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- ⚠️ Trump Turned to Grok on Venezuela
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Imagine asking a chatbot about the fallout of capturing a foreign president. Trump did exactly that with Grok before Maduro’s capture. Grok apparently impressed him later.
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AI INSIGHTS
According to reporting from Time, President Trump reportedly spent hours using Elon Musk’s Grok during a private December 2025 meeting, including asking how Venezuelans might react if President Nicolás Maduro were captured:
Grok reportedly told Trump that Maduro was deeply unpopular and that many Venezuelans would likely celebrate his removal.
About a month later, on January 3, 2026, the U.S. invaded Venezuela and captured Maduro.
A source told Time that Trump later viewed Grok as highly capable after public celebrations followed the operation.
Grok has also reportedly been used by the Pentagon through Gov Grok for military operations, while OpenAI and Anthropic also have relationships with the U.S. defense establishment.
The reporting does not establish that Grok caused the decision to invade Venezuela. It shows that Trump reportedly used the chatbot as one source of information while considering the political consequences.
→ AI chatbots are moving beyond research and productivity into high-stakes government decision-making, where incorrect assumptions or overly confident answers can carry much larger consequences.
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What did Trump reportedly ask Grok about? |
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AI BREAKTHROUGH
More pre-training doesn’t always mean better generalization. A new paper found something surprisingly unstable inside LLM training: mode-hopping. This example is wild: OLMo3-32B
2.17T tokens: 81% accuracy
2.19T tokens: 0%
2.21T tokens: 81.7%
So within just 40B more training tokens, performance collapsed to zero and then almost fully recovered. Researchers compared an earlier 4.5T-token checkpoint with a later 4.9T-token checkpoint. After math fine-tuning:
4.5T checkpoint: 36.3% GPQA
4.9T checkpoint: 29.8%
The later model had seen more data, yet became a worse starting point for both transfer and robustness. They think different internal strategies may compete during pre-training. Sometimes the model leans on generalizable reasoning. Other times, easier shortcut-like patterns take over.
That creates a major problem for how we evaluate models. Training loss can keep improving while the model’s actual reasoning behavior quietly gets worse. The final checkpoint may not always be the best checkpoint.
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