SELECTED BLOG POSTS
AI’s New Training Data: Your Old Work Slacks And Emails
AI labs started off by training their models on the public internet—Reddit threads, Wikipedia entries, digitized books. But they exhausted that — all of it — by late 2024, according to former OpenAI chief scientist Ilya Sutskever. And what’s more, it’s not super helpful for building "agentic" AI: models that can actually do work. But the hand-crafted work that was done during the daily operations of defunct companies like cielo24? That’s a sort of fossil fuel for AI agents. Turns out that if you’re shooting for AI competence in the workplace, you need examples of what doing the work actually looks like — a lot of them.
“Model companies are realizing the noise in the real-world environments is required to accurately test models,” said Ali Ansari, whose company micro1 sells a product to AI labs called “Roots,” a mock holding company where AI agents can practice their skills in tasks like financial services and managing complex calendars.
AI-POwered Knowledge Transfer
Knowledge and expertise are human. Yet used well, AI can assist people in acquiring knowledge, transfering expertise from experienced seniors to juniors, and developing true organizational intelligence.
The intent must be not just to capture and institutionalize knowledge, but to enable the flows of human to human knowledge that are at the heart of all expertise development and the foundation of a dynamic, flourishing organization.
Building an AI-Powered RPG – to Learn Enterprise AI Integration – JAVAPRO International
This article shares insights from my hackathon project “Runes of Reason” – an AI-powered RPG – and uses it to explain concepts for AI integration with Spring AI. Along the way, the game demonstrates how LLMs can fundamentally reshape our whole product design. The RPG is an entertaining project for learning, but many insights can be transferred to real-world enterprise software use cases (summarized in the “Lessons Learned” sections).
Adactio: Journal—Threat models
People talk about the effectiveness (or lack thereof) of large language models as though all tasks are comparable. But it strikes me that there are three broad categories of work that large language models are applied to:
Compression.
Transformation.
Expansion.
Could Microsoft Win The War For Enterprise AI? – JOSH BERSIN
As OpenAI and Anthropic talk about going public, everyone is fascinated. These two companies with battling CEOs make up the drama of today’s AI industry. And as more information comes out from their public offering, everyone is wondering how they will compete with each other. (Listen to my latest podcast on culture in AI companies.)
Well as the drama continues, I’d like to advance a different thesis.
While the enterprise market is filled with players (Google, Amazon, Nvidia, Oracle, Anthropic, OpenAI) and the consumer market may be dominated by Apple and others, the biggest winner of all could be Microsoft. Let me walk you through my thinking.
The Map Runs Out Here: What Enterprise AI Adoption Actually Looks Like | by Yogi Shenoy | Apr, 2026 | Medium
In domain-heavy enterprise development — where context is tacit, complexity is deep, and experienced judgment is irreplaceable — what does meaningful AI adoption actually look like?
Not in a startup. Not in a greenfield project. In a real system, with real constraints, and real people who’ve been doing this for decades.
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