SoftBank to rent AI computing resources to US companies starting next fiscal year

Claude Mythos 5 can find software flaws faster than almost any human. What its return means for crypto bridges, protocols, and DeFi security.

OpenAI has floated giving the US government a 5 percent ownership stake as a way of easing tensions with the Trump administration and blunting mounting public backlash against AI, according to the Financial Times. CEO Sam Altman argued that giving the public a financial interest in the company would be the best way to share the upside of AI, the FT reported, citing two unnamed people familiar with the talks. He's said to have first pitched the idea to Trump early last year. Altman reportedly suggested the 5 percent figure. Based on OpenAI's latest funding round, which ended with the company valued at $852 billion, that stake would be worth … Read the full story at The Verge.

Karp's critique highlights potential shifts in AI business models, emphasizing transparency and control, which could reshape enterprise AI dynamics. The post Palantir CEO criticizes OpenAI and Anthropic for overselling AI to businesses appeared first on Crypto Briefing.

Micron's surge highlights AI's transformative impact on tech investments, shifting capital from traditional assets to high-growth sectors. The post Micron stock surges past 270% in 2026 as AI memory demand reshapes capital flows appeared first on Crypto Briefing.

AI-powered software development tools integrate with your IDE and codebase, helping you to write, refactor, and fix code faster. These tools also make it fast and easy to create and run unit tests and integration tests — tasks that take more time when done manually. Today, .NET developers often use GitHub Copilot, Claude Code, Cursor AI, and even AI chatbots like ChatGPT to generate code. In this article, we’ll cover some best practices you should follow when using AI to generate your C# code. Challenges of using AI-generated code While AI can write code for you, often the generated code does not work as intended. AI may generate code that contains logic errors, bugs, or security vulnerabilities, or code that doesn’t conform to your organization’s coding conventions or quality standards, or code that isn’t compatible with existing architecture. Further, AI may generate code that runs slowly or fails to run at all. These are some of the key challenges organizations face when using AI-ge

As organizations rush to move AI into production, they’re finding that the tools they rely on to monitor traditional software don’t translate cleanly to AI systems. The reason is fundamental: AI doesn’t fail as software does. It doesn’t throw clean error codes or follow predictable execution paths. It drifts, hallucinates, and degrades in ways that are often subtle, intermittent, and hard to reproduce. The result is a growing gap between what teams think observability should provide and what current tools actually deliver. The uncomfortable truth? The AI observability tools we have today are built for yesterday’s problems. To understand where the industry is headed, we need to look at where it is today and why that’s not enough. AI observability today: The era of evals Today’s AI observability landscape is dominated by one concept: evaluation. Most tools focus on scoring model outputs after the fact. They rely on test datasets, human graders, or, increasingly, “LLM-as-a-judge” approach

Ionic Digital, the bitcoin miner formed out of the Celsius bankruptcy, filed to go public on Nasdaq after a sharp pullback in its mining operations and a rapid shift toward AI infrastructure leasing. This article first appeared in The Energy Mag. The original article can be viewed here. The Energy Mag (formerly The Miner Mag) […]

SoftBank's SB Neo initiative signals a strategic shift towards integrated AI infrastructure, emphasizing data sovereignty and market expansion. The post SoftBank establishes SB Neo for US neocloud push, launching FY27 appeared first on Crypto Briefing.