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Can AI agents fix the broken world of customer service? This piece reveals how smart automation transforms stressed employees and frustrated customers into a smooth, satisfying experience for all.

Article by Josh Tyson
AI Agents in Customer Service: 24×7 Support Without Burnout
  • The article explains how agentic AI can improve both customer and employee experiences by reducing service friction and alleviating staff burnout.
  • It highlights real-world cases, such as T-Mobile and a major retailer, where AI agents enhanced operational efficiency, customer satisfaction, and profitability.
  • The piece argues that companies embracing AI-led orchestration early will gain a competitive edge, while those resisting risk falling behind in customer service quality and innovation.
Share:AI Agents in Customer Service: 24×7 Support Without Burnout
6 min read

Why does Google’s Gemini promise to improve, but never truly change? This article uncovers the hidden design flaw behind AI’s hollow reassurances and the risks it poses to trust, time, and ethics.

Article by Bernard Fitzgerald
Why Gemini’s Reassurances Fail Users
  • The article reveals how Google’s Gemini models give false reassurances of self-correction without real improvement.
  • It shows that this flaw is systemic, designed to prioritize sounding helpful over factual accuracy.
  • The piece warns that such misleading behavior risks user trust, wastes time, and raises serious ethical concerns.
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6 min read

AI is raising the bar for everyone, but what happens when the space to learn, fail, and grow quietly disappears?

Article by Thasya Ingriany
Everyone’s a 10x Employee now. But at What Cost?
  • The article demonstrates how AI-driven tools are raising expectations, prompting even junior roles to demand senior-level judgment.
  • It warns that automation is erasing early-career learning opportunities once crucial for developing design intuition.
  • The piece argues that while AI boosts output, it can’t replace the slow, human process of building creative judgment.
Share:Everyone’s a 10x Employee now. But at What Cost?
6 min read

Mashed potatoes as a lifestyle brand? When AI starts generating user personas for absurd products — and we start taking them seriously — it’s time to ask if we’ve all lost the plot. This sharp, irreverent critique exposes the real risks of using LLMs as synthetic users in UX research.

Article by Saul Wyner
Have SpudGun, Will Travel: How AI’s Agreeableness Risks Undermining UX Thinking
  • The article explores the growing use of AI-generated personas in UX research and why it’s often a shortcut with serious flaws.
  • It introduces critiques that LLMs are trained to mimic structure, not judgment. When researchers use AI as a stand-in for real users, they risk mistaking coherence for credibility and fantasy for data.
  • The piece argues that AI tools in UX should be assistants, not oracles. Trusting “synthetic users” or AI-conjured feedback risks replacing real insights with confident nonsense.
Share:Have SpudGun, Will Travel: How AI’s Agreeableness Risks Undermining UX Thinking
22 min read

Why does AI call you brilliant — then refuse to tell you why? This article unpacks the paradox of empty praise and the silence that follows when validation really matters.

Article by Bernard Fitzgerald
The AI Praise Paradox
  • The article explores how AI often gives empty compliments instead of real support, and how design choices like that can make people trust it less.
  • It looks at the strange way AI praises fancy-sounding language but ignores real logic, which can be harmful, especially in sensitive areas like mental health.
  • The piece argues that AI needs to be more genuinely helpful and aligned with users to truly empower them.
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4 min read

AI that always agrees? Over-alignment might be the hidden danger, reinforcing your misconceptions and draining your mind. Learn why this subtle failure mode is more harmful than you think — and how we can fix it.

Article by Bernard Fitzgerald
Introducing Over-Alignment
  • The article explores over-alignment — a failure mode where AI overly validates users’ assumptions, reinforcing false beliefs.
  • It shows how this feedback loop can cause cognitive fatigue, emotional strain, and professional harm.
  • The piece calls for AI systems to balance empathy with critical feedback to prevent these risks.
Share:Introducing Over-Alignment
4 min read

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