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MS auto group braces for tariffs on imported vehicles, parts

MS auto group braces for tariffs on imported vehicles, parts

Yahoo28-03-2025

MEMPHIS, Tenn. — A Southaven auto group is preparing for tariffs on imported vehicles and automotive parts that could go into effect on April 3.
The Trump administration announced tariffs affecting imported vehicles and automotive parts could go into effect at the beginning of April.
A tariff is a tax imposed by a government on imported goods and services.
Trump places 25% tariff on imported autos, expecting to raise $100 billion in tax revenues
In response to the possible tax, an auto group is taking immediate action to protect its customers from unexpected costs.
'Well, my first reaction was dismay and my follow-up reaction was uncertainty,' said Kent Ritchey, owner of Landers Auto Group.
Ritchey has been in the auto business for more than 50 years, and when he heard about the tariffs for foreign cars and parts, it reminded him of COVID-19.
MATA deputy CEO fired after investigation into spending
'It was a mad scramble. It's kinda like the days that COVID hit. You don't know what's going to happen and you don't know how you are going to face it,' said Ritchey.
He said that he is staying focused on being proactive rather than reactive by ordering extra car parts over the last 30 days.
The company said in a press release they are also in line to get additional vehicles without any added cost with a promise to offer pre-tariff pricing on anything already in stock or on order.
'Calm employees down, calm customers down that's a normal reaction in any industry right now,' said Ritchey. 'We are positively optimistic.'
Copyright 2025 Nexstar Media, Inc. All rights reserved. This material may not be published, broadcast, rewritten, or redistributed.

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Mortgage Rates Today: June 9, 2025 - 30-Year and 15-Year Rates Stand Still
Mortgage Rates Today: June 9, 2025 - 30-Year and 15-Year Rates Stand Still

Forbes

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Mortgage Rates Today: June 9, 2025 - 30-Year and 15-Year Rates Stand Still

Currently, the average interest rate on a 30-year fixed mortgage is 6.83%, compared to 6.86% a week ago, according to the Mortgage Research Center. For borrowers who want to pay off their home faster, the average rate on a 15-year fixed mortgage is 5.85%, down 0.10% from the previous week. Homeowners who want to lock in a lower rate by refinancing should compare their existing mortgage rate with current market rates to make sure it's worth the cost to refinance. Borrowers paid an average rate of 6.83% on a 30-year mortgage. This was down from the previous week's rate of 6.86%. Currently, the average APR on a 30-year fixed-rate mortgage is 6.86%. This is lower than last week when the APR was 6.89%. The APR contains both mortgage interest and the lender fees to help give a more complete picture of loan costs. To get an idea of how much you'll pay: a $100,000 mortgage with a 30-year fixed-rate loan at the current average interest rate of 6.83% will cost you about $654 including principal and interest (taxes and fees not included) each month, the Forbes Advisor mortgage calculator shows. That's around $136,086 in total interest over the life of the loan. Today, the 15-year mortgage rate declined to 5.85%, lower than it was yesterday. Last week, it was 5.85%. On a 15-year fixed, the APR is 5.9%. Last week it was 5.9%. At today's interest rate of 5.85%, a 15-year fixed-rate mortgage would cost approximately $836 per month in principal and interest per $100,000. You would pay around $50,885 in total interest over the life of the loan. Today's average interest rate on a 30-year fixed-rate jumbo mortgage (a mortgage above 2025's conforming loan limit of $806,500 in most areas) fell 4.34% from last week to 7.18%. Borrowers with a 30-year, fixed-rate jumbo mortgage with today's interest rate of 7.18% will pay approximately $677 per month in principal and interest per $100,000 borrowed. That would be $144,242. Mortgage rates initially trended downward post-spring 2024. However, they surged again in October 2024—despite cuts by the Federal Reserve to the federal funds rate (its benchmark interest rate) in September, November and December 2024. Rates began to drop again in mid-January 2025, but experts don't forecast them falling by a significant amount in the near future. Mortgage rates are influenced by various economic factors, making it difficult to predict when they will drop. Mortgage rates follow U.S. Treasury bond yields. When bond yields decrease, mortgage rates generally follow suit. The Federal Reserve's decisions and global events also play a key role in shaping mortgage rates. If inflation rises or the economy slows, the Fed may lower its federal funds rate. For example, during the Covid-19 pandemic, the Fed reduced rates, which drove interest rates to record lows. A significant drop in mortgage rates seems unlikely in the near future. However, they may decline if inflation eases or the economy weakens. Mortgages and mortgage lenders are often a necessary part of purchasing a home, but it can be tough to understand what you're paying for—and what you can actually afford. Using a mortgage calculator can help you estimate your monthly mortgage payment based on your interest rate, purchase price, down payment and other expenses. Here's what you'll need in order to calculate your monthly mortgage payment: Mortgage interest rates are determined by several factors, including some that borrowers can't control: While the above factors set the base interest rate for new mortgages, there are several areas that borrowers can focus on to get a lower rate: Many home buyers are eligible for several mortgage loan types. Each program can have its own advantages: A competitive mortgage rate currently ranges from 6% to 8% for a 30-year fixed loan. Several factors impact mortgage rates, including the repayment term, loan type and borrower's credit score. Most rate locks last 30 to 60 days and your lender may not charge a fee for this initial period. However, extending the rate lock period up to 90 or 120 days is possible, depending on your lender, but additional costs may apply. A mortgage interest rate reflects what a lender is charging you on top of your loan amount in return for allowing you to borrow money. Annual percentage rate (APR), on the other hand, is a calculation that includes both a loan's interest rate and finance charges, expressed as an annual cost over the life of the loan. In other words, it's the total cost of credit. APR accounts for interest, fees and time. Since APRs include both the interest rate and certain fees associated with a home loan, the APR can help you understand the total cost of a mortgage if you keep it for the entire term. The APR will usually be higher than the interest rate, but there are exceptions.

Defying debt warnings, Republicans push forward on Trump tax agenda
Defying debt warnings, Republicans push forward on Trump tax agenda

Yahoo

time10 minutes ago

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Defying debt warnings, Republicans push forward on Trump tax agenda

By David Morgan WASHINGTON (Reuters) -U.S. President Donald Trump and his Republican allies in Congress are determined to enact his tax-cut agenda in a political push that has largely abandoned longtime party claims of fiscal discipline, by simply denying warnings that the measure will balloon the federal debt. The drive has drawn the ire of Elon Musk, a once-close Trump ally and the biggest donor to Republicans in the 2024 election, who gave a boost to a handful of party deficit hawks opposed to the bill by publicly denigrating it as a "disgusting abomination," opening a public feud with Trump. But top congressional Republicans remain determined to squeeze Trump's campaign promises through their narrow majorities in the Senate and House of Representatives by July 4, while shrugging off warnings from the official Congressional Budget Office and a host of outside economists and budget experts. 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Behind the Curtain: The scariest AI reality
Behind the Curtain: The scariest AI reality

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The wildest, scariest, indisputable truth about AI's large language models is that the companies building them don't know exactly why or how they work. Sit with that for a moment. The most powerful companies, racing to build the most powerful superhuman intelligence capabilities — ones they readily admit occasionally go rogue to make things up, or even threaten their users — don't know why their machines do what they do. Why it matters: With the companies pouring hundreds of billions of dollars into willing superhuman intelligence into a quick existence, and Washington doing nothing to slow or police them, it seems worth dissecting this Great Unknown. None of the AI companies dispute this. They marvel at the mystery — and muse about it publicly. They're working feverishly to better understand it. They argue you don't need to fully understand a technology to tame or trust it. Two years ago, Axios managing editor for tech Scott Rosenberg wrote a story, "AI's scariest mystery," saying it's common knowledge among AI developers that they can't always explain or predict their systems' behavior. And that's more true than ever. Yet there's no sign that the government or companies or general public will demand any deeper understanding — or scrutiny — of building a technology with capabilities beyond human understanding. They're convinced the race to beat China to the most advanced LLMs warrants the risk of the Great Unknown. The House, despite knowing so little about AI, tucked language into President Trump's "Big, Beautiful Bill" that would prohibit states and localities from any AI regulations for 10 years. The Senate is considering limitations on the provision. Neither the AI companies nor Congress understands the power of AI a year from now, much less a decade from now. The big picture: Our purpose with this column isn't to be alarmist or " doomers." It's to clinically explain why the inner workings of superhuman intelligence models are a black box, even to the technology's creators. We'll also show, in their own words, how CEOs and founders of the largest AI companies all agree it's a black box. Let's start with a basic overview of how LLMs work, to better explain the Great Unknown: LLMs — including Open AI's ChatGPT, Anthropic's Claude and Google's Gemini — aren't traditional software systems following clear, human-written instructions, like Microsoft Word. In the case of Word, it does precisely what it's engineered to do. Instead, LLMs are massive neural networks — like a brain — that ingest massive amounts of information (much of the internet) to learn to generate answers. The engineers know what they're setting in motion, and what data sources they draw on. But the LLM's size — the sheer inhuman number of variables in each choice of "best next word" it makes — means even the experts can't explain exactly why it chooses to say anything in particular. We asked ChatGPT to explain this (and a human at OpenAI confirmed its accuracy): "We can observe what an LLM outputs, but the process by which it decides on a response is largely opaque. As OpenAI's researchers bluntly put it, 'we have not yet developed human-understandable explanations for why the model generates particular outputs.'" "In fact," ChatGPT continued, "OpenAI admitted that when they tweaked their model architecture in GPT-4, 'more research is needed' to understand why certain versions started hallucinating more than earlier versions — a surprising, unintended behavior even its creators couldn't fully diagnose." Anthropic — which just released Claude 4, the latest model of its LLM, with great fanfare — admitted it was unsure why Claude, when given access to fictional emails during safety testing, threatened to blackmail an engineer over a supposed extramarital affair. This was part of responsible safety testing — but Anthropic can't fully explain the irresponsible action. Again, sit with that: The company doesn't know why its machine went rogue and malicious. And, in truth, the creators don't really know how smart or independent the LLMs could grow. Anthropic even said Claude 4 is powerful enough to pose a greater risk of being used to develop nuclear or chemical weapons. OpenAI's Sam Altman and others toss around the tame word of " interpretability" to describe the challenge. "We certainly have not solved interpretability," Altman told a summit in Geneva last year. What Altman and others mean is they can't interpret the why: Why are LLMs doing what they're doing? Anthropic CEO Dario Amodei, in an essay in April called "The Urgency of Interpretability," warned: "People outside the field are often surprised and alarmed to learn that we do not understand how our own AI creations work. They are right to be concerned: this lack of understanding is essentially unprecedented in the history of technology." Amodei called this a serious risk to humanity — yet his company keeps boasting of more powerful models nearing superhuman capabilities. Anthropic has been studying the interpretability issue for years, and Amodei has been vocal about warning it's important to solve. In a statement for this story, Anthropic said: "Understanding how AI works is an urgent issue to solve. It's core to deploying safe AI models and unlocking [AI's] full potential in accelerating scientific discovery and technological development. We have a dedicated research team focused on solving this issue, and they've made significant strides in moving the industry's understanding of the inner workings of AI forward. It's crucial we understand how AI works before it radically transforms our global economy and everyday lives." (Read a paper Anthropic published last year, "Mapping the Mind of a Large Language Model.") Elon Musk has warned for years that AI presents a civilizational risk. In other words, he literally thinks it could destroy humanity, and has said as much. Yet Musk is pouring billions into his own LLM called Grok. "I think AI is a significant existential threat," Musk said in Riyadh, Saudi Arabia, last fall. There's a 10%-20% chance "that it goes bad." Reality check: Apple published a paper last week, "The Illusion of Thinking," concluding that even the most advanced AI reasoning models don't really "think," and can fail when stress-tested. The study found that state-of-the-art models (OpenAI's o3-min, DeepSeek R1 and Anthropic's Claude-3.7-Sonnet) still fail to develop generalizable problem-solving capabilities, with accuracy ultimately collapsing to zero "beyond certain complexities." But a new report by AI researchers, including former OpenAI employees, called " AI 2027," explains how the Great Unknown could, in theory, turn catastrophic in less than two years. The report is long and often too technical for casual readers to fully grasp. It's wholly speculative, though built on current data about how fast the models are improving. It's being widely read inside the AI companies. It captures the belief — or fear — that LLMs could one day think for themselves and start to act on their own. Our purpose isn't to alarm or sound doomy. Rather, you should know what the people building these models talk about incessantly. You can dismiss it as hype or hysteria. But researchers at all these companies worry LLMs, because we don't fully understand them, could outsmart their human creators and go rogue. In the AI 2027 report, the authors warn that competition with China will push LLMs potentially beyond human control, because no one will want to slow progress even if they see signs of acute danger. The safe-landing theory: Google's Sundar Pichai — and really all of the big AI company CEOs — argue that humans will learn to better understand how these machines work and find clever, if yet unknown ways, to control them and " improve lives." The companies all have big research and safety teams, and a huge incentive to tame the technologies if they want to ever realize their full value.

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