Sleepless AI(AI) Regular Invest

NEAR AI, a platform for confidential computing and autonomous AI agents, has introduced a novel method for users to pay for AI computational resources using staking. Instead of traditional monthly subscriptions paid via bank card, users can now lock up a specific amount of $NEAR tokens and receive monthly compute credits based on the staked amount. The tokens are neither spent nor deducted; they remain in the user's ownership and can be returned to their wallet after unstaking. Users have the flexibility to increase or decrease their staked $NEAR depending on their usage of AI services. This staking-based payment mechanism supports 43 models available on NEAR AI, including those from Anthropic, OpenAI, and Google, and is designed for confidential query processing and autonomous AI agent operations. The new system represents the first practical implementation of the "AI money" concept previously proposed by NEAR. The core idea is to directly link the usage of AI services with the cryptoasset underpinning the network.

Sam Altman has revised his earlier predictions about AI rapidly replacing jobs, admitting he overestimated the speed at which AI would eliminate entry-level white-collar roles. Speaking on the "Invest Like the Best" podcast, he stated that people do not truly want an AI CEO, as accountability and human connection remain critical. He found that individuals prefer interacting with people who can be held responsible for decisions. Similarly, NVIDIA's Jensen Huang argued that the narrative of AI destroying jobs is misguided. He distinguishes between tasks and jobs, noting that while AI can automate specific tasks, entire jobs—encompassing communication, judgment, coordination, and accountability—are not eliminated. He cited examples like radiologists and software engineers, where demand for these roles has increased as AI handles repetitive tasks, allowing for business expansion and the creation of more positions. Data from a University of Maryland and LinkUp study supports this, showing that U.S. job postings for new graduates have actually risen, countering the fear of vanishing entry-level roles. However, a significant shift is occurring: the traditional entry-level tasks that help newcomers gain experience are being automated, making initial career access more challenging. The key insight is that as AI takes over standardized tasks, the enduring value of human work shifts toward areas of responsibility, trust-building, and final decision-making—aspects that AI cannot replicate. The real "moat" for professionals lies in these irreplaceable human elements.

Apple's $5 Trillion Moment: Strongest Earnings, Weakest AI Apple released its Q2 2026 (FY Q3 2026) earnings, posting record revenue of $109.42 billion, up 16.4% year-over-year, and net income of $29.79 billion, up 27%. Driven by the iPhone 17's "super cycle" and strong Mac sales, hardware remained the core growth engine. However, the market reaction was muted, with the stock falling sharply after hours, contrasting with rallies in AI-heavy stocks like Microsoft and Nvidia. The report highlights Apple's ongoing AI struggles. After delays, its new Siri AI is finally launching, but its strategy reveals dependencies: partnering with Google's Gemini model overseas and with Alibaba and Baidu in China. This underscores Apple's lag in the foundational AI model race. Apple faces four key pressures: 1) sustaining growth after the iPhone 17's high sales base potentially "cannibalizes" future demand; 2) supply chain constraints and soaring memory costs impacting production and margins; 3) intense competition from AI-native smartphones that could redefine the user interface; and 4) a CEO transition, with hardware chief John Ternus taking over from Tim Cook in September. This stellar quarter serves as both Cook's final report card and the first major test for his successor, as Apple navigates a market increasingly defined by AI prowess rather than hardware alone.

This article argues that the primary threat from AI is not job replacement, but remaining trapped in "wage slavery"—financial dependence on employers. The path to becoming irreplaceable is not resisting AI, but becoming an "unemployable" individual who builds their own meaningful enterprise. The author identifies five key elements for this: Agency (acting without permission), Taste (judging what's worthwhile), Persuasion, Persistence, and Iteration. The solution is to stop being a "pawn" in someone else's game. To start, you must fundamentally change your identity and environment, then engage in rapid, real-world trial and error. While both coding and creating media (content) are powerful, content is more crucial. AI can generate assets, but true value lies in subjective, human-driven content that builds trust and narrative. The actionable advice is to carve out 15 minutes to answer foundational questions: 1) Uncover your "raw material"—what you know deeply or solve effortlessly. 2) Define your contrarian perspective—what common beliefs you think are wrong. The intersection of these answers is your direction. Finally, you must launch by publishing your first core idea immediately, using the feedback to iterate and develop the skills needed for a self-directed life and career.

Ray Dalio, founder of Bridgewater Associates, warns in an interview that the current AI boom shows classic bubble characteristics, which could lead to significant economic downturns as seen in past cycles like 1929 or 2000. He explains that speculative enthusiasm, fueled by debt and overvaluation, often precedes a crash when rising rates or taxation force asset sales, causing widespread losses and recession. Dalio also outlines his "Big Cycle" theory, describing an approximate 80-year pattern where widening wealth gaps, massive government deficits, and shifting geopolitical power (like China's rise) create internal conflict and global instability. He emphasizes that we are in a late-cycle, transitional phase where traditional powers like the US and UK face decline. For personal wealth protection, Dalio advises diversification beyond cash into assets like stocks, bonds, real estate, and particularly gold, which he prefers over Bitcoin. While he holds about 1% of his portfolio in Bitcoin as a non-printable hard asset, he views gold as more secure from technological or governmental threats. Regarding AI's impact, Dalio believes it will disproportionately benefit capital owners, worsening inequality by replacing both physical and cognitive labor. He suggests that human intuition and emotional intelligence, combined with AI, will be key for future workers. On taxation, Dalio argues that wealth taxes are impractical and risk triggering asset sell-offs, reducing productive investment. He points to the UK as a cautionary example of debt, low productivity, and political strife. Geopolitically, Dalio foresees a more regionalized world, with the US showing weakness in prolonged conflicts like with Iran, akin to past imperial declines. The ideal outcome, he suggests, is coexisting powerful blocs (e.g., Americas, China-Asia Pacific) without major war.