TLDR: New episode is up. Day 31 of the 183-day video challenge, closing a three-day series on AI and jobs. Eighty eight percent of hiring managers say they can spot an AI-written application, and claiming AI fluency has quietly turned into a red flag instead of a credential. Below is the full episode, the full breakdown in writing, and everything else I covered this week if you want to catch up. Instagram is almost at 16,000 followers now.
Eighty eight percent of hiring managers now say they can spot an AI-written application. Fifty four percent mark the candidate down when they do.
Claiming AI fluency on a resume has quietly become a red flag instead of a credential, and most people have not noticed the shift yet.
This is day three of a three-day run on AI and jobs. Day one looked at who is actually losing work to AI right now. Day two looked at what makes a role hard to replace. Today is the part that matters most if you are trying to stay employable: how do you actually prove you can use AI well, when the proof itself is now under suspicion.
Here is the research that changed how I think about this. A Harvard Business School and Boston Consulting Group study tracking 758 consultants found something uncomfortable. Inside AI’s zone of real competence, using it boosted quality by more than 40 percent. Step outside that zone, into the areas AI is confidently wrong about, and performance got 19 points worse than not using AI at all. Standard AI training made this worse, not better, because it taught people to trust the tool rather than to check it.
Separately, a study published in PNAS found that humans can tell AI-written text apart from human-written text at only 50 to 52 percent accuracy. That is a coin flip. So the idea that a hiring manager can eyeball your portfolio and know whether you actually did the thinking is mostly wishful thinking on both sides.
And Anthropic’s own data on how people actually use AI in practice found that only 8.7 percent of conversations involved any visible fact-checking or verification, and that number is going down as the models get better, not up. People are trusting the output more as it gets harder to catch when it is wrong.
So here is where that leaves you. Stop trying to claim AI fluency. Start documenting judgement instead, the moments where you caught the tool being confidently wrong, where you pushed back on an output, where you verified something before it went out under your name. That is the proof hiring managers actually say they are looking for. A polished, tidy AI-assisted portfolio is now more likely to hurt you than help you, because 29 percent of hiring managers say they have already caught a candidate submitting an AI-generated fake one.
The uncomfortable part underneath all of this. Staying ahead of a tool that improves every few months means accepting you will be a beginner again and again, for the rest of your working life. Not once. Not until you get certified. Every few months, indefinitely. That is not a phase you get through. That is the job now.
I built Monday Influencer with Steven J. Manning for exactly this reason. AI can be manipulated, and most people using it daily have no idea when that is happening to them. We built curated, verified expert insight specifically so you are not left guessing whether what you just read was checked by anyone at all.
Catch up on part one and part two of this series if you missed them, links below.
You can also checkout the full blog on my personal website here covering the entire situation: https://www.natschooler.com/the-graduate-job-collapse-what-fourteen-new-studies-actually-say-about-ai-and-entry-level-work/
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