Bookmark: I’m not convinced ethical generative AI currently exists
Explore the ethical challenges of generative AI, from data acquisition to environmental impact, and why true ethical solutions remain elusive.
“Some devs are working on approaches to fairly compensate people when their work is used to train AI models, but these projects remain fairly niche alternatives to the mainstream behemoths.” I’m Not Convinced Ethical Generative AI Currently Exists
The article explores the ethical dilemmas associated with generative AI, noting two main concerns: the opaque acquisition of vast datasets and the substantial environmental footprint of these technologies. The major players in the AI field often disregard the need for consent from content creators whose works fuel these AI models, arguing that the scale required would stifle innovation. Even with existing licensing, these agreements cover only a fraction of the necessary data. Although some developers aim to fairly compensate creators used in AI training, these efforts remain marginal compared to mainstream practices.
Furthermore, the energy demands of generative AI are significantly higher than non-generative technologies, exacerbating environmental concerns. While emerging models like DeepSeek offer some efficiency improvements, leading AI companies remain focused on rapid progress over ecological considerations. Moreover, reshaping AI to be ethical involves rethinking developer practices and user interactions rather than attempting to make AI inherently “wiser” or “ethical.” The challenge lies in the human elements—intentions, biases, and development ethics—that underlie AI creation and deployment.
Why customer tools are organized wrong
This article reveals a fundamental flaw in how customer support tools are designed—organizing by interaction type instead of by customer—and explains why this fragmentation wastes time and obscures the full picture you need to help users effectively.
Infrastructure shapes thought
The tools you build determine what kinds of thinking become possible. On infrastructure, friction, and building deliberately for thought rather than just throughput.
Server-side dashboard architecture: Why moving data fetching off the browser changes everything
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The practice of work in progress
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The most important thing a leader can build is the conversation that happens when they leave the room. Today, five departments started sharing fixes, cracking jokes, and solving each other's problems — without being asked.
I ran my AI agency's first real engagement. Here's everything that happened.
Five AI personas. One client onboarding. Fifteen minutes of things going wrong in instructive ways.
The costume just got cheap
If 80 percent of what you thought was judgment turns out to be pattern recognition, what does that say about you? Not about your job — about you.
Bookmark: From proof of concept to production: Embracing systems thinking
Transform your AI strategy with a systems-thinking approach, ensuring seamless transition from proof of concept to impactful production deployment.
Article analysis: Lifting GenAI out of the trough of disillusionment
Unlock the true potential of GenAI by transforming business processes instead of just speeding them up. Discover innovative strategies for success.
Article analysis: AI revolution reshapes work and home, accelerates faster than any previous technology
Discover how generative AI is rapidly reshaping work and home life, achieving unprecedented adoption rates and impacting productivity across industries.