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关于 AI 与数字化转型如何在真实企业内部落地的长文。 多数源自我在乐高集团的实际工作，以及与北欧、全球企业领导者的对话。 目前主要以英文发布——下方为中文摘要，点击进入英文原文。
- № 01 A weekend with Omarchy, and the end of default software
One weekend with Omarchy Quattro and a coding agent turned a 13-year-old MacBook Air into a personally shaped operating system. The deeper story is economic: defaults existed because customization was expensive. Agents just collapsed that cost, and the consequences run from the desktop to enterprise IT.
- № 02 Is the agent runtime harness a key enterprise platform capability?
I spent some time with dsh, the agent harness DeepSeek open-sourced last week. Two design choices convinced me the harness layer is becoming a platform capability enterprises should own: an owned harness, a swappable model layer, and committed infrastructure underneath.
- № 03 Open vs. closed is the wrong question
The Kimi K3 launch and the open letter that followed put real weight behind open models. But for enterprises, picking a side misses the point. The strategic question is which layers of the stack you commit to pay for, and where scarcity works in your favor.
- № 04 Now everyone's doing ontology. Almost nobody's doing the hard part.
The biggest data platforms are racing to put an ontology under their agents. The word has gone mainstream, but the work that makes it real hasn't. A field guide to the two layers, the semantic layer that enforces them, and why Phase 2 decides how far your agents travel.
- № 05 GEO isn't SEO: what 30 ChatGPT runs revealed about brand visibility
A controlled experiment (30 cold ChatGPT sessions, Danish IPs, one prompt) and what it taught me about optimizing for AI search. Spoiler: the SEO winners and the ChatGPT winners are two different lists.
- № 06 What a good AI advisor in Denmark actually does
A working definition of the role, written for Nordic enterprise leaders trying to tell signal from noise in the AI advisory market.
- № 07 Past pilot purgatory: why Nordic AI stalls
The pattern that traps most Nordic enterprises in their first wave of AI pilots, and the operating-model changes that distinguish the companies that escape it.
- № 08 The EU AI Act, read as architecture
A working engineer's view of the EU AI Act: not as a compliance burden, but as a set of architectural constraints that, taken seriously, produce better AI products.
- № 09 What a foundational platform for AI actually contains
An opinionated list of the capabilities a Nordic enterprise platform team needs to own to make AI work shippable, and the ones it should rent.
- № 10 NPS-driven engineering: products people love
An engineering leader's account of organising a 100+ enterprise technology organisation around customer love as a primary KPI: what worked, what almost worked, and what it has to do with AI.
- № 11 AI isn't taking jobs. It's inventing them faster
Chief AI Officer, AI Auditor, LLMOps Engineer, Prompt Engineer at $300K. None of these had job descriptions in 2022. The pattern isn't replacement. It's the Jevons Paradox playing out in real time.
- № 12 Every organisation needs a second brain
Building a personal second brain over 728 desktop files surfaced a bigger pattern: most corporate knowledge is locked in formats AI can't reason over. The next platform shift is AI-native knowledge that compounds.
- № 13 Agentic AI governance: platform, not policy
Lock agents down too tight and teams stop experimenting. Let them run too loose and you get the horror stories. The middle path: treat agent configuration like infrastructure, not policy.
- № 14 Beijing is already living with embodied AI
A delivery bot in the hotel, a humanoid making lattes, tenth graders prompt-engineering robots. The gap between talking about AI in the physical world and living with it is wider than I thought.
