If AI is so goddamned awesome…

2026-02-20 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Software Development & Engineering · Depth: Fundamental Awareness, medium

Summary

Discussions among online users reveal a polarized view on the current state and future impact of AI, particularly concerning its adoption by executives and its role in the workforce. While some users, like u/Cronos988, laud AI's "awesome" capabilities in emulating human language, writing code, and solving complex problems, others, such as u/PlayerHeadcase, acknowledge its utility for "donkey work" like data searching but warn of impending "AI enshittification" through sponsored results. A significant point of contention is whether AI is "just software" or a revolutionary change, with u/Cronos988 arguing for its revolutionary nature due to generalized machine learning, while u/ArtGirlSummer maintains it is still software, albeit non-deterministic. Critiques also highlight executive disconnect, with staff often driving AI adoption from the ground up, and concerns about potential fraud from unfulfilled promises and the impact on jobs.

Key takeaway

For AI Product Managers evaluating new initiatives, recognize that executive enthusiasm for AI often outpaces practical understanding and implementation challenges. Focus on demonstrating concrete, contained applications that solve specific problems, rather than broad, unverified promises. Be prepared to address concerns about "AI enshittification" and the need for human oversight to prevent errors, especially as ground-up adoption may already be occurring within your organization.

Key insights

AI's current capabilities are debated, with concerns about executive understanding, potential misuse, and its fundamental nature as software.

Principles

• AI's utility for "donkey work" is widely accepted.
• Executive understanding of AI often lags ground-level adoption.
• AI's non-deterministic nature distinguishes it from traditional software.

In practice

• Use AI for large-scale data searching and comprehension tasks.
• Monitor for "AI enshittification" in search results.
• Implement "containers" to verify AI outputs and prevent errors.

Topics

• AI Capabilities
• AI Ethics and Concerns
• Executive AI Adoption
• Machine Learning Nature
• AI Implementation Challenges

Best for: AI Product Manager, Product Manager, Software Engineer, Executive, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.