
Trump's tariffs set to cost American employers $83billion, analysis finds
This category of businesses, which collectively employs approximately one-third of private-sector US workers, is particularly reliant on imports from countries such as China, India, and Thailand. The retail and wholesale sectors are identified as especially vulnerable to the import taxes being levied by the Republican president.
The findings directly challenge Mr. Trump's assertions that foreign manufacturers would absorb the costs of these tariffs, instead indicating that the financial burden falls squarely on US companies that depend on imports. While the tariffs implemented under Mr. Trump have not yet triggered widespread inflation, larger corporations like Amazon, Costco, Walmart, and Williams-Sonoma have reportedly delayed the full impact by accumulating inventories before the taxes took effect.
The analysis emerges just ahead of a July 9 deadline set by Mr. Trump for formally establishing tariff rates across dozens of countries. This deadline was introduced after financial markets reacted negatively to his initial tariff announcements in April, prompting a 90-day negotiating period during which most imports faced a 10 per cent baseline tariff. Higher rates apply to goods from China, Mexico, and Canada, alongside separate 50 per cent tariffs on steel and aluminium. Had the initial April 2 tariffs remained in place, the companies examined in the JPMorganChase Institute analysis would have faced a significantly higher direct cost of \:green−background[187].:green−background[6]billion.Underthecurrentrates,the: green − background [187].: green − background [6] billion. Underthecurrentrates, the 82.3 billion figure translates, on average, to \$2,080 per employee, or 3.1 per cent of the average annual payroll, encompassing both importing and non-importing firms.
When questioned about the progress of trade talks, Mr. Trump simply stated on Tuesday: "Everything's going well." The president has indicated his intention to set tariff rates, citing the logistical complexities of negotiating with numerous nations. As the 90-day period concludes, only the United Kingdom has formalised a trade framework with the Trump administration, though Mr. Trump announced a deal with Vietnam on Wednesday, with details pending. India has also signalled it is nearing a trade agreement. The outlook for tariffs remains highly uncertain, exemplified by Mr. Trump's halting and then restarting negotiations with Canada, and his recent threat of more tariffs on Japan unless it increases rice purchases from the US.
A growing body of evidence suggests that further inflation could emerge. Investment bank Goldman Sachs projects that companies will pass on 60 per cent of their tariff costs to consumers. Similarly, the Atlanta Federal Reserve's survey of businesses' inflation expectations indicates that companies could, on average, pass on roughly half their costs from a 10 per cent or 25 per cent tariff without diminishing consumer demand. The JPMorganChase Institute's findings also suggest that while tariffs might strengthen the role of some domestic manufacturers as goods suppliers, wholesalers and retailers, operating on already thin profit margins, may be compelled to pass tariff costs directly to their customers.
Treasury Secretary Scott Bessent commented on the trade talks in a Tuesday interview, asserting that the concessions achieved have impressed career officials across various agencies. "People who have been at Treasury, at Commerce, at USTR for 20 years are saying that these are deals like they've never seen before," Mr. Bessent told Fox News Channel's "Fox & Friends." The treasury secretary added that the Trump administration plans to discuss the specifics of trade deals next week, prioritising a multitrillion-dollar tax cuts package recently passed by the Republican majority in the Senate, the costs of which Mr. Trump hopes to offset with tariff revenues.
Hashtags

Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles


The Independent
34 minutes ago
- The Independent
The 10 moments that defined Keir Starmer's first year in office
Sir Keir Starmer celebrates his first year as prime minister with the largest parliamentary majority in a quarter of a century — a commanding mandate that has brought both opportunity and scrutiny. His tenure so far has been defined by cautious reform and major international resets. From securing a landmark US-UK trade deal with Donald Trump and a string of policy U-turns that have tested party discipline, here are the 10 most significant moments to reflect on his first year.


The Sun
41 minutes ago
- The Sun
Two popular apps loved by millions to shut down forever in DAYS – and you might be owed some cash
TWO popular apps used by millions of mobile users are set to shut down next week - and it might mean you're owed some cash. Mobile apps Pocket and Glitch will both close on 8 July. 3 Pocket was removed from app stores in late May, when sales of subscriptions also stopped. But Pocket's Premium subscribers may be owed refunds for the remaining time left on their subscriptions. A subscription for the read-it-later app costs $4.99 per month or $44.99 (£32.99) per year. So if you paid for an annual membership that will not be fulfilled, you should expect some money to be deposited back onto the card you used for payments. Mozilla, which owns the app, said annual subscribers will receive their refunds after 8 July. No action is necessary - just wait for the money to land. It's worth noting that while the app will shut down next week, users will have until 8 October to download their saved data before all Pocket data is permanently deleted. The Glitch app, aimed at web developers, is also handing out refunds to its subscribers. Those who forked out $96 (£70) for an annual Glitch Pro subscription and have paid time left will receive a refund. Users have until the end of the year to download their projects before all the data is wiped for good. Huge Global Data Breach: 16 Billion Accounts at Risk 3


Geeky Gadgets
an hour ago
- Geeky Gadgets
Model Context Protocol (MCP) Explained With Code Examples)
What if the way AI agents interact with tools and resources could be as seamless as browsing the web? Imagine a world where developers no longer wrestle with custom-built adapters or fragmented integration processes, and AI systems effortlessly connect with APIs, databases, and services. Enter the Model Context Protocol (MCP)—a new framework poised to redefine how AI agents handle complex tasks. By introducing a standardized approach to integration, MCP eliminates inefficiencies, enhances scalability, and simplifies workflows. It's not just a technical upgrade; it's a paradigm shift that could transform the future of AI-driven automation. In this exploration, Assembly AI unpack the core principles and benefits of MCP, revealing how it addresses long-standing challenges in AI-agent integration. From reducing developer workloads to allowing seamless interoperability, MCP offers a unified solution to some of the most pressing issues in AI development. You'll also discover real-world examples that illustrate its potential, such as AI agents performing intricate workflows without the need for custom code. Whether you're a developer, a service provider, or simply curious about the next evolution in AI, this journey into MCP will leave you rethinking what's possible in the realm of automation and innovation. Overview of Model Context Protocol Purpose and Vision of MCP MCP was developed to standardize the interaction between AI agents and external tools or services. Traditionally, developers have relied on custom-built adapters for each service, which increases complexity, maintenance demands, and development time. MCP eliminates these challenges by introducing a unified protocol that enables seamless connections between AI agents and diverse resources. This standardization not only reduces inefficiencies but also unlocks new opportunities for automation and task execution. By providing a consistent framework, MCP enables developers to focus on creating innovative AI solutions rather than managing the intricacies of integration. It also fosters a more collaborative ecosystem where service providers can design tools that are inherently compatible with AI agents, further enhancing the potential for automation and scalability. Challenges Addressed by MCP AI agents, particularly those powered by large language models (LLMs), often encounter significant hurdles when performing multi-step, high-precision, or complex tasks. Existing integration methods exacerbate these challenges by requiring bespoke solutions for each tool or service. This fragmented approach introduces several key obstacles: High Maintenance Demands: Custom-built adapters for individual tools require ongoing updates and troubleshooting. Custom-built adapters for individual tools require ongoing updates and troubleshooting. Security Risks: Inconsistent integration methods can create vulnerabilities and expose sensitive data to potential threats. Inconsistent integration methods can create vulnerabilities and expose sensitive data to potential threats. Limited Scalability: Fragmented processes hinder the ability to scale AI systems efficiently across diverse applications. MCP addresses these issues by shifting the integration responsibility to service providers and offering a standardized framework. This reduces the need for translation layers, simplifies development workflows, and enhances the overall reliability of AI-agent interactions. Understanding Model Context Protocol (MCP) Watch this video on YouTube. Stay informed about the latest in Model Context Protocol (MCP) by exploring our other resources and articles. How MCP Operates MCP employs a client-server architecture to assist interactions between AI agents and external resources. In this model, service providers manage MCP servers that expose tools and capabilities, while AI agents act as clients accessing these tools through the protocol. This architecture introduces several key advantages: Reduced Developer Workload: Service providers handle MCP server management, allowing developers to focus on building AI functionalities. Service providers handle MCP server management, allowing developers to focus on building AI functionalities. Decoupled Implementation: AI agents are abstracted from the specific technical details of external services, allowing more flexible integrations. AI agents are abstracted from the specific technical details of external services, allowing more flexible integrations. Minimized Translation Layers: Traditional API interactions often require complex translation layers, which MCP eliminates or significantly reduces. For example, an AI agent using MCP can interact with a document creation tool without requiring a custom adapter for each API. This abstraction ensures that the integration process is not only more efficient but also more reliable, allowing AI agents to perform tasks with greater precision and consistency. Core Benefits of MCP The adoption of MCP offers several fantastic benefits that enhance the development, functionality, and scalability of AI systems: Simplified Integration: Developers no longer need to create and maintain custom adapters for individual tools or services, reducing time and resource investment. Developers no longer need to create and maintain custom adapters for individual tools or services, reducing time and resource investment. Enhanced Interoperability: MCP enables seamless interaction between AI agents and a wide range of external resources, fostering a more connected ecosystem. MCP enables seamless interaction between AI agents and a wide range of external resources, fostering a more connected ecosystem. Improved Scalability: By decoupling functionality, MCP supports the growth of scalable AI ecosystems capable of handling diverse and complex tasks. By decoupling functionality, MCP supports the growth of scalable AI ecosystems capable of handling diverse and complex tasks. Flexible Tool Composition: AI agents can combine multiple tools to execute intricate workflows more effectively, enhancing their versatility. AI agents can combine multiple tools to execute intricate workflows more effectively, enhancing their versatility. Increased Security: Standardized protocols reduce vulnerabilities associated with ad hoc integration methods, making sure safer interactions. These advantages position MCP as a critical enabler for the future of AI-driven automation and tool integration, making it an essential framework for developers, service providers, and organizations alike. Applications and Real-World Use Cases MCP has already demonstrated its potential in various practical scenarios, showcasing its ability to simplify tool usage and enhance AI-agent capabilities. For instance: Document Management: An AI agent using MCP can integrate with the Google Docs API to generate, edit, and upload documents. By abstracting API interactions, MCP allows the agent to focus on the task rather than the technical details of integration. An AI agent using MCP can integrate with the Google Docs API to generate, edit, and upload documents. By abstracting API interactions, MCP allows the agent to focus on the task rather than the technical details of integration. Mathematical Operations: An AI agent can perform arithmetic calculations by accessing a calculation tool via MCP. This eliminates the need for custom code to handle each mathematical function, streamlining the process. An AI agent can perform arithmetic calculations by accessing a calculation tool via MCP. This eliminates the need for custom code to handle each mathematical function, streamlining the process. Data Analysis: MCP enables AI agents to connect with data visualization tools, allowing them to generate insights and create visual reports without requiring specialized adapters. These examples highlight how MCP simplifies the integration process, making AI agents more versatile and effective in handling diverse tasks across industries. Broader Implications and Future Potential MCP has the potential to become a foundational protocol for AI-agent ecosystems, much like HTTP/HTTPS serves as the backbone of the web. Its standardization encourages widespread adoption by service providers, fostering robust and scalable interactions between AI agents and external resources. As organizations such as OpenAI and others embrace MCP, it is likely to emerge as the universal standard for AI-agent resource integration. Looking ahead, MCP's ability to streamline integration, enhance security, and enable complex task execution positions it as a cornerstone of AI-driven automation. Its evolution will likely play a pivotal role in advancing AI capabilities, shaping interoperable ecosystems, and driving innovation across industries. Media Credit: AssemblyAI Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.