Fed's Barr: inflation to rise, may see some persistence
June 24 (Reuters) - Higher import levies will put upward pressure on prices that may not prove temporary, Federal Reserve Governor Michael Barr said on Tuesday in remarks that suggest he is in no rush to cut interest rates despite what he called "gradual, albeit uneven" progress toward 2% inflation to date.
"I expect inflation to rise due to tariffs," Barr said in remarks prepared to open an event in Omaha, Nebraska aimed at getting feedback on Fed policy and a read on current economic conditions from business and community leaders. "Higher short-term inflation expectations, supply chain adjustments, and second-round effects may cause some inflation persistence."
At the same time, he added, tariffs may slow the economy and drive up unemployment, which has been low and steady. May's unemployment rate was 4.2%.
"There is still considerable uncertainty about tariff policies and their effects," Barr said. "Monetary policy is well positioned to allow us to wait and see how economic conditions unfold."
The Fed last week left short-term borrowing costs in the 4.25%-4.50% range. Fed Chair Jerome Powell in congressional testimony earlier on Tuesday underscored the central bank's "wait-and-see" approach to interest-rate setting as it assesses the effect of tariffs on inflation over the next few months.
Barr's remarks differ from those of Fed Governor Christopher Waller and Fed Vice Chair for Supervision Michelle Bowman, each of whom in recent days said they could see a July rate cut given their view that tariffs are likely to deliver only a one-time bump to inflation.
"Monetary policy sometimes requires tradeoffs - a stance of policy that is necessary to lower inflation, for example, may also lower aggregate demand and slow the economy," Barr said.
"Crucial in balancing our economic goals is determining how policy decisions affect households and businesses, which is why we are here to listen to you."
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Geeky Gadgets
22 minutes ago
- Geeky Gadgets
Inside the Lightning-Fast World of High-Frequency AI Trading Systems
What if the difference between profit and loss in financial markets wasn't measured in seconds, but in nanoseconds? High-frequency AI trading (HFT) systems operate in this razor-thin margin of time, where every microsecond shaved off a process can mean millions of dollars gained—or lost. These systems are feats of engineering, combining ultra-low latency hardware, real-time data pipelines, and algorithmic precision to execute trades faster than the blink of an eye. Yet, behind the scenes, their architecture is a labyrinth of complexity, demanding relentless optimization and innovation. For those curious about the invisible machinery driving modern financial markets, the world of HFT offers a fascinating glimpse into the intersection of technology and economics. In this exploration, ByteMonk uncover the real-time architecture that powers these systems, breaking down the critical components that enable them to process market data, make decisions, and execute trades at unprecedented speeds. From in-memory order books that provide instant snapshots of market activity to FPGA accelerators that push hardware to its limits, each element plays a pivotal role in maintaining the competitive edge of HFT firms. But this isn't just a story of speed; it's also one of precision, risk management, and adaptability in an ever-evolving landscape. By the end, you'll gain a deeper appreciation for the intricate design and relentless optimization that make high-frequency AI trading possible—an architecture where milliseconds are a luxury, and every decision is a race against time. High-Frequency Trading Overview What Drives High-Frequency Trading? At its core, high-frequency AI trading revolves around the use of sophisticated algorithms and machines to execute trades at lightning-fast speeds. The primary objective is to exploit minute price discrepancies across markets or instruments, generating profits through high trade volumes. In this environment, speed is paramount—delays measured in microseconds can determine the difference between profit and loss. To achieve this level of performance, HFT systems prioritize: Minimizing latency to ensure trades are executed faster than competitors. Real-time data handling to process and analyze market information instantly. Precision in execution to capitalize on fleeting opportunities with minimal error. Every aspect of an HFT system's design is carefully optimized to meet these demands, making sure it operates with unparalleled efficiency. How Real-Time Market Data Powers HFT The backbone of any HFT system lies in its ability to ingest and process market data in real time. Exchanges broadcast market data through ultra-low latency networks, which HFT systems capture using specialized hardware such as network interface cards (NICs) equipped with kernel bypass technology. This bypass eliminates delays caused by traditional operating system processes, allowing data to flow directly into the system. Once captured, market data feed handlers decode and transform raw data into actionable formats. This transformation enables algorithms to perform rapid analysis and make split-second AI trading decisions. The ability to process data with such speed and accuracy is what allows HFT systems to stay ahead in highly competitive markets. Trading System Real-Time Architecture Explored Watch this video on YouTube. Explore further guides and articles from our vast library that you may find relevant to your interests in AI automation. The Role of In-Memory Order Books An in-memory order book is a pivotal component of HFT systems, providing a live, up-to-the-moment view of market activity. Unlike traditional storage methods, which rely on disk-based systems, in-memory order books store data directly in memory. This eliminates the latency associated with disk access, making sure that trading decisions are based on the most current market conditions. In addition to speed, in-memory order books offer fault tolerance, allowing the system to recover quickly in the event of failures. These order books serve as the foundation for trading algorithms, allowing them to evaluate market conditions and execute strategies with minimal delay. By maintaining a real-time snapshot of the market, HFT systems can respond to changes with unparalleled precision. Event-Driven Pipelines: The Engine of Speed HFT systems rely on event-driven architectures to process data and execute trades at exceptional speeds. These pipelines are optimized for high throughput and low latency, often employing lock-free mechanisms to avoid bottlenecks. Nanosecond-precision timestamps play a critical role in sequencing and synchronizing events. By making sure that decisions are based on the most accurate and timely information, event-driven pipelines enable HFT systems to operate with unmatched efficiency. This architecture is the driving force behind the speed and reliability of high-frequency trading. FPGA Acceleration: Hardware for Speed Field Programmable Gate Arrays (FPGAs) are a cornerstone of HFT systems, providing hardware-level acceleration for critical tasks. These components are customized to perform specific operations, such as arbitrage calculations or market-making, at speeds far beyond what traditional software can achieve. By embedding logic directly into hardware, FPGAs enable ultra-fast decision-making and execution. This hardware-level optimization gives HFT firms a significant competitive advantage, allowing them to process data and execute trades faster than their competitors. The integration of FPGAs into HFT systems exemplifies the importance of specialized hardware in achieving peak performance. Algorithmic Strategy Engines The strategy engines within HFT systems are responsible for analyzing market conditions and executing trades. These engines are tailored to the specific trading strategies employed by the firm, which may include: Statistical arbitrage : Analyzing historical price relationships to identify profitable opportunities. : Analyzing historical price relationships to identify profitable opportunities. Machine learning models: Adapting to evolving market conditions for dynamic decision-making. Regardless of the approach, these engines are carefully optimized for both speed and accuracy. By using advanced algorithms, HFT systems can respond to market changes in real time, making sure they remain competitive in fast-moving environments. Smart Order Routing and Risk Management Smart order routing is a critical feature of HFT systems, making sure that trades are executed at the best possible prices across multiple exchanges. These routers continuously evaluate market conditions, directing orders to the most favorable venues. Simultaneously, pre-trade risk management systems perform essential checks to prevent financial errors and ensure compliance with regulatory requirements. By integrating these components, HFT systems safeguard the integrity and stability of their operations, minimizing risks while maximizing profitability. Order Management and Continuous Monitoring Centralized monitoring systems provide real-time visibility into the trading process, tracking the status of orders, execution performance, and overall system health. These tools enable operators to detect and resolve issues such as latency spikes or hardware failures quickly. Continuous monitoring ensures that the system operates at peak efficiency, minimizing downtime and maximizing profitability. By maintaining a constant watch over system performance, HFT firms can address potential problems before they escalate, making sure uninterrupted AI trading operations. Relentless Optimization for Precision HFT AI trading systems are in a perpetual state of refinement, with engineers focusing on reducing latency and enhancing performance. This process involves the seamless integration of hardware and software, including: FPGA-based accelerators for hardware-level speed enhancements. for hardware-level speed enhancements. Specialized network hardware to reduce transmission delays. By co-designing these components, HFT systems achieve the precision and speed required to thrive in the fast-paced world of financial markets. This relentless pursuit of optimization ensures that HFT systems remain at the cutting edge of trading technology. Media Credit: ByteMonk 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.
BBC News
25 minutes ago
- BBC News
US Judge sides with AI firm Anthropic over copyright issue
A US judge has ruled that using books to train artificial intelligence (AI) software is not a violation of US copyright decision came out of a lawsuit brought last year against AI firm Anthropic by three writers, a novelist, and two non-fiction authors, who accused the firm of stealing their work to train its Claude AI model and build a multi-billion dollar business. In his ruling, Judge William Alsup wrote that Anthropic's use of the authors' books was "exceedingly transformative" and therefore allowed under US he rejected Anthropic's request to dismiss the case, ruling the firm would have to stand trial over its use of pirated copies to build their library of material. Anthropic, a firm backed by Amazon and Google's parent company, Alphabet, could face up to $150,000 in damages per copyrighted firm holds more than seven million pirated books in a "central library" according to the ruling is among the first to weigh in on a question that is the subject of numerous legal battles across the industry - how Large Language Models (LLMs) can legitimately learn from existing material."Like any reader aspiring to be a writer, Anthropic's LLMs trained upon works, not to race ahead and replicate or supplant them — but to turn a hard corner and create something different," Judge Alsup wrote."If this training process reasonably required making copies within the LLM or otherwise, those copies were engaged in a transformative use," he noted that the authors did not claim that the training led to "infringing knockoffs" with replicas of their works being generated for users of the Claude they had, he wrote, "this would be a different case". Similar legal battles have emerged over the AI industry's use of other media and content, from journalistic articles to music and month, Disney and Universal filed a lawsuit against AI image generator Midjourney, accusing it of BBC is also considering legal action over the unauthorised use of its response to the legal battles, some AI companies have responded by striking deals with creators of the original materials, or their publishers, to license material for Alsup allowed Anthropic's "fair use" defence, paving the way for future legal judgements. However, he said Anthropic had violated the authors' rights by saving pirated copies of their books as part of a "central library of all the books in the world".In a statement Anthropic said it was pleased by the judge's recognition that its use of the works was transformative, but disagreed with the decision to hold a trial about how some of the books were obtained and used. The company said it remained confident in its case, and was evaluating its options.A lawyer for the authors declined to authors who brought the case are Andrea Bartz, a best-selling mystery thriller writer, whose novels include We Were Never Here and The Last Ferry Out, and non-fiction writers Charles Graeber and Kirk Wallace Johnson.
Reuters
an hour ago
- Reuters
Breakingviews - California and tech embrace one more knotty fling
NEW YORK, June 24 (Reuters Breakingviews) - The uneasy partnership between California's politicians and technology barons is based on mutual need. Taxes on capital gains help legislators balance the budget. Venture capitalists depend on the cluster of workers, firms and schools surrounding Silicon Valley. Both hope that the rise of artificial intelligence will shower them with wealth. Yet it could fatally weaken the relationship, too. Half of the world's ten most valuable companies – from iPhone maker Apple to Google owner Alphabet – are headquartered in California. Many now benefit from an AI gold rush, as investor hype lifts valuations to dizzying levels. San Francisco-based OpenAI, which helped spark the mania, is now valued at $300 billion. Thinking Machines Lab, founded by ex-OpenAI employee Mira Murati, just snagged $2 billion in funds at a $10 billion valuation, the Financial Times reported. It's by far the largest seed round ever, according to Crunchbase. The Golden State relies on these windfalls for the rich. About 60% of general revenue comes from personal income taxes, almost half of which are paid by the top 1% of earners. This cohort depends on capital gains that surge and tumble with the broader market. The AI boom is driving a new upswing: California expects to reap $6 billion, opens new tab more than originally projected this fiscal year. Governor Gavin Newsom and legislators could use more. Lawmakers approved a preliminary $325 billion budget plan for the coming fiscal year. The two branches must now haggle over details, and how to best paper over a $12 billion budget gap. That hole is expected to grow in future years. Raising tax rates seems a tough ask. California's top earners pay two-thirds more of their family income in state and local levies than the U.S. average, according to the Institute of Taxation and Economic Policy, opens new tab. A boost from rising valuations therefore would be especially handy. Investors backing the industry, like Andreessen Horowitz or Founders Fund, probably would prefer to keep their lucre. They can't easily decamp, though. Yes, Tesla boss Elon Musk – after building a $1 trillion company – relocated to Texas. For venture capitalists seeking the next superstar founder, though, the density of engineers, programmers and specialized vendors is unparalleled outside of the Bay Area. They are further fed by top-flight institutions like Stanford University. Yet the current AI boom is premised, at least partially, on automating much of this away. Programming is one of the jobs most at threat. Tech firms are laying off workers at a steady clip. If employment shrinks dramatically, so too will tax revenues – and, ultimately, the human and financial ties binding the industry and the state. Follow Robert Cyran on Bluesky, opens new tab.
