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The meme coin boom has made some Web3 bros incredibly rich. But a new study published on Cornell Universitys arXiv suggests the ecosystem is better understood as a place of extreme churn, flimsy infrastructure, and a surprising number of scammy projects that disappear quickly. Researchers Alberto Maria Mongardini at the Technical University of Denmark and Alessandro Mei at the Sapienza University of Rome built MemeChain, an open-source, cross-chain dataset of 34,988 meme coins across Ethereum, BNB Smart Chain (BSC), Solana, and Base. The system combines on-chain records with off-chain legitimacy signals such as token logos, social links, and archived website HTML. MemeChain found that through mid-January 2025, 1,801 tokens, or around 5% of all the coins tracked, stopped trading within 24 hours of launch. Nearly half showed zero transaction activity from mid-October to mid-December 2024, suggesting many projects burn out within weeks. Around 10% of meme coins on the BNB Smart Chain lasted only a single day, compared with roughly 0.1% on Solana. Mongardini said the team began by watching this wave of new created meme coins across chains in 2024 and asking who was trying to exploit the hype and FOMO. While he expected some level of alleged impropriety, he was surprised by the scale of scammy rug pulls and short-lived meme coins. Its shocking to me, he said. Some indicators helped signal whether a coin was likely to rug pull. While 74.8% of tokens claimed an associated website, only 32.1% of those sites returned a working HTTP 200 response when tested. The researchers also found widespread use of cheap registrars and short-lived hosting, which Mongardini described as very, very fragile infrastructure built with very, very low effort because creators want to capitalize as soon as possible. The aim of developing tools that can warn investors about high-risk trades is to make a safer environment for people that [use] the crypto markets, Mei said, though he added that doing so in real time is very expensive. Buyer beware.
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E-Commerce
The distance between a world-changing innovation and its funding often comes down to four minutesthe average time a human reviewer tends to spend on an initial grant application. In those four minutes, reviewers must assess alignment, eligibility, innovation potential, and team capacity, all while maintaining consistency across thousands of applications. It’s an impossible ask that leads to an impossible choice: either slow down and review fewer ideas or speed up and risk missing transformative ones. At MIT Solve, we’ve spent a year exploring a third option: teaching AI to handle the repetitive parts of review so humans can invest real time where judgment matters most. WHY AI, AND WHY NOW In 2025, Solve received nearly 3,000 applications to our Global Challenges. Even a cursory four-minute review per application would add up to 25 full working days. Like many mission-driven organizations, we dont want to trade rigor for speed. We want both. That led us to a core question many funders are now asking: How can AI help us evaluate more opportunities, more fairly and more efficiently, without compromising judgment or values? To answer this question, we partnered with researchers from Harvard Business School, the University of Washington, and ESSEC Business School to study how AI could support early-stage grant review, one of the most time-intensive and high-volume stages of the funding lifecycle. WHAT WE TESTED AND WHAT WE LEARNED The research team developed an AI system (based on GPT-4o mini) to support application screening and tested it across reviewers with varying levels of experience. The goal was to understand where AI adds value and where it doesnt. Three insights stood out: 1. AI performs best on objective criteria. The system reliably assessed baseline eligibility and alignment with funding priorities, identifying whether applications met requirements or fit clearly defined geographic or programmatic focus areas. 2. AI is more helpful to less experienced reviewers. Less experienced reviewers made more consistent decisions when supported by AI insights, while experienced reviewers used AI selectively as a secondary input. 3. The biggest gain was standardization at scale. AI made judgments more consistent across reviewers, regardless of their experience, creating a stronger foundation for the second level of review and human decision-making. HOW THIS TRANSLATES INTO REAL-WORLD IMPACT At Solve, the first stage of our review process focuses on filtering out incomplete, ineligible, or weak-fit applications, freeing human reviewers to spend more time on the most promising ideas. We designed our AI tool with humans firmly in the loop, focused on the repetitive, pattern-based nature of initial screening that makes it uniquely suited for AI augmentation. The tool: Screens out applications with no realistic path forward. Supports reviewers with a passing probability score, a clear recommendation (Pass, Fail, or Review), and a transparent explanation. When the 2025 application cycle closed with 2,901 submissions, the system categorized them as follows: 43% Pass; 16% Fail; and 41% Review. That meant our team could focus deeply on just 41% of the applicationscutting total screening time down to ten dayswhile maintaining confidence in the quality of the results. THE BIGGER TAKEAWAY FOR PHILANTHROPY Every hour saved during the early stages of evaluation is an hour redirected toward the higher-value work that humans excel at: engaging more deeply with innovators and getting bold, under-resourced ideas one step closer to funding. Our early results show strong alignment between AI-supported screening and human judgment. More importantly, they demonstrate that its possible to design AI systems that respect nuance, preserve accountability, and scale decision-making responsibly. The philanthropic sector processes millions of applications annually, with acceptance rates often below 5%. If we’re going to reject 95% of ideas, we owe applicantsespecially those historically excluded from fundinga genuine review. Dividing responsibility, with humans making decisions and AI eliminating rote review, makes it that much more possible at scale. It’s a practical step toward the thoroughness our missions demand. Hala Hanna is the executive director and Pooja Wagh is the director of operations and impact at MIT Solve.
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E-Commerce
Planned layoffs have now reached their highest rate since 2009’s Great Recession. The data comes from Challenger, Gray & Christmas’ new layoffs report, which revealed that U.S.-based employers announced 108,435 job cuts in January, marking the highest rate to start a year since 2009. Also notable, in the same month, just 5,306 planned hires were announcedthe lowest total on record for January. According to the data, that means layoffs are up a staggering 118% from the same period a year ago, and 205% from December 2025. Generally, we see a high number of job cuts in the first quarter, but this is a high total for January, Andy Challenger, workplace expert and chief revenue officer for the firm, said in the report. It means most of these plans were set at the end of 2025, signaling employers are less-than-optimistic about the outlook for 2026. The most hard-hit sectors for layoffs are transportation, technology, and health care industries. According to a Reuters report, 31,243 planned cuts came from United Parcel Service (UPS). UPS plans to close 24 facilities in 2026, as part of a major restructuring effort. On the tech side, 22,291 tech job cuts, most came from Amazon, as the company announced plans to lay off 16,000 corporate employees. Some of you might ask if this is the beginning of a new rhythmwhere we announce broad reductions every few months, wrote Beth Galetti, senior vice president of people experience and technology at Amazon, in an announcement last week. Thats not our plan. But just as we always have, every team will continue to evaluate the ownership, speed, and capacity to invent for customers, and make adjustments as appropriate. Meanwhile, the healthcare sector has been battling as a result of federal funding cuts with 17,107 job cuts announced in January, making it the largest cut since April 2020. Healthcare providers and hospital systems are grappling with inflation and high labor costs, Challenger said. Lower reimbursements from Medicaid and Medicare are also hitting hospital systems. These pressures are leading to job cuts, as well as other cutting measures, such as some pay and benefits. Its very difficult for leaders of these companies to tighten budgets while not sacrificing patient care. Additionally, the Labor Department reported that job openings are down to the lowest rate since September 2020, as vacancies fell to 6.5 million in December.Of course, many have been quick to blame AI for a surging number of layoffs. But some experts say that it has more to do with current economic conditions, and that AI could be being used as a mere scapegoat. In a post on BlueSky responding to the new data, CNBC journalist Carl Quintanilla shared a quote attributed to market researcher Renaissance Macro Research (RENMAC), referencing the Challenger report and explaining the real reasons behind the downslide: …While there is quite a bit of attention on AI driving layoffs, most of the reasons cited in this data set are about closing, economic conditions, restructuring, and loss of contract. AI is a comparatively small factor behind the January jump in layoff news. That aligns with data from entities like the Brookings Institution and Yale University, which found that sectors (including ones especially susceptible to AI) havent seen drastic changes in the amount of available jobs since ChatGPT debuted in 2022. Still, other experts continue to believe that AI’s toll on the job market will be crushing. We are at the beginning of a multi-decade progress development that will have a major impact on the labor market, said Gad Levanon, chief economist at the Burning Glass Institute, a workforce research firm, told CNBC last year. Theres probably much more in the tank, he said.
Category:
E-Commerce
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