The first thank-you email looked perfect.
Professional. Polished. Exactly the type of message a hiring manager would appreciate after an interview.
Then the second email arrived.
Same structure. Same wording. Same sentences.
Then the third.
Word for word identical.
Anne Hathaway was sitting in the middle of a hiring process when she realized every candidate had made the same mistake: they had all used AI without adding anything personal.
During an interview promoting the upcoming sequel to The Devil Wears Prada, Hathaway explained that she noticed multiple candidates sending identical ChatGPT-generated thank-you emails.
Her reaction?
“I was like, oh no… I see something I’m not supposed to see.”
The lesson was simple:
AI can help you get noticed.
But using it without adding your own voice can make you invisible.
For years, career experts have called thank-you emails a “secret weapon.”
Most candidates never send one.
A thoughtful message after an interview shows effort, attention, and genuine interest.
But AI has changed the game.
The same tool that helps candidates write faster is now creating a new problem: everyone starts sounding the same.
Perfectly structured paragraphs.
Generic compliments.
Phrases like “passionate about,” “results-oriented,” and “excited about this opportunity.”
These phrases might sound professional, but recruiters are seeing them repeatedly.
A Resume-Now survey found that many employers reject application materials that clearly appear AI-generated.
The problem isn’t using AI.
The problem is sending something that feels like it came from a machine.
The pressure behind AI usage is understandable.
Many job seekers are applying to dozens or even hundreds of positions.
The hiring process can feel automated, with applicants fighting against algorithms just to get noticed.
AI feels like the obvious solution.
Why spend an hour writing a customized email when a chatbot can create one in seconds?
The problem is that thousands of other people are doing exactly the same thing.
The “shortcut” becomes the thing that removes your advantage.
A thank-you email is supposed to prove that you were paying attention.
A generic AI response can prove the opposite.
The best way to use AI is as a starting point, not the final version.
Let AI help you organize your thoughts.
Let it improve your grammar.
Let it suggest a structure.
But the final message should include something only you could write.
A simple example:
Instead of saying:
“Thank you for the opportunity to discuss the role. I am excited about the possibility of contributing to your team.”
Write something specific:
“I appreciated your explanation of how the marketing team works with product development. The way you described that collaboration was one of the things that made the role stand out to me.”
That one sentence proves you were actually there.
It shows you listened.
It gives the hiring manager something real to remember.
As AI becomes better at creating polished content, human details become more valuable.
The goal isn’t to write like a novelist.
The goal is to sound like yourself.
A perfect AI-generated email can still fail if it says nothing about you.
A simple, slightly imperfect message with a real observation from the interview can make a much stronger impression.
The best candidates won’t be the ones who avoid AI.
They will be the ones who know how to use it without disappearing behind it.
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Supercharge your productivity with HubSpot’s comprehensive guide.
AI search engines may be creating a new problem: a feedback loop where AI-generated content is increasingly used to train and inform future AI answers. New research from Graphite suggests this could lead to “AI search collapse,” where recommendations become narrower, more repetitive, and easier to manipulate as models rely on content created by previous AI systems.
The concern is that instead of exposing users to the diversity of the open web, AI search could gradually converge around the same ideas and sources. Companies are already trying to influence these systems through “GEO” (generative engine optimization), creating content designed to be cited by AI. While some consistency is useful for factual questions, researchers warn that too much AI-driven convergence could reduce discovery, originality, and the variety of perspectives people encounter online.
OpenAI has partnered with Broadcom to develop Jalapeño, a custom AI chip designed specifically for running large language models more efficiently. The processor is the first in a planned series of in-house AI accelerators aimed at giving OpenAI more control over the infrastructure behind ChatGPT, Codex, its API, and future AI agents. Early versions are already running internal models, with deployment expected by the end of 2026.
The chip was built from scratch around the needs of modern AI inference, focusing on improving performance, reducing power usage, and limiting unnecessary data movement. OpenAI says the nine-month development cycle was accelerated by close hardware-software collaboration and the use of AI tools during chip design. The move highlights the growing race among AI companies to build their own computing infrastructure as demand for AI workloads continues to explode.
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That’s it for today.
AI is moving fast - models are getting better, tools are getting cheaper, and the gap between “people who use AI” and “people who don’t” keeps widening.
The only real advantage left is speed of learning.
Until next time: stay AI smart, stay ahead, and keep building with the future instead of reacting to it.
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