OpenAI Raises $4 Billion for Deployment Joint Venture, Valued at $10 Billion

Why do some people think AI will change the world, while others think it’s ordinary? Karpathy’s two diagnoses

Karpathy pointed out that AI cognition gaps come from two diagnoses: 1) having only used free/older models, which cannot reflect the capability of the most advanced agentic models; 2) ability improvements are only significant in highly technical domains and they show asymmetry. This causes paid users and general readers to see different phenomena, leading to mutual misunderstandings in parallel worlds. The takeaway for readers in Taiwan is that only by actually using the most advanced models to complete production tasks and verify the basis for judgments can you avoid being swayed by media myths.

Applied Digital Secures $300 Million Bridge Financing Led by Goldman Sachs for AI Data Centers

According to Globenewswire, Applied Digital secured $300 million in senior secured bridge financing led by Goldman Sachs on May 4 to advance its AI data center development. The financing is collateralized by project assets, can be repaid early without penalties, and the company plans to pursue

Karpathy “Let LLMs argue with themselves”: a 4-step method to counter thinking biases with AI

This article summarizes Andrej Karpathy’s four-step method for “making an LLM argue against its own claims”: first, use the LLM to refine an initial draft; then, start a new conversation and ask it to propose opposing arguments and provide evidence; next, turn it into a complete rebuttal article; finally, compare both sides and the verifiable evidence, with humans making the judgment. Remind that the LLM may exhibit sycophancy—don’t treat agreement as truth; use it instead as an argument-generating machine rather than a final arbiter. You can also have two LLMs debate each other to reduce bias. This method is especially valuable for content creation in 2026, improving cognitive depth and reducing homogenization.

Sam Altman and Dario Amodei are both unbearable! AI doomsday talk and relative deprivation have made Americans increasingly resent AI

A well-known Silicon Valley podcast, 《All-In Podcast》, discussed the AI industry and offered a rather sharp observation: American society’s attitude toward AI is shifting negative, and the most concrete outlet for this sentiment is the data centers that AI companies are racing to build across the U.S. The source of this backlash may include AI doomsday fears, fear of unemployment, or a deeper sense of dissatisfaction: it seems that yet another wave of technological innovation will only make a small number of people get rich, while most people’s lives show no clear improvement. Local U.S. governments have already overturned data center construction cases On the show, Chamath Palihapitiya said that the current problems facing the AI industry are not just competition among models, capital expenditures, or a shortage of compute power, but that “the American people, overall, are becoming increasingly repulsed by the whole issue of AI.” He noted that the source of this repulsion may include AI companies

Community Questions Ideological Bias in Mainstream AI Models on May 4

According to BlockBeats, on May 4, AI community user X Freeze questioned whether mainstream AI models including ChatGPT, Claude, and Gemini show systematic bias by being less aligned with conservative positions on issues such as gender, immigration, and crime. The user suggested that as AI