u/sahilrathi859

Saw this making the rounds and figured it's worth a post since it hasn't gotten much coverage outside NVIDIA's own blog.

NVIDIA released Nemotron 3 Ultra on June 4. It's a 550B parameter MoE model (55B active per token), open weights, released alongside training data and recipes.

A few things that stood out to me:

• 1M token context window, so you can throw an entire codebase or hours of transcript at it in one go
• NVIDIA claims up to ~6x higher inference throughput vs other open models in its class (Kimi K2.6, GLM-5.1, Qwen-3.5), though take vendor benchmarks with the usual grain of salt
• It's specifically tuned for agentic workflows — multi-step coding, tool use, long research chains — rather than optimized for single-turn chatbot benchmarks
• You can run it locally through Ollama if you've got the hardware for it (fair warning: full BF16 weights need something like 8x H100s, this isn't a laptop model)

Worth noting this isn't really a "Claude Code killer" the way some clickbait framing has suggested — it's a base model, not an agent harness, so it's more of a building block than a direct product comparison.

Independent numbers from Artificial Analysis put it at the top of the Intelligence Index for US open-weight models specifically, though Kimi K2.6 still leads on raw benchmarks overall if you're not tied to NVIDIA hardware.

Anyone here tested it yet? Curious how it actually performs on real agent tasks vs the benchmark numbers.

A recent move by Zeb Evans, CEO of ClickUp, has sparked significant discussion. He made the bold decision to fire 22% of his workforce, stating he didn't need more people but rather individuals who could direct machines. This decision coincided with his announcement of transforming ClickUp into a "100x organization," aiming for three AI agents for every employee.

In the same week he cut the workforce, ClickUp deployed around 3000 AI agents, most of which operate independently, processing data, organizing information, and monitoring activity without requiring human judgment. His approach is straightforward: if a task doesn't need human judgment, automate it.

This shift has fundamentally changed how work is done at ClickUp. Marketers now create landing pages themselves, and project managers build and iterate in real-time without waiting for design and research. Even Evans has taken on individual contributor work, designing landing pages and writing emails, as the bottlenecks have been removed.

However, this transformation came at a cost: 290 employees were let go, while those who remained were offered million-dollar salary bands if they could deliver 100x output by directing AI systems. This trade-off presents a reality of fewer employees, higher stakes, and significantly increased pay for those capable of orchestrating AI.

A key takeaway from this situation is: "If you automate your own job with AI, you will always have a job." Those who grasp this concept early may find themselves in leadership roles, while others may be left wondering what happened. Is this smart restructuring or a warning sign for industries everywhere?