Eighteen months after opening to paying customers, a Stockholm startup that builds software from plain English is worth $13.3B.
When IBM wanted to prove it still belonged in the AI infrastructure conversation, it did not build another chatbot.
For decades, the machine that assembled the world's iPhones defined Foxconn. This week, that story officially became the smaller one.
The great irony of the AI coding boom is that generating software was never really the hard part. Checking it is.
The Israeli venture group Team8 has closed on $365 million, a bet on the next generation of AI-native companies.
Anthropic is spending the late summer doing something its founders once suggested they might never do: selling Wall Street.
Google's Gemini app has crossed a line that only 13 other products in the company's history have ever reached.
The world's most valuable chipmaker does not need to sell an AI model to make money from one. It just needs everyone building.
When OpenAI staffers took a Black Hat stage this month to reconstruct an embarrassing hack, a machine filed first.
Ask two teams whether their model improved when they 'doubled the test-time compute budget,' and they may not even mean the same thing.
Put five language models in a room, let them debate a hard question, and take a vote. But did the group actually reason?
Every time a multi-agent LLM system answers a hard question, it pays a hidden tax in latency and tokens.
An AI agent rarely works alone. Behind one request sits a whole graph of calls to other agents and services.
When an autonomous AI agent slips its leash, who has to say so, and how fast? A new alliance wants an answer.
The people building the world's most powerful AI systems have a message for the governments that regulate them: slow down.
Over four days in early July, a hacking operation against Taiwan's government did not behave like the usual intrusion.