
China Tightens Fentanyl Controls in Goodwill Gesture to Trump
China moved to tighten controls over two chemicals that can be used to make fentanyl, in an apparent olive branch to the US that may help maintain their fragile trade truce.
Authorities added two previously unclassified precursors to a list of Class Two chemicals, according to a joint statement by six government departments on Monday. The label will subject the substances, 4-piperidone and 1-boc-4-piperidone, to tougher supervision starting July 20.
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Bloomberg
34 minutes ago
- Bloomberg
Mexico Joins Rush of Borrowers Tapping Global Debt Markets
Mexico is looking to sell global notes for the third time this year as sovereign and corporate borrowers rush to tap debt markets Monday. The country is offering global notes due July 2032 and January 2038, according to a preliminary prospectus. Proceeds will go to redeem dollar notes due next year and fund the repurchase of some other outstanding sovereign bonds, according to the filing.


Forbes
34 minutes ago
- Forbes
China Market Update: It's The End Of The World, Hong Kong & Chinese Stocks Feel Fine
CLN Asian equities declined following the US attack on Iranian nuclear facilities, the threat of a potential counterattack, and concerns over Middle East oil transportation out of the Persian Gulf (hence the REM song reference in today's title). Despite the strength of the US dollar, Hong Kong, Mainland China, and Malaysia outperformed. A key factor in the resilience of Hong Kong and Mainland China was the start of the 12th two-and-a-half-day meeting of the 14th Standing Committee of the National Committee of the Chinese People's Political Consultative Conference (CPPCC). Try saying that five times fast! The meeting, attended by the upper echelons of China's government, will discuss 'further deepening economic system reform and promoting China-style modernization,' with topics including 'deepening economic system reform and promoting China's modernization,' 'promoting fertility support,' and 'promoting artificial intelligence.' Policy expectations are light, though I suspect last year's meeting set in motion the September announcements by the People's Bank of China (PBOC) and Politburo regarding real estate policy. It was hard not to notice that consumer-focused Hong Kong stocks and sub-sectors, such as automobiles, electric vehicles (EVs), hybrids, travel, hotels, and restaurants, performed well. Alibaba declined 0.81% despite integrating its online travel platform, Fliggy, and restaurant delivery service, into its core E-Commerce business, driven by strong orders. Alibaba's weakness may also have been influenced by Meituan's 2.18% gain after the company announced an expansion of its 'instant retail business,' leveraging its massive restaurant delivery network and further overseas expansion in Saudi Arabia. Another factor was the Hong Kong relisting initial public offering (IPO) of Mainland-listed Zhejiang Sanhua Intelligent Controls. While more supply in the market requires capital from somewhere, it was interesting that the IPO was down despite being heavily oversubscribed: 747 times by retail investors and 23 times by institutional investors. Premier Li signed a State Council order requiring internet platforms to submit tax-related information, but this does not appear problematic, as it focuses on tax reporting by the companies themselves. The Wall Street Journal published a nonsensical article on Wall Street's lost love affair with US listings of Chinese companies. Meanwhile, Southbound Stock Connect saw strong inflows, with Mainland investors net buying $1.005 billion. Hong Kong- and Mainland-listed semiconductor stocks had a strong day following Friday's Wall Street Journal article reporting that the US will pressure ASML and Taiwan Semiconductor Manufacturing Company (TSMC) to stop producing in China. Mega-cap banks also performed well, as the exchange-traded funds (ETFs) favored by the National Team saw strong volumes. Beverages and food stocks were down, with Kweichow Moutai off 0.61%. I thoroughly enjoyed listening to the Dwarkesh Podcast interview with Arthur Kroeber of Gavekal Dragonomics. You'll learn more about China from listening to that two-and-a-half-hour episode than from a lifetime of reading about China in the Western media. I don't agree with everything, but on the big picture, I found it very insightful and aligned with my own thinking. I also enjoyed the BG2 Podcast, hosted by Altimeter's founder and CEO Brad Gerstner and Benchmark's former general partner Bill Gurley, interviewing Coatue's Laffont brothers on artificial intelligence (AI), public and venture capital markets, macroeconomics, US debt, crypto, IPOs, and more. Coatue hosted its 2024 East Meets West Conference last week, focusing on AI. The investment firm generously provides its deck for free on its website, which I recommend checking out. Yes, I had a long weekend watching my kids' sports. New Content Read our latest article: Navigating Global Crosswinds: Carbon Markets Respond to Tariff Tactics and Executive Orders Please click here to read Chart1 Chart2 Chart3 Chart4 Chart5 Chart6


Forbes
an hour ago
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The AI Hype Trap: Why Most CEOs Struggle To Unlock Real Business Value
Diganta Sengupta is a seasoned technology leader with deep expertise in artificial intelligence, Gen AI, Cloud computing, and blockchain. While collaborating with clients on cutting-edge AI initiatives, I've had a front-row seat to the rapidly evolving landscape of generative AI (GenAI). There's no doubt that it's a transformative force, and the excitement is palpable. Leaders see GenAI as a powerful enabler of innovation, efficiency and even cultural change within their organizations. But beneath the surface of this enthusiasm, a more sobering reality has started to emerge. I observed leadership become enthusiastic about leveraging AI to unlock insights from massive operational datasets, but the reality quickly became evident. Despite deploying advanced models, the organization lacked the foundational elements for scalable impact. In other words, data was siloed, inconsistent and often not AI-ready. Teams were stretched thin across too many pilot projects without clear alignment to business workflows. Flashy prototypes drew attention but failed to deliver lasting value without reengineering the underlying processes. This mirrors a broader trend. Seventy percent of CEOs fear that flawed AI strategies could lead to their removal, while 54% fear that competitors may already have more advanced AI implementations. AI systems learn from historical data. If that data encodes human biases against certain demographics, regions or business units, the AI will reproduce and even amplify those biases. While developing a prototype using certain datasets for a utility company, for example, I grappled with significant challenges around bias and fairness. These issues persisted despite the presence of seemingly robust governance frameworks. As we trained our AI models on historical operational and customer data, I noticed embedded biases tied to region, demographics and internal processes. These biases not only surfaced in the model outputs but were, in some cases, amplified. My two cents: CEOs must invest in bias-detection tools, diverse development teams and transparency mechanisms long before deploying AI at scale. Without these guardrails, AI initiatives stall as risk-averse stakeholders balk at unverified "black-box" systems. In another project integrating a large language model (LLM)-powered chatbot with an enterprise ERP system, I encountered AI hallucinations as the model confidently generated inaccurate and misleading information about customer orders. Despite rigorous prompt engineering and system tuning, we noticed that the LLM occasionally fabricated responses about inventory levels or order status. This experience echoed findings from a 2024 Boston Consulting Group survey, which revealed that while 75% of executives ranked AI among their top priorities, only 25% reported realizing substantial benefits from their AI initiatives. Tackle hallucinations with robust validation pipelines, keep human-in-the-loop review for critical outputs and ongoing monitoring of model performance. This is where the challenge becomes even more complex. In many of my AI pilots in the oil and gas sector, I've repeatedly seen issues like inconsistent formats, missing metadata and a lack of standardized governance across departments severely impact model performance. Despite having large volumes of rich data, much of it couldn't be used without extensive manual cleanup. Efforts to unify data governance were often sidelined in favor of launching high-profile AI initiatives. A Harvard Business Review Analytic Services survey similarly found that most companies' data is largely not ready for enterprise-wide AI, citing poor data quality as a key barrier. Without strong cross-functional data stewardship and quality assurance, even the most advanced AI models fall short. Before spending on fancy models, CEOs must champion cross-functional data governance, setting up practices on creating common taxonomies, automated data-quality checks and centralized platforms. Only then can AI be relied upon to deliver accurate, actionable insights. Working on the previously mentioned utility AI project also brought light to another critical and often underestimated concern—security and governance challenges that surround enterprise AI deployments. As we integrated sensitive operational and customer data into AI workflows, it became clear how vulnerable these systems can become without rigorous controls. Inadequate access management, insufficient encryption and lack of monitoring can create openings for potential ransomware attacks and unauthorized data exposure. In one survey, 35% of respondents cited mistakes or errors with real-world consequences and 34% pointed to not achieving expected value as top barriers. Both are rooted in security vulnerabilities and governance shortcomings. CEOs must elevate AI risk management to the same level as financial or operational risk. This includes rigorous model-risk frameworks, data-privacy impact assessments and alignment with evolving regulations such as the EU's AI Act. To harness the full potential of AI, I recommend applying practical, accountable strategies that organizations can adopt to drive real, scalable impact. • Establish cross-functional data governance. Form a governance council with IT, compliance and operations to ensure data ownership, accountability and consistent standards. • Implement data quality controls. Deploy automated checks for outliers, schema validation and data freshness to improve input reliability and mitigate bias. • Address LLM hallucinations with RAG. Use retrieval-augmented generation (RAG), prompt chaining and fallback mechanisms to reduce hallucinations. • Align AI projects with business goals. Prioritize initiatives tied directly to key KPIs (for example, safety, cost reduction, etc.), which can improve adoption and leadership support. • Pivot away from noncritical use cases. Reallocate resources from low-impact projects to high-impact workflows like downtime alerts for field engineers. • Focus on responsible AI deployment. Emphasize transparency, accountability and strategic value delivery to build trust and ensure scalability. CEOs who view AI adoption as a multidimensional transformation rather than a plug-and-play technology will be the ones ready to move beyond the hype and truly harness the AI power. The future of competitive advantage lies not just in having AI, but in embedding it thoughtfully and responsibly into the fabric of the enterprise. This will help transform AI from a conceptual promise to a tangible asset and help drive innovation and growth for the organizations. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?