OpenAI's nonprofit will control a $500B entity while owning $100B+ in equity—an unprecedented governance experiment. Microsoft formalizes partnership even as both companies hedge through diversification. Regulators hold the keys.
FTC orders seven AI giants to reveal how their companion chatbots affect children after teen suicide cases involving ChatGPT and Character.AI. Meta faces particular scrutiny over internal docs permitting romantic chats with minors.
Large U.S. companies just hit the brakes on AI—adoption fell from 14% to 12% in two months, the first decline since tracking began. MIT research explains why: 95% of enterprise pilots deliver zero ROI. The gap between AI hype and workflow reality is widening.
Amazon's cloud division just unveiled its first quantum chip, arriving fashionably late to Silicon Valley's latest tech party. They named it Ocelot, combining their love for cats and oscillators in one puzzling portmanteau.
The timing is pointed. Google and Microsoft flaunted their quantum hardware recently, making AWS's entrance feel like a calculated response to the quantum arms race.
Ocelot's design is deceptively simple: two silicon squares stacked like the world's tiniest sandwich. It uses a "cat qubit" system - named after Schrödinger's famous thought experiment where quantum particles, like his hypothetical cat, exist in multiple states simultaneously. Five qubits handle the computing while four more play quantum error control. AWS claims this architecture could slash quantum computing costs by 90% compared to other leading approaches.
The team's findings, published in Nature, represent a significant milestone. But Oskar Painter, AWS's quantum hardware chief, keeps expectations grounded: useful quantum computers are still a decade or two away, he says. In an industry prone to hype, such candor is as rare as a quantum particle staying put.
Why this matters:
While everyone's building quantum computers, Amazon's cost-efficient approach could finally make quantum computing commercially viable
The race for quantum supremacy just got more interesting: it's not just about who gets there first, but who gets there affordably
Tech translator with German roots who fled to Silicon Valley chaos. Decodes startup noise from San Francisco. Launched implicator.ai to slice through AI's daily madness—crisp, clear, with Teutonic precision and sarcasm.
E-Mail: marcus@implicator.ai
Large U.S. companies just hit the brakes on AI—adoption fell from 14% to 12% in two months, the first decline since tracking began. MIT research explains why: 95% of enterprise pilots deliver zero ROI. The gap between AI hype and workflow reality is widening.
Students embrace AI faster than schools can write rules. While 85% use AI for coursework, institutions stall on policy—and tech giants step in with billions in training programs to fill the vacuum. The question: who gets to define learning standards?
First survey of 283 AI benchmarks exposes systematic flaws undermining evaluation: data contamination inflating scores, cultural biases creating unfair assessments, missing process evaluation. The measurement crisis threatens deployment decisions.
Tech giants spent billions upgrading Siri, Alexa, and Google Assistant with AI. Americans still use them for weather checks and timers—exactly like 2018. Fresh YouGov data reveals why the utility gap persists.