In a meeting with President Trump, Meta CEO Mark Zuckerberg announces a staggering $600 billion US investment plan by 2028, only to admit in a hot mic gaffe that he fabricated the figure, while tech rivals share AI investment plans.
Massive investments by tech giants into AI data centers are driving U.S. economic growth, with spending on AI infrastructure surpassing consumer spending and nearing 2% of GDP, diverting capital from other sectors like venture capital and manufacturing.
OpenAI collaborates with Broadcom on custom AI chips scheduled for launch in 2024, aiming to meet soaring computing demands and reduce reliance on Nvidia while utilizing the chips internally rather than selling them to customers.
OpenAI projects a staggering $115 billion cash burn by 2029, $80 billion higher than earlier estimates, as soaring costs drive the AI giant to develop its own chips and data centers to power its cutting-edge technology.
In a landmark settlement, Anthropic AI pays $1.5 billion to resolve copyright claims from authors over an estimated 500,000 books used to train its AI models, compensating writers around $3,000 per book.
The U.S. Navy aggressively develops AI-powered combat assistants and decision aids, integrating machine learning into the AN/BYG-1 submarine combat system for enhanced tactical control, payload integration, and AI-driven decision support in undersea warfare.
OpenAI unveils an AI-powered job matching platform challenging LinkedIn, aiming to connect businesses with workers using AI, while also offering AI fluency certifications through OpenAI Academy.
AI is predicted to disrupt an astounding 99% of jobs by 2030, warns AI safety researcher Roman Yampolskiy, with even professions like coding and prompt engineering not immune to automation as AI becomes increasingly advanced and capable of handling all tasks, potentially rendering retraining efforts insufficient.
Vibe coding hype overshadows real software bottlenecks, as the promise of fast code development misses the fact that validation through continuous discovery, not coding speed, is the real bottleneck, taking weeks or months to determine if changes lead to growth, revenue, or retention.
Shattering the illusion of AI reasoning, researchers expose that large language models cannot truly 'reason' like humans, with their chain-of-thought processes revealed as a 'brittle mirage' relying on pattern matching, prompting calls for clarity and precision in describing AI capabilities.