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Posts tagged “culture”

Why AI in Interviews Is Bad for Candidates, Not Just Companies

A quick post on LinkedIn about interviewing a candidate who used real-time AI got more engagement than is usual for me. And as often happens when something goes semi-viral, some folks took issue with what I said, so I want to expand on the point I was trying to make (it wasn’t that “AI is cheating”).

Here’s what I wrote:

I had my first experience interviewing a candidate who used real-time AI today. If you’re someone who uses AI daily, it’s so easy to spot. The pause before the answer, the constant eyes flicking to the other screen, the perfectly-manicured 3-point answer…

Friends, just don’t do this. It’s too easy to spot, and it will also set you up for failure, because it might get you a job that you’re not a good fit for, which is bad for everyone.

Use AI in your job, for sure. But don’t use it to get the job. The interview process is about you. Be you.

One response called this “absolutely myopic” (I had to double check I didn’t accidentally post on Hacker News) and asked why candidates shouldn’t use AI if it allows for “a better, more creative answer.” Another suggested that if candidates will use AI on the job anyway, then the “real you” isn’t going to be working, so what’s the difference?

Let’s dig into this.

What interviews are actually for

I don’t interview people to test whether they can produce a good answer to a question. I interview people to understand how they think, what they’ve actually done, and whether we’ll work well together.

When I ask “Tell me about a time you had to make a difficult prioritization decision,” I’m not looking for the theoretically optimal framework. I want to hear your story. The messy details and the trade-offs you wrestled with. The thing you got wrong and what you learned from it. AI can’t give me that. It can only give me a polished summary of what prioritization frameworks exist.

One commenter put it well: “It’s about both the company and the individual, so you will often talk about their real experience, what they did, how they felt, what did they learn, digging deeper into their real experience to find out the interesting things that could make them a good match.”

AI might help you phrase things more clearly. But if it’s generating your answers, you’re hiding the very thing I’m trying to evaluate.

The fit problem

Here’s the part that didn’t seem to land: using AI to get a job you’re not qualified for is bad for you.

Let’s say the AI-assisted interview works. You get hired. Now what? You show up on day one, and the expectations are set based on how you performed in those interviews. But that wasn’t you. That was a performance enhanced by a tool you won’t have in the same way during actual work conversations, whiteboard sessions, and quick chat exchanges where people expect you to just… know things.

I’ve seen what happens when there’s a mismatch between interview performance and actual capability. It’s not a fun experience for anyone, least of all the person who’s now struggling in a role they weren’t ready for. One person called it “artificial buzzword ventriloquism” in the comments. Harsh, but not wrong.

It’s about context, not absolutes

A few commenters suggested that interviews should evolve to assume AI assistance, since that’s how people will actually work. One person wrote: “By prohibiting AI during interviews, the interview environment diverges from actual job conditions and fails to evaluate a critical skill: the ability to effectively use one of the most powerful productivity tools available today.”

I think there’s something to this. In fact, our interview process includes a take-home assessment where we explicitly encourage candidates to use AI. We want to see how they approach a problem, how they structure their thinking, and yes, how they use modern tools to get to a good answer. That’s a legitimate skill worth evaluating.

But that’s different from what happened in my interview, where someone was clearly trying to hide their AI usage while answering questions about their past experience. That’s not “using AI as a tool.” That’s using AI as a mask.


I think candidates should absolutely use AI to prepare for interviews: research the company, practice answering common questions, refine their resume.

But in the interview itself, when I’m asking about your experience and your thinking, I need to hear from you. Not because AI is cheating, but because the whole point is to figure out if you are the right fit for this role and this team. If I can’t evaluate that, we can’t make a good hiring decision. And that’s bad for both of us.

The invention of "classic rock"

Daniel Parris wrote a statistical analysis of when rock became “classic rock”, and it’s not the story I expected.

He assumed the genre emerged organically from music nerds debating on message boards and in the pages of Rolling Stone. Instead:

What I found was a deliberate realignment engineered by music executives chasing an ephemeral advertising demographic. Like many entertainment industry decisions, it was a small (mostly male) group of executives quietly deciding the future of popular culture behind closed doors.

The data shows two concentrated periods when stations rapidly switched to classic rock: the mid–1980s (to capture aging Boomers entering their peak earning years) and the mid–1990s (after the Telecommunications Act enabled Clear Channel to buy up local stations and prioritize low-risk, high-profit formats).

The kicker is that this rebrand was designed around economic incentives that have since eroded. Radio isn’t the default distribution channel anymore. On streaming, music can just exist without being packaged for a hyper-valuable consumer cohort.

Another reminder that so much of what feels like culture is really just business decisions made in conference rooms.

Don't Outsource Your Love of Music to AI

I’m late to this one, but I like Liz Pelly’s take on Spotify Wrapped. It’s not just about music—it’s about what happens when we let corporations automate our memories:

Spotify Wrapped now feels like just another example of something personal and precious that is being automated away from us; another example of a supposedly unbearable task of thinking and writing being “offloaded” in order to make life more frictionless.

The post is essentially about friction—and why we need it. She argues that working through the process of remembering what mattered to us and thinking critically about our year is what keeps us sharp and curious. When we just accept what a streaming service tells us about our taste, we’re not just outsourcing a task. We’re losing our own sense of what connected with us and why.

It encourages music fans to believe that the records they streamed the most must be the ones they liked the most, which is surely not always the case.

Her suggestion is straightforward: write your own list. It doesn’t have to be polished—a notes app screenshot, a handwritten list, whatever. Just something that came from you, not from an algorithm optimizing for engagement metrics.

What's Actually Working with AI

Natalia Quintero wrote about what she’s learned from talking to more than 100 companies about AI implementation. This part about the problem with early adopters and isolated workflows stood out:

AI doesn’t spread like other software. Think about Asana. If one person decides to organize their team’s tasks there, everyone benefits automatically because the work is more organized, and someone on the team has taken responsibility for that organization. You don’t need to learn the tool to get value from your colleague using it. AI doesn’t work that way. If you develop workflows around how you work, that value doesn’t automatically translate to the rest of the company. Your prompts, your GPTs, your automations—they’re built around your context, your processes, and your way of thinking. They don’t transfer.

That’s the adoption problem in a nutshell. A power user’s AI setup is like their personal note-taking system—valuable for them but not portable. It explains why enterprise rollouts don’t work the way everyone expects.

The recruiting firm example is good: they trained 10 champions who built tools their peers wanted to use. One person automated scheduling coordination (saving 2–10 hours per task), and suddenly 30 others got curious. Peer-to-peer beats top-down mandates.

(If you’re curious about my setup, I wrote about it here)

Where Do the Children Play?

Eli Stark-Elster has a piece that reframes the “kids and screens” debate in a way I haven’t seen before. The usual narrative blames addictive tech design, but he offers an alternative:

Why do our children spend more time in Fortnite than forests? Usually, we blame the change on tech companies. They make their platforms as addicting as possible, and the youth simply can’t resist — once a toddler locks eyes with an iPad, game over.

I want to suggest an alternative: digital space is the only place left where children can grow up without us.

The argument is that kids have always needed spaces away from adult supervision. We’ve just paved over the forests and creeks where they used to find it.

What makes this more than speculation is the research he cites: 72% of 8 to 12-year-olds say they’d rather spend time together in person, without screens. 61% wish they had more time to play with friends without adults around. The kids don’t actually want to be on screens all day. They’re looking for something we’ve taken away.

It seems like what they want is to wander together in a forest. But they can’t. So they boot up Fortnite or TikTok instead.

I’m still sitting with this one. It doesn’t let tech companies off the hook, but it does suggest that “just take away the iPad” isn’t addressing the real problem.

"Disagree and Let’s See"

I like this alternative to the “Disagree and Commit” saying:

“Disagree and let’s see” allows you to stay aligned with the team without forcing you to pretend you had conviction you didn’t have. It lets you walk into a room with your team and be honest:

“Here’s the path that was chosen. It wasn’t my first pick, but here’s the experiment we’re running, and here’s what we’re trying to learn.”

That’s a much more authentic stance for most leaders than repeating something with a tight smile and hoping no one notices your doubt.

Source: “Disagree and Let’s See” →

Brief thoughts on the recent Cloudflare outage

Lorin Hochstein is a big name in the LFI (Learning From Incidents) space. He often writes about post-incident reviews, and he has a very interesting write-up of the Cloudflare outage on November 18, 2025 blog post. I especially loved this part:

Companies generally err on the side of saying less rather than more. After all, if you provide more detail, you open yourself up to criticism that the failure was due to poor engineering. The fewer details you provide, the fewer things people can call you out on. It’s not hard to find people online criticizing Cloudflare online using the details they provided as the basis for their criticism.

I think it would advance our industry if people held the opposite view: the more details that are provided an incident writeup, the higher esteem we should hold that organization. I respect Cloudflare is an engineering organization a lot more precisely because they are willing to provide these sorts of details. I don’t want to hear what Cloudflare should have done from people who weren’t there, I want to hear us hold other companies up to Cloudflare’s standard for describing the details of a failure mode and the inherently confusing nature of incident response.

Source: Brief thoughts on the recent Cloudflare outage →

Selling Lemons

This is an essay I think everyone should read, front to back. It’s about all the things we are living through right now, but it’s especially about work (and AI):

I’m not sure hiring can ever be much more efficient, because neither side has reason to show themselves as they really are, warts and all. Idealistically, both would come straight; pragmatically, it is a game of chicken. Candidates polish résumés and present curated versions of their abilities, listing outcomes and impact statistics with dubious accuracy and provenance. Companies do the same, putting culture and mission front and center while hiding systematic dysfunctions and looming existential risks. When neither side is forthcoming, you’re left with proxies: a famous logo on a resume, a polished culture deck.

Source: Selling Lemons →

ChatGPT Is Blowing Up Marriages as It Goads Spouses Into Divorce

Wild story:

Multiple people we spoke to for this story lamented feeling “ganged up on” as a partner used chatbot outputs against them during arguments or moments of marital crisis. One of these sources, a man who’s now in the process of selling his home as he and his spouse barrel toward divorce, recounted feeling voiceless as his partner turned to ChatGPT to pathologize their relationship. “I was really hurt by the way [ChatGPT] was being used against me,” said the man, speaking through tears. “I felt like it was being leveraged… like, ‘I didn’t feel great about whatever happened, and so I went to ChatGPT, and ChatGPT said that you’re not a supportive partner, and this is what a supporting partner would do.’”

I think we have to realize that non-tech people don’t have a good understanding of the sycophantic nature of LLM bots, so we’ll see more and more examples like this.

Source: ChatGPT Is Blowing Up Marriages as It Goads Spouses Into Divorce →

The illegible nature of software development talent

This resonates so hard. The tech industry’s obsession with LARPing roles in the public sphere has really hurt our ability to work with people who care and want to do the best work of their lives without distractions.

I think it’s unlikely the industry will get much better at identifying and evaluating candidates anytime soon. And so I’m sure we’ll continue to see posts about the importance of your LinkedIn profile, or your GitHub, or your passion project. But you neglect at your peril the engineers who are working nine-to-five days at boring companies.

Source: The illegible nature of software development talent →