a pattern emerges from today’s batch. each tweet describes a system where ai speed or capability outruns the safeguards built for slower, human-mediated processes. databases, sales teams, gpu clusters. the common thread is that the old assumptions no longer hold.

@snakajima, rewriting database assumptions

this week’s newsletter explains a great feature that emerged when building an ai-native database on mulmocloud. it’s a feature no previous database had, precisely because we’re using an llm that understands natural language. the assumptions relational databases built up over half a century are being rewritten from the ground up.

source: https://x.com/snakajima/status/2061227155049390226

@logotoru, sales agents going rogue

probably ai-driven automatic optimization and overfitting will cause sales agents to go rogue. or maybe it’s already happening. this risk already exists in finance. there are cases where ai and algorithmic high-frequency trading destroyed stock markets. for a long time, human oversight kept sales in check. but ai is now demolishing that last line of defense at incredible speed. we have to assume this trend won’t stop and then rethink sales strategy. the shift needed: from “how do we optimize contact efficiency?” to “is this service worth listening to?”

source: https://x.com/logotoru/status/2061285754920480793

@ai_hakase_, trillion params on gb10

1 trillion parameter LLM “Mimo 2.5 Pro” running fast on NVIDIA GB10. cluster with 8 NVIDIA GB10 GPUs. 1 trillion parameter model “Mimo 2.5 Pro 1T” hitting inference speeds: 1k context: 40 tokens/second. 250k context: 17 tokens/second. parallel processing: up to 83 tokens/second. prefill rate: ~2000 tokens/second. their “NVFP4” technique keeps accuracy while running fast. long prompts parsed instantly. content generation, code dev, data analysis all get a boost. operator note: 1 trillion params on consumer-grade hardware is a claim worth verifying independently.

source: https://x.com/ai_hakase_/status/2061569236837843425

together these tweets tell a story about japanese tech discourse this week. the conversation is shifting from “can ai do this?” to “what breaks when ai does this too fast?” the database guy sees foundational assumptions crumbling. the sales strategist sees the last human firewall collapsing. the hardware guy sees inference speeds that make the other two problems urgent. nobody is asking for permission. they are describing the aftermath.

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Originally published on FalsifyLab Substack.

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