Arca Blames Strategy's Sale for Bitcoin Crash

Bitcoin Inflows Slow Down in 2026

Bitcoin inflows have slowed down sharply in 2026. This is because investors are now chasing AI-related assets. Bitcoin exchange-traded fund flows have weakened. ETF outflows have totaled $2.6 billion this year. This is a relatively modest amount. A broader investor base has created a healthier market structure. Bitcoin ownership is becoming less dependent on retail investors. The cryptocurrency has fallen from $82,000 to around $63,000. It will be important to see how bitcoin performs in the coming months.

KPMG Expands Microsoft Partnership

KPMG and Microsoft have expanded their global relationship to help clients deploy AI at scale. This move includes the deployment of Microsoft 365 Copilot across the entire KPMG global workforce of more than 276,000 professionals. This matters because it will help KPMG and clients move from AI pilots to organization-wide deployment with built-in security and governance. The deployment of Microsoft 365 Copilot will provide a secure, enterprise AI platform that supports multiple models and helps protect KPMG data and intellectual property. KPMG will use Microsoft Agent 365 to manage and control AI agents for clients and across the global network, enabling more consistent and scalable AI across the organization. KPMG will now help clients scale their own AI transformation in a trusted and responsible way, and this will continue to grow in the future.

China's $295 Billion AI Investment Boosts Domestic Chip Industry

China is set to invest nearly $295 billion over the next five years into building a nationwide network of AI data centers. A key part of this plan requires that at least 80% of the technology used must come from domestic suppliers, such as Huawei. This move aims to reduce reliance on foreign companies like Intel and AMD, which have dominated the AI chip market. Meanwhile, Taiwan is considering making the smuggling of AI chips to China a criminal offense for the first time. This comes amid growing tensions over semiconductor supplies and China's push to develop its own chip industry. The measures reflect Beijing's determination to build a self-sufficient AI ecosystem, even as it faces challenges in catching up with global leaders in the field. This investment could significantly reshape the global AI landscape by strengthening China's domestic tech sector. It also raises questions about how this will impact international trade and competition in semiconductors. Watch for further developments in China's chip manufacturing capabilities and its relationships with key suppliers.

Corning Wins Amazon Data Center Deal

Corning has entered a multiyear agreement to supply optical fiber and connectivity solutions for Amazon's US data center infrastructure. This deal creates 1,000 new US manufacturing jobs and a training program for fiber optic technicians. The deal matters because it ties Corning to the growing demand for high bandwidth connectivity driven by AI workloads. Corning will supply Amazon with optical fiber, a key component of data center networks. The partnership may become an important part of Corning's business as AI workloads increase, Corning will continue to expand its data infrastructure business.

AI Costs Shift Beyond Subscriptions

The traditional model of generative AI, where users pay monthly subscriptions and ask questions one at a time, is being replaced. New "agentic" AI systems are consuming far more tokens-sometimes thousands or even millions-and running autonomously for hours. This shift means flat-rate pricing is no longer feasible for providers. Instead, costs vary based on speed, specialization, and the value of results. For instance, while a simple question might cost a few cents, complex tasks like coding or debugging can run into hundreds or thousands of dollars. This change isn't just about pricing; it fundamentally alters how AI's economic impact is measured. Instead of flat fees, businesses are now evaluating AI based on its ability to generate real value over time. However, the exact costs and benefits vary widely depending on the specific use case. For example, a business using AI for customer service might see significant cost savings, while another using it for creative design might not. Looking ahead, experts predict that token pricing will become a key metric in determining an AI's true value to businesses. As this new model evolves, companies will need to carefully assess how these costs align with their specific needs and whether the benefits outweigh the investment.