DeepSeek situation

I think most of you heard about DeepSeek R1 and it's capabilities in the last days so here is a brief overview for the crypto world:

🆚Short comparison of Openai o1 vs DeepSeek

OpenAI o1:

- High training demands: requires massive computational resources (e.g., GPUs/TPUs), large datasets, and significant time.

- High operational costs: needs powerful infrastructure (e.g., GPU/TPU clusters) for real-time inference.

- Optimized for versatility, but this increases resource requirements.

- Suitable for a wide range of tasks, but at a higher cost.

DeepSeek R1:

- Likely a specialized, task-specific model.

- Lower training demands: can be trained with smaller datasets and fewer computational resources.

- Lower operational costs: can run efficiently on less powerful hardware or optimized infrastructure.

- - Focused on efficiency and specific use cases, reducing overall resource needs.

More cost-effective for targeted applications compared to general-purpose models like O1.

- In summary, OpenAI O1 is more resource-intensive due to its general-purpose nature, while DeepSeek R1 is likely more efficient and cost-effective for specialized tasks.

For whom is it bullish and for whom bearish

🐻 Bearish:

- NVDA & computation power providers (RNDR, AKT) at least temporarily

- Crypto projects working on their own LLM model (that can't utilize DeepSeek)

🐂 Bullish:

- AI agent launchpads like $VIRTUAL, $BID, etc. -> why? Because they can simply implement and utilize DeepSeek to empower their agents

AI agents itself

- Model aggregators + $TAO (which runs DeepSeek on https://t.co/sxVD3KHcev)

- Anything that can be tied to custom LLM

⚠️ Potential risks:

- DeepSeek is under pressure atm. With API implementation and the limited capacity it can be overloaded which can bring potential problems

- For now their "Search" component faces it and is overloaded and not functional for me personally