The conversation around AI is shifting, and my chat with Clemens Scholz at KubeCon really drove that home.
AI Is Just a Regular Workload Now
We are moving past the phase where AI is just this “shiny, fancy future.” As Clemens pointed out, AI is now just a regular workload. The focus has shifted from the novelty of it to the practicalities of the infrastructure: how do we run these compute-heavy workloads in the most optimal and efficient way possible?
This resonates with what I have been seeing across my AI on Kubernetes consulting work. Enterprises are no longer asking “should we use AI?” — they are asking:
- How do we schedule GPU workloads efficiently across multi-tenant clusters?
- How do we manage inference at scale without blowing the budget?
- How do we observe and debug AI pipelines like any other production service?
The infrastructure conversation has matured. AI workloads need the same rigor we apply to databases, message queues, and web services — proper resource management, autoscaling, monitoring, and capacity planning.
The Digital Companion: Building a Second Brain
But beyond the infrastructure, the day-to-day human impact is what is really fascinating. We talked about how AI is evolving into a true digital companion.
Clemens shared a great example from a recent CNCF chapter talk where a developer built a “second brain” using Obsidian, Markdown, and Claude to organize and accelerate their daily workflows.
The stack is elegant in its simplicity:
- Obsidian as the knowledge base — all notes in plain Markdown, locally stored, fully searchable
- Markdown as the universal format — no vendor lock-in, portable, version-controllable
- Claude as the reasoning layer — connecting dots across notes, surfacing relevant context, drafting from existing knowledge
It is not about AI replacing the work — it is about having a powerful assistant that helps you do what you already do, just significantly faster and more effectively. The results might still vary from day to day, but the value of having a dedicated digital companion is undeniable.
The Future of AI Is Integrated
The future of AI is not just generative — it is integrated. It is our second brain, our coding assistant, and a core part of our daily routines.
This is exactly the shift I see happening across the industry:
- Phase 1 (2023-2024): “AI is cool, let’s experiment”
- Phase 2 (2025): “AI is useful, let’s build products”
- Phase 3 (2026+): “AI is infrastructure, let’s optimize”
We are firmly in Phase 3. The organizations winning with AI are not the ones with the most impressive demos — they are the ones who have integrated AI into their engineering workflows as seamlessly as they integrated CI/CD a decade ago.
Always great to catch up with brilliant minds like Clemens who look past the hype and focus on the practical reality of where tech is heading.
Start Building Your Second Brain
If you want to start building your own AI-assisted second brain:
- Pick a knowledge base — Obsidian, Notion, or even a git repo of Markdown files
- Commit to capturing — write down insights, decisions, and context daily
- Connect an AI layer — use Claude, GPT, or a local model to query and synthesize your notes
- Iterate on prompts — the magic is in teaching the AI how to use your specific knowledge graph
How are you integrating AI into your day-to-day workflow? Are you building a “second brain” yet?
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- KubeCon Europe 2026: Multi-Tenant GPUs on Bare Metal
- Content Credentials: How C2PA Is Building Trust in the Age of Generative AI
- KubeCon Europe 2026 Community Connections
About the Author
I am Luca Berton, AI and Cloud Advisor. I help enterprises integrate AI into their infrastructure and workflows — from GPU clusters to developer productivity. Book a consultation.
