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US Bonds Climb as Economic Data Backs Bets on Two 2025 Fed Cuts

US Bonds Climb as Economic Data Backs Bets on Two 2025 Fed Cuts

Yahoo15-05-2025

(Bloomberg) -- Treasuries gained as a fresh spate of economic data offered signs of ebbing economic activity and dimming inflation, supporting bets that the Federal Reserve will cut interest rates twice this year.
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The bond gains on Thursday came after several Wall Street strategists boosted their yield forecasts earlier in the week as their firms' economic teams pushed back expectations for when the Fed will resume easing policy. Still, swaps traders maintained wagers on two quarter-point Fed cuts by year-end. The dollar edged lower.
Treasury yields were down as much as 10 basis points, after long maturities were whipsawed earlier in the day by large trades that briefly pushed the 30-year bond to nearly 5%. Rates on debt that matures from five to seven years led the declines.
'Bad news is good news for the bond market' as the latest data could be signs of a weaker economy that may allow the Fed to cut rates sooner than anticipated, said Zachary Griffiths, head of investment-grade and macroeconomic strategy at CreditSights Inc. 'Our base case remains the Fed stays on hold this year as we expect below-potential growth, but no recession, and tariffs - even at the current levels - likely to produce sustained inflationary pressure.'
Prices paid to US producers unexpectedly declined in April by the most in five years, suggesting companies are absorbing some of the hit from higher tariffs. Meanwhile, growth in US retail sales decelerated notably as consumers pulled back spending on imported goods amid concerns about rising prices from levies.
Two-year yields, which are most sensitive to Fed policy, slid as much as nine basis points to 3.96%, while the benchmark 10-year yield dropped a similar amount to 4.44%. Investors have been increasingly wary of buying long-term securities given concern about the US fiscal trajectory, and the 30-year yield dropped by a smaller magnitude.
While swaps traders were fully pricing in the Fed's next reduction for October, the odds in the market were also strong for a move in September. That's ahead of some Wall Street economists, who pushed out their forecasts for the next cut after the recent cooling in US-China trade tensions. For the rest of the year, swaps contracts imply about 0.54 percentage point in total Fed cuts.
The rates strategy teams at TD Securities, JPMorgan Chase & Co. and Bank of America were among those that boosted their forecasts for Treasury yields over recent days.
In the meantime, Republican lawmakers' draft plan for sweeping tax cuts has been moving ahead, sparking renewed focus on the US fiscal trajectory, with the package projected to worsen the federal deficit and lift the government's debt burden.
Jamie Dimon, chief executive officer of JPMorgan Chase, said in a Bloomberg Television interview Thursday that the US deficit and debt load is an issue.
'It creates risk of inflation to me. It creates risk of higher long-term rates,' Dimon said at JPMorgan's annual Global Markets Conference in Paris. That might slow growth and create a stagflation scenario, he added.
Even higher short-term yields risk causing a hit to the fiscal outlook given US debt managers have hoisted sales of bills in recent years. At least $9.3 trillion of federal debt is slated to mature and roll over within a year – in addition to the approximately $2 trillion that the Treasury will need to issue over the next year to cover the federal deficit, according to the Peterson Foundation.
Traders have received through the week signals from Fed policy makers that they're comfortable keeping rates unchanged as they await more clarity on how the administration's trade policy will affect growth and inflation. Last week, the Fed opted to hold rates steady while it waits for further evidence on the strength of the economy.
'Weaker consumer spending and lower inflation could give the Fed some room to ease later in the year if these trends continue,' said David Berson, chief US economist at Cumberland Advisors.
In prepared remarks released on Thursday on the Fed's framework review, Fed Chair Jerome Powell held off on providing insights on the near-term outlook for monetary policy.
--With assistance from Aline Oyamada and Edward Bolingbroke.
(Updates rates, adds strategist comments.)
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Defying debt warnings, Republicans push forward on Trump tax agenda
Defying debt warnings, Republicans push forward on Trump tax agenda

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Defying debt warnings, Republicans push forward on Trump tax agenda

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High Throughput Process Development Strategic Business Report 2025-2030: Increasing Focus on Speed and Efficiency in Drug Discovery Expands Market Opportunities
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High Throughput Process Development Strategic Business Report 2025-2030: Increasing Focus on Speed and Efficiency in Drug Discovery Expands Market Opportunities

The global High Throughput Process Development (HTPD) market, pivotal for the biopharmaceutical industry, is projected to grow from $15.3 billion in 2024 to $24.6 billion by 2030, at a CAGR of 8.3%. Key drivers include technological innovations in automation and data analytics. The market is segmented by product types, including consumables and software, with major growth forecasted in regions such as China and the U.S. The report offers insights into market trends, regional analysis, and profiles leading companies like Agilent Technologies and Danaher Corporation. High Throughput Process Development Market Dublin, June 09, 2025 (GLOBE NEWSWIRE) -- The "High Throughput Process Development - Global Strategic Business Report" report has been added to global market for High Throughput Process Development was valued at US$15.3 Billion in 2024 and is projected to reach US$24.6 Billion by 2030, growing at a CAGR of 8.3% from 2024 to 2030. This comprehensive report provides an in-depth analysis of market trends, drivers, and forecasts, helping you make informed business decisions. The report includes the most recent global tariff developments and how they impact the High Throughput Process Development market. The growth in the high throughput process development market is driven by several factors, including the increasing complexity of biologic drugs, the rising demand for personalized medicines, and advancements in automation and data analytics. As the biopharmaceutical industry shifts toward more complex therapeutics, such as gene and cell therapies, the need for efficient process optimization is more critical than ever. HTPD allows companies to streamline the development of these therapies, reducing both time and costs. The growing demand for biosimilars, which require rapid process development to compete with branded biologics, is also contributing to market growth. Additionally, the adoption of automation and machine learning in process development is further enhancing the efficiency and accuracy of bioprocess optimization, driving the market's throughput process development (HTPD) is a critical tool in the biopharmaceutical industry, enabling faster, more efficient development of biologic drugs. HTPD involves the use of automated, miniaturized experiments that allow researchers to test multiple process conditions simultaneously, significantly accelerating the optimization of production processes for therapeutic proteins, vaccines, and monoclonal antibodies. The ability to screen numerous variables at once reduces the time and resources needed to scale up bioprocesses, from lab-scale to commercial manufacturing. As biopharmaceutical companies face increasing pressure to reduce costs and bring therapies to market faster, HTPD has become indispensable in ensuring both process efficiency and product Are Technological Innovations Shaping High Throughput Process Development?Technological innovations, particularly in automation, robotics, and data analytics, have revolutionized high throughput process development. Automation systems now allow for the simultaneous running of hundreds of experiments, enabling researchers to optimize cell culture conditions, purification processes, and formulation strategies more efficiently. The integration of advanced data analytics and machine learning into HTPD platforms has further enhanced process optimization, allowing for predictive modeling and real-time decision-making. Microfluidic technologies, which allow for the precise control of small volumes of liquids, are also playing a critical role in reducing reagent use and increasing the speed of bioprocess optimization. These technologies are enabling biopharmaceutical companies to streamline their development timelines and reduce production Do Market Segments Define the Growth of the High Throughput Process Development Market?Product types include consumables, software, and instruments, with consumables like reagents and microplates holding the largest market share due to their constant need in HTPD workflows. Services such as contract research and process development outsourcing are also expanding, as biopharmaceutical companies increasingly seek external expertise to accelerate drug development. The biopharmaceutical industry, particularly companies focused on biologics such as monoclonal antibodies, is the primary end-user of HTPD, as it enables rapid scale-up from R&D to manufacturing. Other key users include academic and research institutes involved in biotechnology Scope Report Features: Comprehensive Market Data: Independent analysis of annual sales and market forecasts in US$ Million from 2024 to 2030. In-Depth Regional Analysis: Detailed insights into key markets, including the U.S., China, Japan, Canada, Europe, Asia-Pacific, Latin America, Middle East, and Africa. Company Profiles: Coverage of players such as Agilent Technologies, Aurora Biomed, Axxam S.P.A., Bio-Rad Laboratories, Danaher Corporation and more. Complimentary Updates: Receive free report updates for one year to keep you informed of the latest market developments. Key Insights: Market Growth: Understand the significant growth trajectory of the Monoclonal Antibodies segment, which is expected to reach US$17.8 Billion by 2030 with a CAGR of a 9.1%. The Other Molecule Types segment is also set to grow at 6.5% CAGR over the analysis period. Regional Analysis: Gain insights into the U.S. market, valued at $3.9 Billion in 2024, and China, forecasted to grow at an impressive 12.6% CAGR to reach $6.4 Billion by 2030. Discover growth trends in other key regions, including Japan, Canada, Germany, and the Asia-Pacific. Segments Molecule Type (Monoclonal Antibodies, Other Molecule Types) Component (Tools & Systems, Consumables, Software, Other Components) Technology (Chromatography, Ultraviolet-Visible Spectroscopy, Other Technologies) End-Use (Biopharmaceutical Companies, Contract Research Organizations, Academic Research Institutes) Tariff Impact Analysis: Key Insights for 2025What's Included in This Edition: Tariff-adjusted market forecasts by region and segment Analysis of cost and supply chain implications by sourcing and trade exposure Strategic insights into geographic shifts Buyers receive a free July 2025 update with: Finalized tariff impacts and new trade agreement effects Updated projections reflecting global sourcing and cost shifts Expanded country-specific coverage across the industry Key Attributes: Report Attribute Details No. of Pages 89 Forecast Period 2024 - 2030 Estimated Market Value (USD) in 2024 $15.3 Billion Forecasted Market Value (USD) by 2030 $24.6 Billion Compound Annual Growth Rate 8.3% Regions Covered Global Key Topics Covered: MARKET OVERVIEW Influencer Market Insights Tariff Impact on Global Supply Chain Patterns Global Economic Update High Throughput Process Development - Global Key Competitors Percentage Market Share in 2024 (E) Competitive Market Presence - Strong/Active/Niche/Trivial for Players Worldwide in 2024 (E) MARKET TRENDS & DRIVERS Rising Demand for Biopharmaceuticals Drives Growth in High Throughput Process Development Technological Advancements in Automation and Robotics Propel Innovation in High Throughput Process Development Increasing Focus on Speed and Efficiency in Drug Discovery Expands Addressable Market for High Throughput Solutions Growing Adoption of High Throughput Technologies in Vaccine Development Spurs Market Growth Surge in Demand for Personalized Medicine Strengthens Business Case for High Throughput Process Development Increasing Use of High Throughput Screening in Protein and Antibody Discovery Expands Market Potential Technological Innovations in Microfluidics and Lab Automation Propel Market Innovation Growing Focus on Reducing Time-to-Market for Pharmaceuticals Drives Adoption of High Throughput Processes Rising Use of Artificial Intelligence (AI) in High Throughput Data Analysis Strengthens Competitive Advantage Increasing Focus on Process Optimization and Scalability in Biopharmaceutical Production Drives Innovation Rising Demand for High Throughput Analytical Techniques in Quality Control Expands Market Opportunities FOCUS ON SELECT PLAYERS:Some of the 33 companies featured in this report Agilent Technologies Aurora Biomed Axxam S.P.A. Bio-Rad Laboratories Danaher Corporation Eppendorf GE Healthcare Hamilton Company Merck Millipore Perkinelmer Sartorius Stedim Biotech Tecan Group Thermo Fisher Scientific, Inc. For more information about this report visit About is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends. Attachment High Throughput Process Development Market CONTACT: CONTACT: Laura Wood,Senior Press Manager press@ For E.S.T Office Hours Call 1-917-300-0470 For U.S./ CAN Toll Free Call 1-800-526-8630 For GMT Office Hours Call +353-1-416-8900Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data

Behind the Curtain: The scariest AI reality
Behind the Curtain: The scariest AI reality

Axios

time21 minutes ago

  • Axios

Behind the Curtain: The scariest AI reality

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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