One administrative change ("stopped public comments") extrapolated to investigation status.
CLAIM RATING: D (Low)
LABELS: Speculative, incomplete information, inference from silence, potentially misleading framing
LOWEST CLAIM SCORE: D (Electronic shelf label pricing, FTC investigation status)
HIGHEST CLAIM SCORE: C (Different prices charged simultaneously, behavioral segmentation through patents, inflation attribution, collusion concerns)
AVERAGE CLAIM SCORE: C (Medium)
The investigation presents compelling experimental evidence of price variation but relies heavily on patent analysis and corporate denial to infer intentional discrimination. While concerning and warranting regulatory scrutiny, claims exceed proven facts. Strengths include rigorous methodology with 400+ volunteers and documented price gaps. Weaknesses include small sample sizes, inability to definitively prove algorithmic causation, and speculation about operational implementation. The investigation effectively raises awareness but should be viewed as raising important questions rather than proving systematic abuse. Regulatory investigation and transparency requirements are reasonable responses, though consumers should await independent verification before accepting all claims as established fact.
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