Colleges will soon have to fork over millions of dollars as Trump's bill extends a tax on their endowments
The bill — which was unanimously opposed by Democrats in the House and the Senate — was sold by Trump as a method for providing tax relief and expanding American wealth.
Among its various provisions, the bill levies a tiered tax on university endowments, which previously were not taxable. The new tax is justified by the Trump administration as a way to prevent universities from "[abusing] generous benefits provided through the tax code".
The taxes range from 1.4 percent to 8 percent at the wealthiest institutions.
Universities rely on their endowments to fund essential operations and to provide services to their students.
In 2017, during Trump's first term, Congress started taxing universities with endowments of $500,000 or more per student at 1.4 percent. The new bill expands that.
Harvard and Princeton both have endowments of $2 million or more per student. They will be subject to an 8 percent tax on their investment income. It could be worse; the original House bill set the tax at 21 percent, and Vice President J.D. Vance — himself a product of a wealthy school and its endowment funding — wanted to set the tax at 35 percent, according to the New York Times.
Harvard has the largest endowment of any U.S. university, with an estimated $53.2 billion. At an 8 percent tax rate, the school will be paying approximately $4.24 billion to the federal government.
Only nine U.S. universities qualify for the highest tax rate; Harvard, Yale, Princeton, MIT, Stanford, CIT, Juilliard, Amherst and Pomona.
Schools with endowments of between $750,000 and $2m per student will be taxed at 4 percent.
That tier includes universities such as Notre Dame, Dartmouth, the Mayo Clinic College of Medicine, Baylor, Northwestern, Johns Hopkins, Duke, Vanderbilt, the University of Chicago, Columbia and Brown.
In the case of a school such as the University of Chicago, the college has an endowment of $850,000 per student, so it will be taxed at the 4 percent rate. With an endowment estimated at $10.4 billion, the university will pay $416 million each year.
The University of Notre Dame's endowment is approximately $18 billion, so it will pay around $720 million each year.
All other schools that qualify — meaning they have at least 500 tuition-paying students — will be taxed at 1.4 percent.
The tiered endowment taxes aren't the only changes to higher education that the "big, beautiful bill" has introduced.
Student loans are also being re-tooled. A lifetime borrowing cap of $100,000 for graduate students and a $200,000 cap for law and med school students is now in place.
The bill also set a $65,000 cap on Parent PLUS loans, which are unsubsidized loans that parents can use to support their dependent undergraduate students. Those loans will no longer be eligible for repayment programs.
Repaying student loans has also changed. The new bill puts limits on deferments and forbearances and replaces existing income-based payment plans — aimed at helping lower-income borrowers — with two ways to repay.
One plan is a standard repayment plan that lets borrowers pay over 10 to 25 years based on their loan amounts, regardless of their income. The other is labeled at a "Repayment Assistance Plan" that is based on a percentage of a borrower's discretionary income.
The approximately eight million borrowers enrolled in former President Joe Biden's SAVE repayment plan will have to wait a little longer for a judge to rule on the program's legality.
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Finextra
34 minutes ago
- Finextra
Context Engineering for Financial Services: By Steve Wilcockson
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He emphasizes that in real-world, industrial-strength LLM applications, the challenge entails filling the model's context window with just the right mix of information. He thinks about context engineering as both a science—because it involves structured systems and system-level thinking, data pipelines, and optimization —and an art, because it requires intuition about how LLMs interpret and prioritize information. His analysis reflects two of my predictions for 2025 one highlighting the increasing impact of uncertainty and another a growing appreciation of knowledge. Tech mortals offered further useful comments on the threads, two of my favorites being: 'Owning knowledge no longer sets anyone apart; what matters is pattern literacy—the ability to frame a goal, spot exactly what you don't know, and pull in just the right strands of information while an AI loom weaves those strands into coherent solutions.' weaves those strands into coherent solutions.' 'It also feels like 'leadership' Tobi. How to give enough information, goal and then empower.' I love the AI loom analogy, in part because it corresponds with one of my favorite data descriptors, the "Contextual Fabric". I like the leadership positivity too, because the AI looms and contextual fabrics, are led by and empowered by humanity. Here's my spin, to take or leave. Knowledge, based on data, isn't singular, it's contingent, contextual. Knowledge and thus the contextual fabric of data on which it is embedded is ever changing, constantly shifting, dependent on situations and needs. I believe knowledge is shaped by who speaks, who listens, and what about. That is, to a large extent, led by power and the powerful. Whether in Latin, science, religious education, finance and now AI, what counts as 'truth' is often a function of who gets to tell the story. It's not just about what you know, but how, why, and where you know it, and who told you it. But of course it's not that simple; agency matters - the peasant can become an abbot, the council house schoolgirl can become a Nobel prize-winning scientist, a frontier barbarian can become a Roman emperor. For AI, truth to power is held by the big tech firms and grounded on bias, but on the other it's democratizing in that all of us and our experiences help train and ground AI, in theory at least. I digress. For AI-informed decision intelligence, context will likely be the new computation that makes GenAI tooling more useful than simply being an oft-hallucinating stochastic parrot, while enhancing traditional AI - predictive machine learning, for example - to be increasingly relevant and affordable for the enterprise. Context Engineering for FinTech Context engineering—the art of shaping the data, metadata, and relationships that feed AI—may become the most critical discipline in tech. This is like gold for those of us in the FinTech data engineering space, because we're the dudes helping you create your own context. I'll explore how five different contextual approaches, all representing data engineering-relevant vendors I have worked for —technical computing, vector-based, time-series, graph and geospatial platforms—can support context engineering. Parameterizing with Technical Computing Technical computing tools – think R, Julia, MATLAB and Python's SciPy stack - can integrate domain-specific data directly into the model's environment through structured inputs, simulations, and real-time sensor data, normally as vectors, tables or matrices. For example, in engineering or robotics applications, an AI model can be fed with contextual information such as system dynamics, environmental parameters, or control constraints. Thus the model can make decisions that are not just statistically sound but also physically meaningful within the modeled system. They can dynamically update the context window of an AI model, for example in scenarios like predictive maintenance or adaptive control, where AI must continuously adapt to new data. By embedding contextual cues, like historical trends, operational thresholds, or user-defined rules, such tools help ground the model's outputs in the specific realities of the task or domain. Financial Services Use Cases Quantitative Strategy Simulation Simulate trading strategies and feed results into an LLM for interpretation or optimization. Stress Testing Financial Models Run Monte Carlo simulations or scenario analyses and use the outputs to inform LLMs about potential systemic risks. Vectors and the Semantics of Similarity Vector embeddings are closely related to the linear algebra of technical computing, but they bring semantic context to the table. Typically stored in so-called vector databases, they encode meaning into high-dimensional space, allowing AI to retrieve through search not just exact matches, but conceptual neighbors. They thus allow for multiple stochastically arranged answers, not just one. Until recently, vector embeddings and vector databases have been primary providers of enterprise context to LLMs, shoehorning all types of data as searchable mathematical vectors. Their downside is their brute force and compute-intensive approach to storing and searching data. That said, they use similar transfer learning approaches – and deep neural nets – to those that drive LLMs. As expensive, powerful brute force vehicles of Retrieval-Augmented Generation (RAG), vector databases don't simply just store documents but understand them, and have an increasingly proven place for enabling LLMs to ground their outputs in relevant, contextualized knowledge. Financial Services Use Cases Customer Support Automation Retrieve similar past queries, regulatory documents, or product FAQs to inform LLM responses in real-time. Fraud Pattern Matching Embed transaction descriptions and retrieve similar fraud cases to help the model assess risk or flag suspicious behavior. Time-Series, Temporal and Streaming Context Time-series database and analytics providers, and in-memory and columnar databases that can organize their data structures by time, specialize in knowing about the when. They can ensure temporal context—the heartbeat of many use cases in financial markets as well as IoT, and edge computing- grounds AI at the right time with time-denominated sequential accuracy. Streaming systems, like Kafka, Flink, et al can also facilitate the real-time central nervous systems of financial event-based systems. It's not just about having access to time-stamped data, but analyzing it in motion, enabling AI to detect patterns, anomalies, and causality, as close as possible to real time. In context engineering, this is gold. Whether it's fraud that happens in milliseconds or sensor data populating insurance telematics, temporal granularity can be the difference between insight and noise, with context stored and delivered by what some might see as a data timehouse. Financial Services Use Cases Market Anomaly Detection Injecting real-time price, volume, and volatility data into an LLM's context allows it to detect and explain unusual market behavior. High-Frequency Trading Insights Feed LLMs with microsecond-level trade data to analyze execution quality or latency arbitrage. Graphs That Know Who's Who Graph and relationship-focussed providers play a powerful role in context engineering by structuring and surfacing relationships between entities that are otherwise hidden in raw data. In the context of large language models (LLMs), graph platforms can dynamically populate the model's context window with relevant, interconnected knowledge—such as relationships between people, organizations, events, or transactions. They enable the model to reason more effectively, disambiguate entities, and generate responses that are grounded in a rich, structured understanding of the domain. Graphs can act as a contextual memory layer through GraphRAG and Contextual RAG, ensuring that the LLM operates with awareness of the most relevant and trustworthy information. For example, graph databases - or other environments, e.g. Spark, that can store graph data types as accessible files, e.g. Parquet, HDFS - can be used to retrieve a subgraph of relevant nodes and edges based on a user query, which can then be serialized into natural language or structured prompts for the LLM. Platforms that focus graph context around entity resolution and contextual decision intelligence can enrich the model's context with high-confidence, real-world connections—especially useful in domains like fraud detection, anti-money laundering, or customer intelligence. Think of them as like Shakespeare's Comedy of Errors meets Netflix's Department Q. Two Antipholuses and two Dromios rather than 1 of each in Comedy of Errors? Only 1 Jennings brother to investigate in Department Q's case, and where does Kelly MacDonald fit into anything? Entity resolution and graph context can help resolve and connect them in a way that more standard data repositories and analytics tools struggle with. LLMs cannot function without correct and contingent knowledge of people, places, things and the relationships between them, though to be sure many types of AI can also help discover the connections and resolve entities in the first place. 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Context Engineering Beyond the Limits of Data, Knowledge & Truths Context engineering I believe recognizes that data is partial, and that knowledge and perhaps truth or truths needs to be situated, connected, and interpreted. Whether through graphs, time-series, vectors, tech computing platforms, or geospatial layering, AI depends on weaving the right contextual strands together. Where AI represents the loom, the five types of platforms I describe are like the spindles, needles, and dyes drawing on their respective contextual fabrics of ever changing data, driving threads of knowledge—contingent, contextual, and ready for action.
The Guardian
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- The Guardian
I chaired the FCC. The 60 Minutes settlement shows Trump has weaponized the agency
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The Guardian
2 hours ago
- The Guardian
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