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I live in a rent-stabilized apartment in New York City; can't leave
My rent-stabilized apartment in New York City's West Village has drawbacks, but they don't outweigh the good deal it is to live here. I can't leave.
Emory humanities, social science faculty debate artificial intelligence, tech-free classrooms
As some universities across the country implement prohibitive bans on student devices in classes, professors consider the benefits and drawbacks of technology and AI in higher education
Want a solution to plastic pollution? Scientists urge UN to require global reporting
Plastic is a wonder material that has molded itself to fit a wide variety of applications. But it comes with a host of drawbacks that aren't fully considered. That's why the United Nations has convened ...
Dimensity 9600 Pro To Bring Desktop-Level Performance To Smartphones With New Architecture & 5GHz Target Speed, At A Significant Trade-Off
A tipster states that MediaTek is bringing incredible performance gains with its Dimensity 9600 Pro, but there will be one major drawback to this move
We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
Most Sports Parents Say There's More Good Than Harm In Youth Sports, Survey Finds
By Dennis Thompson HealthDay ReporterMONDAY, Sept. 21, 2026 (HealthDay News) — Sports parents overwhelmingly say the benefits of organized competition outweigh the drawbacks for them and their kids, a new survey has found. Nearly 9 out of 10 sports parents (89%) said the benefits of youth sports ...
4 Drawbacks Of Buying A Nintendo Switch 2
Generally, the Nintendo Switch 2 is a great console. Are there any drawbacks to buying one now? Our expert weighs in on the cons of buying Nintendo's flagship.
Emory humanities, social science faculty debate artificial intelligence, tech-free classrooms
As some universities across the country implement prohibitive bans on student devices in classes, professors consider the benefits and drawbacks of technology and AI in higher education.
Ella Langley paused concert to address online bullies
Ella Langley's rapid rise hasn't come without its drawbacks, and the country singer is getting candid about the dark side of life in the spotlight.
Vast majority of sports parents feel the benefits of youth sports outweigh the drawbacks
Findings from a new Associated Press/Ipsos poll of parents of K-12 children enrolled in organized sports
How Americans’ Financial Health Is Faring In The “K-Shaped” Economy
Hey all, Jason here. Money20/20 is still two weeks away, but I’ve already started packing (not procrastinating for once!). My calendar is already filling up with various sessions I want to catch, meetings, and of course happy hours, dinners, and other side events. If you’ll be in Vegas and want to catch up, let me know by replying to this email, and we can try to find a time amidst the chaos of the Venetian. A big if not totally unexpected piece of news dropped on Friday: the Independent Community Bankers of America, a trade group that represents smaller U.S. banks, filed a lawsuit against the OCC and Comptroller Jonathan Gould, arguing that the regulator’s move to grant trust bank charters to firms seeking to use them to conduct substantial non-fiduciary activities exceeds the authorities granted to the OCC by Congress. I haven’t had time to fully digest the legal filing or speak to folks in my network about it, but you can expect to see coverage and analysis on it in next week’s newsletter. Subscribe or Support by Upgrading The NerdCon agenda just dropped. It’s stacked. Partner content: Nubank built an AI-first bank. Chime built its own banking core. Figure and Valon are rebuilding the mortgage stack. At NerdCon, you’ll hear from the people behind those bets: what they chose, what they learned, and what they’re still figuring out. The agenda is live, with leaders from Nubank, Chime, OpenAI, Remitly, Mercury, Figure, Valon and more. Follow the mainstage conversations, bring the problem your team is wrestling with to a hands-on workshop, or pull up a chair at a roundtable. Pick your quests. Meet us in San Diego, November 18–20. Fintech Business Weekly readers save 20% with code FBW20. Explore the Agenda Things To Know & Other Good Reads Federal Reserve Board finalizes changes to enhance the transparency and public accountability of its stress test and reduce volatility in its stress test-related capital requirements (Federal Reserve Board of Governors) FDIC Announces Conclusion of Independent Monitorship (FDIC) Modernizing Financial Regulation: Initial Observations from eSLR (Fed Vice Chair for Supervision Michelle Bowman) The Data Version of Godzilla versus Kong: FRED Takes on AI (Fed Governor Christopher Waller) Financing the AI buildout (Brookings) Q3’26: Rules for Banks but Not for Crypto (Fintech Takes Banking) The Inevitability of Local Stablecoins (Lombard Notes) Delusions of AI Governance: The Human-in-the-Loop Comfort Blanket (Fintech Snark Tank) Stripe agrees to acquire Parafin to expand revenue opportunities for platforms and help small businesses grow (Stripe) Listen: Fighting Fraud in the Age of AI, with SEON’s Tamas Kadar (Fintech Business Weekly) How Americans’ Financial Health Is Faring In The “K-Shaped” Economy If there is one overarching theme across the economy and politics in the U.S. at the moment, it’s “affordability.” The term is vague enough to encompass a panoply of factors shaping Americans’ day-to-day lives: rising costs for the hallmarks of being “middle class,” including healthcare, housing, childcare, and education, inflation and rising interest rates, and a job market with the specter of AI hovering over it. At the same time, policy shifts under the Trump administration have resulted in reduced subsidies to those who get health insurance under the Affordable Care Act and new restrictions on qualifying for Medicaid and SNAP, the impacts of which have yet to be fully felt. These factors are contributing to declining consumer confidence and general dissatisfaction with the economy, despite continuing GDP growth and stock market records. This divergence is encapsulated in the idea of the “K-shaped” economy, in which a small proportion of Americans have seen their wealth balloon, while the majority of American households struggle to preserve the lifestyle they have. Perhaps the greatest determinant of which branch of the “K” a household is on is whether their wealth and income are primarily derived from employment vs. from assets. Nearly two decades of low interest rates and, more recently, elevated rates of inflation have benefited asset owners, while those whose income is primarily or solely derived from labor have largely seen real purchasing power stagnate or decline. This is reflected in recent Bureau of Labor Statistics data showing that labor share of U.S. GDP — the fraction of economic output that accrues to workers as compensation in exchange for their labor — dropped to just 52.8% in Q2 2026, the lowest since the BLS began keeping records. In 1947, approximately 2/3rds of economic output accrued to workers; even as recently as 2001, labor’s share of GDP was 64.1%. Against this backdrop, we’ve seen the rise of more credit and credit-like products: cash advance apps, “no-fee” overdraft, earned wage access, and buy now pay later, which are often used as small-dollar short-term borrowing mechanisms to meet immediate consumption needs or pay other bills (eg utilities, cell phone, other debt payments). With the markers of a middle class existence and, increasingly, basic financial stability seemingly out of reach for many, it should be no surprise there’s been an increase in “financial nihilism,” something industry peers like Alex Johnson and Frank Rotman have discussed and analyzed the roots of. The explosion of gambling and gambling-adjacent products and services — often marketed under the guise of being an “investment” — is inextricably intertwined with the rise of financial nihilism. Crypto, sports betting, and, more recently, prediction markets offer an escape or even hope of sorts, like a contemporary, digital version of a scratch-off lottery ticket, while leaving the overwhelming number of people who use such products worse off. Share of Households That Are Financially “Vulnerable” Ticks Up The Financial Health Network’s 2026 Financial Health Pulse® report adds context on how American households are faring. (I linked to this report in last week’s newsletter, but wanted to take time to further unpack the data in the report this week.) The Financial Health Network publishes the report annually, providing insight into how Americans’ financial circumstances are changing over time. The 2026 report is derived from a survey fielded in April and May 2026. The report leverages survey responses to assess indicators of financial health across spending, saving, borrowing, and planning/protecting and to determine a zero to 100 “FinHealth” score. Those with scores between 0 and 39 are considered “Financially Vulnerable,” consumers with scores ranging between 40 and 79 are defined as “Financially Coping,” and those with scores of 80 to 100 are “Financially Healthy.” The report found that moderate improvements in 2025 were reversed, with the share of respondents considered financially “vulnerable” rebounding to 17%. The longitudinal data reflect the impact of pandemic-era programs, like expanded unemployment, cash stimulus payments, and pauses of federal student loan payments. The positive impacts of those programs, as reflected in the Financial Health Pulse reports, had largely disappeared by 2023. While pandemic-era inflation — to be fair, in part caused by the various support and stimulus measures — had come down from as high as 9% in 2022, it has rebounded since the start of Trump’s second term, with tariffs and energy market disruptions owing to the war in Iran pushing prices back up. Specific indicators in the survey that deteriorated from 2025 to 2026 include the share of respondents spending less than their income over the past 12 months, the share paying all bills on time over the past 12 months, the share that have a manageable amount or no debt, the share that are “moderately” or “very” confident their insurance is adequate to cover them in an emergency, and the share that “somewhat” or “strongly” agree that their household plans ahead financially. Unsurprisingly, lower- and moderate-income households are more likely to struggle to pay their bills on time and have any funds leftover to save. The share of low-income households whose income exceeded their spending dropped from 35% in 2025 to 31% in 2026, while the share of upper-income households whose income exceeded expenses remained unchanged at 63%. Households with student loans or revolving credit card debt reported having “a bit more” or “far more” debt than was manageable at rates far higher than those not carrying these types of debts. The share of those with student loan debt indicating their debt load was too high to manage increased from 50% in 2025 to 55% in 2026, while the share of those with revolving credit card debt saying the same ticked up from 51% in 2025 to 54% in 2026. Households that had student loans were markedly more likely to be financially “coping” or “vulnerable” vs. those without student loans. In 2026, 27% of households with student loans were considered financially vulnerable, a jump of 6% points vs. 2025. Households without student loans saw just a 1% point increase (not statistically significant) in those considered financially vulnerable. Households’ perception of the affordability of categories of goods and services paints a stark picture, with less than one-fourth considering higher education affordable, about a third deeming childcare affordable, and less than half viewing healthcare as affordable. Fintech Business Weekly is made possible by the generous support of paying subscribers — bringing you independent analysis of banking, fintech, and crypto without fear or favor. You can support my work by becoming a paying subscriber if you aren’t already. Paying subscribers enjoy: access to the entire archive of nearly six years of newsletters extended versions of the weekly newsletter, with additional content and analysis and (for founding member tier) quarterly personal 1:1 fintech Q&A / strategy calls with me, tapping into my unparalleled knowledge of the intersection of banking and fintech and experience working in the sector, including helping to launch Goldman Sachs’ retail bank Marcus Support Fintech Business Weekly You can also support Fintech Business Weekly by sponsoring a newsletter or podcast, putting you in front of 93,000+ of the most influential decision makers in banking, fintech, and crypto. Learn more about sponsor opportunities or request a media kit by dropping me an email. Democratic Senate Report Highlights How Iran Uses Tether to Evade U.S. Sanctions Iran uses stablecoins, specifically Tether, to evade sanctions, fund regional proxies, and to purchase military drones, a report released last week by the Senate Permanent Subcommittee on Investigations says. The report was authored by committee ranking member Senator Richard Blumenthal (D-CT) and the minority staff. Crypto and stablecoin proponents will often push back on criticism that criminals make use of these assets by arguing that, on an absolute dollar basis, far more illicit funds flow through traditional banking systems and payment rails than via crypto and stablecoins. And while that is likely true, it elides the reality presented in news story after news story: whether state actors — Russia, Iran, North Korea — or criminal groups, those looking to engage in illicit transactions increasingly favor stablecoins and, specifically, Tether (USDT). The report from the Senate Permanent Subcommittee on Investigations released last week adds context to how Iran uses Tether has a lynchpin in its shadow banking system. The report analyzed 846 wallets that have been sanctioned or targeted for seizure, finding that 84% of them transacted exclusively or primarily in Tether. The ability to create near limitless, anonymous wallets and to move funds effectively instantaneously and irreversibly have made crypto a favored financial mechanism for bad actors. Stablecoins solved key drawbacks of bitcoin, namely, the original cryptocurrency’s highly volatile value in dollars. And while Tether (the company) has the technical ability to “freeze” or destroy funds, the company is often limited and reactive in its approach to doing so, the Senate report argues. Tether, which is nominally based in crypto-friendly El Salvador, “has stated that its compliance with OFAC sanctions is ‘voluntary’ and that it follows ‘OFAC guidelines,’” the Senate report says. Owing to these favorable attributes, Tether “became a primary cryptocurrency for Iran, Hamas, Hizballah, and the Houthis beginning in 2023 and has expanded in scale since,” according to the report. Tether’s role in Iran’s shadow banking system, the report says, is enhanced by crypto exchanges like Bybit, Kyrrex, OKX, Gate, and Binance, and through “over the counter” exchanges and hawala networks. The consequences of these financial flows aren’t hypothetical. The Senate report links Tether as a funds transfer mechanism to Iranian proxies in the region, including Hizballah, the Houthis, and Hamas. Tether has also been used to make payments to secure components necessary to manufacture drones, the report says. The report concludes by arguing that “[s]tablecoin issuers with a significant nexus to the United States, particularly those that offer dollar-denominated stablecoins, should be subject to American sanctions law rather than being allowed to hide behind foreign jurisdictions.” Stablecoin issuers like Tether must be held accountable for repeated failures to prevent illicit finance and sanctions violations by law enforcement, the Department of Justice, the Securities and Exchange Commission, and OFAC, the report argues. [Paying Subscriber Exclusive] OUSD Goes Live, Make Your Own Neobank, U.S. Sanctions Russia’s A7 Network As “Transnational Criminal Organization” OpenUSD, the stablecoin issued by the Open Standard consortium that includes Stripe, Visa, Mastercard, core providers, crypto firms, and numerous banks, went live last week. Social commerce platform Whop raised eyebrows in fintech by offering creators on its platform the ability to launch their own neobanks, which the company describes as “great businesses that are easy to run,” in just 15 minutes. And OFAC and FinCEN target Russia’s “shadow banking” A7 Network — more on these stories after the paywall.
Trying Qatar Airways' business class flights for 26 hours; mostly good
I flew 26 hours in Qatar Airways business class on different flights. The lie-flat seats, food, and amenities were good, but there were drawbacks.
Trump must divulge financial details about businesses in $10bn BBC lawsuit
US judge’s ruling could open up window into first family’s multiple ventures, in drawback of lawsuit on media
I suspected cancer lurked in my body. I hoped this test would ease my fears.
The FDA is expected to approve a new kind of blood test that may allow earlier diagnosis and less invasive, more effective treatment. But doctors warn of drawbacks.
Why Don't We Put More Turbines Underwater? The Pros And Cons Of Tidal Energy
Underwater turbines would be great for zero-carbon and high power output, but it has some drawbacks, including high upfront costs and some inconsistency.
The surprising health benefits – and one drawback
Despite their small size, sesame seeds are remarkably nutrient-dense.
4 Of The Biggest Drawbacks Of Driving A C8 Corvette
At this point, it is safe to say the C8 Corvette is a success, but that doesn't mean it's perfect. Owners and drivers have reported a few common drawbacks.
iPhone Fold to Pave Way for Thinner, Brighter Display on iPhone Air 2
The iPhone Fold will be the first Apple device to adopt a Samsung-made OLED technology called CoE (Color Filter on Encapsulation), which could make the display brighter and thinner than previous panels, reports The Elec. In a traditional OLED panel, a polarizing film sits above the display to cut reflections and improve contrast. The drawback is that this film also absorbs some of the OLED's own light, reducing brightness and efficiency.
Jaffrey couple cut energy costs with NHSaves
Evan Clements and Precia Harthcock of Jaffrey love their 1926 home, but they acknowledge the drawbacks of houses built in that era. The room that is now their sitting room, for example, was once a porch that had been enclosed and is noticeably colder than other parts of the house. The second floor was much hotter than the first floor, and their kitchen was drafty.
Too much coffee may weaken bones, study finds
Following nearly 10,000 older women for 10 years, researchers found that tea drinking was associated with slightly higher hip bone density. Moderate coffee consumption did not appear harmful, but drinking more than five cups a day was linked to lower bone density. The findings suggest tea may offer a small bone-health boost, while excessive coffee could carry some drawbacks.
Trying Japan Airlines business class; not the best, but worth it
Flying business class to Japan on Japan Airlines (JAL) had some great perks, like good food and a comfy seat, and a few drawbacks for a long flight.
3 Trade Targets Atlanta Hawks Should Avoid at All Costs
The Atlanta Hawks could make a major trade, but Domantas Sabonis, Kevin Durant and Anthony Davis would each bring significant drawbacks.
I Bought a Used iPhone 13 Mini and Learned a Hard Lesson
Commentary: The iPhone Air intrigued me but I didn't want to spend $1,000. So I got a $353 used iPhone 13 Mini, and found it quite capable despite a major drawback.