# DeepSeek V4, OpenAI GPT 5.5, and MCP Security Risks

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Text version: https://stenobird.com/podcast/ai-convo-cast-7218040/deepseek-v4-openai-gpt-5-5-and-mcp-security-risks.md
Podcast: [AI Convo Cast](https://stenobird.com/podcast/ai-convo-cast-7218040)
Published: 2026-04-25T09:15:00+00:00
Episode link: https://podcasters.spotify.com/pod/show/aiconvocast/episodes/DeepSeek-V4--OpenAI-GPT-5-5--and-MCP-Security-Risks-e3ieko9
Audio file: https://anchor.fm/s/101530384/podcast/play/119017673/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2026-3-25%2Faf6a19f8-8f31-2ed9-abed-a348d8e11f04.mp3
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Duration seconds: 1063

## Resource

In this episode, we discuss DeepSeek V4 and its one million token context window, OpenAI GPT 5.5 pricing and agentic coding performance, MCP security risks, and Microsoft Research AutoAdapt for domain adaptation. We break down DeepSeek V4 Pro and V4 Flash, mixture of experts efficiency, long context AI agents, and why developers are comparing GPT 5.5 coding gains against higher token costs. We also examine the MCP Model Context Protocol controversy involving toolchains like LangChain, LiteLLM, Flowise, Cursor, and VS Code, plus how AutoAdapt automates RAG, fine tuning, LoRA, and enterprise LLM adaptation under budget, latency, privacy, and accuracy constraints. https://www.aiconvocast.com Help support the podcast by using our affiliate links: Eleven Labs: https://try.elevenlabs.io/ibl30sgkibkv Disclaimer: This podcast is an independent production and is not affiliated with, endorsed by, or sponsored by DeepSeek, OpenAI, Microsoft, Anthropic, NVIDIA, LangChain, LiteLLM, Flowise, Cursor, VS Code, Eleven Labs, or any other entities mentioned unless explicitly stated. The content provided is purely for informational and entertainment purposes only and does not constitute professional, financial, legal, security, or technical advice. Some links may be affiliate links, and we may earn a commission if you use them, at no additional cost to you.

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## Transcript

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