THE AI SLOWDOWN ISN'T ABOUT INTELLIGENCE. IT'S ABOUT ECONOMICS.
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AI can now follow a conversation and warn when it begins to resemble a scam. New local and secure-cloud approaches reveal the harder challenge: protecting people without creating unnecessary access to their private messages, draining their phones, or overwhelming them with warnings they cannot trust.
At its Texas reveal, Tesla sent the new Roadster upside down and into the air. SpaceX thrusters and technology borrowed from Optimus offered a glimpse of how Musk’s engineering projects can come together.
AI agents are beginning to explore chip designs, run simulations, and learn from tool feedback. New research shows why the important question is how much independently checked improvement a team can achieve within its engineering budget.
Robots can complete impressive tasks and still struggle after a missed grasp or a misplaced object. New research on failure recovery asks a more practical question: can a robot recognize what went wrong, repair the situation, and continue without creating another problem?
As AI agents take on longer-running work, remembering more is only part of the problem. Reliable memory also needs a way to revise outdated facts, distinguish decisions from assumptions, and verify what still holds before acting.
Donald Trump wants America to stop calling it AI and start calling it SI — Super Intelligence. On the surface, it looks like a presidential obsession with words. But the change points to something bigger: as AI becomes ubiquitous, the battle is shifting from who builds the technology to who gets to define what the next era of intelligence is called.
Finding a camera clip, checking on the dog and keeping a family memory are everyday reasons to put AI in a home hub.
Artificial intelligence has already changed how scientists search, model and analyze. Now it is beginning to move deeper into the research process itself. New systems can generate hypotheses, design experiments, produce formal proofs and revise their own ideas based on results. The shift is subtle but important: AI is no longer only helping scientists work faster. It is starting to participate in the scientific method.