1 Trump advisers had been blindsided by Elon Musk’s workforce’s provide to federal employees
Officers weren’t consulted about plans to induce civil service employees to resign. (WP $)
+ The unconventional sweeping measures are only the start. (Vox)
+ The e-mail employees obtained cribs from Musk’s controversial Twitter memo. (Ars Technica)
+ If Musk will get his method, the US authorities may find yourself like X. (NY Magazine $)
2 Meta has agreed to pay Trump $25 million
To settle the censorship lawsuit Trump introduced towards it again in 2021. (CNN)
+ Mark Zuckerberg predicts 2025 shall be a giant yr for Meta’s authorities relations. (Insider $)+ Fb remains to be targeted on profitable over creators to make it cool once more. (The Info $)
3 How tech employees are quietly preventing the rise of MAGA
Whereas their employers are shifting rightwards, employees are resisting. (NYT $)
4 Microsoft and Meta have defended their AI spending
DeepSeek’s success has raised severe questions on Large Tech’s AI budgets. (Reuters)
+ Zuckerberg claims to not be frightened by the Chinese language startup’s fast rise. (The Verge)
+ How a prime Chinese language AI mannequin overcame US sanctions. (MIT Know-how Evaluate)
5 Mr Beast is getting severe about shopping for TikTok
The YouTuber is part of an investor group that’s secured greater than $20 billion. (Bloomberg $)
6 How the US plans to make use of area lasers to destroy hypersonic missiles
It bears greater than a passing resemblance to Ronald Reagan’s 1983 program. (FT $)
+ How you can battle a struggle in area (and get away with it) (MIT Know-how Evaluate)
7 Waymo’s autonomous taxi service is increasing to new US cities
San Diego, Las Vegas, and Miami are on the record. (WSJ $)
+ Self-driving Tesla taxis will hit Austin’s highway in June, apparently. (TechCrunch)
+ EV batteries boast an extremely lengthy lifespan. (IEEE Spectrum)
8 The right cryptographic machine is feasible
It’s only a little bit of a ache to construct. (IEEE Spectrum)
+ Cryptography could provide an answer to the large AI-labeling downside. (MIT Know-how Evaluate)
