Mark Zuckerberg spent $14.3 billion to hire the guy who trained everyone else’s AI. Now we get to see if it was worth it. Meta dropped Muse Spark this week — the first model from Superintelligence Labs, led by former Scale AI CEO Alexandr Wang. It’s proprietary. It’s consumer-focused. And it represents the most dramatic strategic pivot in Meta’s AI history: the company that championed open-source AI just went closed. ...
Mark Zuckerberg just played his most expensive hand yet. Meta unveiled Muse Spark, the first AI model from its Superintelligence Labs — the division built on a $14.3 billion Scale AI investment and a talent raid that cost hundreds of millions in individual engineer packages. After more than a year in the wilderness following the embarrassing Llama 4 launch, Meta says it’s back. But here’s the twist nobody expected: the company that championed open-source AI just went proprietary. ...
The AI cost curve didn’t just bend — it snapped. Chinese AI lab MiniMax just released M2.7, a model that scores within spitting distance of Claude Opus 4.6 and GPT-5.4 on coding benchmarks, runs on modest hardware, and costs $0.30 per million input tokens. That’s roughly 17x cheaper than Opus on input and 21x cheaper on output. But the price isn’t even the headline. The headline is how they built it: M2.7 helped build itself. ...
Nvidia doesn’t just want to sell you the shovels anymore. It wants to dig the gold too. Buried inside a financial filing and confirmed by executives in interviews with WIRED, Nvidia plans to spend $26 billion over five years building open-weight AI models. To prove this isn’t vaporware, they simultaneously dropped Nemotron 3 Super — a 128-billion-parameter beast with a hybrid Mamba-Transformer architecture that’s already topping agentic AI benchmarks. This is the most strategically significant move in AI since Meta released the original Llama. Here’s why it matters. ...
There’s a dirty secret in the AI agent world: most teams running Claude Opus are burning money for bragging rights. Don’t get me wrong — Opus 4.6 is a beast. It tops SWE-bench at 80.9%, handles 200K context windows without breaking a sweat, and orchestrates multi-tool workflows like a conductor with perfect pitch. But at $15 per million tokens (blended), it’s the filet mignon of language models. And most of us are building tacos. ...