Blog

Notes from the field on adopting AI without losing control of it.

This is the private-ai.ai blog. We'll be posting on different AI topics and the latest AI news, filtered through one lens: what actually works for small and mid-sized companies trying to adopt AI without losing control of their data. Expect posts on open-weight models, running inference locally, RAG, and the other tools and techniques that make private, on-prem-capable AI practical — not just theoretical.

• Local AI agents just left the desktop. The security question is the one that matters now.

  Microsoft and Nvidia's new agentic laptop brings on-device AI to a device you actually carry around. The interesting problem isn't the economics anymore — it's what happens when an autonomous agent has your files, browser, and credentials.

• NVIDIA's fourth open-weight bet in a year, and why the pattern matters more than any one of them

  Nvidia-backed Reflection is prepping a US open-weight model to challenge DeepSeek and Qwen. On its own, that's a funding story. As the fourth NVIDIA move into open weights this year, it's something else.

• Apple's pitch to the enterprise: "there's no cost per token"

  Apple just told corporate IT buyers to stop renting AI compute and start owning it. That's not a tech curiosity — it's the exact business case for private AI, from the last company you'd expect to make it.

• NVIDIA is buying the front door to open weights. Here's why that's the real story.

  NVIDIA's $12.9B deal for Hugging Face puts hardware, model investment, and the platform that distributes open weights under one roof. What that concentration means if you're betting on private AI.

• NVIDIA just productized the thing I've been doing by hand: your own devices as one AI cluster

  NVIDIA's new PAIR tool routes AI inference across whatever Macs and RTX PCs you already own. Here's why a free tool from the industry's biggest hardware vendor is a bigger signal than it looks.

• Why this week's $7B NVIDIA deal matters for your AI strategy, not just NVIDIA's

  NVIDIA just spent $7 billion to strengthen its open-weight AI models. Here's what that trajectory actually means if you're deciding how to invest in AI for your business.

• A short history of neural networks (and why it matters for your AI vendor decisions)

  Neural networks have died and been reborn at least three times since 1943. Understanding that pattern is the fastest way to see through today's AI hype.

• Starting late, building anyway

  Why this site exists: catching up on AI hands-on, on my own hardware, in public.