BAIRD SLASHES NVIDIA PRICE TARGET 60%, WARNS OF AI SLOWDOWN Baird downgraded Nvidia ($NVDA ) to Neutral from Outperform, slashing its price target to $200 from $500. Analyst Tristan Gerra warns that 2027 data-center forecasts face delays amid supply constraints, potentially softer demand and reduced visibility. Hyperscaler AI spending growth could slow to 40% or less in 2027, while 10–30% of channel orders may face preemptive cuts. Despite strong H300 demand, Baird also flags uncertainty surrounding CEO Jensen Huang's tenure into 2027. The warning: Nvidia's AI business remains strong, but growth expectations and leadership risks could weigh heavily on the stock.

Baidu just dropped ERNIE-5.5-Flash-Base, and the weights are available. 280B MoE, only 14B active per token, pretrained from scratch on 33.5T tokens. It's a pure base model with no mid- or post-training, so labs can adapt it for domain-specific tasks, reasoning, and agents. What makes it exciting is how hard it pushes on efficiency: - Only 6 of 43 layers keep token-level access to the full context. The other 37 squeeze every 128 tokens into a single cached entry, which keeps the KV cache small. Context still goes up to 1M tokens for fine-tuning and inference. - Multi-Router MoE: several routers pick the experts together instead of just one, without increasing activated expert compute or communication costs. - ECHO: multi-token prediction runs through the model's own final layer, no separate MTP module needed. And the early numbers look strong. In Baidu's preliminary evals against DeepSeek-V4-Flash-Base, a model of almost the same size: - MMLU-Pro: 78.4 vs. 69.2 - HumanEval+: 82.3 vs. 69.5 - LiveCodeBench: 40.1 vs. 31.9 Weights are live on Hugging Face in FP8 and BF16: The model license excludes the EU, UK, and South Korea from its permitted territory.

Baidu killed the MTP module everyone copied from DeepSeek ERNIE 5.5 Flash just runs its own last layer a second time to guess the token after next. They call it ECHO. Trained on only 1 extra token and it still drafts several ahead for speculative decoding, w/ higher acceptance than an MTP module trained the same way With a normal MTP module the future token loss mostly trains the bolted on module. ECHO puts that loss on the real model so the backbone itself learns to look ahead, and Baidu says reasoning and code got clearly better. The leftover ECHO layer is like 10 times smaller than the MTP module tencent bolted on Hy3, a model the same size