Moving forward, we will pivot our content from business cases on everyday brands to something we believe matters more right now, Artificial Intelligence.
Our goal is to share real, practical ways AI is reshaping how we interact with the world both in our careers and personal lives.
If you like this new direction, reply “Hell yes!”. If you liked the previous content style, reply “Go back!”.
AI is Taking the Filmmaking Industry by Storm
A Filipino man walks through his childhood backyard in Hawaii, footsteps swooshing through the grass and birds chirping in the background. As he approaches a shrine at the base of a starfruit tree, a sudden gust of wind knocks the contents over causing the man to trip and fall, striking his head on a root of a tree and knocking him unconscious. When he wakes, a woman in a clay mask is holding a sword to his throat.
This opening scene from "Murmuray" feels like any other indie short film, except filmmaker Brad Tangonan made the entire thing using AI.
He wrote the script himself, built a shot list, and fed his creative vision into Google's AI. The result? A film he never could have afforded to make traditionally.
This is the quiet revolution happening in independent filmmaking. AI is empowering storytellers by removing the budget barrier that kept great stories from being told.
Similarly, the AI model from Bytedance, Seedance 2.0, is making waves this week, creating Hollywood quality films without the Hollywood price tag. See it in action here.
The question now is what kind of filmmaking survives when AI can dramatically impact the speed, scale, and cost of production.
Everyone has access to the same AI models. So why are some companies seeing dramatically better results than others? It comes down to context.
A new analysis of 50+ enterprises, mostly Fortune 500, found that companies in the same industry, using the same tools and systems, executed in distinctly different ways. The divergence showed up in behavior: which signals people responded to, who got looped in and when, which exceptions triggered action, and how teams balanced speed against risk.
This accumulated institutional knowledge, or "context", is exactly what AI systems lack by default. AI models are general by design. Without explicit grounding in how your organization actually operates and past learnings, they flatten the nuances that make a company distinctive.
As AI becomes commoditized, the competitive moat shifts from access to models toward the quality of organizational context you can feed them. Companies that invest in capturing execution patterns—not just logging outcomes, but mapping how decisions are made—will compound that advantage.
What this means for you: "Context engineering" is emerging as critical infrastructure. If you're in engineering, product, or ops, knowing how to capture organizational knowledge for AI systems is becoming increasingly valuable.
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