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Here we go again: latest Trump tariff deadline looms amid inflation concerns

Here we go again: latest Trump tariff deadline looms amid inflation concerns

The Guardian20 hours ago
When Donald Trump unveiled his 'liberation day' tariffs in the spring, only to pull the plug days later as panic tore through global markets, his officials scrambled to present the climbdown as temporary.
Three months of frenetic talks would enable the Trump administration to strike dozens of trade agreements with countries across the world, they claimed. 'We're going to run,' the White House trade adviser Peter Navarro told Fox Business Network. 'Ninety deals in 90 days is possible.'
The 90-day pause Trump ordered on his steep tariffs is almost up, and 90 deals have not materialized. The US is again on the brink of launching a trade assault against dozens of countries, with rates including 27% on Kazakhstan, 47% on Madagascar and 36% on Thailand.
'I'm not thinking about the pause,' the president claimed during a briefing with reporters earlier this week, when asked about Wednesday's deadline. 'I'll be writing letters to a lot of countries. And I think you're just starting to understand the process.'
Business leaders, lobbyists, economists and investors might disagree. Even officials in Trump's own administration have at times struggled to keep up. Another cliff edge has reared into view, forcing them to return to a familiar question: will he actually go through with this?
'I would suspect he's serious,' said Marc Busch, professor of international business diplomacy at Georgetown University. 'I think he's going to give a pass to the countries negotiating in good faith. But as of 9 July, a lot of the news will be big tariffs that the US hasn't seen since the 1930s are in effect.'
A handful of agreements have emerged, cooling some tensions. A partial deal with the UK was first to emerge, before a delicate truce with China, and a pact with Vietnam. Officials are also said to be closing in on a 'framework' arrangement with the EU.
But these breakthroughs have been significantly narrower than conventional free trade agreements, which can take years to hammer out. 'These aren't real trade deals. These are cessations of hostility,' said Busch. 'These are purchasing agreements that may or may not appease Trump for maybe a little while, thrown in with some aspirational stuff.'
Even if Trump extends the 90-day pause next week, or strikes myriad deals at breakneck pace, current tariff levels are still much higher than they were before his return to office. The effects of this are still filtering through to prices for US consumers.
'The US economy is definitely, I would say, breaking more to the positive than would have been the narrative, or the expectation, kind of right after liberation day,' said John Waldron, president of Goldman Sachs. 'There's still an expectation that we're going to see more inflation over the course of the summer.'
Mid-sized businesses in the US face an estimated $82.3bn in additional costs if the US maintains a 10% universal rate on all imports, as well as higher rates of 55% on China and 25% on Mexico and Canada, according to analysis by the JPMorganChase Institute.
Such firms 'often play a crucial role in regional economies and as part of larger supply chains', said analysts at the institute. 'If they struggle, it may cause ripple effects for other businesses and their communities.'
If the 'liberation day' tariffs are reimposed after the pause, costs would rise significantly. But even if they are not, the duties Trump has already introduced – and remain in force – are leaving companies with a hefty bill.
The administration's playbook, of hiking tariffs on a country dramatically and then cutting them back as a result of an agreement, is 'like a retailer that one day increases prices by 100% and another day announces a 30% sale', said Busch. 'It's quite extraordinary that we're still debating this issue,' he added. 'American businesses are already eating and passing on parts of these tariffs to consumers.'
No senior federal official has been more vocal about this reality than Jerome Powell, chair of the Federal Reserve, who – despite Trump's public demands and attacks – has kept US interest rates on hold while waiting to see how the administration's trade strategy pans out.
'Someone has to pay for the tariffs,' Powell said at a recent press conference, noting how the cost filters through a supply chain, from the initial manufacturer through to the customer buying a product. 'All through that chain, people will be trying not to be the ones who pick up the cost.
'But ultimately, the cost of the tariff has to be paid and some of it will fall on the end consumer. We know that. That's what businesses say. That's what the data says from past evidence. So we know that's coming.'
Trump does not see it this way, insisting that tariffs are taxes on other countries, rather than US businesses and consumers.
Whatever happens over the next few days, those attempting to take a longer-term view believe the main actions he has taken in recent months – like imposing blanket 10% tariffs – could remain in place for many years to come.
'We think it's likely that high and broad-based tariffs are here to stay because, of all the purported goals of trade policy, they're proving most successful at raising revenue,' said Michael Pearce, deputy chief US economist at Oxford Economics. 'Given the fiscal challenges that lie ahead, those revenues will be hard for future administrations to replace.'
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Context Engineering for Financial Services: By Steve Wilcockson
Context Engineering for Financial Services: By Steve Wilcockson

Finextra

time31 minutes ago

  • Finextra

Context Engineering for Financial Services: By Steve Wilcockson

The hottest discussion in AI right now, at least the one not about Agentic AI, is about how "context engineering" is more important than prompt engineering, how you give AI the data and information it needs to make decisions, and it cannot (and must not) be a solely technical function. "'Context' is actually how your company operates; the ideal versions of your reports, documents & processes that the AI can use as a model; the tone & voice of your organization. It is a cross-functional problem.' So says renowned Tech Influencer and Associate Professor at Wharton School, Ethan Molick. He in turn cites fellow Tech Influencer Andrej Karpathy on X, who in turn cites Tobi Lutke, CEO of Shopify: "It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM. " The three together - Molick, Karpathy and Lutke - make for a powerful triumvirate of Tech-influencers. Karpathy consolidates the subject nicely. 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.' 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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. 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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. Financial Services Use Cases AML and KYC Investigations Surface hidden connections between accounts, transactions, and entities to inform LLMs during risk assessments. Credit Risk Analysis Use relationship graphs to understand borrower affiliations, guarantors, and exposure networks. Seeing the World in Geospatial Layers Geospatial platforms support context engineering by embedding spatial awareness into AI systems, enabling them to reason about location, proximity, movement, and environmental context. They can provide rich, structured data layers (e.g., terrain, infrastructure, demographics, weather) that can be dynamically retrieved and injected into an LLM's context window. This allows the model to generate responses that are not only linguistically coherent but also geographically grounded. For example, in disaster response, a geospatial platform can provide real-time satellite imagery, flood zones, and population density maps. This data can be translated into structured prompts or visual inputs for an AI model tasked with coordinating relief efforts or summarizing risk. Similarly, in urban planning or logistics, geospatial context helps the model understand constraints like traffic patterns, zoning laws, or accessibility. In essence, geospatial platforms act as a spatial memory layer, enriching the model's understanding of the physical world and enabling more accurate, context-aware decision-making. Financial Services Use Cases Branch Network Optimization Combine demographic, economic, and competitor data to help LLMs recommend new branch locations. Climate Risk Assessment Integrate flood zones, wildfire risk, or urban heat maps to evaluate the environmental exposure of mortgage and insurance portfolios. 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.

I chaired the FCC. The 60 Minutes settlement shows Trump has weaponized the agency
I chaired the FCC. The 60 Minutes settlement shows Trump has weaponized the agency

The Guardian

timean hour ago

  • The Guardian

I chaired the FCC. The 60 Minutes settlement shows Trump has weaponized the agency

It is time to unfurl the 'Mission Accomplished' banner at the Federal Communications Commission (FCC). Paramount Global, the parent of CBS Television, has agreed to pay $16m to settle a lawsuit brought by Donald Trump over the editing of a 60 Minutes interview with Kamala Harris. Presumably, the FCC can now cease its slow-walking of the Paramount-Skydance Media merger. Just two days after the president took office, the agency's new chair, Brendan Carr, inserted the FCC into the issues in the Trump lawsuit that alleged 'news distortion'. As the New York Post headlined: 'Trump's FCC pick Brendan Carr says '60 Minutes' editing scandal could affect Paramount-Skydance merger review.' That lawsuit was filed in the final week of the 2024 presidential campaign under the Texas Deceptive Trade Practices Act, a statute historically used against false advertising. The case was filed in a single-judge federal district court that one legal publication characterized as 'a favored jurisdiction for conservative legal causes and plaintiffs'. CBS characterized the case as 'without merit'. The 60 Minutes broadcast aired in October; the day before, a different excerpt had appeared on Face the Nation. Soon after, the Center for American Rights – a group that describes itself as 'a public interest law firm dedicated to protecting Americans' most fundamental constitutional rights' – filed a complaint at the FCC alleging CBS had engaged in 'significant and substantial news alteration'. The complaint was dismissed as seeking 'to weaponize the licensing authority of the FCC in a way that is fundamentally at odds with the First Amendment'. Immediately upon becoming the FCC chair, Carr reversed that decision and ordered a formal proceeding on the matter (but let stand the dismissal of a complaint against a local Fox station over its 2020 election coverage). The election of Trump and the installation of a Trump-appointed FCC chair transformed the Paramount/CBS merger from a review of the public interest merits of the transfer of broadcast licenses into a broader question that included the 60 Minutes editing. Carr told an interviewer: 'I'm pretty confident that the news distortion complaint over the 60 Minutes transcript is something that is likely to arise in the context of the FCC review of that transaction.' The formal paperwork for FCC approval of the license transfers was submitted 10 months ago, on 6 September 2024. Now that the lawsuit has been settled, it will be interesting to see how quickly the FCC acts. The CBS case is just one example of the tactical leverage the Trump FCC regularly exerts over those it regulates. Carr, who wrote the FCC chapter in the 'Project 2025' Maga blueprint, has not been shy about using this authority to achieve such political goals. Even before formally assuming the FCC chair position, Carr began exercising chair-like authority to advance the Maga agenda. This began with a letter to the CEOs of Alphabet (Google and YouTube), Meta (Facebook and Instagram), Microsoft and Apple alleging: 'you participated in a censorship cartel … [that is] an affront to Americans' constitutional freedoms and must be completely dismantled.' Going beyond traditional FCC authority, he threatened: 'As you know, Big Tech's prized liability shield, Section 230, is codified in the Communications Act, which the FCC administers.' Carr suggested he might investigate whether those editorial decisions were made in good faith. Recently, Carr conditioned the approval of Verizon's acquisition of Frontier Communications on Verizon agreeing to drop its corporate diversity, equity and inclusion (DEI) policies. Continuing his anti-diversity efforts, he launched an investigation into Comcast Corporation because it promotes DEI as 'a core value of our business'. In his pre-FCC chair days, Carr championed press freedom. In a 2021 statement, he wrote: 'A newsroom's decision about what stories to cover and how to frame them should be beyond the reach of any government official.' Once he became Trump's FCC chair, however, he not only picked up on the 60 Minutes matter, but also launched an investigation into the public broadcasters NPR and PBS 'regarding the airing of … programming across your broadcast member stations'. The FCC's regulatory authority directly covers about one-sixth of the American economy while also affecting the other five-sixths that rely on the nation's communications networks. What was once an independent, policy-based agency has been transformed into a performance-based agency, using any leverage it can discover or invent to further the Trump Maga message. Tom Wheeler was the chair of the Federal Communications Commission from 2013 to 2017

U.S. completes deportation of 8 men to South Sudan after weeks of legal wrangling
U.S. completes deportation of 8 men to South Sudan after weeks of legal wrangling

NBC News

timean hour ago

  • NBC News

U.S. completes deportation of 8 men to South Sudan after weeks of legal wrangling

WASHINGTON — Eight men deported from the United States in May and held under guard for weeks at an American military base in the African nation of Djibouti while their legal challenges played out in court have now reached the Trump administration's intended destination, war-torn South Sudan, a country the State Department advises against travel to due to 'crime, kidnapping, and armed conflict.' The immigrants from Cuba, Laos, Mexico, Myanmar, Vietnam and South Sudan arrived in South Sudan on Friday after a federal judge cleared the way for the Trump administration to relocate them in a case that had gone to the Supreme Court, which had permitted their removal from the U.S. Administration officials said the men had been convicted of violent crimes in the U.S. 'This was a win for the rule of law, safety and security of the American people,' said Homeland Security spokeswoman Tricia McLaughlin in a statement Saturday announcing the men's arrival in South Sudan, a chaotic country in danger once more of collapsing into civil war. The Supreme Court on Thursday cleared the way for the transfer of the men who had been put on a flight in May bound for South Sudan. That meant that the South Sudan transfer could be completed after the flight was detoured to a base in Djibouti, where they men were held in a converted shipping container. The flight was detoured after a federal judge found the administration had violated his order by failing to allow the men a chance to challenge the removal. The court's conservative majority had ruled in June that immigration officials could quickly deport people to third countries. The majority halted an order that had allowed immigrants to challenge any removals to countries outside their homeland where they could be in danger. A flurry of court hearings on Independence Day resulted a temporary hold on the deportations while a judge evaluated a last-ditch appeal by the men before the judge decided he was powerless to halt their removals and that the person best positioned to rule on the request was a Boston judge whose rulings led to the initial halt of the administration's effort to begin deportations to South Sudan. By Friday evening, that judge had issued a brief ruling concluding the Supreme Court had tied his hands. The men had final orders of removal, Immigration and Customs Enforcement officials have said. Authorities have reached agreements with other countries to house immigrants if authorities cannot quickly send them back to their homelands.

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